DataCon Sofia’s Post

In this modern era, most companies use their key resources to the maximum advantage in Big Data analytics applications namely #Datalake and #DataWarehouse. This helps to store data and brings insight into an in-depth understanding of people and places to deliver objectives for better decision-making. Know more at: https://lnkd.in/gaN4qG_t Databricks William Rathinasamy Sekhar Reddy Anuj Kumar Sen Lawrance Amburose Brindha Sendhil Praveen Kumar C Rashika S Parthiban Raja xavier don bosco

Nice summary of modern big data modeling by John Souza. Here's how it matches my experience: - Yes, denormalization is good, it can do miracles for the end-user experience - I'm not 100% sure that SCD2 is unnecessary. But, snapshot dimensions deserve more consideration. - Surrogate keys are indeed evil and must be avoided. While we're at it, I assume nobody uses data vault concepts (hubs, links, and satellites) in big data. - There's no need to argue on "star schema vs snowflake schema", use whatever makes sense, and denormalize as needed. #bigdata #datamodeling https://lnkd.in/gaJNFxY7

Discover the high level potential of Databricks Lakehouse for modern data management in my new blog. DataTheta #databricks #lakehouse #dataanalytics #dataengineering

The General Availability of Delta Lake Liquid Clustering has been announced, revolutionizing data management in the Databricks Data Intelligence Platform. Liquid Clustering is a game-changer that: * Replaces table partitioning and ZORDER * Simplifies data layout decisions * Provides flexibility to redefine clustering keys without data rewrites * Improves query performance by up to 12x compared to traditional methods Key benefits: * Simple implementation with optimal clustering performance * Fast write times to clustered tables, reducing costs * Record-level concurrency support with DatabricksIQ Already, hundreds of customers have seen significant improvements, with over 100 petabytes written and nearly 20 exabytes read from Liquid clustered tables. Industry leaders like Shell and Cisco are praising its simplicity and performance gains. As Bryce Bartmann from Shell notes, "Delta Lake Liquid Clustering improved our time series queries up to 10x and was remarkably simple to implement." Ready to supercharge your data management? Learn more about Liquid Clustering and how it can transform your data strategy! #Databricks #DataManagement #LiquidClustering #DataIntelligence

Success in data analytics doesn't just happen; it requires the right strategies and techniques! 🌟 The recent insights on optimizing Databricks queries are a perfect reminder of this. With approaches like Adaptive Query Execution and utilizing the Photon engine, we can drastically improve query performance, decrease costs, and enhance overall efficiency. 🚀 It's eye-opening how proper management of cluster resources and regular data maintenance can pave the way for better data processing. Have you ever implemented any of these techniques or discovered others that made a difference in your projects? Share your experiences below – let’s inspire each other! 💬✨ #DataAnalytics #Databricks #QueryOptimization #BigData #MachineLearning #DataScience #CostEfficiency https://lnkd.in/gYGzVwVY

Hi Connections, Day 6 : 🚀 Revolutionizing Data Management with Liquid Clustering in Delta Tables - Databricks 🚀 Traditionally, customers have relied on a combination of Hive-style partitioning and ZORDERing to speed up read queries and enable concurrent writers. While effective, this approach comes with its own set of challenges.( I will explain those in my next Post) In my previous post, we explored how ZORDER and partitioning work, and the scenarios in which each is most effective. However, as data grows exponentially, the ability to redefine clustering columns (grouping and sorting the same data together) without data rewrites becomes incredibly valuable. Enter Liquid Clustering in Delta tables! 🌊 As the name suggests, "Liquid" Clustering allows you to change your cluster keys/columns as your data grows and business requirements evolve. This innovative data management technique replaces traditional table partitioning and ZORDERing, eliminating the need for constant fine-tuning of your data layout to achieve optimal query performance. Key Benefits: Flexibility: Redefine clustering keys without the need for data rewrites. Evolves with Your Needs: Allows your data layout to adapt alongside your analytic needs over time – something that was never possible with traditional partitioning. Liquid Clustering is a game-changer for managing large datasets efficiently and effectively. Embrace the future of data management with Delta tables! 💡 #DataManagement #DeltaTables #LiquidClustering #BigData #DataAnalytics #Innovation

