Chaos Genius’ Post

🚀 **New Blog: Databricks Query Optimization – 2024 Guide** 🚀 Just published a comprehensive guide on boosting Databricks query performance for 2024. Whether you're a data pro or just getting started, this blog has actionable insights to help you optimize your workflows. Read more: [https://lnkd.in/dxH9UQKe) #Databricks #QueryOptimization Hevo Data

Here is a post that our CEO sent some days ago, and which is interesting for everybody who is reviewing the existing Data Warehouse strategy…. As always, and questions….or any interest in talk about this? Just let me know…

Databricks SQL is an enabler for cloud cost savings, simplified architecture and higher data quality / accuracy. A single platform for all your data use cases is key to the Lakehouse. Here is a highlight to all the Databricks SQL innovations over the past 2 years.

Thanks for sharing Ali Ghodsi. Great summary of reasons by #bricksters Gaurav Saraf and Kevin Clugage Databricks is making waves in #datawarehousing.

Databricks moving as fast as ever in supporting the needs of our customers! Case in point, great story on the incredible momentum that our data warehouse has had with our customers over the past 2 years! #databricks #lakehouse #datawarehouse

Ever wondered WHY our data warehouse in Databricks is doing so well in the market? Check out this short blog with screen shots, it summarizes well all the innovations we did just in the recent 2 years: https://lnkd.in/gHrMt4bG

What has columns, rows, and is read all over? You guessed it: tables! What may seem like data engineering table stakes (see what we did there!) is far from it. Table formats like Iceberg, Delta, Databricks Delta Live Tables, and Snowflake Dynamic Tables can support new workflows that allow data engineers to deliver data more quickly – and with data observability in place, more reliably as well. That's why we're thrilled to release Monte Carlo's support for these table types. Read more: https://lnkd.in/enigUNFd #dataengineering #iceberg #databricks #snowflake #dataquality

𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 𝗟𝗶𝗾𝘂𝗶𝗱 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 - 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗱𝗮𝘁𝗮 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 In Databricks Runtime 13.2 and later, 𝗟𝗶𝗾𝘂𝗶𝗱 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗳𝗼𝗿 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝘀 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗮𝗯𝗹𝗲 𝗽𝗮𝗿𝘁𝗶𝘁𝗶𝗼𝗻𝗶𝗻𝗴 and ZORDER, streamlining data layout decisions and boosting query performance. 𝗟𝗶𝗾𝘂𝗶𝗱 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 provides the flexibility to redefine clustering keys without rewriting existing data, allowing dynamic adaptation to evolving analysis requirements. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁 from optimized query performance, improved concurrency, and a data layout that seamlessly evolves over time. 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 use cases, implementation steps and comparisons with traditional clustering methods. 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 how to select clustering keys for optimal performance, utilize row-level parallelism, and trigger clustering on existing tables. 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿 the potential - and limitations - of liquid clustering for efficient data processing and analysis. #Databricks #DataManagement #LiquidClustering #QueryPerformance #DataAnalytics #DeltaLake #Lakehouse

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

Delta tables are a new type of table in Databricks that provide a powerful and efficient way to work with big data. They are optimized for fast, read-intensive, large-scale data processing and are ideal for use cases such as data lakes. Delta tables also offer a number of other advantages over traditional data lake tables, such as: ACID transactions Data versioning Data management Efficient queries Overall, Delta tables provide a powerful and flexible way to store, manage, and process big data in Databricks, and are a great choice for data lake use cases. So I have explained most of the features of the Delta Table in the below Medium post. https://lnkd.in/g88XEcrc #databricks #bigdata #dataengineering #datacafe