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Stream Processing - Windows

Data Modeling - Entities and Events

Real-Time Data Processing with Kafka Streams: Simplifying Stream Processing Applications

What's Inside the Recent Massive Google Search API Leak?

সাম্প্রতিক ব্যাপক Google অনুসন্ধান API লিক ভিতরে কি?

Was steckt hinter dem jüngsten massiven API-Leck bei der Google-Suche?

¿Qué hay dentro de la reciente filtración masiva de la API de búsqueda de Google?

Que contient la récente fuite massive de l’API de recherche Google ?

हाल ही में हुए बड़े पैमाने पर गूगल सर्च एपीआई लीक के पीछे क्या है?

最近の大規模な Google 検索 API 漏洩の内容とは?

최근 대규모 Google 검색 API 유출 내용은 무엇입니까?

O que há por dentro do recente vazamento massivo da API de pesquisa do Google?

Что скрывается за недавней массовой утечкой API поиска Google?

Son Zamanlardaki Büyük Google Arama API Sızıntısının İçinde Neler Var?

Có gì bên trong vụ rò rỉ API tìm kiếm lớn gần đây của Google?

最近大规模的 Google 搜索 API 泄漏到底是怎么回事？

Unlocking the Power of Data Lakes for Embedded Analytics in Multi-Tenant SaaS

মাল্টি-টেন্যান্ট SaaS-এ এমবেডেড অ্যানালিটিক্সের জন্য ডেটা লেকের পাওয়ার আনলক করা

Entfesseln Sie die Leistungsfähigkeit von Data Lakes für eingebettete Analysen in Multi-Tenant-SaaS

Liberando el poder de los lagos de datos para análisis integrados en SaaS multiinquilino

Libérer la puissance des lacs de données pour l'analyse intégrée dans les SaaS multi-locataires

मल्टी-टेनेंट SaaS में एम्बेडेड एनालिटिक्स के लिए डेटा लेक्स की शक्ति को अनलॉक करना

マルチテナント SaaS の組み込み分析のためのデータレイクのパワーを解き放つ

멀티 테넌트 SaaS에 내장된 분석을 위한 데이터 레이크의 강력한 기능 활용

Desbloqueando o poder dos data lakes para análises incorporadas em SaaS multilocatário

Раскрытие возможностей озер данных для встроенной аналитики в мультитенантном SaaS

Çok Kiracılı SaaS'ta Gömülü Analitik için Data Lakes'in Gücünü Ortaya Çıkarma

Khai thác sức mạnh của Hồ dữ liệu cho Phân tích nhúng trong SaaS nhiều người thuê

释放数据湖的力量，实现多租户 SaaS 中的嵌入式分析

POWER LEADER: Piwik PRO's Piotr Korzeniowski on digital analytics and advertising under HIPAA

