Daato

Daytona, a pioneering development environment management (DEM) platform offering secure and scalable hybrid development environments, just closed a $2 million pre-seed funding round. I've spoken with CEO and co-founder to learn more about the investment, Daytona's traction, and future plans. Ivan Burazin How much money did you raise in this round? We raised $2 million, which exceeded our initial target. There was tremendous interest from leading dev tools investors who recognized the potential of our solution. Who participated in the round? When we structured our Pre-Seed round, we intentionally sought support from experienced founders and key players in the developer tool industry, individuals whose reputations are linked to the tools you probably use daily. We received backing from angel investors like Abhinav Asthana (Founder of ), Prashanth Chandrasekar (CEO of ), Charity Majors (Co-Founder of ), Paul Copplestone (Co-Founder of ), Luke Kanies (Founder of ) and many others. Their domain expertise will help us refine our product and scale efficiently. Postman StackOverflow Honeycomb Supabase Puppet To bolster our capacity to meet growing demand, we also teamed up with Emerging Europe, a firm managing $2.7 billion in assets and an investor in over 35 billion-dollar enterprises such as GitLab, Twilio, Algolia, among others. Additionally, we're excited to reveal involvement from , , and , all of whom have supported top-tier companies, including Anthropic, Stripe, and OpenAI. 500 Tiny.vc Silicon Gardens Firestreak Ventures One of our angels, Christian Bach from , was impressed by our dedication to improving developer environments. He believes in our mission to make building more straightforward, a mission that's in line with the larger aim of boosting developer efficiency. Netlify standardized, secure developer environments What is your company’s current valuation? The round was done on an SAFE, reflecting the immense market potential for secure, self-hosted development environments tailored to enterprise needs. Our early traction with large Fortune 500 customers validates the strong demand. uncapped What does Daytona offer today? Daytona provides a self-hosted that gives enterprises an enterprise-grade alternative to GitHub Codespaces. Developers can spin up secure, compliant coding environments matching production infrastructure with a simple CLI command or click in the Daytona dashboard. development environment management platform Daytona eliminates complex configuration and security challenges while empowering developers with flexible, instantly available workspaces. We aim to be the industry standard for dev environments served through internal developer platforms. How do you differentiate from competitors? Unlike other development environment managers, Daytona is designed for self-managed deployment on internal infrastructure, whether cloud or local data center. This enables complete data control, security, and policy enforcement for regulated industries. Who are the founders and key team members? My co-founders, (CTO) and (Chief Architect), and I lead an experienced team of cloud dev tools veterans. We have pioneered the cloud IDE space with and have over 15 years of experience understanding developer needs. Building on this experience, Daytona offers unmatched flexibility, security, and simplicity. Vedran Jukic Goran Draganic Codeanywhere Our technical leadership designed Daytona's architecture leveraging Kubernetes, Kata Containers, and other leading orchestration technologies. We're a product-obsessed team focused on delivering exceptional solutions. How will you invest the funding? The funding will help us accelerate hiring across engineering, sales, and marketing to keep pace with customer demand. We'll also be refining our product capabilities, exploring strategic partnerships, and ramping up sales and marketing efforts. What’s exciting about your traction? We already have large enterprise customers in industries like aerospace, banking, and telecommunications. Our early success in securing Fortune 500 customers indicates the immense market appetite for enterprise-tailored development environments. What are your revenue goals? As this is an early stage, we are focused on product-market fit and scaling our customer base. In the forthcoming period, our goal is to double our revenue. Our vision is ambitious but achievable based on the enthusiasm we're seeing. What's your favorite pitch deck slide? My favorite slide captures the all-too-familiar pre-coding routine developers endure. It illustrates the steps before actual coding can begin, such as switching git branches, refreshing local environments, downloading libraries, installing tools, enduring build times, updating dependencies, compiling the project, and waiting for tests to run. When scaled across multiple projects and teams, this sequence of tasks significantly amplifies the inefficiency and frustration. This slide resonates deeply with our audience because it highlights the universal pain points in the development process, and it sets the stage for Daytona to present our compelling solution. It effectively underscores the necessity for a system like ours, which streamlines these cumbersome processes, allowing developers to focus on what they do best—coding. What problem does Daytona solve? Daytona addresses the challenges enterprises face in building secure, compliant development environments cost-effectively. We eliminate the need for expensive in-house platforms by providing an off-the-shelf solution purpose-built for regulated industries. The rise of infrastructure-as-code and integrated IDEs created the perfect timing for our standardized environment approach. We make robust dev environments accessible. What are the biggest risks? There is always execution risk with bringing a new product to market. However, our experience, technical expertise, customer validation, and exceptional investor support give us confidence. Our biggest challenge is keeping up with surging enterprise demand. We'll be smart about scaling sustainably. Why will Daytona grow exponentially? Every software team needs standardized environments to eliminate configuration drift and boost velocity. We have a track record as founders who helped pioneer cloud-based dev tools. With tailored enterprise capabilities, incredible support, and perfect timing, Daytona is poised for tremendous growth. The market pain points are clear, the solution works, and customers are validating the value. We enable enterprises to deliver better software faster through Daytona's simplicity and security. It's a very exciting time!

Interview with Daytona CEO Ivan Burazin on $2M Pre-Seed Round

The Noonification: DataOps: the Future of Data Engineering (9/17/2023)

The Noonification: DataOps: the Future of Data Engineering (9/9/2023)

