StatMuse: Josh Hart tonight: 13 PTS 19 REB 10 AST 3rd Knick to reach those numbers in a game (David Lee in 2010, Richie Guerin in 1957). statmuse.com/nba/player/jos…

StatMuse: Josh Hart tonight: 13 PTS 19 REB 10 AST 3rd Knick to reach those numbers in a game (David …

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Unsupervised Investments (I): A Guide to AI Investors

The burgeoning big data landscape is set to transform a range of industries over the coming years, and for global supply chains in dire need of more comprehensive levels of transparency and optimization, the data revolution couldn’t come soon enough. The global big data analytics market size already swelled to a value of $307.51 billion in 2023 and is expected to grow to $924.39 billion by 2032, representing a CAGR of 13% over the forecast period. Today, global supply chains can suffer from myriad inefficiencies that hinder their performance with troubling consequences. Factors like regulatory compliance, quality control, tracing faulty goods, and the ability to manage vendors can all cause disruptions that may hinder vendor relationships and adversely impact customer satisfaction. Environmental and geopolitical factors have also caused fresh turmoil in supply chains in recent months, with conflict in the Middle East impacting routes through the Suez Canal while drought is actively slowing the passage of goods through the Panama Canal. Although digital transformation in supply chains can’t actively reverse the impact of climate change, it can help to offer efficient solutions that counter the slowing and costly effects of disruption for global organizations. But how can firms maintain their productivity through data? Let’s take a deeper look at the scale of impact the big data revolution can have on global supply chains and vendor management: Next-Generation Forecasting Forecasting empowers organizations to anticipate emerging problems or issues that could impact supply chain performance in the future. Big data processes like predictive modeling and trend forecasting can provide companies with stronger insights in anticipating customer spending, emerging trends, and wider market changes. There are three key areas where next-generation forecasting will pave the way for more supply chain efficiency, and they include: Anticipating demand: Here, big data can be utilized to create predictive models through the analysis of historical data and market trends that anticipate customer behavior and seasonal trends to efficiently manage inventories and deliver personalized experiences for customers. Data suggests that this already has a positive impact on brand performance, and a recent McKinsey report suggests a 115% higher ROI and 93% higher profits can be achieved by companies using customer analytics reports. Further down the supply chain, manufacturers and shipping firms can use these analytical models to predict production demand and delivery analytics. Optimizing inventories: Because big data analytics can help to forecast demand fluctuations and seasonality patterns, it can also utilize reservoirs of historical sales data, current inventory levels, and future demand forecasts to analyze and determine the optimal inventory rates for every product's business stock. We’ve already seen retail giants like Amazon embrace big data for this purpose and to simplify key decision-making processes. Mapping production: Big data can streamline production schedules by actively monitoring machine utilization, the availability of staff, and the accessibility of materials. By integrating this data from production systems and a variety of other sources, it’s possible to achieve a fully optimized production process that limits idleness and bolsters efficiency on a comprehensive scale. For a more holistic supply chain overview, companies can partner with logistics firms that can deliver more supply chain resilience models along with more alternative solutions. With many supply chains impacted by issues in the Suez and Panama canals, the merits of utilizing a logistics firm that can accommodate the hard-to-predict disruptions and drawbacks like geopolitical events, extreme weather, and any other form of disruption and offer a multimodal alternative is clear. Integrating Artificial Intelligence Big data has an intrinsic relationship with artificial intelligence, and we’re already seeing the development of a key focus on data reliability and relevance among companies seeking to optimize supply chains through AI. Here, it’s essential to achieve lower latency and utilize data that can be rapidly analyzed to deliver more real-time decision-making. Artificial intelligence can also be an asset for active compliance management to help brands stay in tune with their values by actively vetting the companies within a supply chain and their processes. In the future, we can expect AI and data analytics to become better integrated with different forms of structured and unstructured data, and this will help businesses make use of logistics data directly to improve supply chain management. The beauty of AI in the data management process is that generative tools can help to provide data in a variety of different formats, including image files and PDFs, which can be more effectively utilized by various tools for recognition and understanding. Leveraging a Transparent Supply Chain One area where big data can excel in supply chain management is through the transparency it affords supply chains from the top all the way down. Through multifaceted data sourcing from suppliers, warehouses, transportation providers, and customers, businesses can obtain real-time insights into live inventory levels, shipment statuses, and demand patterns, which can then be actively monitored through artificial intelligence programs. These transparent measures are already in use today among leading retailers, and Walmart actively leans on big data analytics to monitor product sales and inventory levels to restock shelves at a pace that’s consistent with delivery forecasts and supplier production rates–helping to actively save costs and uphold the strongest levels of customer satisfaction. In addition, a recent study by Capgemini found that companies investing in real-time supply chain visibility can benefit from a 50% reduction in instances of out-of-stock inventory and a 10% improvement in perfect order delivery. Unprecedented Chain Management Big data can also optimize existing processes at an unprecedented level. While it can leverage fresh efficiency throughout the production of goods and inventory levels, these insights can also help to cut costs significantly and drive efficiency when it comes to delivery fleet management. Here, data can optimize route deployment, delivery schedules, and item location to help customers receive their orders faster without vehicles having to repeat preventable processes over and over. UPS has utilized this perk of big data exceptionally well within its shipping processes. Radars and sensors are used to capture data as packages move throughout the supply chain, while big data insights optimize driver routes to ensure that packages arrive by their expected date. This has helped UPS to save 1.6 million gallons of gasoline in their truck each year, saving costs across the board. Likewise, big data brings efficiency to businesses in managing vendors. Intelligent payout solutions backed by real-time reporting and analytics can help streamline the vendor management process and ensure that all facets of a supply chain are paid for their work in a way that enhances liquidity and improves relationships throughout the chain. Here, payouts can be leveraged on the delivery of goods in a fully compliant manner to deliver automation to time-consuming processes backed by data analytics. Unlocking Supply Chain Efficiency Emerging technology like big data analytics is helping to transform the operational efficiency of companies across a variety of industries. Digitalization within supply chains can help companies make more intelligent decisions while tracking emerging trends to ensure that nobody misses a beat when inventory demands or requirements change. As big data continues to become more sophisticated, its functionality within the supply chain will grow. This will help businesses avoid the pitfalls of inefficient chains in the future and continue delivering for customers in a semi-automated manner. The burgeoning big data landscape is set to transform a range of industries over the coming years, and for global supply chains in dire need of more comprehensive levels of transparency and optimization, the data revolution couldn’t come soon enough. The global big data analytics market size already swelled to a value of $307.51 billion in 2023 and is expected to grow to $924.39 billion by 2032 , representing a CAGR of 13% over the forecast period. expected to grow to $924.39 billion by 2032 expected to grow to $924.39 billion by 2032 Today, global supply chains can suffer from myriad inefficiencies that hinder their performance with troubling consequences. Factors like regulatory compliance, quality control, tracing faulty goods, and the ability to manage vendors can all cause disruptions that may hinder vendor relationships and adversely impact customer satisfaction. Environmental and geopolitical factors have also caused fresh turmoil in supply chains in recent months, with conflict in the Middle East impacting routes through the Suez Canal while drought is actively slowing the passage of goods through the Panama Canal. caused fresh turmoil caused fresh turmoil Although digital transformation in supply chains can’t actively reverse the impact of climate change, it can help to offer efficient solutions that counter the slowing and costly effects of disruption for global organizations. But how can firms maintain their productivity through data? Let’s take a deeper look at the scale of impact the big data revolution can have on global supply chains and vendor management: Next-Generation Forecasting Forecasting empowers organizations to anticipate emerging problems or issues that could impact supply chain performance in the future. Big data processes like predictive modeling and trend forecasting can provide companies with stronger insights in anticipating customer spending, emerging trends, and wider market changes. There are three key areas where next-generation forecasting will pave the way for more supply chain efficiency , and they include: pave the way for more supply chain efficiency pave the way for more supply chain efficiency Anticipating demand: Here, big data can be utilized to create predictive models through the analysis of historical data and market trends that anticipate customer behavior and seasonal trends to efficiently manage inventories and deliver personalized experiences for customers. Anticipating demand: Here, big data can be utilized to create predictive models through the analysis of historical data and market trends that anticipate customer behavior and seasonal trends to efficiently manage inventories and deliver personalized experiences for customers. Anticipating demand: Here, big data can be utilized to create predictive models through the analysis of historical data and market trends that anticipate customer behavior and seasonal trends to efficiently manage inventories and deliver personalized experiences for customers. Data suggests that this already has a positive impact on brand performance, and a recent McKinsey report suggests a 115% higher ROI and 93% higher profits can be achieved by companies using customer analytics reports. Further down the supply chain, manufacturers and shipping firms can use these analytical models to predict production demand and delivery analytics. Optimizing inventories: Because big data analytics can help to forecast demand fluctuations and seasonality patterns, it can also utilize reservoirs of historical sales data, current inventory levels, and future demand forecasts to analyze and determine the optimal inventory rates for every product's business stock. Optimizing inventories: Because big data analytics can help to forecast demand fluctuations and seasonality patterns, it can also utilize reservoirs of historical sales data, current inventory levels, and future demand forecasts to analyze and determine the optimal inventory rates for every product's business stock. Optimizing inventories: Because big data analytics can help to forecast demand fluctuations and seasonality patterns, it can also utilize reservoirs of historical sales data, current inventory levels, and future demand forecasts to analyze and determine the optimal inventory rates for every product's business stock. We’ve already seen retail giants like Amazon embrace big data for this purpose and to simplify key decision-making processes. Mapping production: Big data can streamline production schedules by actively monitoring machine utilization, the availability of staff, and the accessibility of materials. By integrating this data from production systems and a variety of other sources, it’s possible to achieve a fully optimized production process that limits idleness and bolsters efficiency on a comprehensive scale. Mapping production: Big data can streamline production schedules by actively monitoring machine utilization, the availability of staff, and the accessibility of materials. By integrating this data from production systems and a variety of other sources, it’s possible to achieve a fully optimized production process that limits idleness and bolsters efficiency on a comprehensive scale. For a more holistic supply chain overview, companies can partner with logistics firms that can deliver more supply chain resilience models along with more alternative solutions. With many supply chains impacted by issues in the Suez and Panama canals, the merits of utilizing a logistics firm that can accommodate the hard-to-predict disruptions and drawbacks like geopolitical events, extreme weather, and any other form of disruption and offer a multimodal alternative is clear. Integrating Artificial Intelligence Big data has an intrinsic relationship with artificial intelligence, and we’re already seeing the development of a key focus on data reliability and relevance among companies seeking to optimize supply chains through AI. Here, it’s essential to achieve lower latency and utilize data that can be rapidly analyzed to deliver more real-time decision-making. rapidly analyzed rapidly analyzed Artificial intelligence can also be an asset for active compliance management to help brands stay in tune with their values by actively vetting the companies within a supply chain and their processes. In the future, we can expect AI and data analytics to become better integrated with different forms of structured and unstructured data, and this will help businesses make use of logistics data directly to improve supply chain management. The beauty of AI in the data management process is that generative tools can help to provide data in a variety of different formats, including image files and PDFs, which can be more effectively utilized by various tools for recognition and understanding. Leveraging a Transparent Supply Chain One area where big data can excel in supply chain management is through the transparency it affords supply chains from the top all the way down. Through multifaceted data sourcing from suppliers, warehouses, transportation providers, and customers, businesses can obtain real-time insights into live inventory levels, shipment statuses, and demand patterns, which can then be actively monitored through artificial intelligence programs. These transparent measures are already in use today among leading retailers, and Walmart actively leans on big data analytics to monitor product sales and inventory levels to restock shelves at a pace that’s consistent with delivery forecasts and supplier production rates–helping to actively save costs and uphold the strongest levels of customer satisfaction. In addition, a recent study by Capgemini found that companies investing in real-time supply chain visibility can benefit from a 50% reduction in instances of out-of-stock inventory and a 10% improvement in perfect order delivery. 50% reduction 50% reduction Unprecedented Chain Management Big data can also optimize existing processes at an unprecedented level. While it can leverage fresh efficiency throughout the production of goods and inventory levels, these insights can also help to cut costs significantly and drive efficiency when it comes to delivery fleet management. Here, data can optimize route deployment, delivery schedules, and item location to help customers receive their orders faster without vehicles having to repeat preventable processes over and over. UPS has utilized this perk of big data exceptionally well within its shipping processes. Radars and sensors are used to capture data as packages move throughout the supply chain, while big data insights optimize driver routes to ensure that packages arrive by their expected date. This has helped UPS to save 1.6 million gallons of gasoline in their truck each year, saving costs across the board. save 1.6 million gallons save 1.6 million gallons Likewise, big data brings efficiency to businesses in managing vendors . Intelligent payout solutions backed by real-time reporting and analytics can help streamline the vendor management process and ensure that all facets of a supply chain are paid for their work in a way that enhances liquidity and improves relationships throughout the chain. vendors vendors Here, payouts can be leveraged on the delivery of goods in a fully compliant manner to deliver automation to time-consuming processes backed by data analytics. Unlocking Supply Chain Efficiency Emerging technology like big data analytics is helping to transform the operational efficiency of companies across a variety of industries. Digitalization within supply chains can help companies make more intelligent decisions while tracking emerging trends to ensure that nobody misses a beat when inventory demands or requirements change. As big data continues to become more sophisticated, its functionality within the supply chain will grow. This will help businesses avoid the pitfalls of inefficient chains in the future and continue delivering for customers in a semi-automated manner.