Why Embedded Analytics Matters: Unleashing Data Insights within Applications is becoming an indispensable capability for modern SaaS applications across industries. By embedding analytics directly into applications, insights can guide internal application users and external customers to enable better and faster decision-making. A strong embedded analytics solution that SaaS companies can benefit from starts with the data layer. Many SaaS companies try to determine the best database for their SaaS solution and quite often it becomes an AWS Redshift vs Snowflake comparison. Embedded analytics Exporting data to external business intelligence tools for analysis is becoming less common. Leading organizations are realizing the competitive advantage and of using live data within their apps, so choosing the right database matters. monetization opportunities Data Warehousing: The Engine Powering Embedded Analytics To enable real-time and/or multi-tenant embedded analytics, applications need a high-performance data warehousing layer that can efficiently process queries and serve up data analysis. The data warehouse organizes and stores data from various sources specifically for use cases that span reporting, data visualization, dashboards, and analytics applications. Choosing the right data warehouse is therefore critical. Choosing the Right Tool: Redshift vs Snowflake Two leading cloud data warehouse contenders that show great promise for embedded use cases are AWS Redshift and Snowflake. Both platforms offer advantages such as scalability and flexibility which suit them well for embedded analytics. We compare the two options across crucial criteria to determine which choice best meets embedded needs. Redshift vs Snowflake: Comparing Strengths and Weaknesses AWS Redshift AWS Redshift is a fully managed, petabyte-scale data warehousing service provided by (AWS). It is a cloud-based, massively parallel processing (MPP) database optimized for analytical and reporting workloads. This makes it useful for powering dashboards, ad-hoc queries, and data warehousing. Amazon Web Services Redshift provides fast query performance by using columnar storage and parallel processing to quickly analyze large datasets using multiple nodes. Many enterprises rely on Redshift given its ability to handle heavy analytics workloads. To manage those larger workloads, Redshift can scale storage and compute capacity independently. This offers you the flexibility to pay only for what you need. Scalability and Performance: Brute Force Meets Efficiency with Redshift A pioneer in cloud data warehousing, Redshift offers fast query performance leveraging a massively parallel processing (MPP) architecture optimized for high throughput analytics workloads. Redshift allows scaling compute and storage separately on demand, automatically distributing data across nodes. Performance remains high even with ultra-large datasets and complex queries. Users have reported 50-100x faster queries near the petabyte scale. Cost-Effectiveness: Pay-as-You-Go vs Predictability As part of AWS, Redshift offers pay-as-you-go pricing allowing optimization of costs based on current needs. However, costs can vary significantly based on changing query volumes, underlying data sizes, and other factors – making longer-term budgets and forecasts difficult. Cost optimization requires continual fine-tuning of Redshift clusters and workload monitoring. For embedded analytics specifically, this cost model requires careful management as SaaS usage is meant to grow over time. Deployment and Management: The AWS Ecosystem Advantage Being natively part of AWS, Redshift enables deployment leveraging other AWS services for storage, ETL, monitoring, and more. Companies already using AWS experience less management overhead as a result. But reliance on AWS also leads to vendor lock-in – migrating to other platforms would require significant re-architecture. User-friendliness: Is Redshift Beginner-Friendly? Redshift exposes a standard SQL interface for executing queries. However optimal configuration and cost management require deeper expertise in areas like cluster sizing, workload management, and query optimization. The platform may present a learning curve for beginners. Snowflake Snowflake is a cloud-based data warehousing service that offers a unique architecture optimized for scalability, flexibility, and performance in the cloud. It utilizes a multi-cluster, shared data architecture to efficiently separate storage and computing. This allows independent scaling of resources to match workload demands. Snowflake also has native support for public clouds AWS, Azure, and GCP cloud platforms. The decoupled storage/compute architecture can auto-scale clusters and warehouse capacity based on query volumes and data sizes. This provides high concurrency and performance, similar to Redshift. Snowflake uses a SQL database engine optimized for data warehousing workloads such as analytics, dashboards, reporting, etc. Elastic Power: Scale on Demand, Pay for What You Use with Snowflake Snowflake pioneered a unique cloud-native architecture optimized for flexibility and scalability. The decoupled storage and compute allow auto-scaling to handle extreme workloads without overload. Snowflake also offers per-second pricing – pay only for the capacity used per query without paying for idle clusters. This has similar concerns to Redshift for embedded analytics use cases. As SaaS usage increases, companies realize that usage remains consistent throughout the day, contrary to their initial expectations. These cost increases present challenges for using Snowflake with embedded analytics. Cloud Agnostic Freedom: Beyond the AWS Walls A multi-cloud and hybrid cloud option, Snowflake avoids vendor lock-in by deploying across AWS, Azure, and GCP. Snowflake offers easy migration between clouds with push-button cloud failover capabilities. Snowflake also offers flexibility to query data in external stores without copying across the warehouse. Rich Data