In today's dynamic digital landscape, data is frequently dubbed as the new oil. However, as with crude oil, the true value of data lies not in its raw state but in its refinement: processing, analyzing, and leveraging it to inform decisions. and Data Engineering often intertwine, especially as DevOps principles are increasingly applied in the data domain, giving rise to the term "DataOps." As data volumes have surged and the pace of business has intensified, the methodologies used to manage and harness data have consequently evolved. Enter DataOps. DevOps Introduction to DataOps At its core, DataOps can be understood as the application of DevOps principles to data workflows. If DevOps is about enhancing software development through continuous delivery, integration, and collaboration, DataOps takes a leaf out of that book but specifically targets the unique challenges and intricacies of the data domain. The focus here is not just on the data itself but on the processes, systems, and teams that handle it, ensuring a streamlined flow of data through its lifecycle. Definition: The concept of DataOps didn't emerge in isolation. It was born out of a confluence of needs. With businesses increasingly relying on data-driven insights and with data sources becoming more varied and voluminous, traditional data management practices began to show their limitations. Speed, scalability, and collaboration became paramount. DataOps, as an approach, traces its roots back to these industry demands and the successful paradigms established by DevOps. Over time, as the industry started recognizing the gaps between data teams (from engineers to scientists to analysts) and the operational challenges they faced, DataOps began to solidify as a distinct discipline in data management. Evolution: Parallels between DevOps and DataOps The continuous evolution in technology and business demands has brought about various methodologies and practices. While DevOps primarily emerged as a bridge between software development and IT operations, ensuring swift and efficient software releases, its foundational principles found resonance in another crucial domain: data engineering. This synergy paved the way for DataOps. To grasp the essence of DataOps, one must first understand its parallels with DevOps. Shared Principles: Just as DevOps emphasizes the and infrastructure changes to ensure consistent and rapid delivery, DataOps recognizes the need for automating data pipelines. This automation reduces manual intervention, minimizes errors, and accelerates data processing. For instance, where DevOps might automate code testing and deployment, DataOps automates data validation, transformation, and loading processes. Imagine a global e-commerce platform. Where DevOps might ensure that the platform itself remains bug-free and user-friendly through automated testing, DataOps ensures that when a customer views a product, the stock count, price, and other product details are up-to-date and accurate, achieved through automated data validation and loading. Automation: automation of software deployment The heart of DevOps lies in the , which ensures that code changes are continuously integrated, tested, and delivered to production. Similarly, DataOps adopts CI/CD but in the realm of data. It ensures that data from various sources integrates seamlessly, is consistently refined, and is delivered to data warehouses or analytics tools without hiccups. This continuous flow ensures that businesses always have access to timely and reliable insights. Continuous Integration and Continuous Delivery (CI/CD): CI/CD pipeline DevOps introduced a culture where developers and IT operations teams collaborated closely, breaking down silos. DataOps extends this collaborative approach to include data engineers, data scientists, and business stakeholders. The objective is the same: fostering an environment where cross-functional collaboration results in optimal outcomes, whether it's software products in DevOps or data-driven insights in DataOps. For instance, a tech company launching a new device might need both software updates (managed by DevOps) and real-time market data for pricing and promotion (handled by DataOps). Seamless collaboration between these teams can ensure a successful launch. Collaboration: Customization for Data: While the foundational principles might be shared, it's essential to understand that data has its own set of complexities. This is where DataOps customizes these principles. Unlike code repositories, which are relatively standardized, data can come from a plethora of sources: IoT devices, user interactions, databases, third-party APIs, and more. DataOps ensures that the principle of continuous integration is agile enough to handle this diversity, integrating data from varied sources into a cohesive dataset. Diverse Data Sources: Data isn't always clean or reliable. Unlike software bugs that are generally consistent in nature, data anomalies can be random and multifaceted. DataOps, drawing from the automation principle, incorporates automated data quality checks and validation processes, ensuring that the data utilized is accurate and trustworthy. Data Quality and Integrity: Data volumes are ever-growing. While DevOps deals with the scalability of applications and infrastructure, DataOps must ensure that and pipelines scale efficiently, processing ever-larger datasets without performance bottlenecks. Scalability and Performance: data infrastructures In essence, DataOps is not just a mere adoption of DevOps principles but a meticulous adaptation, ensuring these principles serve the unique demands and challenges of the data world. It's this careful melding of principles and customizations that makes DataOps a vital methodology in today's data-driven enterprises. Importance of DataOps In the evolving landscape of data-driven decision-making, organizations are under constant pressure to efficiently utilize their data resources. With the sheer volume and diversity of data at play, traditional data management techniques often fall short of delivering timely insights. DataOps, as a solution, steps into this arena, emphasizing not just the methods but the value it brings to the table. The importance of DataOps is manifold, addressing time-to-value, collaborative efficiency, and data quality with remarkable efficacy. Time-to-Value: In the world of business, time is money. The quicker raw data is converted into actionable insights, the faster organizations can make informed decisions, be it launching a new product, refining marketing strategies, or identifying operational inefficiencies. DataOps plays a pivotal role here: Streamlined Workflows: By applying continuous integration and delivery principles, DataOps ensures that data pipelines are smooth, reducing the lag between data acquisition and data consumption. Data validation, transformation, and loading are automated, minimizing manual interventions that often lead to delays. Automated Processes: Airlines use DataOps to quickly process vast amounts of flight data, weather conditions, and passenger preferences to optimize flight schedules, pricing, and in-flight services. This immediate processing can lead to improved customer experience and efficient operations. Collaborative Efficiency: The true potential of data is realized when diverse teams —including data engineers, data scientists, and business stakeholders—work in unison. DataOps fosters this synergy. DataOps encourages the use of platforms where teams can collaboratively view, access, and work on data. This shared workspace reduces back-and-forths and promotes parallel processing. Unified Data Platforms: With clear communication channels, teams are better aligned in their goals, ensuring that the data engineering process serves the larger business objectives. Shared Objectives: Data Quality and Reliability: High-quality insights require high-quality data. With the vastness and varied nature of data sources, ensuring data consistency and reliability is paramount. This is where DataOps shines: Just as code undergoes tests in DevOps, DataOps incorporates automatic data quality assessments, ensuring anomalies are detected and rectified early in the pipeline. Consider financial institutions like , where trading algorithms are based on vast amounts of data. It's not just about quantity; the quality of this data is paramount. A single discrepancy could lead to significant financial discrepancies. Through DataOps' automated quality checks, the bank ensures that its algorithms operate on accurate, validated data, reducing potential risks. Automated Quality Checks: JP Morgan Borrowing from DevOps, DataOps often uses version control for data, ensuring that every stakeholder accesses the most recent, consistent version of the dataset. Version Control: Feedback Loops: Continuous monitoring of data pipelines means that any discrepancies in data quality are flagged and fed back into the system for improvements. This iterative approach enhances the reliability of the data over time. In the midst of an information age where data is plentiful but actionable insights are gold, DataOps stands as a beacon, guiding organizations to effectively harness their data potential. By focusing on quick turnarounds, collaboration, and quality, DataOps ensures that data engineering is not just about managing data but truly empowering businesses. Challenges Addressed by DataOps In today's digital era, businesses are inundated with data. Yet, while data is undeniably a valuable