Unlocking the Invaluable Role of Big Data in Modern Supply Chain Management

Seamless integration of tools and data sources has become the cornerstone of efficiency and innovation. HubSpot stands as a prominent figure for all things Sales and Marketing. However, in this blog, we . delve into how HubSpot data finds applications in custom business operations lying outside its out-of-the-box capabilities We'll explore how integration empowers developers to craft bespoke tools that merge HubSpot data with that of other sources, enhancing operational efficiency across enterprises. low-code The Need for Custom Operational Efficiency Tools Companies today are driven by the need for seamless data integration across diverse tools. of Frendy Fashion. Their customer success representatives must pull data from HubSpot, Zendesk, and Stripe for every return/refund query, creating a consolidated profile to process requests. This process consumes significant time and hampers query resolution speed. Integrating these data sources into a single point of truth can expedite response times and enhance customer experiences. Consider this hypothetical case The Versatility of HubSpot Data The integration of HubSpot's data with various tools empowers companies to optimize operations, refine customer interactions, and boost efficiency. Consider these use cases: : Join HubSpot data with marketing analytics platforms to track and measure the success of campaigns. This provides valuable insights into the and channels. Marketing Campaign Insights effectiveness of different marketing strategies : Combine with financial and sales data through integration to generate accurate sales forecasts. This , and setting achievable sales targets. Sales Forecasting HubSpot's CRM data aids in strategic planning, resource allocation : Link HubSpot with an e-commerce platform to gain a comprehensive view of customer behavior. This integration can help , and optimize the customer journey. E-commerce Integration tailor marketing efforts, offer personalized product recommendations : Integrate HubSpot with event management tools to seamlessly manage event registrations, track attendance, and automate follow-up communication. This . Event Management enhances lead nurturing and engagement after events : Connect HubSpot with supply chain systems to , shipping information, and inventory availability. This ensures accurate and timely communication to customers. Supply Chain Management provide real-time updates on order statuses : Integrate HubSpot with financial systems to gain . This integration supports credit control, risk assessment, and financial decision-making. Financial Insights insights into customer transaction history and payment status : Integrate HubSpot with customer support ticketing systems and communication tools. This integration allows customer support representatives to , enabling them to provide more personalized and effective support. It also facilitates seamless coordination among support teams and enhances issue resolution speed. Enhanced Customer Support access comprehensive customer profiles, purchase histories, and interactions The Landscape of HubSpot Integration : HubSpot readily connects with tools that enhance marketing and sales efforts. Yet, as businesses evolve, so do their operational needs. The demand for , project management systems, e-commerce platforms, and beyond. To cater to these diverse demands, we must look beyond the boundaries of native integrations. Native Integrations with HubSpot personalized experiences necessitates connections with specialized tools : Imagine your marketing team gathering invaluable insights from HubSpot, syncing seamlessly with your custom analytics dashboard. Or your sales teams leveraging HubSpot data in real-time to execute targeted strategies that resonate with potential clients. These scenarios often demand more than standard integrations can provide. While HubSpot effortlessly links with several tools, the complexity of that transcends these connections. Challenges of Complex Integrations business operations demands a level of customization and precision Unlocking Potential With Low-Code Integration: Why Developers Need More Going back to Frendy Fashion’s story. The gap is clear, however, developer bandwidth remains a critical issue, so internal ops requirements take a backseat. That’s where low-code integration comes in to connect disparate data sources, designing a custom interface and giving the CSRs a personalized app to resolve queries faster. Developer Constraints and Complex Integrations While HubSpot offers a plethora of features, not every team has the luxury of time and resources to embark on extensive internal tool development. Often, the demands of customer-facing operations take precedence, leaving little room for additional development work. Moreover, building tools that seamlessly integrate HubSpot's data with other systems can be intricate and time-consuming, requiring specialized developer expertise. Enter Low-Code Development Low-code tools streamline integration and app development with a visual interface to build custom tools. Low-code platforms offer pre-built UI components and ready connectors to databases and applications like SQL database, Airtable, Google Sheets, HubSpot, Trello, Zendesk, Stripe, and more; automating user authentication and data handling. Low-code platforms simplify development, bringing in an abstraction layer while reducing the need for extensive coding, and offer pre-built connectors, user management, security, and automation blocks. They handle updates and scalability, making maintenance easier. While custom integrations provide tailored solutions, low-code tools excel in speed, simplicity, and accessibility, making them a preferable choice for organizations seeking quicker integration deployment without the hassles of handling dependencies on libraries, frameworks, and intensive development resources. Real-Life Success Stories: Low-Code Integration in Action : A financial services company seamlessly connects HubSpot with its client database using low-code. This integration automates client onboarding, ensuring data consistency across platforms and delivering a personalized experience from the get-go. Personalized Client Onboarding : An e-commerce retailer integrates HubSpot with its customer support system. This low-code integration captures customer interactions across platforms, providing the sales and support teams with a unified customer view that enhances engagement and support efficiency. Holistic Customer Insights : A marketing agency combines HubSpot with its social media scheduling tool through low-code. By syncing HubSpot's lead data with social media schedules, the agency orchestrates targeted campaigns based on real-time insights. Automated Social Media Campaigns : An education institution integrates HubSpot with its accounting software via low-code. This integration automates the process of tracking payments, ensuring timely follow-ups and accurate financial management. Effortless Financial Tracking : An event planning company integrates HubSpot with its event management platform using low-code. Attendee data flows seamlessly between systems, enabling targeted communication and an enhanced event experience. Personalized Event Management : A data-driven marketing team integrates HubSpot with its data analytics tool via low-code. This integration empowers the team to create highly targeted campaigns based on real-time customer behavior insights. Insights-Driven Marketing These examples showcase the power of integrating HubSpot with multiple tools and data sources to build custom solutions that enhance efficiency, customer engagement, and decision-making across various business functions. Types of Low-Code Tools to Consume HubSpot (and Other) APIs BPM Workflow tools – , , Kissflow Pipefy Workato In the automation tools category, processes are defined by triggers, actions, and well-defined stages. Tools like Kissflow, and Workato are pretty popular for their workflow capabilities. These platforms are industry-agnostic, driving business process workflows for simple to complex processes. Integration platforms – , , Zapier Make Tray Integration platforms include the likes of Zapier, Tray, Make (previously Integromat), etc. Zapier, in particular, has done an impressive job at providing ready-to-use zaps for non-coders to get their hands dirty with marketing integrations. All-in-one platforms (App builders) – , , DronaHQ Glide Monday You also get platforms that provide a host of more capabilities such as creative designing, UI capabilities, integration using APIs, user access management, distribution management, and of course, automation capabilities to run trigger-based workflows. Platforms include  DronaHQ, Bubble, Glide, Retool (each offering its own set of specialties), and more. The Uniqueness of Custom Solutions In the ever-evolving landscape of business technology, the synergy between HubSpot and low-code integration stands as a testament to innovation. While HubSpot's rich features empower businesses, custom operational efficiency tools often require a higher level of personalization. The constraints of developer availability and complex integrations can hinder progress. This is where low-code development shines, democratizing the creation of custom tools and bridging the gap between HubSpot's capabilities and unique business needs. By embracing low-code development, businesses can empower teams to swiftly build tools that streamline operations, enhance customer experiences, and unlock the full potential of HubSpot's data-rich environment.