Ecosystem: Seamless Integration and Collaboration Snowflake is a strong hub for sharing and exchanging data. It helps teams, partners, and other stakeholders access and collaborate on data easily. Snowflake also offers extensive compatibility with third-party tools. Future-Proof Innovation: Embracing the Evolution of Analytics With rapid innovation across query processing, security, compliance, and machine learning capabilities, Snowflake is leading the way in cutting-edge features for modern internal analytics. Their unique architecture choices make it easy to evolve the platform over time. Organizations can benefit from new capabilities without migrations. Embedded Analytics: Where Redshift and Snowflake Shine (and Stumble) Real-Time Insights: Delivering Data at the Speed of Thought to SaaS Users Embedded analytics requires querying and aggregating live, real-time data with minimal latency to drive contextual insights and guided action within apps. Both Redshift and Snowflake leverage MPP architectures to enable speedy analysis across large datasets. Slight advantages go to Snowflake for its adaptive elastic scaling and per-second pricing which optimizes costs for spiky query workloads common in real-time dashboards and applications. Simplicity and Integration: Seamless Embedding for User Delight For delightful embedded experiences, analytics components need easy integration and simple configuration within applications built using various programming languages, frameworks and platforms. Both data warehouses offer standard JDBC/ODBC connectivity for executing SQL queries from within apps. Redshift may have quicker learning curves for current AWS application teams. But Snowflake offers SDKs for more turnkey embedding across diverse tech stacks. Security and Compliance: Building Trust with Embedded Data Embedded analytics puts live data directly into apps, so security and controls are paramount. Both Snowflake and Redshift enable enterprise-grade user access controls, encryption and data governance capabilities leveraging the underlying cloud infrastructures. For highly regulated industries, Snowflake offers additional native capabilities to track data usage, mask sensitive data and implement fine-grained access policies. Big Data Challenges of Redshift vs Snowflake: When Volume and Variety Demand More As use cases expand to big data sources like , clickstreams or genomics data, the volume, velocity and variety of data can push conventional systems over the edge. Ingesting semi-structured data like JSON events get tricky. (Although ) IoT analytics Qrvey handles all data natively Serverless options on Snowflake like Snowpark handle varied data with less friction. Handling data volumes above 100s of TB can stretch Redshift capabilities. At massive scales, Snowflake better absorbs extreme spikes in storage and concurrent users. Picking the Champion for Your Use Case in This Redshift vs Snowflake Decision Cost Considerations: Balancing Budget and Performance AWS Redshift follows typical cloud pay-as-you-go pricing with node-based commitments. Cost efficiencies kick in at higher scales above a few TB. Snowflake’s per-second pricing and adaptive scaling remove overhead for idle clusters. But per-second billing can also lead to unexpected spikes on shared systems with uneven workloads. Cross-cloud deployment, data sharing and BYOL options on Snowflake provide more levers for optimization. Read more about or try our . Snowflake cost optimization Snowflake Cost Optimization Calculator Technical Requirements: Matching Capabilities to Needs Redshift provides a tightly coupled solution with quick time-to-value for simpler analytics integrated into AWS-centric application environments. More complex use cases like large-scale machine learning, and hybrid transactional/analytical processing may benefit from Snowflake’s more advanced architecture. Snowflake better fulfills needs for multi-cloud flexibility or rich data-sharing ecosystems. Choosing a Platform to Grow With: Redshift vs Snowflake Snowflake’s platform is cloud-based offering fast innovation in security, compliance, data science, and governance. This makes it an ideal solution for the long term…assuming costs are kept in check. The underlying separation of storage and computing eases future migrations. Future-proofing for unforeseen changes favors Snowflake, but Redshift is still likely a good option. Redshift vs Snowflake: Collaboration and Hybrid Solutions The data warehousing landscape continues to evolve rapidly, with the boundaries between Redshift, Snowflake and other platforms becoming more porous over time. Rather than a winner-take-all dynamic, we see increasing convergence and collaboration between platforms. Many organizations leverage hybrid solutions with Redshift for high-intensity operational workloads integrated with Snowflake for larger-scale data science experiments. Connectors like the recently launched make interoperation easier. AWS Redshift integration for Snowflake As analytics use cases grow more sophisticated, matching the ideal platform to each specific embedded scenario will unlock more value than a one-size-fits-all choice. Takeaway: Embracing the Right Data Warehouse for Your Embedded Analytics Journey The data warehousing engine powering embedded analytics should align with technical requirements, cost constraints, and future ambitions. Both AWS Redshift and Snowflake bring unique strengths as the foundation for real-time data applications. How Qrvey is Different At Qrvey, we know that a strong data layer is the foundation that makes any embedded analytics solution successful. We are the only solution with a built-in data warehouse layer made for multi-tenant, security-first embedded analytics. However, did you know that while we connect with Redshift, Snowflake, PostGres and more, we know don’t use any of these for our native data warehouse? Discover to power our embedded analytics for SaaS applications solution. why we chose AWS OpenSearch Also published here.