asset, it comes with its own set of challenges. These challenges, if not addressed, can hinder an organization's ability to make informed decisions, strategize effectively, and maintain a competitive edge. DataOps, as a forward-thinking methodology, aims to alleviate these pain points. Let's delve into the core challenges DataOps addresses: Scalability: With data streaming in from a myriad of sources, data infrastructures often buckle under the strain of ever-expanding data volumes. Traditional systems might be ill-equipped to handle this influx, leading to performance degradation and bottlenecks. DataOps encourages the use of cloud-based solutions and containerization, allowing data infrastructures to dynamically scale as per the data volume. This ensures smooth and efficient data processing irrespective of the data load. For instance, Netflix, known for its massive user base, leverages DataOps principles to handle petabytes of data daily, ensuring that their recommendation algorithms and content delivery networks run efficiently. Dynamic Scaling: Through continuous monitoring and feedback loops, DataOps ensures that resources are optimally utilized, preventing over-provisioning and wastage. Resource Optimization: Data Variety: Data heterogeneity poses another challenge. Organizations handle data that ranges from structured datasets in relational databases to unstructured data from social media, logs, and IoT devices. DataOps promotes the creation of platforms where diverse datasets can be integrated, transformed, and standardized, thus offering a cohesive view of data. Coca-Cola, for example, integrates data from various sources such as sales figures, social media feedback, and supply chain data using DataOps techniques to gain a holistic view of its global operations. Unified Data Platforms: DataOps practices often emphasize robust metadata management, aiding in understanding, categorizing, and utilizing diverse datasets effectively. Metadata Management: Real-time Processing: In an age where real-time insights can be a game-changer, the demand for real-time data processing has surged. This requires handling data streams efficiently and processing them without delays. DataOps ensures that data pipelines are designed for low latency, handling data streams efficiently and delivering real-time insights. utilize DataOps to process real-time data on traffic, driver availability, and user demand to efficiently match drivers with riders. Streamlined Pipelines: Companies like Uber DataOps often leans on event-driven architectures, which respond in real-time to data changes or specific events, ensuring timely data processing and analytics. Event-Driven Architectures: Data Security and Compliance: With data breaches becoming increasingly common and regulations like GDPR in place, data security and compliance cannot be overlooked. DataOps emphasizes encrypting data at rest and in transit, ensuring that sensitive information is always secure. End-to-End Encryption: With tools integrated into the DataOps workflow, compliance checks are automated, ensuring data handling adheres to regulatory standards consistently. Automated Compliance Checks: Access Control: Role-based access controls are instituted, making sure that only authorized personnel can access and modify sensitive data. In addressing these challenges, DataOps transforms the daunting task of data management into a streamlined, efficient, and secure process. Organizations armed with DataOps are better positioned to harness their data's potential, ensuring that data-related challenges are not roadblocks but mere stepping stones toward a data-driven future. Best Practices for Implementing DataOps Implementing DataOps can significantly refine an organization's data engineering processes, but the effectiveness of this methodology hinges on the adoption of best practices. These practices act as guideposts, ensuring that DataOps not only integrates smoothly into the existing data ecosystem but also realizes its transformative potential. Here's a comprehensive look at these best practices: Setting Objectives: Defining the North Star: Before diving into DataOps, organizations must outline their primary goals. Whether it’s streamlining data processing, bolstering data quality, or fostering inter-departmental collaboration, a well-articulated objective is pivotal. Companies like Airbnb have emphasized the importance of setting clear objectives when transitioning to DataOps, which allowed them to streamline their vast property and user data more efficiently. Key Performance Indicators (KPIs) act as tangible metrics of success. By setting quantifiable targets, organizations can gauge the efficacy of their DataOps practices and iteratively refine their approach. Without a set target, many organizations find themselves adrift in the vast sea of data, leading to wasted resources and missed opportunities. Quantifying Success: Building the Right Team: The essence of DataOps lies in interdisciplinary teamwork. Assembling a diverse group comprising data scientists, engineers, and operations experts ensures a holistic approach to data challenges. Tech giants like Google stress the importance of diverse teams, drawing on varied expertise to tackle complex data scenarios. Cross-functional Collaboration: The fluid nature of data mandates that the team remains updated with prevailing trends and methodologies. Regular workshops and training sessions help maintain a cutting-edge team. Moreover, fostering soft skills like adaptability, effective communication, and problem-solving enhances the team's ability to navigate the challenges of DataOps efficiently. Continuous Training: Choosing the Right Tools: The market is inundated with tools designed for containerization, orchestration, version control, and monitoring. Organizations should carefully assess their requirements, conduct pilot tests, and choose tools that align with their objectives and infrastructure. The plethora of available tools can be overwhelming. Prioritizing tools like Apache Airflow for orchestration or Docker for containerization, after meticulous assessment, can be beneficial. It’s also prudent to be wary of tools that promise the moon but might not align with the organization's specific needs or existing infrastructure. Assessment and Evaluation: The chosen tools should seamlessly integrate with existing systems, ensuring that the transition to a DataOps approach is smooth and devoid of disruptions. Integration Capabilities: Promoting a DataOps Culture: Collaboration is the bedrock of DataOps. Creating a milieu where open dialogue is the norm and where teams from diverse domains unite to solve data challenges is paramount. Organizations that sideline this collaborative ethos often find themselves grappling with inefficiencies, even if they possess advanced tools. Fostering Collaboration: Just as continuous integration and delivery are integral to DataOps, so is continuous feedback. Regularly collecting feedback from team members and stakeholders and acting on it refines the DataOps process over time. Feedback Loops: In the fast-evolving world of data, learning never stops. Promoting a culture where team members are encouraged to learn, experiment, and innovate ensures that the organization remains at the forefront of data management best practices. Lifelong Learning: In summary, while DataOps holds the promise of revolutionizing , the key to unlocking this potential lies in adhering to these best practices. They act as the bedrock upon which successful DataOps implementation is built, ensuring that data processes are agile, efficient, and in perfect harmony with business objectives. data engineering Conclusion The Transformative Potential of DataOps As we've journeyed through the intricacies of DataOps, one thing is clear: its transformative potential in the realm of data engineering is immense. This isn't just another buzzword or fleeting trend. It represents a paradigm shift. By incorporating DevOps principles into data workflows, organizations can experience heightened efficiency. Gone are the days of siloed operations, where data engineers, data scientists, and business stakeholders worked in isolation. DataOps bridges these divides, fostering a collaborative ecosystem where data-driven decisions are expedited, aligned with business objectives, and rooted in high-quality, reliable data. Future Outlook of DataOps Looking ahead, the future of DataOps seems promising and exciting. As technology continues to advance, we can anticipate the emergence of even more sophisticated tools that further simplify data engineering tasks, promoting automation and ensuring even tighter integration of data processes. Moreover, as organizations globally recognize the merits of DataOps, we might witness the development of new methodologies, best practices, and standards that further refine this discipline. Furthermore, the growing emphasis on and machine learning will likely intertwine with DataOps. This union will give birth to intelligent data operations, where predictive analytics, automation, and adaptive data workflows become the norm. artificial intelligence In conclusion, DataOps stands at the cusp of revolutionizing the world of data engineering. Its principles, methodologies, and practices hold the key to navigating the complexities of today's data-driven world. As organizations continue to embrace and evolve with DataOps, the future of data engineering looks bright, collaborative, and extraordinarily efficient.