Unleashing HubSpot's Potential: Leveraging Low-Code Integration for Hyper-Personalized Apps

Newton’s Laws in Society: How Social Forces Move Physical Bodies

সমাজে নিউটনের আইন: সামাজিক শক্তিগুলি কীভাবে শারীরিক দেহগুলিকে স্থানান্তরিত করে

Newtons Gesetze in der Gesellschaft: Wie soziale Kräfte physische Körper bewegen

Las leyes de Newton en la sociedad: cómo las fuerzas sociales mueven los cuerpos físicos

Les lois de Newton dans la société : comment les forces sociales déplacent les corps physiques

समाज में न्यूटन के नियम: सामाजिक शक्तियां भौतिक निकायों को कैसे स्थानांतरित करती हैं

社会におけるニュートンの法則: 社会力がどのように物体を動かすか

뉴턴의 사회 법칙: 사회적 힘이 육체를 움직이는 방법

As Leis de Newton na Sociedade: Como as Forças Sociais Movem os Corpos Físicos

Законы Ньютона в обществе: как социальные силы движут физическими телами

Newton'un Toplumdaki Yasaları: Sosyal Güçler Fiziksel Bedenleri Nasıl Hareket Eder?

Định luật Newton trong xã hội: Các lực lượng xã hội di chuyển cơ thể vật chất như thế nào

社会中的牛顿定律：社会力量如何移动身体

Hi There! So, I believe each of us has encountered a situation when, after modifying an entity in the database, it's necessary to notify other systems about this change. For example, upon accepting an order, we need to inform all related services and teams about the arrival of a new order. The first solution that comes to mind is straightforward: have each service or database polled at regular intervals to check for new orders or status updates. But what if the logic becomes too complex and needs to be segmented? This is where we turn to the event-driven approach, where we broadcast what happened with our objects. is an architectural pattern that allows systems to asynchronously exchange messages or events. The main advantage of EDA is its ability to increase system scalability and flexibility by minimizing direct dependencies between its components. Event-driven architecture (EDA) This facilitates easier adaptation to changes and growth, ensures higher resilience, and simplifies the integration of diverse services. However, a challenge arises in synchronizing data between the database and the messaging system. If we first record data about a new order in the database and then attempt to send a message to Kafka, we cannot guarantee that the message will be sent. A potential producer error could lead to the loss of critical notifications. actually Attempting to wrap these operations in a single transaction (writing to the database and sending to Kafka, followed by a transaction commit) faces fundamental difficulties, as Kafka operates outside the transaction context of databases. This necessitates an alternative solution that ensures atomicity and delivery guarantees for messages without compromising the system's performance and reliability. Here is where the comes to the rescue. Outbox pattern https://microservices.io/patterns/data/transactional-outbox.html?embedable=true The Outbox pattern involves creating a special table (outbox) in the database, where an event intended for the messaging system is recorded within the same transaction as the main data changes. Thus, both operations - changing the state in the main table and creating an event in the outbox table - are guaranteed to be executed together. Subsequently, a dedicated connector (e.g., Kafka Connect) asynchronously reads data from the outbox table and sends it to Kafka, thereby ensuring the delivery of events to the end recipients. This approach achieves high reliability and data consistency without adding undue complexity to the system's architecture or compromising its performance. Let’s review it. The Problem ACID is an acronym describing key transaction properties in databases: A transaction is either executed in full or not executed at all. Atomicity: A transaction transitions the database from one consistent state to another. Consistency: The execution of one transaction doesn't impact the execution of others. Isolation: Completed transactions persist in the database, even in case of system failures. Durability: In distributed systems, the requirement to synchronize data across multiple services is a common challenge. The conventional Two-Phase Commit (2PC) approach is often deemed suboptimal due to its sluggish and blocking nature. Moreover, dealing with data integrity recovery in the event of a system failure poses significant difficulties. The Outbox Pattern emerges as a remedy to address the need for atomicity in writing operations to the database and dispatching messages to a message queue. The core of this solution involves incorporating an additional table within the service's database, referred to as the "Outbox." This table acts as a buffer, storing messages destined for dispatch to a message queue. How the Outbox Pattern Works: Local Transaction: When performing a business operation, data changes are written to the respective database tables. A message intended for other services is placed in the Outbox table within the same transaction. Message Publication: Pollers periodically scan the Outbox table for new messages. Each message from the Outbox table is published to the corresponding message queue. After successful publication, the message is either deleted from the Outbox table or marked as sent. Let’s Code Let's break down this process step by step with a simple example, utilizing Go. You can find the source code and additional materials at the following . link Step 1: Setting Up the Order Service We'll start by creating a service for handling orders. This service will be responsible for receiving orders and recording them in the database. A key component will be implementing the "Outbox" pattern to ensure that every change in order status is reliably transmitted to Kafka. For our case, we need to create only one method: curl -X POST
  http://localhost:8080/api/v1/create-order
  -H 'Content-Type: application/json'
  -d '{
    "name": "John",
    "surname": "Doe",
    "details": {
      "code": 123,
      "operation_code": 456,
      "transaction_code": "sdfsdfsdfs"
    }
  }' The core concept of this service involves writing to both the orders table and the outbox table within a single transaction. For instance, in Go, this would be implemented as follows: func (r *OrderRepository) CreateOrder(userUUID string, order domain.Order) (*int32, error) {
	det := order.Details
	var id int32

	tx, err := r.db.Begin()
	if err != nil {
		return nil, err
	}

	err = tx.QueryRow(
		query,
		order.Name, order.Surname, det.Code, det.OperationCode, det.TransactionCode, StatusCreated).Scan(&id)

	if err != nil {
		tx.Rollback()
		return nil, err
	}

	// Save to outbox
	err = r.createOutbox(userUUID, order)
	if err != nil {
		tx.Rollback()
		return nil, err
	}