AWS Redshift vs Snowflake: A Comprehensive Guide to Embedded Analytics Solutions

AWS রেডশিফ্ট বনাম স্নোফ্লেক: এম্বেডেড অ্যানালিটিক্স সলিউশনের জন্য একটি ব্যাপক গাইড

AWS Redshift vs. Snowflake: Ein umfassender Leitfaden für eingebettete Analyselösungen

AWS Redshift vs Snowflake: una guía completa para soluciones de análisis integradas

AWS Redshift vs Snowflake : un guide complet des solutions d'analyse embarquées

एडब्ल्यूएस रेडशिफ्ट बनाम स्नोफ्लेक: एंबेडेड एनालिटिक्स सॉल्यूशंस के लिए एक व्यापक गाइड

AWS Redshift と Snowflake: 組み込み分析ソリューションの包括的なガイド

AWS Redshift와 Snowflake 비교: 내장형 분석 솔루션에 대한 종합 가이드

AWS Redshift vs Snowflake: um guia abrangente para soluções analíticas incorporadas

AWS Redshift против Snowflake: комплексное руководство по решениям для встроенной аналитики

AWS Redshift ve Snowflake Karşılaştırması: Yerleşik Analitik Çözümlerine Yönelik Kapsamlı Bir Kılavuz

AWS Redshift vs Snowflake: Hướng dẫn toàn diện về giải pháp phân tích nhúng

AWS Redshift 与 Snowflake：嵌入式分析解决方案综合指南

Some of you might remember the article I wrote last time: , it was extremely productive, and even though the like vs dislike proportion favors the latter many of you provided valuable insight into how to improve berrynews.org SEO ranking and Domain Authority, thank you for that. SEO is DEAD ❌ Based on your replies I've compiled a list of how to optimize your SEO and although many had valid points, a platform that aggregates news articles from various sources would be close to impossible to improve, I want to consider it as a challenge🦾 Answers to my 'What actions should I take?' question This brings me to the first actionable improvement point: Understand that SEO takes time. Two weeks is typically not long enough to see significant results, especially for keywords with high search volumes. I'll post an update once more time has passed and I've had the time to develop the SEO. Patience is key: Target relevant search queries. Being on the first page for low-volume searches won't drive much traffic. Aim for higher-volume, relevant keywords. Keyword optimization is an important factor and you have built-in tools in Google Search and Bing Search where you can find out what would work best for your website and update the metadata accordingly. Focus on relevant keywords: Optimize beyond just page speed. Focus on creating great content, building domain authority, acquiring quality backlinks, and setting up technical aspects like metadata and sitemaps properly. On the topic of backlinks, I will explain more later in this article. SEO is a comprehensive strategy: SEO in highly competitive sectors, like news, demands more work and strategic planning. Truer words have never been spoken, as I said challenge ACCEPTED🫡 Competitive niches require more effort: Dedicate time to understanding and implementing SEO best practices, including meta tags, content writing, and backlink strategies. Results will show over time. I've just finished reading "The Beginner's Guide to SEO" by MOZ and now moved to "Search Engine Optimization (SEO) Starter Guide" from Google Search. Invest time in SEO: Adjust your expectations for SEO outcomes. Initial traffic might be low, and growth is typically incremental. Consistent effort is required to see improvements and we shall see this in a couple of months when I post the update Set realistic expectations: Plan for SEO as a long-term strategy, giving it a few months to start showing results. Early expectations for quick gains are often unrealistic. This was one extremely valid but hard-to-achieve bullet point that I am working on. Long-term strategy: These points emphasize the importance of patience, relevant content targeting, comprehensive SEO strategies, and setting realistic expectations in the competitive landscape of online content. tl;dr: How exactly do you improve SEO? 🧘 if you want the full-length version you can find it . I will try as best as possible to answer these questions for myself, but they are a blueprint for any SEO improvements. Keyword research: here Ask what your users want. Ad-free news Time efficiency: A single platform to host them all Multiple sources and languages Unfiltered content What are your users searching for? TBD How often are your users searching the terms? The top 10 is unachievable but the top 100 is more than better What is your competition doing? Keywords by competitor Keywords by season Is it Easter already? 