DataOps: the Future of Data Engineering

ডেটাঅপস: ডেটা ইঞ্জিনিয়ারিংয়ের ভবিষ্যত

DataOps: die Zukunft des Data Engineering

DataOps: el futuro de la ingeniería de datos

DataOps : l'avenir de l'ingénierie des données

डेटाऑप्स: डेटा इंजीनियरिंग का भविष्य

DataOps: データ エンジニアリングの未来

DataOps: 데이터 엔지니어링의 미래

DataOps: o futuro da engenharia de dados

DataOps: будущее инженерии данных

DataOps: Veri Mühendisliğinin Geleceği

DataOps: Tương lai của Kỹ thuật dữ liệu

DataOps：数据工程的未来

Introduction: Data engineering has evolved into specialized fields like DataOps and VectorOps to address the challenges of managing data and vector embeddings, respectively. As large language models (LLMs) like GPT-4 emerge, managing high-dimensional data structures becomes increasingly important. LangChain, an LLM-powered application development framework, integrates with DataOps and VectorOps processes and utilizes vector databases to create data-aware, interactive applications. DataOps: Streamlining Data Management and Processes: DataOps focuses on enhancing collaboration, integration, and automation of data flows across organizations. Implementing DataOps practices promotes data accuracy, consistency, and accessibility, which are vital for data-aware applications using LLMs. Key DataOps technologies include Apache Kafka for real-time data streaming, Apache Airflow for workflow management, and Apache NiFi for data routing and transformation. References: https://medium.com/codex/5-steps-to-implementing-a-successful-dataops-practice-f553effd7367 https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2022/01/24/how-to-implement-a-successful-dataops-strategy-and-why-it-matters/?sh=56e354d5e87c https://www.linkedin.com/pulse/dataops-practical-understanding-implementation-taaha-khan-/ VectorOps: Tackling High-Dimensional Data Challenges: VectorOps specializes in managing vector embeddings, a critical component of LLMs. Traditional databases struggle with high-dimensional data structures, whereas vector databases excel in handling them. Integrating VectorOps with DataOps processes enables efficient large-scale vector embedding management, paving the way for innovative applications using LLMs. Popular vector database technologies used in VectorOps include FAISS, Milvus, and Pinecone. References: https://thenewstack.io/vector-databases-long-term-memory-for-artificial-intelligence https://www.pinecone.io/learn/vector-database/ https://techgenix.com/rise-of-vector-databases/ LangChain: Building Data-Aware LLM Applications: LangChain facilitates the development of applications that connect to data sources, interact with their environment, and utilize the power of LLMs. It offers various modules, such as Models, Prompts, Memory, Indexes, Chains, and Agents. The Indexes module focuses on structuring documents for optimal LLM interaction and retrieval, making it an excellent candidate for VectorOps and vector database integration. LangChain supports integration with LLMs like GPT-4, BERT, and RoBERTa. """
Example of creating a chat bot with GPT-Index & LangChain.
"""

from llama_index import SimpleDirectoryReader, GPTListIndex, readers, GPTSimpleVectorIndex, LLMPredictor, PromptHelper
from langchain import OpenAI
import sys
import os
from IPython.display import Markdown, display


def construct_index(directory_path):
    # set maximum input size
    max_input_size = 4096
    # set number of output tokens
    num_outputs = 2000
    # set maximum chunk overlap
    max_chunk_overlap = 20
    # set chunk size limit
    chunk_size_limit = 600 

    # define LLM
    llm_predictor = LLMPredictor(llm=OpenAI(temperature=0.5, model_name="text-davinci-003", max_tokens=num_outputs))
    prompt_helper = PromptHelper(max_input_size, num_outputs, max_chunk_overlap, chunk_size_limit=chunk_size_limit)
 
    documents = SimpleDirectoryReader(directory_path).load_data()
    
    index = GPTSimpleVectorIndex(
        documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
    )

    index.save_to_disk('index.json')

    return index

def ask_ai():
    index = GPTSimpleVectorIndex.load_from_disk('index.json')
    while True: 
        query = input("What do you want to ask? ")
        response = index.query(query, response_mode="compact")
        display(Markdown(f"Response: <b>{response.response}</b>")) References: https://python.langchain.com/en/latest/index.html https://github.com/hwchase17/langchain https://github.com/gkamradt/langchain-tutorials/blob/main/LangChain Cookbook Part 2 - Use Cases.ipynb Integrating DataOps, VectorOps, and LangChain: DataOps, VectorOps, and LangChain integration create powerful applications that combine efficient data management, high-dimensional data processing, and LLM capabilities. Some key advantages of this integration include: Enhanced similarity search: LangChain's integration with vector databases and VectorOps enables efficient near-neighbor matching, providing comprehensive results beyond traditional keyword-based searches. Scalability: DataOps and VectorOps processes ensure the efficient handling of large-scale vector embeddings, making the framework ideal for managing data in the era of LLMs and other complex models. """
As described here: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/pinecone.html
"""

import pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import Pinecone
from langchain.document_loaders import TextLoader

loader = TextLoader('../../../state_of_the_union.txt')
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