	return &id, tx.Commit()
} As a result, we’ll expect messages like this: Step 2: Configuring Kafka JDBC Connector Next, we'll set up the Kafka JDBC Connector to automatically transfer messages from the outbox table to Kafka. This allows other services to subscribe to these events and react to changes in order statuses in real-time. For the Kafka connector, I'll be using the JDBC connector. Find a good way for you to install it: https://docs.confluent.io/kafka-connectors/jdbc/current/source-connector/overview.html?embedable=true#install-the-jdbc-sink-connector After installation, just call connector API: curl http://localhost:8083/connector-plugins We’ll expect to see: [{"class":"io.confluent.connect.jdbc.JdbcSinkConnector","type":"sink","version":"10.7.5"},
{"class":"io.confluent.connect.jdbc.JdbcSourceConnector","type":"source","version":"10.7.5"},
{"class":"org.apache.kafka.connect.mirror.MirrorCheckpointConnector","type":"source","version":"7.6.0-ccs"},
{"class":"org.apache.kafka.connect.mirror.MirrorHeartbeatConnector","type":"source","version":"7.6.0-ccs"},
{"class":"org.apache.kafka.connect.mirror.MirrorSourceConnector","type":"source","version":"7.6.0-ccs"}] Next, we need to create settings for the source. Be careful at this stage, as we are providing credentials for our connector. I recommend creating separate credentials with read-only permissions for the outbox table so that this service can only observe changes. Overall, there are plenty of best practices in this regard, but we won't delve into them here. {
  "name": "jdbc-source-connector",
  "config": {
    "connector.class": "io.confluent.connect.jdbc.JdbcSourceConnector",
    "connection.url": "jdbc:postgresql://postgres:5432/postgres",
    "connection.user": "postgres",
    "connection.password": "postgres",
    "topic.prefix": "microshop.order-",
    "mode": "incrementing",
    "incrementing.column.name": "id",
    "table.whitelist": "outbox",
    "poll.interval.ms": "5000",
    "value.converter": "org.apache.kafka.connect.json.JsonConverter",
    "key.converter": "org.apache.kafka.connect.storage.StringConverter",
    "key.converter.schemas.enable": "false",
    "transforms": "createKey",
    "transforms.createKey.type": "org.apache.kafka.connect.transforms.ValueToKey",
    "transforms.createKey.fields": "user_id"
  }
} Set - our table. "table.whitelist": "outbox" Set - our topic. "topic.prefix": "microshop.order-" Set - our message key. "transforms.createKey.fields": "user_id" More about settings and fields: … here And send it. curl -X POST -H "Content-Type: application/json" --data @jdbc-source-config.json http://localhost:8083/connectors And that's it! Now, you just need to create an order, and check if everything is working and if messages are being recorded in the tables. To view what we have in Kafka, I use Kafka UI for such tasks, which allows us to quickly see what we wrote there and if it's working. Step 3: Creating the Background Processing Service We'll create a background service that listens for messages from Kafka and executes the appropriate processing logic. For instance, this service might be responsible for updating order statuses or initiating delivery processes. Full schema: The last service is a background (order-background-service). In our case, it’s just a simple service that can consume messages from Kafka. ...
	err = c.SubscribeTopics([]string{"microshop.order-outbox"}, nil)
	if err != nil {
		fmt.Fprintf(os.Stderr, "Failed to subscribe to topics: %s\n", err)
		os.Exit(1)
	}

...

	ev := c.Poll(cfg.Poll)
	if ev == nil {
	  continue
	}

	switch e := ev.(type) {
		case *kafka.Message:

		// Create an instance of Outbox to store the unmarshaled data
		var outbox domain.Outbox

		// Unmarshal the JSON message into the Outbox struct
		err := json.Unmarshal(e.Value, &outbox)
		if err != nil {
			fmt.Println("Error decoding message:", err)
			return
		}
		fmt.Println("Payload:", outbox.Payload)

		_, err = c.CommitMessage(e)
		if err != nil {	
			fmt.Printf("Failed to commit message: %v\n", err)
		} Be Careful! It is essential to pause here and reflect on the following: as we can observe, entries are now being recorded in tables atomically, ensuring ACID compliance. However, our pipeline has been expanded to include both a Kafka connector and Kafka itself. It is crucial to remember the delivery guarantees that Kafka promises. Apache Kafka offers developers various levels of message delivery guarantees, enabling a balance between performance and reliability according to application requirements. These guarantees are based on the concepts of atomicity, durability, and isolation. Here are the primary delivery guarantees provided by Kafka: : This foundational guarantee ensures a message is delivered at least once. It implies that, in the event of failures or retries, a message may be delivered or processed more than once. This guarantee is facilitated by retry mechanisms in case of errors and acknowledgments from consumers. At Least Once : In this mode, messages can be lost but will not be delivered more than once. This is the fastest and least reliable mode, as it does not account for retries in case of errors. This approach may be acceptable for scenarios where the loss of some messages is not critical. At Most Once : This is the most complex and desirable guarantee, ensuring that each message is delivered and processed “exactly once”, eliminating duplication or loss. Kafka has supported "exactly once" delivery since version 0.11, using a combination of techniques such as idempotent producers and transactions to ensure atomicity across various topics and consumers. Exactly Once The choice among these guarantees depends on your application's requirements for reliability and performance. If you need more information about it just let me know. In conclusion, implementing an Outbox pattern alongside a reliable Kafka connector like the JDBC connector offers a robust solution for orchestrating data changes across microservices. This approach ensures atomicity and consistency when updating multiple systems, significantly reducing the chances of data inconsistencies. This method serves as a crucial first step toward an Event-Driven Architecture (EDA). By introducing a reliable messaging system, like Kafka, and seamlessly integrating it with a transactional database, we establish a foundation for distributing events across the microservices landscape. This approach becomes particularly valuable when considering the need for analytics, as all relevant events are readily available for consumption. Analytics teams can effortlessly retrieve and process these events, contributing to the generation of valuable insights and reports. In essence, the Outbox pattern, coupled with Kafka integration, not only addresses immediate concerns of data consistency but also lays the groundwork for a scalable, event-driven ecosystem that is essential for modern microservices architectures. This facilitates the seamless flow of information between services, unlocking the potential for enhanced analytics and real-time decision-making. If you're interested in learning more about advanced topics such as managing deletions from the Outbox table and further insights into working with this setup, consider subscribing for future updates. Your subscription will help gauge the interest in this topic and guide the direction of upcoming articles. Stay tuned for more in-depth explorations into the intricacies of microservices architectures and event-driven systems! Useful Links Kafka UI: click here Kafka JDBC connector: click here Outbox pattern: click here