𝗧𝗶𝗺𝗲 𝗧𝗿𝗮𝘃𝗲𝗹 𝗛𝗮𝗰𝗸𝘀 𝗳𝗼𝗿 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀! #𝗦𝗖𝗗 𝗠𝗮𝗱𝗲 𝗘𝗮𝘀𝘆 Hey Data Enthusiasts! 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗦𝗹𝗼𝘄𝗹𝘆 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗗𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝘀 (𝗦𝗖𝗗) 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲?  Building and maintaining complex archival processes can be a real drag. Databricks Time Travel is here to whisk you away from that headache! 𝗪𝗵𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗺𝗮𝗴𝗶𝗰?  Databricks lets you travel back in time and query your data lakehouse at any point in history. This eliminates the need for intricate, custom-built archives for SCD. 𝗦𝗖𝗗𝘀 𝗶𝗻 𝗮 𝗦𝗻𝗮𝗽: Imagine a customer table with an "address" field. An address change shouldn't erase the previous one – you need that historical context! With Databricks Time Travel: - Update your customer table with the new address. - Databricks automatically captures the change as a new version. - Now you can query the table as of ANY point in time, retrieving the relevant address for that specific historical snapshot. 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲𝘀: - 𝗗𝗶𝘁𝗰𝗵 𝗺𝗮𝗻𝘂𝗮𝗹 𝗮𝗿𝗰𝗵𝗶𝘃𝗶𝗻𝗴: Time Travel handles versioning automatically, saving you precious development hours. - 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗦𝗖𝗗 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Focus on data logic, not convoluted archival pipelines. - 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Uncover historical trends and patterns with greater ease. 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲:  Imagine analyzing customer purchase behavior by region. With Time Travel, you can see how demographics or marketing campaigns impacted purchases across different time periods in your data lakehouse. 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗧𝗶𝗺𝗲 𝗧𝗿𝗮𝘃𝗲𝗹 𝗲𝗺𝗽𝗼𝘄𝗲𝗿𝘀 𝘆𝗼𝘂 𝘁𝗼: - 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀𝗹𝘆 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗖𝗗: Focus on extracting data insights, not infrastructure woes. - 𝗨𝗻𝗹𝗼𝗰𝗸 𝗵𝗶𝘀𝘁𝗼𝗿𝗶𝗰𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀: Gain a richer understanding of your data's evolution over time. - 𝗕𝗼𝗼𝘀𝘁 𝗱𝗮𝘁𝗮 𝗮𝗴𝗶𝗹𝗶𝘁𝘆: Respond swiftly to changing business needs with readily available historical data in your data lakehouse. Stop wasting time building archives! Travel through time with Databricks and revolutionize your SCD strategy in the data lakehouse. #Databricks #TimeTravel #DataLakehouse #SCD # Let's hear it in the comments! How are you currently handling SCDs in your data lakehouse? Coditas Mitul Bid Shirish Bhatt Shubham Upadhyay Sanket Kelkar Stuti Mishra Dhimant Gandhi Asif Khan Gaurav Nandode Aditya Khare

When creating data pipelines, one of the challenges for #dataengineering teams is to define a partition strategy that avoids data skewness and impacts on performance. In the context of #databricks, the addition of clustered index or Z Order can enhance query performance, but it also introduces complexity in data layout decisions. #deltalake Liquid Clustering, however, replaces table partitioning and Z Order to simplify data layout decisions and optimise query performance. This approach aims to provide a more straightforward solution for data layout, thereby addressing the challenge of defining a good strategy to avoid data skewness and its impact on performance. Available on Databricks Runtime 13.3 LTS and above. https://lnkd.in/ejpepBFT

I am glad to be collaborating with a friend and fellow Data Engineer Giannis Konstantakopoulos on this article. Have you tried Liquid Clustering yet? Will it replace traditional partitioning? Leave your thoughts and experience in the comments. Subscribe and stay ahead of the curve. #urbandataengineer