# initialize pinecone
pinecone.init(
    api_key=PINECONE_API_KEY,  # find at app.pinecone.io
    environment=PINECONE_ENV  # next to api key in console
)

index_name = "langchain-demo"

docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)

# if you already have an index, you can load it like this
# docsearch = Pinecone.from_existing_index(index_name, embeddings)

query = "What did the president say about Ketanji Brown Jackson"
docs = docsearch.similarity_search(query)

print(docs[0].page_content) Flexibility: LangChain allows users to perform searches based on high-level descriptions rather than relying on specific keywords or metadata classifications, enabling more versatile applications. Reference: https://mattboegner.com/knowledge-retrieval-architecture-for-llms/ https://simonwillison.net/2023/Jan/13/semantic-search-answers/ https://www.codingthesmartway.com/unlocking-the-power-of-llms-with-langchain-a-beginners-guide/ https://python.langchain.com/en/latest/modules/models/llms/integrations.html Conclusion: By focusing on DataOps, VectorOps, and LangChain, developers can create advanced, data-driven applications that leverage the power of LLMs and vector embeddings. Efficient data management through DataOps, coupled with VectorOps' expertise in high-dimensional data processing, unlocks the full potential of LLMs like GPT-4, BERT, and RoBERTa. LangChain serves as the bridge between these two fields, enabling developers to build innovative applications that can revolutionize industries and reshape the future of data-driven solutions. https://www.youtube.com/watch?v=UO699Szp82M&embedable=true As the need for data-aware applications and large-scale vector embedding management grows, the synergistic integration of DataOps, VectorOps, and LangChain will play a critical role in shaping the next generation of data-driven applications. By keeping up-to-date with the latest developments in these fields and embracing cutting-edge technologies, developers can stay ahead of the curve and lead the way toward a data-centric future.

The New Data Engineering Landscape: DataOps, VectorOps, and LangChain

Some 95 percent of businesses say they struggle with processing complex data, such as unstructured records and multi-source flows, according to . At the same time, about 90 percent of all data out . This is in itself indicative of the data conundrum facing businesses today. Data is growing more complex by the day, and to keep up with it, companies are forced to build, operate, and constantly upgrade a sophisticated low-latency pipeline infrastructure. TechJury there is unstructured Building even a simple Extract, Transform, Load (ETL) pipeline requires tireless hours of coding—several weeks on average. Furthermore, ETL requires efforts from multiple teams. First, data engineers must build data collection tools and ensure data validity. Software engineers must then develop the pipelines to pull the data into warehouses or lakes for analysts, the end-users, to make sure it is aligned with the business question. If it isn’t, as it happens way too often, the process goes back to square one, bleeding the company’s budget dry. The resulting workflow is linear and rigid: developers are usually forced to wait before data engineers are done, and analysts, for their part, have to wait for them both. Analysts cannot generate insights without enough data, which in turn requires a pipeline up and running. So what ends up happening is too many teams have nothing to do as the expenses continue to tick up. Even worse, in more dynamic environments, data collected with so much effort and investment can simply become outdated before the set is even complete. Furthermore, in my own experience, even the most business-minded tech enthusiasts are seldom excited about data. Developers would much rather focus on the product’s functionality than the boring data collection itself. Data engineers prefer handling data integrity, their core task, to the company’s informational plumbing. And as for data scientists, I imagine few of them get their fix spending . hours loading and cleaning the data More than just lost time The flaws of this approach stretch beyond the realms of wasted time and high costs. Today’s data-management philosophy is code-centric, meaning everyone involved, including the analysts, must be a coder. Data and software engineers write code to start capturing and preprocessing data. Then, analysts and data scientists must write code to fetch it and make the extra transformations needed for analysis or training models. Even in the rare case where all of them are outstanding coders, the result is still sluggish due to the use of Python and Java, interpreted languages that are generally slower than compiled ones. Another implication is that the legacy companies trying to tap data, the data immigrants, face an increasingly uphill struggle. They often lack the tech aptitude of their new-age counterparts, and the colossal price tag and time associated with developing data infrastructure will only weigh them down more. By the same account, companies who want to tap new data sources have to go through the full process as well, which slows down their progress. Tougher still, nobody is really able to innovate with data simply because even the most basic analysis requires substantial investment. Innovation takes time and requires all hands on deck, but with today’s DataOps workflow, both are a luxury. Day-to-day tasks simply take all of that away, leaving data teams no room for experimentation and more creative ideas. The problem comes down to the reliance on the ETL mantra. Building a rigid data infrastructure made up of pipelines tailored to very specific business questions with limited transformation abilities only perpetuates the conundrum at hand. It is heavy on resources and hampers productivity and innovation while failing to drive real progress. To truly unlock their potential, companies looking to tap data must embrace a whole new design and management paradigm. The simplistic ETL just doesn’t cut it. Businesses need data playgrounds, solutions that will make the necessary processed data instantly accessible to those in the enterprise who need it. Building the data playground At its core, a data playground is a strategy that moves the focus from code to the data itself, making it easily accessible to relevant teams with little need for further development. It is rooted in the idea of collaborative design that fosters cooperation between teams instead of locking them into a rigid linear sequence of tasks. While all teams retain their overall functions, the process breaks up the dependencies between them, allowing all of them to focus on their own tasks. At the core of the strategy is the playground itself, a software platform powering all of the company’s data operations. Software engineers will use this platform to develop Data Apps, which would feature both extraction and transformation capabilities. Data engineers would then use these Apps to produce validated data sources, ready to fuel the analysis by data scientists, who will be able to make extra transformations using the App’s built-in functions, if needed. The approach leaves the code-heavy tasks on the backend to the engineers, while the analysts are free to use the low-code to no-code front-end to build up new pipelines and transform data on the go. The playground brings more than the basic ETL to the table since it also allows the analysts to configure, redesign, and create a data flow on the go, within hours, not weeks. By collecting the backlog of Data Apps and data sources, the company can conduct integrated analysis to streamline the data operations and workflows, enriching the playground in an offline manner. Businesses looking to scale up with data need to understand success is not just about collecting as much data as possible. It is just as much, if not even more, about how you manage what you have collected, and whether you can make changes and amendments on the go without having to rebuild the entire pipeline and waiting for more data. Granted, it’s a careful balancing act. But to be flexible, innovative, and ultimately effective, businesses must manage their DataOps in a way that plays to the employees’ strengths and makes data easy for them to access, not gathering it for months and years on end with no use.