🐇🥚 Keywords by region (Bonus) What type of search types do we have? Informational queries - how fall is Brad Pitt Navigational queries - just Google search "berry news" if you don't know the full link Transactional queries: buy a concert ticket Commercial research Local queries: best oliebollen in Den Haag? Tools: - (PAID) The tool extracts accurate search volume data by using live clickstream data. Moz Keyword Explorer - (FREE) Google's AdWords Keyword Planner has historically been the most common starting point for SEO keyword research. Google Keyword Planner - Google’s keyword trend tool is great for finding seasonal keyword fluctuations. Google Trends (PAID) - This free tool populates commonly searched-for questions around a specific keyword. AnswerThePublic (PAID) - Provides some really neat competitive keyword data. SpyFu Keyword Research Tool as backlinks (links from other websites to yours) are a significant ranking factor for search engines. Developing an effective backlink strategy: Backlink strategy is an essential component of SEO, Understanding backlinks Quality over quantity: One high-quality backlink from a reputable site is worth more than many low-quality links from dubious sources. Relevance: Backlinks from sites within your niche or industry are more beneficial as they are seen as more relevant. Diverse Link Profile: A natural backlink profile has links from various sources and types (e.g., blogs, news sites, educational sites) Content creation Valuable content: Create high-quality, informative, and engaging content that others naturally want to link to. Shareable assets: Infographics, research studies, comprehensive guides, and thought leadership articles are examples of content that typically attract backlinks. As recently "announced" berrynews Europe and Spain "article share feature" Outreach and networking Blogger outreach: Reach out to bloggers or influencers in your industry for guest posting opportunities or to share your content. Relationship building: Develop relationships with industry leaders and content creators. Networking can lead to organic backlinks. Utilize social media and forums Share your content on social media platforms and relevant online communities. While most social media links are nofollow, they can lead to exposure and natural backlinking. Broken link building Identify broken links on other websites and suggest your content as a replacement. To be honest, this one I didn't properly understand. Guest blogging Write articles for other websites in your industry. Include a link back to your website within the content or in your author bio. If anyone wants to connect you can find me at @email Leverage PR and publicity Utilize press releases or reach out to news outlets for coverage of significant events, studies, or announcements from your company. Monitor your backlink profile Use tools like , , or to monitor your backlinks. Keep an eye out for spammy links that might harm your site’s reputation. Ahrefs SEMrush Moz Competitor analysis Analyze the backlink profiles of your competitors to understand where they are getting their links from. This can provide ideas for your own strategy Be Active in industry forums and blogs Participate in discussions on industry forums and comment on relevant blogs. Make sure your contributions are meaningful; this can lead to natural backlink opportunities. Directories and listings Get listed in reputable directories relevant to your industry. Avoid low-quality directories as they can be harmful. Best practices and caution Avoid black-hat techniques: Buying links, participating in link schemes, or excessive link exchanges can lead to penalties from search engines. Natural growth: Build your backlinks gradually and naturally. A sudden spike in backlinks can appear manipulative to search engines. Focus on value: Always focus on providing value through your content and interactions. High-quality content that serves the user's needs is more likely to earn backlinks. Developing a strong backlink profile takes time and effort, but it's a vital part of a comprehensive SEO strategy. Remember, the goal is to build a natural-looking backlink profile that grows organically over time. This brings my article to an end. If you are not familiar with some of these terms, I suggest that you check out the . And this funny message Glossary As always, thank you stranger for your time and if you want to "make acquaintance" you can always subscribe to my Hackernoon account. Until next time much love from Den Haag 🫶