Unlocking Microservices Reliability With ACID and the Outbox Pattern

The idea behind the Internet of Value (IoV) is that , with the help of cryptocurrency and blockchain technology. money should be able to be sent as quickly as information This change is similar to how the internet changed the way people share knowledge. The internet made it possible to instantly send and receive information around the world. Cryptocurrencies and blockchain aim to do the same for value, making transactions faster and more transparent without the need for middlemen. Decentralized Payment Protocols in the Internet of Value Decentralized payment protocols are an interesting shift in how we handle digital transactions. Instead of relying on centralized systems or sticking with the isolated nature of traditional blockchains, these protocols aim to streamline things by cutting out the middleman and allowing transactions to happen directly between peers. The idea is pretty simple: decentralized payment protocols use a network of nodes to verify and validate transactions instead of a single authority overseeing transactions. This decentralized approach contrasts with traditional systems, where banks or other central entities usually handle transaction processing, often leading to delays and extra costs. What's intriguing is how these protocols are designed to work across different blockchains, a big departure from how things typically operate. This interoperability means you can move your assets between various networks more seamlessly without relying on third-party exchanges or complex conversions. Security is also a key consideration. While some systems heavily depend on smart contracts to enforce transaction terms, decentralized payment protocols take a different route, aiming for simplicity and robustness. They use advanced cryptographic techniques and consensus algorithms to ensure secure and reliable transactions. How Decentralized Payment Protocols Work Within the IoV Decentralized payment systems make it possible for money to be sent quickly and easily across the internet by leveraging blockchain technology. They are also pretty versatile, catering to different needs, including the integration of cryptocurrency payments for businesses. Below are some examples of decentralized payment platforms and how they enable the transfer of value across different use cases. How RippleNet Enables Cross-Border Payment Solutions RippleNet is a platform that enhances cross-border payment efficiency through its  messaging, settlement, and foreign exchange (FX) management features. One of the essential aspects of RippleNet is RippleNet Home, which streamlines management tasks across the network. Users can initiate and monitor payments, troubleshoot issues, analyze payment execution, manage accounts and user profiles, and retrieve API credentials, among other functionalities. RippleNet Home is a centralized hub for overseeing various aspects of business operations within the RippleNet ecosystem. On-Demand Liquidity (ODL) is a liquidity solution offered by RippleNet. It enables instant settlement without needing pre-funded capital in destination markets. ODL facilitates real-time settlement of payment funds, access to global payout rails, upfront visibility of fees, and automatic payout to beneficiaries' bank accounts, enhancing the efficiency and transparency of cross-border transactions. RippleNet's flexibility, particularly with ODL integration, caters to diverse customer needs across multiple use cases. Payment service providers can leverage ODL for cross-border remittances, while fintech companies can facilitate business-to-business transactions for small-to-medium enterprises. Additionally, corporations can utilize ODL for internal treasury payments to accounts in foreign countries, optimizing liquidity management and reducing transaction costs. Various transaction types are supported within the RippleNet ecosystem, each tailored to specific use cases. Individual payments involve settling individual customer or business payments using ODL, with XRP funding the sender wallet and recipients receiving fiat payouts in receive markets. Bulk funding allows instant topping up of sender positions with receivers, facilitating efficient remittances and B2B payments. Internal treasury transactions enable intra-company transfers for customers with accounts in foreign countries, streamlining cross-border treasury operations. How Kima Enables Asset Transfers without Smart Contracts Kima facilitates asset transfers across various blockchain networks and traditional bank accounts, employing a specialized chain designed for direct transactions with the source and destination networks or banks. Unlike relying on smart contracts, which can be susceptible to exploits, Kima ensures the security of sensitive funds in transit by utilizing proven technologies such as Threshold Signature Schemes (TSS) and Trusted Execution Environments (TEEs) like Intel SGX, which have demonstrated resilience in real-world scenarios. In its approach to settlement security, Kima adopts a straightforward principle: if assets aren't present in a vulnerable environment, they can't be compromised. This philosophy drives Kima's avoidance of smart contracts, oracles, and external relayers, eliminating potential points of failure. Instead, Kima's technical framework comprises three key components: External Accounts (EOAs), TSS and TEEs, and Efficient Liquidity Management (LiMa). EOAs serve as native blockchain wallets acting as pools, enhancing security by eliminating the need for smart contracts across every blockchain and mitigating vulnerabilities associated with cross-contract messaging. TSS and TEEs enable secure key sharing among parties without direct exposure, bolstering the system's resilience against critical compromises. Additionally, Kima employs a Liquidity Management algorithm to optimize the distribution of assets across pools, ensuring effective service delivery. Additionally, Kima bridges the gap between traditional and decentralized finance systems by offering support for fiat integration through an opt-in compliance system. This protocol-level integration enables access to legacy systems like fiat payments while adhering to regulatory requirements, including KYC anti-money laundering verifications. Recognizing the challenges inherent in achieving compatibility within the web3 ecosystem, Kima emphasizes a holistic approach. Rather than treating compatibility issues as isolated problems, Kima views them as interconnected symptoms of a broader issue. Kima keeps liquidity pools on each combined layer-1 blockchain by enabling smooth cross-chain exchange without token wrapping. This lets users move assets directly between chains without creating synthetic tokens. At the core of Kima's functionality lies its blockchain, orchestrating asset synchronization across multiple layer-1 blockchains through committee-based consensus. This consensus mechanism relies on rotating sets of wardens responsible for ensuring asset pool synchronization and authorizing withdrawals on destination chains only upon confirmation of corresponding deposits on source chains. Kima leverages TSS protocols within TEEs to enhance security and privacy in cross-chain transfers. By employing TSS, Kima enables participants to collaboratively generate and control signing keys securely, ensuring that a predetermined threshold of participants must engage in authorizing transactions. Through proactive secret sharing and the use of Intel SGX enclaves, Kima fortifies the security of its TSS implementation, minimizing the risk of key compromises and unauthorized access. How Coinbase Commerce Integrates Cryptocurrencies into Retail Transactions Coinbase Commerce facilitates the integration of cryptocurrency payments into merchants' operations via an API or user interface. This enables customers to pay directly from their devices using the blockchain for settlement. Merchants and customers benefit from guaranteed settlement without exposure to cryptocurrency volatility. Payments are automatically settled into stablecoins, providing merchants with stability. Additionally, Coinbase Commerce supports various wallets and exchanges, allowing merchants to deposit funds into their preferred storage solutions. The Commerce Onchain Payment Protocol simplifies the payment process, enabling merchants to accept various cryptocurrencies. Customers can utilize their verified Coinbase accounts to make seamless payments to any Commerce merchant. Transactions are confirmed instantly, leveraging cost-effective networks like Base and Polygon. Coinbase Commerce operates on the Onchain Payment Protocol, an open-source standard to streamline cryptocurrency payments. Unlike traditional pull payments, where merchants initiate transactions, cryptocurrency payments rely on a push system initiated by customers. Upon payment initiation, Coinbase Commerce creates a Charge representing the requested payment, generating a unique payment link and Universally Unique Identifier (UUID). Customer transactions are broadcast to the cryptocurrency network for validation, with Coinbase monitoring the blockchain for inbound payments and updating charge statuses accordingly. For non-developers, Coinbase Commerce offers user-friendly solutions such as embedding payment buttons on websites or creating hosted checkout pages. These options require minimal coding and provide a straightforward experience for customers. Merchants can integrate Coinbase Commerce into existing e-commerce platforms to accept cryptocurrency payments without extensive technical expertise. Final Thoughts As blockchain technology improves and decentralized payment systems improve, the Internet of Value looks like it could have a bright future. Adding digital assets to financial systems and creating new decentralized financial services could lead to significant growth and broad use. The IoV could affect e-commerce, banks, and money transfers, making things easier, cheaper, and safer. In general, I'm hopeful about the IoV's future.