Data Playgrounds are The Cure for Slow and Inefficient DataOps

Procesando Datos Para Deep Learning: Datasets, visualizaciones y DataLoaders en PyTorch

Por favor clic el artículo original： http://www.octoparse.es/blog/70-fuentes-de-datos-gratuitas-en-2020 Todos gran visualización de datos comienza con datos buenos y limpios. La mayoría de la gente cree que recopilar grandes datos sería un trabajo difícil, pero simplemente no es cierto. Hay miles de conjuntos de datos gratuitos disponibles en línea website, listos para ser analizados y visualizados por cualquier persona. Aquí hemos reunido 70 fuentes de datos gratuitas para 2020 sobre , gobierno, delincuencia, salud, datos financieros y económicos, marketing y redes sociales, periodismo y medios, bienes raíces, directorio, revisión de empresas y más. Fuente de Datos Gratuita: Gobierno : Es la primera etapa y proporciona información gratuita sobre el clima y la delincuencia en la web por parte del gobierno de los Estados Unidos. Data.gov : Aquí hay conjuntos de datos de todos los departamentos centrales del Reino Unido, así como de muchas otras autoridades locales y públicas. Actúa como un portal a todo tipo de información sobre todo, incluyendo negocios y economía, crimen y justicia, defensa, educación, medio ambiente, gobierno, salud, sociedad y transporte. Data.gov.uk : Este sitio contiene las últimas estadísticas gubernamentales sobre la vida de los ciudadanos estadounidenses, incluida la población, la economía, la educación, la geografía y más. U.S. Census Bureau : Datos sobre todos los países del mundo; se enfoca en temas de historia, gobierno, población, economía, energía, geografía, comunicaciones, transporte, militares y transnacionales para 267 países. The CIA World Factbook : Socrata es una compañía de software impulsada por la misión que es otro lugar interesante para explorar datos relacionados con el gobierno con algunas herramientas de visualización integradas. Sus datos como servicio han sido adoptados por más de 1200 agencias gubernamentales para datos abiertos, gestión del desempeño y gobierno basado en datos. Socrata : Portal de datos abiertos de la Unión Europea: es el único punto de acceso a una creciente gama de datos de las instituciones y otros organismos de la Unión Europea. Los aumentos de datos incluyen el desarrollo económico dentro de la UE y la transparencia dentro de las instituciones de la UE, incluidos datos geográficos, geopolíticos y financieros, estadísticas, resultados electorales, actos jurídicos y datos sobre delincuencia, salud, medio ambiente, transporte e investigación científica. Podrían reutilizarse en diferentes bases de datos e informes. Y más, una variedad de formatos digitales están disponibles en las instituciones de la UE y otros organismos de la UE. El portal proporciona un catálogo estandarizado, una lista de aplicaciones y herramientas web que reutilizan estos datos, un editor de consultas de punto final SPARQL y acceso a la rest API, y consejos sobre cómo hacer el mejor uso del sitio. European Union Open Data Portal es un proyecto piloto con muchos conjuntos de datos gubernamentales y geoespaciales. Le ayuda a explorar cómo el gobierno de Canadá crea una mayor transparencia, responsabilidad, aumenta la participación ciudadana e impulsa la innovación y las oportunidades económicas a través de datos abiertos, información abierta y diálogo abierto. Canada Open Data : ofrece datos abiertos del gobierno de US, La UE, Canadá, CKAN y más. Datacatalogs.org : El Centro Nacional de Estadísticas de Educación (NCES) es la principal entidad federal para recopilar y analizar datos relacionados con la educación en los U.S./otras naciones. U.S. National Center for Education Statistics : La colección del Servicio de datos del Reino Unido incluye importantes encuestas patrocinadas por el gobierno del Reino Unido, encuestas transnacionales, estudios longitudinales, datos del censo del Reino Unido, agregado internacional, datos comerciales y datos cualitativos. UK Data Service Fuente de Datos Gratuita: Crimen : El Programa UCR ha sido el punto de partida para ejecutivos de la ley, estudiantes, investigadores, miembros de los medios de comunicación y el público que busca información sobre delitos en los Estados Unidos. Uniform Crime Reporting : Statistical crime informes y publicaciones estadísticas sobre delitos que detallan delitos específicos y describen tendencias para comprender las amenazas de delitos a nivel local y nacional. FBI Crime Statistics : información sobre todo lo relacionado con el sistema de justicia penal de los U.S., Incluidas las muertes relacionadas con el arresto, el censo de los reclusos en la cárcel, la encuesta nacional de laboratorios de delitos de ADN, Investigaciones de organismos encargados de hacer cumplir la ley, etc. Bureau of Justice Statistics : Búsqueda nacional de delincuentes sexuales: es un recurso de seguridad pública sin precedentes que permite el acceso público a los datos de delincuentes sexuales en todo el país. Presenta la información más actualizada proporcionada por cada Jurisdicción. National Sex Offender Search Fuente de Datos Gratuita: Salud : Aquí encontrará un archivo de datos comprimido de la base de datos Drugs@FDA. Drugs@FDA se actualiza diariamente, y este archivo de datos se actualiza una vez por semana, el martes. U.S. Food & Drug Administration : UNICEF gathers evidence on the situation of children and women around the world. The data sets include accurate, nationally representative data from household surveys and other sources. UNICEF : Estadísticas sobre nutrición, enfermedades y salud en más de 150 países. : 125 años de datos de atención médica de EE. UU., Incluidos datos de Medicare a nivel de reclamos, epidemiología y estadísticas de población. World Health Organisation Healthdata.gov : Conjuntos de datos de salud del Servicio Nacional de Salud del Reino Unido. La organización produce más de 260 publicaciones estadísticas oficiales y nacionales. Esto incluye datos comparativos nacionales para usos secundarios, desarrollados a partir de las estadísticas de episodios long-running hospitalarios que pueden ayudar a los tomadores de decisiones locales a mejorar la calidad y la eficiencia de la atención de primera línea. NHS Health and Social Care Information Centre Fuente de Datos Gratuita: datos financieros y económicos : Estadísticas educativas sobre todo, desde finanzas hasta indicadores de prestación de servicios. World Bank Open Data :Una fuente de información increíblemente útil que incluye informes de estabilidad financiera mundial, informes económicos regionales, estadísticas financieras internacionales, tipos de cambio, dirección comercial y más. IMF Economic Data : Acceso gratuito a datos detallados del comercio mundial con visualizaciones. UN Comtrade es un depósito de estadísticas oficiales de comercio internacional y tablas analíticas relevantes. Se puede acceder a todos los datos a través de la API. UN Comtrade Database : Con datos sobre más de 60,000 compañías que cubren 300 años, Global Financial Data proporciona una fuente especial única para analizar los giros y vueltas de la economía global. Global Financial Data : Cotizaciones y gráficos de acciones en tiempo real, noticias financieras, conversiones de divisas o carteras rastreadas Google Finance . : El Explorador de datos públicos de Google proporciona datos públicos y pronósticos de una variedad de organizaciones internacionales e instituciones académicas, incluidos el Banco Mundial, la OCDE, Eurostat y la Universidad de Denver. Estos se pueden mostrar como gráficos de líneas, gráficos de barras, diagramas de sección transversal o en mapas. Google Public Data Explorer :Estadísticas macroeconómicas y de la industria oficiales de U.S., Principalmente informes sobre el producto interno bruto (PIB) de los Estados Unidos y sus diversas unidades. También proporcionan información sobre ingresos personales, ganancias corporativas y gastos del gobierno en sus Cuentas Nacionales de Ingresos y Productos (NIPAs). U.S. Bureau of Economic Analysis :Plentiful links to anything related to finance, no matter how obscure, including World Development Indicators Online, World Bank Open Data, Global Financial Data, International Monetary Fund Statistical Databases, y . Financial Data Finder at OSU EMIS Intelligence : Datos macro, datos de la industria, datos de productividad, datos comerciales, finanzas internacionales, datos y más. National Bureau of Economic Research : Conjuntos de datos trimestrales de información extraída de las exhibiciones a informes financieros