SEO is not DEAD ✅

SEO মৃত নয় ✅

SEO ist nicht TOT ✅

El SEO no está MUERTO ✅

Le référencement n'est pas MORT ✅

SEO ख़त्म नहीं हुआ है ✅

SEOは死んでいない ✅

SEO는 죽지 않았습니다 ✅

SEO não está MORTO ✅

SEO не МЕРТВО ✅

SEO ÖLÜ değil ✅

SEO không CHẾT ✅

SEO 还没有死 ✅

Today’s business requires accurate and timely data in order to make decisions. Data-driven applications and dashboards built on time series data are found in every enterprise. A great deal of business data is time series - from system and application metrics to weather, stock prices, network telemetry, IoT sensors, all rely on complex time series analysis and visualization to provide business insight. There are more than a few databases that are built for this kind of analysis, such as Clickhouse (for more on Clickhouse and MinIO see ), Snowflake, Amazon’s TimeStream and RedShift, and Apache Druid. A typical workflow involves gathering data, likely being streamed in real time or pulled from a data lake, transforming and ingesting it into a database, and then building applications and dashboards using SQL queries running against the database. Integrating ClickHouse with MinIO This blog post focuses on , specifically how to run Druid with MinIO as deep storage. We’ll go into more detail later, but in short Druid saves data to S3 deep storage and swaps it in and out of local memory as needed when running queries. We’re going to install MinIO, install Druid, edit some configuration files to tell Druid to use MinIO, and then import demo data and visualize it using . Apache Druid Apache Superset Prepare to be amazed at how quickly you can build a high-performance analytics and visualization stack with open source products. Druid Overview Druid is one of the fastest data stores around - it was built to combine the advantages of data warehouses, time series databases and search systems. Druid achieves tremendously speedy query responses by combining the key characteristics of these three systems into its ingestion layer, storage format, query layer and core architecture. Druid is typically deployed with other open source technologies such as Apache Kafka, Apache Flink and sits between a storage or processing layer and the end user, serving as a query layer for analytic workloads. To learn more about how Druid works, please see . Apache Druid 101 Druid is cloud-native and relies on a microservice-based architecture. Each core service in Druid (ingestion, query, coordination) can be deployed and scaled independently in containers or on commodity hardware. In addition, Druid services can independently fail and restart or be updated without impacting other services. Druid has several process types: processes manage data availability on the cluster. Coordinator processes control the assignment of data ingestion workloads. Overlord processes handle queries from external clients. Broker processes are optional; they route requests to Brokers, Coordinators, and Overlords. Router processes store queryable data. Historical processes ingest data. MiddleManager These processes are typically deployed in groups onto three different server or node types: : Runs Coordinator and Overlord processes, manages data availability and ingestion. Master : Runs Broker and optional Router processes, handles queries from external clients. Query : Runs Historical and MiddleManager processes, executes ingestion workloads and stores all queryable data. Data Druid has three major external dependencies: deep storage, metadata storage and ZooKeeper. Briefly, metadata is used to track segment usage and task information, and is usually handled by PostgreSQL or MySQL. Zookeeper is used for internal service discovery and coordination. As this is the MinIO blog, we’re going to focus on deep storage. Deep storage is a shared file system accessible by every Druid server where data is stored after it is ingested. In a single-server deployment this is simply a local drive. In clustered deployments, deep storage would take the form of a distributed object store like S3 or MinIO, HDFS or a network mounted filesystem. is how Druid transfers data between processes and backs up data. Druid stores data in files called segments. There’s an internal mapping that pulls segments from deep storage to cache them on local drives and in memory. In order to minimize latency, queries run against segments locally, not against segments stored in deep storage. Deep storage enables Druid’s elastic, fault tolerant design. Druid provides durability by bootstrapping from deep storage in the event that a data node goes down and comes back up. One caveat is that you’ll need enough free space on data nodes and in deep storage for the data you want to work with in Druid. Deep storage Druid uses the S3 API for object storage for deep storage. Using S3 API compatible object storage such as MinIO gives you the freedom to run Druid anywhere, which is something you might want to do to improve performance and security. MinIO is a superb deep storage for Druid because it has excellent S3 compatibility, the performance to meet requirements for real time OLAP and, perhaps most of all, the ability to deliver performance at scale. MinIO also provides necessary and . data protection lifecycle management You can run Druid, MinIO and Superset directly on Linux or in containers on Docker or Kubernetes. There are already some great resources out there that show you how to get Druid up and running, so if for some reason this blog post doesn’t satisfy you, check out for Linux, for Docker and for Kubernetes. Apache Druid Quickstart Docker · Apache Druid Clustered Apache Druid® on your Laptop Installing MinIO If you’re not already running MinIO, here are . The process is described thoroughly in . installation instructions The MinIO Quickstart Guide On Linux, for example, download MinIO and run it (feel free to change the root password - and remember it): wget https://dl.min.io/server/minio/release/linux-amd64/minio
chmod +x minio