The Role of Decentralized Payment Platforms in the Internet of Value

If the complex and costly hardware required for traditional mining has been a deterrent for you, or if you’re weary of the relentless grind to boost transaction volumes just to earn fees, now there’s a refreshing alternative. Discover the brand-new mobile mining experience that ‘ ’ provides, where your smartphone becomes your portal to a rewarding ‘verify to earn’ journey. Massive Introduction We’re thrilled to introduce , our innovative mobile mining app, poised for launch alongside the upcoming at beginning of April. As the first project in the blockchain sector to leverage ‘ZKVM+DePIN+AI’ technology, Ola is at the forefront, pioneering a new, fair, open and user-centric approach to blockchain interaction with this initiative, Massive. Massive Ola Testnet Alpha Massive simplifies the blockchain experience by removing the need for expensive hardware or technical expertise. Our vision for Massive is direct and clear: to democratize web3 access by providing a platform where anyone with a smartphone can participate and earn rewards through simple verification tasks. Our commitment with Massive is to lower the entry barriers to blockchain technology, making it accessible and equitable for all users. By introducing the ‘verify to earn’ concept, we’re not only changing how people mine; we’re also transforming their role within the blockchain ecosystem from passive observers to active participants. Massive, as a crucial part of Ola’s development strategy, . Ola is set to open applications for invitation codes on March 18th, releasing the first set of 1,000 codes for node registration. This initial offering is limited to 1,000 nodes, with registrations being accepted on a first-come, first-served basis. Additionally, the first 1,000 nodes will have the privilege of generating invitation codes, allowing them to invite newcomers with these codes to earn rewards. plans to allocate about 10% of Ola’s token to verifiers & miners, with an initial 1% being used for Ola Testnet Alpha Join us as we embark on this exciting journey to make blockchain technology intuitive, inclusive, and rewarding for everyone, everywhere. Background The industry has tirelessly explored various strategies to engage traditional users (those outside the web3 ecosystem), including gaming, infrastructure, and incentivizing through airdrops. Notable efforts like StepN’s ‘move to earn’ and Starkware’s airdrops for non-Web3 developers momentarily captured high interest, hinting at potential breakthroughs. However, their inability to sustain long-term momentum and impact highlighted the critical need for innovative, enduring approaches that truly resonate with and retain users in the blockchain realm. “We recognized the need for a standout feature that resonates deeply with users; thus, Massive was conceived as our bold solution to this industry challenge,” - Simon Chen, co-founder and CEO of Ola Ola’s mission with Massive is to catalyze the widespread adoption of web3, advocating for a decentralized and fair digital ecosystem. By introducing the ‘verify to earn’ concept, where anyone with a smartphone can participate in mining, Massive aims to significantly lower these barriers. Additionally, users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. This approach will foster a more inclusive environment, encouraging a broader demographic to engage with, understand, and contribute to the web3 space, while receiving long-term rewards. The introduction of Massive is a strategic move toward making blockchain more accessible and engaging for the general public, thus infusing the industry with new participants and fresh perspectives. Through this initiative, Ola envisions transforming mere observers into active contributors, nurturing a dynamic and sustainable blockchain community. Massive: Fair Opportunities for All In the realm of traditional mining, such as the Proof of Work (POW) mining mechanism, the requirement for costly hardware presents a significant barrier to ordinary users. When developing Massive, Ola focused on reducing entry barriers, enhancing user experience, and increasing user rewards, all while guaranteeing utmost fairness for EVERY user. Massive is a decentralized authentication network composed of various mobile elements. In terms of user-friendliness, Massive boasts several key features: User Access Threshold: : Users can easily access Massive by simply downloading the necessary app and wallet on their smartphones. Low Entry Barrier : Massive supports a broad range of mobile models, compatible with both Android and iOS (coming soon) systems. Minimal Device Requirements User Rewards (10% of Ola’s tokens): : Users receive rewards by completing verification tasks, providing a straightforward way to earn. Verify to Earn : In addition to earning verification rewards, users can also gain additional returns by inviting newcomers with a referral code and through staking activities. Multiple Reward Streams User Experience: : Users can join or leave the network at their convenience, offering the flexibility to suit individual needs. Flexible Access and Exit : Without the need for complex operations or maintaining high transaction volumes, Massive simplifies user participation. Ease of Use : Compared to distributing funds across multiple addresses, using a smartphone to participate presents a lower barrier for the average user. User-Friendly Enhanced Network Security and Intelligence: : Massive harnesses AI+zk technology to elevate network security and intelligence, utilizing AI for nuanced analysis of node service statuses. This results in refined task distribution and heightened network efficiency. AI + ZK Integration Platform Fairness and Sustainability: : Massive ensures fairness for all users, where everyone’s time and contributions are equally valued. Fair : Users can engage with multiple projects over time on their smartphones, enhancing the longevity and diversity of their participation. Sustainable Massive Changes the Game: Say Hi to Your New Web Roles At Massive, your role is fundamentally transformed. Gone are the days of having to “slave away” by artificially boosting transaction volumes and amounts to earn incentives. Now, all you need is a standard smartphone to connect. By simply staying active and verifying, you can consistently earn rewards. But your role evolves further; once you connect as an Ola node, you become an essential part of maintaining the network’s security. You might have felt the imbalance before, as web3 whales could distribute their funds across multiple addresses to amplify their earnings, a feat not so easily replicated by purchasing multiple smartphones. Hence, Massive levels the playing field, offering a balanced opportunity for both whales and retailers. On the Massive platform, everyone participates and contributes in the same manner, ensuring fairness. This approach incentivizes a broader spectrum of users to engage with the web3 ecosystem, lowering entry barriers and bolstering decentralization and equity within the system. With Massive, every participant has the chance to earn rewards fairly, fostering wider participation and a healthier network. Distinct from other staking or mining models where more staking could mean more chances to validate, Massive champions decentralization and equity. Every node stakes an equal value, and by merely staying online and completing verification tasks, rewards can be earned. Time is valued equally across all participants, ensuring that mining on Massive is inherently fair. This paradigm encourages broader and more equitable user engagement, reinforcing the network’s integrity and community inclusiveness. Looking Ahead: Shaping the Future of Blockchain and Web3 Mobile Experiences with ZK Technology As we gaze into the future, our expectations for the Massive are set even higher: Firstly, as a purely decentralized ZK verification network, Massive is poised to accelerate the modularization of blockchain design. Secondly, with an increasing influx of new users joining the blockchain, we anticipate the emergence of genuine Web3 mobile technology. This evolution promises not only to propel the advancement of blockchain technology and invigorate the ecosystem but also to offer users a more equitable, open, and convenient digital experience. Collectively, these efforts aim to facilitate the widespread adoption and development of blockchain technology, steering the industry toward a more prosperous future. Join the Venture! Ola is tackling blockchain’s biggest hurdles: fairness and accessibility, with its gamechanging ‘Massive’ app, leveraging “ZKVM+DePin+AI” technology. Massive simplify blockchain interactions with a ‘verify to earn’ model, opening the digital door for anyone with a smartphone. 🗓️ March 18 marks the launch of Ola’s first 1,000 node registration codes, with mining, new features, and greater rewards soon to follow. Early nodes gain the advantage of creating invite codes for collaborative reward earning. Join us on this venture and be among the first to benefit! About us is an open-source Hybrid ZK Rollup that delivers programmable scalability and privacy to blockchains, empowering individuals through data ownership. Incubated by cryptographers and engineers of Sin7y Labs in 2022, Ola offers a layer2 ZK Stack, featuring Ola-lang, OlaVM, OlaUI, OlaOS, and a series of developer-friendly tools, facilitating the development of any type of contracts. In 2023, Ola launched the Ambassador Program and OVP Points Rewards, now with 20+ global ambassadors. For 2024, Ola plans to roll out public testnet, mainnet, mining mechanism (PoW, ZKP, & Verifier), and a range of incentive programs steadily. If you are interested in joining or partnering with us to contribute to a fair, decentralized, and permissionless world, please contact us at . Ola contact@olavm.org | | | | | | | | | | Website Twitter Discord Telegram Whitepaper GitHub LinkedIn YouTube HackMD Medium HackerNoon

The First Ola Mobile Mining App: A New ‘Verify to Earn’ Paradigm with a 10% Token Airdrop Plan

প্রথম ওলা মোবাইল মাইনিং অ্যাপ: একটি 10% টোকেন এয়ারড্রপ প্ল্যান সহ একটি নতুন 'ভেরিফাই টু আর্ন' দৃষ্টান্ত

Die erste mobile Mining-App von Ola: Ein neues „Verify to Earn“-Paradigma mit einem 10 %-Token-Airdrop-Plan

La primera aplicación de minería móvil de Ola: un nuevo paradigma de "verificar para ganar" con un plan de lanzamiento aéreo de tokens del 10%

La première application Ola Mobile Mining : un nouveau paradigme « Vérifier pour gagner » avec un plan Airdrop de 10 % de jetons

पहला ओला मोबाइल माइनिंग ऐप: 10% टोकन एयरड्रॉप प्लान के साथ एक नया 'वेरिफाई टू अर्न' प्रतिमान

最初の Ola モバイル マイニング アプリ: 10% トークン エアドロップ プランを備えた新しい「検証して獲得」パラダイム

최초의 Ola 모바일 채굴 앱: 10% 토큰 에어드롭 계획을 통한 새로운 '수익 확인' 패러다임

O primeiro aplicativo de mineração móvel Ola: um novo paradigma de ‘verificar para ganhar’ com um plano de lançamento aéreo de token de 10%

Первое мобильное приложение для майнинга Ola: новая парадигма «подтверди, чтобы заработать» с планом Airdrop 10% токенов

İlk Ola Mobil Madencilik Uygulaması: %10 Token Airdrop Planıyla Yeni Bir "Kazanmak İçin Doğrula" Paradigması

Ứng dụng khai thác di động Ola đầu tiên: Mô hình 'Xác minh để kiếm tiền' mới với kế hoạch thả 10% mã thông báo

第一个 Ola 移动挖矿应用程序：新的“验证赚取”范式和 10% 代币空投计划

This tutorial shows you how to deploy and automate a Docker container using Pulumi in Google Cloud Platform's Cloud Run with minimum permissions. Here's the GitHub Repository for reference: https://github.com/superjose/deploy-to-cloud-run-go Get the CLIs: . Create a Google Cloud Account Install Google Cloud CLI Install Pulumi CLI - We need it to build the Docker container. Install Docker Bootstrap the project: Create a directory: pulumi mkdir pulumi Run to initialize a pulumi project with Go (It can be your language of choice pulumi new go Navigate to the directory. pulumi cd pulumi Navigate to and create a new project. cloud.google.com Take note of the (Usually the project's name). project-id Generate the necessary permissions using gcloud CLI Login with the Google Auth CLI: gcloud auth login (Optional) Set the project in google cloud CLI (Can be changed anytime). This saves you from passing into every command. --project [PROJECT-ID] gcloud If your machine has multiple GCP projects, skip this step and pass the flag into every command. --project gcloud Create a service account (The account that Pulumi will connect to): gcloud iam service-accounts create pulumi-gcp --description="Pulumi GCP" Download the credentials for the service accounts and store them locally (Remember to replace with your GCP Project Id): [PROJECT-ID] gcloud iam service-accounts keys create ~/keys/gcp/pulumi-service-account-key-file.json --iam-account=pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com Set Pulumi's gcp credentials config path:
(This will connect the service account with Pulumi) pulumi config set gcp:credentials ~/keys/gcp/pulumi-service-account-key-file.json Set the GCP Project by doing: pulumi config set gcp:project [PROJECT-ID] Create a file (Inside the dir) and add the required permissions in : roles.gcp.yml pulumi includedPermissions # https://cloud.google.com/iam/docs/creating-custom-roles#creating
# Yaml to define the Pulumi GCP Roles that need to be created with gcloud CLI
title: Pulumi GCP Roles
description: |
 This policy ensures that all GCP roles are created using Pulumi.
stage: GA
# https://cloud.google.com/iam/docs/permissions-reference
includedPermissions:
 - serviceusage.services.list
 - serviceusage.services.enable
 - serviceusage.services.disable
 - serviceusage.services.get
 - serviceusage.services.use
 # Permissions for GCR
 - storage.objects.create
 - storage.objects.delete # Optional: only include if you need to delete images
 - storage.objects.get
 # Permissions for Google Artifact Registry
 - artifactregistry.repositories.create
 - artifactregistry.repositories.delete
 - artifactregistry.repositories.get
 - artifactregistry.repositories.list
 - artifactregistry.repositories.update
 - artifactregistry.repositories.downloadArtifacts
 - artifactregistry.repositories.uploadArtifacts
 - artifactregistry.repositories.deleteArtifacts
 # Permissions
 # Permissions
 - run.services.create
 - run.services.get
 - run.services.list
 - run.services.update
 - run.services.delete
 - run.services.getIamPolicy
 - run.services.setIamPolicy
 - iam.serviceAccounts.actAs
# NOTE: This should be removed the first time you're creating a role.
# This etag is to update the current active role (As GCP lets you manage multiple roles)
# I'm commenting it out so I can always replace the role
# etag: BwYS74Xx5y4= Create the with the file above:
(We assume we're running this code from the directory) pulumi_admin_role pulumi gcloud iam roles create pulumi_admin_role --project=[PROJECT-ID] --file='./roles.gcp.yml' In case you need to make edits, change the file and use: gcloud iam roles update pulumi_admin_role --project=[PROJECT-ID] --file='./roles.gcp.yml' We're also adding the role (I haven't found a better way) (Otherwise we'd get 403 errors when refreshing and updating in Pulumi)1 serviceAccountAdmin gcloud projects add-iam-policy-binding [PROJECT-ID] --role roles/iam.serviceAccountAdmin --member serviceAccount:pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com (The following will add the new role to the service account) gcloud projects add-iam-policy-binding [PROJECT-ID] --role projects/[PROJECT-ID]/roles/pulumi_admin_role --member serviceAccount:pulumi-gcp@[PROJECT-ID].iam.gserviceaccount.com Our in the directory:
(Check the code for comments!) main.go pulumi We enable the required services (Artifact Registry, and Cloud Run). Artifact Registry is used to host the docker container image. Cloud Runner will launch the Docker image from Artifact Registry. We build the docker image locally (We specify the platform in case you're using an ARM chip like M1, M2, Snapdragon SQ, X Elite, etc.) We create a chain of "DependsOn" to notify Pulumi:
5.1 Services need to be enabled first
5.2 We create the Artifact Repository
5.3 We build the docker image and push it to Artifact Registry.
5.4 We pull the Docker Image from Artifact Registry and run it.
5.5 We add IAM permissions so it can be accessed from anywhere. package main