corporativos presentados a la Comisión. : Visualizaciones de datos sobre la economía. U.S. Securities and Exchange Commission Visualizing Economics :Financial Times proporciona una amplia gama de información, noticias y servicios para la comunidad empresarial global. Financial Times Fuente de Datos Gratuita: Marketing y Redes Sociales : Explore los conjuntos de datos públicos de Amazon Web Services por categoría para obtener una gran cantidad de información. Amazon API Gateway permite a los desarrolladores conectar de forma segura aplicaciones móviles y web a API que se ejecutan en Amazon Web (AWS) Lambda, Amazon EC2 u otros servicios web de acceso público que se alojan fuera de AWS. Amazon API : ASTA es la asociación de profesionales de viajes más grande del mundo. Proporciona información a los miembros, incluidas las agencias de viajes y las empresas cuyos productos venden, como tours, cruceros, hoteles, alquiler de automóviles, etc. American Society of Travel Agents :Social Mention es una plataforma de búsqueda y análisis de redes sociales que agrega contenido generado por los usuarios de todo el universo en un solo flujo de información. Social Mention : Google Trends muestra la frecuencia de ingresar términos de búsqueda específicos en varios idiomas en relación con el volumen de búsqueda total en todo el mundo. Google Trends : Aprenda a publicar y recuperar datos de Facebook utilizando la API Graph. Facebook API : La plataforma de Twitter conecta su sitio web o aplicación con la conversación mundial que tiene lugar en Twitter. : La plataforma de API de Instagram se puede utilizar para crear aplicaciones y servicios sin automatizados, auténticos y de alta calidad. Twitter API Instagram API : La API de Foursquare le brinda acceso a nuestra base de datos de lugares de clase mundial y la capacidad de interactuar con los usuarios y comerciantes de Foursquare. Foursquare API : Un gran depósito de datos de marketing. Puede encontrar las últimas estadísticas y tendencias de marketing aquí. También proporciona herramientas para marketing en redes sociales, gestión de contenido, análisis web, páginas de destino y optimización de motores de búsqueda. HubSpot : Información sobre SEO que incluye investigación de palabras clave, creación de enlaces, auditorías de sitios e información de optimización de páginas para ayudar a las empresas a tener una mejor visión de la posición que tienen en los motores de búsqueda y cómo mejorar su clasificación. Moz : Las últimas noticias, estudios e investigaciones sobre marketing de contenidos. Content Marketing Institute Fuente de Datos Gratuita: Periodismo y Medios – Busque artículos de Times desde 1851 hasta hoy, recuperando titulares, resúmenes y enlaces a multimedia asociados. También puede buscar reseñas de libros, listados de eventos de Nueva York, críticas de películas, historias principales con imágenes y más. The New York Times Developer Network :La de contenido AP le permite buscar y descargar contenido utilizando sus propias herramientas editoriales, sin tener que visitar los portales AP. Proporciona acceso a imágenes de AP, propiedad de un miembro y de terceros, y videos producidos por AP y terceros seleccionados. Associated Press API API : Es un motor de búsqueda en línea que registra las frecuencias de cualquier conjunto de cadenas de búsqueda delimitadas por comas utilizando un recuento anual de n-gramos encontrados en fuentes impresas entre 1500 y 2008 en los corpus de texto de Google. Google Books Ngram Viewer : Wikipedia offers free copies of all available content to interested users. Wikipedia Database : Es un sitio web que se centra en el análisis de encuestas de opinión, política, economía y blogs de deportes. LLos datos y el código en Github están detrás de la historia y la interacción de FiveThirtyEight. FiveThirtyEight : Google Scholar es un motor de búsqueda web de acceso gratuito que indexa el texto completo o los metadatos de la literatura académica en una variedad de formatos y disciplinas de publicación. Incluye la mayoría de las revistas y libros académicos en línea revisados ​​por pares, documentos de conferencias, tesis y disertaciones, preimpresiones, resúmenes, informes técnicos y otra literatura académica, incluidas las opiniones de los tribunales y las patentes. Google Scholar Free Data Source: Real Estate : Castles son una exitosa agencia independiente de propiedad privada. Establecidos en 1981, ofrecen un servicio integral que incorpora ventas residenciales, alquileres y administración, y encuestas y valoraciones. Castles : RealEstate.com sirve como el mejor recurso para los compradores de vivienda por primera vez, ofreciendo herramientas fáciles de entender y asesoramiento experto en cada etapa del proceso. Realestate.com : Gumtree es el primer sitio de anuncios clasificados gratuitos en el Reino Unido. Comprar y vender artículos, automóviles, propiedades y encontrar u ofrecer trabajos en su área está disponible en el sitio web. Gumtree : Proporciona un enfoque innovador de base de datos para ventas residenciales, alquileres y administración. James Hayward : Japan’s property website. Lifull Home’s : Italy’s property website. Immobiliare.it : Italy’s property website. Subito : El sitio web inmobiliario líder de Bélgica. Immoweb Fuente de Datos Gratuita: Directorio de Negocios y Revisión : LinkedIn es un servicio de redes sociales orientado a los negocios y el empleo que opera a través de sitios web y aplicaciones móviles. Tiene 500 millones de miembros en 200 países y puede encontrar el directorio de negocios aquí. LinkedIn : OpenCorporates es la base de datos abierta más grande de compañías y datos de compañías en el mundo, con más de 100 millones de compañías en un número similar de jurisdicciones. Nuestro objetivo principal es hacer que la información sobre las empresas sea más utilizable y esté más ampliamente disponible para el beneficio público, particularmente para abordar el uso de las empresas con fines criminales o antisociales, por ejemplo, corrupción, lavado de dinero y crimen organizado. OpenCorporates : La fuente original para encontrar y conectarse con fontaneros locales, personal de mantenimiento, mecánicos, abogados, dentistas y más. Yellowpages : Craigslist es un sitio web estadounidense de anuncios clasificados con secciones dedicadas a empleos, vivienda, anuncios personales, en venta, artículos buscados, servicios, comunidad, conciertos, currículums y foros de discusión. Craigslist : Fundado en 1886, GAF se ha convertido en el mayor fabricante de techos comerciales y residenciales de América del Norte (Fuente: estudio del Grupo Fredonia). Nuestro éxito en el crecimiento de la compañía a casi $ 3 mil millones en ventas ha sido el resultado de nuestra incesante búsqueda de la calidad, combinada con la experiencia líder en la industria y soluciones integrales para techos. Jim Schnepper es el presidente de GAF, una subsidiaria operativa de Standard Industries. Cuando busca proteger las cosas que más valora, estas son solo algunas de las razones por las que creemos que debe elegir GAF. GAF Master Elite Contractor : Aquí puede encontrar contratistas, remodeladores, instaladores o constructores en los US y Canadá en su proyecto residencial o comercial. CertainTeed : Toda la información sobre las empresas en California. Companies in California : Manta es uno de los mayores recursos en línea que ofrece productos, servicios y educativas oportunidades. El directorio de Manta cuenta con millones de visitantes únicos cada mes que buscan en la base de datos integral empresas individuales, segmentos de la industria y listados geográficos específicos. Manta : Directorio sobre startups en la UE.、 EU-Startups : Directorio de abogados. La Asociación de Abogados de Kansas (KBA) se fundó en 1882 como una asociación voluntaria para profesionales legales dedicados y tiene más de 7,000 miembros, incluidos abogados, jueces, estudiantes de derecho y asistentes legales. Kansas Bar Association Fuente de datos gratuita: Otros sitios web del portal : Directorio sobre software de negocios y reseñas. Capterra : Fuente de datos para empleos y oportunidades de carrera. Monster : Directorio sobre trabajos e información sobre información privilegiada sobre empresas con revisiones de empleados, herramientas salariales personalizadas y más. Glassdoor : Directorio sobre servicio de automóviles, MOT o reparación de automóviles The Good Garage Scheme : Información sobre fragancias. OSMOZ : Una herramienta de extracción de datos gratuita para recopilar todos los datos web mencionados anteriormente en línea. Octoparse