MINIO_ROOT_USER=admin MINIO_ROOT_PASSWORD=password ./minio server /mnt/data --console-address ":9001" Launch a browser and connect to MinIO Server at using the root user and password you configured earlier. http://<your-IP-address>:9000 The first time you log in, you’ll be taken to the Buckets page. Create the bucket . druidbucket In the real world, you would probably want to secure access to your Druid data, but we don’t have to do that in our lab. You would create a service account (under the Identity menu), edit the user policy to only allow access to and in the Druid configuration below use the service account’s access key and secret key. druidbucket Installing Druid The easiest way to test drive Druid is via Docker. Follow the instructions  and use this . This creates a container for each Druid service, as well as Zookeeper and PostgresSQL. This method uses an to specify the Druid configuration. Download to where you’ve saved Druid’s containers. For the instructions below (Configure Druid for MinIO), you can edit the environment file to pass the variables to standard files or edit the configuration files directly within the container. Docker · Apache Druid docker-compose.yaml environment file this file Druid configuration Start Druid with . Once the cluster comes up, open a browser and navigate to to use the Druid console. docker-compose up http://localhost:8888 Configure Druid for MinIO Now it’s time to configure Druid to use MinIO for deep storage. In this step, you will need to edit Druid config files (or the Docker environment files as described above) to reference MinIO and then restart Druid. The process is fully described in and we’re including an abbreviated version here for quick reference. How to Configure Druid to Use Minio as Deep Storage - DZone Big Data Depending on how you’ve installed Druid, In the file, add to . Add extensions by name, in quotes and separated by a comma, for example: conf/druid/_common/common.runtime.properties "druid-s3-extensions" druid.extensions.loadList druid.extensions.loadList=["druid-parser-route", "druid-s3-extensions"] According to the above referenced article, the S3 extension for deep storage relies on jets3t under the hood. You need to create a file on the class path. For this example, we’ll create a new inside the directory with the following: jets3t.properties jets3t.properties conf/druid/_common s3service.s3-endpoint=localhost
s3service.s3-endpoint-http-port=9000
s3service.disable-dns-buckets=true
s3service.https-only=false Now, comment out the configurations for local storage under the “Deep Storage” section and add appropriate values for Minio. In my case, I’m running MinIO on the same machine as Druid so the S3 Endpoint URL includes localhost and a port. If you’re running on another machine or in a container, you’ll need to change this line to address MinIO in your environment. After this, the “Deep Storage” section should look like: #
# Deep storage
#
# For local disk (only viable in a cluster if this is a network mount):
# druid.storage.type=local
# druid.storage.storageDirectory=var/druid/segments
# For HDFS:
# druid.storage.type=hdfs
# druid.storage.storageDirectory=/druid/segments
# For S3:
druid.storage.type=s3
druid.storage.bucket=druidbucket
druid.storage.baseKey=druid/segments
druid.s3.accessKey= <insert your MinIO accessKey>
druid.s3.secretKey= <insert your MinIO secretKey>
druid.s3.protocol=http
druid.s3.enabePathStyleAccess=true
druid.s3.endpoint.signingRegion=us-east-1
druid.s3.endpoint.url=http://localhost:9000/ We’re also going to configure Druid to store indexing service logs in Minio. It is a good practice to store everything in one place to make it easier to find - Druid is complicated enough without having to search multiple locations for logs. Update the “Indexing service logs” section with appropriate values in . conf/druid/_common/common.runtime.properties After this, the “Indexing service logs” section should look like: # Indexing service logs
# For local disk (only viable in a cluster if this is a network mount):
# druid.indexer.logs.type=file
# druid.indexer.logs.directory=var/druid/indexing-logs
# For HDFS:
# druid.indexer.logs.type=hdfs
# druid.indexer.logs.directory=/druid/indexing-logs
# For S3:
druid.indexer.logs.type=s3
druid.indexer.logs.s3Bucket=druidbucket
druid.indexer.logs.s3Prefix=druid/indexing-logs OK! That covers all of the configuration changes to make to Druid. Save the file and restart your cluster. As you work with Druid, you will see segments and indices written to . druidbucket Installing Apache Superset Apache Superset is a powerful visualization tool that is frequently used with Druid. Superset is a cloud-native business intelligence application that is fast, lightweight, feature-packed, and, best of all, it is free! Superset’s intuitive interface makes crafting interactive dashboards relatively easy. Superset provides out-of-the-box support for most SQL databases. Superset integrates with authentication backends such as the database, OpenID, LDAP and OAuth to secure visualizations. As cloud-native software, Superset is highly scalable and highly available. It was designed to scale out to large, distributed environments. We’re more interested in getting started with Superset than we are with scaling it in production, so we’re going to do the simplest installation we can using . This is the fastest way to try out Superset on Linux or Mac OSX. Docker Compose If you don’t already have Docker installed, please follow . Once you’ve installed Docker, please . Docker’s instructions install Docker Compose Open a terminal window and clone the Superset repo: $ git clone https://github.com/apache/superset.git This will create a superset folder in your current directory. Navigate to that directory and run the following commands (which may take several minutes to complete): $ docker-compose -f docker-compose-non-dev.yml pull