import (
	"errors"
	"log"
	"os"

	"github.com/joho/godotenv"
	"github.com/pulumi/pulumi-docker/sdk/v3/go/docker"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/artifactregistry"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/cloudrun"
	"github.com/pulumi/pulumi-gcp/sdk/v7/go/gcp/projects"
	"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)

// This is the name you created in Google Cloud Platform (GCP).
const gcpProjectId = "deploy-to-cloud-run-go"

// The Docker Image Name
const dockerImageName = "my-app-docker"
const artifactRegistryServiceName = "artifact-registry-api"
const artifactRegistryRepoName = "my-app-artifact-repo"
const artifactRegistryRepoLocation = "us-east1"
const cloudRunAdminServiceName = "cloud-run-admin-service"
const cloudRunServiceName = "cloud-run-service"

// For more info: https://cloud.google.com/run/docs/locations
const cloudRunLocation = "us-east1"

// The tag for the Docker image
const imageTag = "latest"

// This is a url like: us-east1-docker.pkg.dev
// It is used to push the Docker image to Google Container Registry
// For more info: https://cloud.google.com/container-registry/docs/pushing-and-pulling
// The format is: <region>-docker.pkg.dev
var dockerGCPServer = cloudRunLocation + "-docker.pkg.dev"

// The full path to the Docker image
// It is used to deploy the Docker image to Google Cloud Run
// The format is: <region>-docker.pkg.dev/<project-id>/<repo-name>/<image-name>:<tag>
// For more info: https://cloud.google.com/run/docs/deploying
// Example: us-east1-docker.pkg.dev/deploy-to-cloud-run-go/my-app--artifact-repo/my-app-docker:latest
var dockerImageWithPath = dockerGCPServer + "/" + gcpProjectId + "/" + artifactRegistryRepoName + "/" + dockerImageName + ":" + imageTag

func main() {

	// Load the .env file
	err := godotenv.Load()

	if err != nil {
		log.Fatal("Error loading .env file")
	}

	pulumi.Run(func(ctx *pulumi.Context) error {

		enabledServices, serviceResultErr := enableServices(ctx)

		if serviceResultErr != nil {
			return serviceResultErr
		}

		artifactRegistryRepo, createArtifactErr := createArtifactRegistryNewRepository(ctx, &enabledServices)
		if createArtifactErr != nil {
			return createArtifactErr
		}

		dockerImage, buildAndPushErr := buildAndPushToContainerRegistry(ctx, &enabledServices, artifactRegistryRepo)

		if buildAndPushErr != nil {
			return buildAndPushErr
		}

		deployContainerErr := deployContainerToCloudRun(ctx, &enabledServices, dockerImage)

		if deployContainerErr != nil {
			return deployContainerErr
		}

		return nil
	})
}

type EnabledServices struct {
	CloudRunService *projects.Service `pulumi:"cloudRunService"`
	ArtifactRegistryService *projects.Service `pulumi:"artifactRegistryService"`
}

func enableServices(ctx *pulumi.Context) (EnabledServices, error) {

	cloudRunService, cloudRunAdminErr := projects.NewService(ctx, cloudRunAdminServiceName, &projects.ServiceArgs{
		Service: pulumi.String("run.googleapis.com"),
		Project: pulumi.String(gcpProjectId),
	})

	if cloudRunAdminErr != nil {
		return EnabledServices{}, cloudRunAdminErr
	}

	artifactRegistryService, err := projects.NewService(ctx, artifactRegistryServiceName, &projects.ServiceArgs{
		Service: pulumi.String("artifactregistry.googleapis.com"),
	})

	if err != nil {
		return EnabledServices{}, err
	}
	return EnabledServices{
		CloudRunService: cloudRunService,
		ArtifactRegistryService: artifactRegistryService,
	}, nil
}

func createArtifactRegistryNewRepository(ctx *pulumi.Context, enabledServices *EnabledServices) (*artifactregistry.Repository, error) {

	if enabledServices == nil || enabledServices.ArtifactRegistryService == nil {
		return nil, errors.New("enabledServices cannot be nil")
	}

	dependingResources := []pulumi.Resource{
		enabledServices.ArtifactRegistryService,
	}

	repo, err := artifactregistry.NewRepository(ctx, artifactRegistryRepoName, &artifactregistry.RepositoryArgs{
		Location: pulumi.String(artifactRegistryRepoLocation),
		RepositoryId: pulumi.String(artifactRegistryRepoName),
		Format: pulumi.String("DOCKER"),
		Description: pulumi.String("The repository that will hold social-log Docker images."),
	}, pulumi.DependsOn(dependingResources))

	if err != nil {
		return nil, err
	}

	return repo, nil
}

func buildAndPushToContainerRegistry(ctx *pulumi.Context, enabledServices *EnabledServices, artifactRegistryRepo *artifactregistry.Repository) (*docker.Image, error) {

	if enabledServices == nil || enabledServices.ArtifactRegistryService == nil {
		return nil, errors.New("enabledServices cannot be nil")
	}

	if artifactRegistryRepo == nil {
		return nil, errors.New("artifactRegistryRepo cannot be nil")
	}

	// Lookup GOOGLE_CREDENTIALS environment variable which should hold the path to the JSON key file
	jsonKeyPath, present := os.LookupEnv("GOOGLE_CREDENTIALS_FILE_PATH")
	if !present {
		return nil, errors.New("GOOGLE_CREDENTIALS_FILE_PATH environment variable is not set")
	}

	// Read the JSON key file
	jsonKey, err := os.ReadFile(jsonKeyPath)
	if err != nil {
		return nil, err
	}

	dependingSources := []pulumi.Resource{
		enabledServices.ArtifactRegistryService,
		artifactRegistryRepo,
	}

	// Build and push Docker image to Google Container Registry using the JSON key
	image, err := docker.NewImage(ctx, dockerImageName, &docker.ImageArgs{
		Build: &docker.DockerBuildArgs{
			Context: pulumi.String("../"), // Adjust the context according to your project structure