Big Data: 70 Increíbles Fuentes de Datos Gratuitas que Debes Conocer para 2020

বিগ ডেটা: 70টি অবিশ্বাস্য ফ্রি ডেটা উত্স যা আপনার 2020 এর জন্য জানা উচিত

Big Data: 70 unglaubliche kostenlose Datenquellen, die Sie für 2020 kennen sollten

Big Data : 70 sources de données gratuites incroyables à connaître pour 2020

बड़ा डेटा: 70 अविश्वसनीय मुफ़्त डेटा स्रोत जो आपको 2020 के लिए जानना चाहिए

ビッグデータ: 2020 年に知っておくべき 70 の驚くべき無料データ ソース

빅 데이터: 2020년에 알아야 할 70가지 놀라운 무료 데이터 소스

Big Data: 70 fontes de dados gratuitas incríveis que você deve conhecer para 2020

Большие данные: 70 невероятных бесплатных источников данных, которые вы должны знать в 2020 году

Büyük Veri: 2020 İçin Bilmeniz Gereken 70 İnanılmaz Ücretsiz Veri Kaynağı

Dữ liệu lớn: 70 nguồn dữ liệu miễn phí đáng kinh ngạc bạn nên biết cho năm 2020

大数据：2020 年您应该了解的 70 个令人难以置信的免费数据源

DAATO

daato.net

-- employees

Since n.d.

COMPANY RANKING

DAATO

EVERGREEN INDEX #3253

Representing this company? Click here to claim and customize this page!

Daato WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

DAATO

daato.net

-- employees

Since n.d.

COMPANY RANKING

DAATO

EVERGREEN INDEX #3253

RELATED COMPANIESRANK

STORIES ABOUT DAATO

Representing this company? Click here to claim and customize this page!

Daato WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!