$ docker-compose -f docker-compose-non-dev.yml up You’ll see a whole lot of initialization text pass by on your terminal. Superset is ready once you see something like this: superset_app            | Loaded your LOCAL configuration at [/app/docker/pythonpath_dev/superset_config.py]
superset_app            | 127.0.0.1 - - [12/May/2022:23:59:35 +0000] "GET /health HTTP/1.1" 200 2 "-" "curl/7.74.0" Open a browser and navigate to to access Superset. Log in with the default username “admin” and password “admin”. http://localhost:8088 After logging in to Superset, you must connect it to Druid. On the top right, click the “+” to add a database. From the dropdown list select Apache Druid. Then name the connection and enter the following under SQLALCHEMY URI, making sure to update the IP address or replace it with localhost as necessary. If we had protected the Druid API by requiring authentication, we would also enter a username and password. druid://10.0.0.10:8888/druid/v2/sql Test the connection to make sure it works, and then click Finish. Woo-hoo! You now have Druid, MinIO and Superset running in your environment. With the triumvirate of Druid, SuperSet and MinIO, we can have a complete and timely view of real-time data. There are numerous organizations that have built web-scale data-drive applications on top of this combination for clickstream analytics (web and mobile analytics), risk/fraud analysis, network telemetry analytics (network performance monitoring), supply chain analytics (manufacturing metrics) and application performance metrics. This cloud-native analytics and visualization stack is flexible and extensible, and is scalable and performant so you can build applications that delight your users. Give them the power to monitor, analyze and visualize real-time data. Let them slice and dice, filter and group by any combination of columns so they can make conclusive action-oriented data-driven decisions at the pace business demands. Putting it All Together In order to demonstrate a bit of the functionality we’ve built, we’ll load a tutorial data file of Wikipedia edits over time into Druid and then visualize the edits in SuperSet. Remember, this is backed by MinIO as deep storage. The Wikipedia edits are covered in more detail in . Quickstart · Apache Druid Ingesting into Druid Druid loads data by submitting an ingestion task spec in JSON to the Overlord server. You can build an ingestion spec using the web GUI or write it yourself and schedule it. Once you build and tune an ingestion spec, you can reuse the JSON so you don't have to repeat the task. In production, teams frequently schedule and automate ingestion. Open a browser and load the Druid web interface at . http://localhost:8888 At the top of the window, click Load Data. From here, select Example Data, then select Wikipedia Edits from the drop-down on the right and click the blue Load example button. The web GUI walks you through building an ingestion spec that will read data from a source and store it in segments on deep storage. Ingestion is based on a time stamp that is used to partition and sort data, while data is stored as columns of dimensions and metrics. For more information about how to optimize for performance on ingestion, please see . Data Modeling and Query Performance in Apache Druid With the tutorial data, we’ll simply click through the ingestion workflow accepting the defaults, or click Edit JSON spec and then click Submit. If you wanted (or needed) to, you could look at the raw file as JSON and parse it. Druid creates its own time column called to organize and segment data. Because time is so important to Druid, it’s very good at figuring out how to handle timestamps regardless of their format. In addition, we can transform data, and optimize for query performance by partitioning data and sizing segments. Finally, you can tune the ingestion job in terms of threads and memory utilization, then schedule it and run it. __time The GUI sends you to view Tasks: This is a very small ingestion job so it finishes almost immediately. From here, you could run queries against the data in Druid and get lightning-fast responses. You can run queries via the web GUI or the API. Druid finds itself behind many a dashboard because of its ability to provide excellent query performance over massive amounts of data at scale. Visualizing in Superset Return to the browser where you’ve logged into Superset, and once again click the “+” on the top right of the home screen. Then click Chart. We could run individual SQL queries at the bottom of the screen. Dashboards are groups of individual queries. Responses to repetitive queries are cached in Superset to improve performance. Select the dataset and then select a visualization. Superset includes a number of time-series visualizations. We’re going to choose Time-series Chart and then click New Chart. druid.wikipedia Initially, you won’t see any results, but don’t let that fool you. First, remove the Time Range by setting it to “no filter”. The Time Grain is set too high at “day”, so change that to “5 minute”. We’re now viewing a chart of the number of Wikipedia edits happening in 5 minute windows in our sample data. You can filter and group results using Superset. To save the chart, click Save at the top of the screen, then give the chart a name and save it. By now you’ve learned the basic steps to install Druid and MinIO, ingest your first data set and generate your first Superset chart. A helpful next step could be to learn more about . Exploring Data in Superset Massively Scalable Analytics In this blog post, we introduced a group of cloud-native applications: Apache Druid for analytics, Apache Superset for visualization and MinIO for S3 deep storage. This simple demo is the start of building a cloud-native analytics and visualization stack, and can be extended with Apache Spark for ML or Jupyter for data science. Thanks for reading through this demo. If you have any questions, please send us an email at hello@min.io, or join the and ask away. MinIO slack channel Also published . here

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