			ExtraOptions: pulumi.StringArray{
				// This option is needed for devices running on ARM architecture, such as Apple M1/M2/MX CPUs
				pulumi.String("--platform=linux/amd64"),
			},
		},
		ImageName: pulumi.String(dockerImageWithPath),
		Registry: &docker.ImageRegistryArgs{
			Server: pulumi.String(dockerGCPServer),
			Username: pulumi.String("_json_key"), // Special username for GCP
			Password: pulumi.String(string(jsonKey)), // Provide the contents of the key file
		},
	}, pulumi.DependsOn(dependingSources))
	if err != nil {
		return nil, err
	}

	return image, nil
}

func deployContainerToCloudRun(ctx *pulumi.Context, enabledServices *EnabledServices, dockerImage *docker.Image) error {

	if enabledServices == nil || enabledServices.CloudRunService == nil {
		return errors.New("enabledServices cannot be nil")
	}

	if dockerImage == nil {
		return errors.New("dockerImage cannot be nil")
	}

	dependingSources := []pulumi.Resource{
		enabledServices.CloudRunService,
		dockerImage,
	}

	appService, err := cloudrun.NewService(ctx, cloudRunServiceName, &cloudrun.ServiceArgs{
		Location: pulumi.String(cloudRunLocation), // Choose the appropriate region for your service
		Template: &cloudrun.ServiceTemplateArgs{
			Spec: &cloudrun.ServiceTemplateSpecArgs{
				Containers: cloudrun.ServiceTemplateSpecContainerArray{
					&cloudrun.ServiceTemplateSpecContainerArgs{
						Image: dockerImage.ImageName,
						Resources: &cloudrun.ServiceTemplateSpecContainerResourcesArgs{
							Limits: pulumi.StringMap{
								"memory": pulumi.String("256Mi"), // Adjust the memory limit as needed
							},
						},
					},
				},
			},
		},
		Traffics: cloudrun.ServiceTrafficArray{
			&cloudrun.ServiceTrafficArgs{
				Percent: pulumi.Int(100),
				LatestRevision: pulumi.Bool(true),
			},
		},
	}, pulumi.DependsOn(dependingSources))

	if err != nil {
		return err
	}

	_, iamErr := cloudrun.NewIamMember(ctx, "invoker", &cloudrun.IamMemberArgs{
		Service: appService.Name,
		Location: appService.Location,
		Role: pulumi.String("roles/run.invoker"),
		Member: pulumi.String("allUsers"),
	})

	if iamErr != nil {
		return iamErr
	}

	ctx.Export("containerUrl", appService.Statuses.Index(pulumi.Int(0)).Url().ToOutput(ctx.Context()))
	return nil
} Update the in your Dockerfile:
There's a , and in which you need to export your environment variable to ENV known issue here HOME /root # Set the ENV HOME before your ENTRYPOINT.
ENV HOME=/root

# This is specific to your project. 
ENTRYPOINT ["/whatever-is-your-entrypoint"] Create a .env file in the pulumi directory: Set the * to the one you saved on full path 11) GOOGLE_CREDENTIALS_FILE_PATH="/Users/myusername/keys/gcp/pulumi-service-account-key-file.json" Run pulumi up You should be up and running! Extras The go.mod module social-log-go

go 1.21

toolchain go1.22.0

require (
	github.com/pulumi/pulumi-gcp/sdk/v7 v7.11.2
	github.com/pulumi/pulumi/sdk/v3 v3.108.1
)

require (
	dario.cat/mergo v1.0.0 // indirect
	github.com/Masterminds/semver v1.5.0 // indirect
	github.com/Microsoft/go-winio v0.6.1 // indirect
	github.com/ProtonMail/go-crypto v0.0.0-20230828082145-3c4c8a2d2371 // indirect
	github.com/aead/chacha20 v0.0.0-20180709150244-8b13a72661da // indirect
	github.com/agext/levenshtein v1.2.3 // indirect
	github.com/apparentlymart/go-textseg/v13 v13.0.0 // indirect
	github.com/atotto/clipboard v0.1.4 // indirect
	github.com/aymanbagabas/go-osc52/v2 v2.0.1 // indirect
	github.com/blang/semver v3.5.1+incompatible // indirect
	github.com/charmbracelet/bubbles v0.16.1 // indirect
	github.com/charmbracelet/bubbletea v0.24.2 // indirect
	github.com/charmbracelet/lipgloss v0.7.1 // indirect
	github.com/cheggaaa/pb v1.0.29 // indirect
	github.com/cloudflare/circl v1.3.7 // indirect
	github.com/containerd/console v1.0.4-0.20230313162750-1ae8d489ac81 // indirect
	github.com/cyphar/filepath-securejoin v0.2.4 // indirect
	github.com/djherbis/times v1.5.0 // indirect
	github.com/emirpasic/gods v1.18.1 // indirect
	github.com/go-git/gcfg v1.5.1-0.20230307220236-3a3c6141e376 // indirect
	github.com/go-git/go-billy/v5 v5.5.0 // indirect
	github.com/go-git/go-git/v5 v5.11.0 // indirect
	github.com/gogo/protobuf v1.3.2 // indirect
	github.com/golang/glog v1.1.0 // indirect
	github.com/golang/groupcache v0.0.0-20210331224755-41bb18bfe9da // indirect
	github.com/golang/protobuf v1.5.3 // indirect
	github.com/google/uuid v1.3.0 // indirect
	github.com/grpc-ecosystem/grpc-opentracing v0.0.0-20180507213350-8e809c8a8645 // indirect
	github.com/hashicorp/errwrap v1.1.0 // indirect
	github.com/hashicorp/go-multierror v1.1.1 // indirect
	github.com/hashicorp/hcl/v2 v2.17.0 // indirect
	github.com/inconshreveable/mousetrap v1.1.0 // indirect
	github.com/jbenet/go-context v0.0.0-20150711004518-d14ea06fba99 // indirect
	github.com/joho/godotenv v1.5.1 // indirect
	github.com/kevinburke/ssh_config v1.2.0 // indirect
	github.com/lucasb-eyer/go-colorful v1.2.0 // indirect
	github.com/mattn/go-isatty v0.0.19 // indirect
	github.com/mattn/go-localereader v0.0.1 // indirect
	github.com/mattn/go-runewidth v0.0.15 // indirect
	github.com/mitchellh/go-ps v1.0.0 // indirect
	github.com/mitchellh/go-wordwrap v1.0.1 // indirect
	github.com/muesli/ansi v0.0.0-20230316100256-276c6243b2f6 // indirect
	github.com/muesli/cancelreader v0.2.2 // indirect
	github.com/muesli/reflow v0.3.0 // indirect
	github.com/muesli/termenv v0.15.2 // indirect
	github.com/opentracing/basictracer-go v1.1.0 // indirect
	github.com/opentracing/opentracing-go v1.2.0 // indirect
	github.com/pgavlin/fx v0.1.6 // indirect
	github.com/pjbgf/sha1cd v0.3.0 // indirect
	github.com/pkg/errors v0.9.1 // indirect
	github.com/pkg/term v1.1.0 // indirect
	github.com/pulumi/appdash v0.0.0-20231130102222-75f619a67231 // indirect
	github.com/pulumi/esc v0.6.2 // indirect
	github.com/pulumi/pulumi-docker/sdk/v3 v3.6.1 // indirect
	github.com/rivo/uniseg v0.4.4 // indirect
	github.com/rogpeppe/go-internal v1.11.0 // indirect
	github.com/sabhiram/go-gitignore v0.0.0-20210923224102-525f6e181f06 // indirect
	github.com/santhosh-tekuri/jsonschema/v5 v5.0.0 // indirect
	github.com/sergi/go-diff v1.3.1 // indirect
	github.com/skeema/knownhosts v1.2.1 // indirect
	github.com/spf13/cobra v1.7.0 // indirect
	github.com/spf13/pflag v1.0.5 // indirect
	github.com/texttheater/golang-levenshtein v1.0.1 // indirect
	github.com/tweekmonster/luser v0.0.0-20161003172636-3fa38070dbd7 // indirect
	github.com/uber/jaeger-client-go v2.30.0+incompatible // indirect
	github.com/uber/jaeger-lib v2.4.1+incompatible // indirect
	github.com/xanzy/ssh-agent v0.3.3 // indirect
	github.com/zclconf/go-cty v1.13.2 // indirect
	go.uber.org/atomic v1.9.0 // indirect
	golang.org/x/crypto v0.17.0 // indirect
	golang.org/x/exp v0.0.0-20231110203233-9a3e6036ecaa // indirect
	golang.org/x/mod v0.14.0 // indirect
	golang.org/x/net v0.19.0 // indirect
	golang.org/x/sync v0.5.0 // indirect
	golang.org/x/sys v0.15.0 // indirect
	golang.org/x/term v0.15.0 // indirect
	golang.org/x/text v0.14.0 // indirect
	golang.org/x/tools v0.15.0 // indirect
	google.golang.org/genproto/googleapis/rpc v0.0.0-20230706204954-ccb25ca9f130 // indirect
	google.golang.org/grpc v1.57.1 // indirect
	google.golang.org/protobuf v1.31.0 // indirect
	gopkg.in/warnings.v0 v0.1.2 // indirect
	gopkg.in/yaml.v3 v3.0.1 // indirect
	lukechampine.com/frand v1.4.2 // indirect
) If you include this Run , and this will fetch all the packages for you go.mod go tidy Footnotes I fought against permissions for 5 days. The predefined GCP role brought in the additional permissions needed. 1* serviceAccountAdmin

STATMUSE

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Since 2014

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STATMUSE

statmuse.com

11-50 employees

Since 2014

COMPANY RANKING

STATMUSE

EVERGREEN INDEX #10505

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