CapsicoHealth’s Post

Tilda v2.5.1 was released a few weeks ago but didn't get a chance yet to talk about it. https://lnkd.in/gxXzbSPJ This release's main feature is a significant extension of the DATETIME type to implement richer timezone semantics. Handling Timezones is always a complex endeavor (don't trust people who say it's nothing!) and with this release, more expected behavior is baked in the platform. We have also strengthened masking rules to make dynamic data masking more powerful. And finally, we implemented some major performance enhancement in our simple command-line-based data transfer capabilities between #Postgres and #BigQuery. There are also opportunities to contribute and i can spend time mentoring newcomers. In particular, our build environment is not great. Someone who knows how to do it right in GitHub and Gradle would be very welcome!

🏹 🦆 You’ve probably heard of Apache Arrow. And maybe you’ve heard of DuckDB. But have you ever heard of a BI platform powered by a combination of these technologies? Read about how our team of developers implemented DuckDB and Apache Arrow, thanks to the FlexQuery engine, and saw significant speed and memory efficiency of the GoodData platform. #dataanalytics #datavisualization #businessintelligence #BI #ApacheArrow #DuckDB #FlexQuery

We are big fans of DuckDB, a simple and yet super-fast open-source database optimized for analytical query workloads with a columnar-vectorized query execution engine. And DuckDB integration with Apache Arrow memory format is a huge plus. So, when we were looking for an #SQL interface for the GoodData FlexQuery, DuckDB was an easy choice. This is part of GoodData's composable data services vision, and our zero-copy approach gives our customers lighting-fast caching, pre-aggregations, data federation, and other benefits of our #Analytics #Lake.

I wrote blog, about "Reasons and Solutions for Deserialization Failure of Data Persisted in Json while Representing State with Types in C#" In some cases, Postgres JSONB data is not compatible for the #dotnet 's json. #Csharp #postgress https://lnkd.in/g7-JU9i4

In our last post, we discussed snapshot isolation and its issues, like lost updates and write-skew. Serializable isolation, the highest level, eliminates all concurrency problems. Let’s explore this today. ⮕ About: Serializability Isolation is the highest level of isolation in database management systems (DBMS). It ensures that transactions are executed so that their final effect on the database is equivalent to executing them sequentially, even if they run concurrently. ⮕ Features: Conflicts between transactions are managed to ensure the result is as if they were executed in a serial order. It guarantees that the database remains consistent before and after the transactions. The execution order of transactions does not matter as long as the outcome matches some serial execution. ⮕ Methods To Achieve Serializability: 1. → Two-Phase Locking (2PL): A locking protocol where transactions acquire locks during the "growing phase" and release them during the "shrinking phase." 2. → Timestamp Ordering: Transactions are assigned timestamps, and the system ensures that their execution order respects the timestamp order. 3. → Validation-Based Protocols: Transactions perform operations without locking resources and validate their execution at the commit time to ensure serializability. ⮕ UseCases: 1. → Critical Systems: Financial systems, booking engines, and inventory management systems require serializability to prevent anomalies like lost updates or inconsistent reads. 2. → Data Integrity Priority: When maintaining data accuracy is more important than performance, serializability is used. Read more about this at - https://lnkd.in/dKBy9emu Do follow me, For more such content - Yash Gupta #serializable #isolation #serializability #evolution #saga #redis #snapshot #redis #db #id #sql #follow #hash #support #sql #interview #sql #apache #bigquery #data #persist #spanner #google #239yash

🚀 Tqla is a SQL query builder that leverages Golang's text templating. 🛠️ Looking for a powerful tool to handle dynamic SQL queries in Golang? Check out Tqla! It offers: 🔒 Security: Prevent SQL injection with text templating. 📈 Performance: Efficient SQL query generation. ⚙️ Maintainability: Simplify complex query logic. Discover more with Tqla: https://lnkd.in/g8ugc3v2 #golang #sql #opensource #github

Thinking to create and conduct an online hands-on lab which will show how to build a realtime dataflow from #Postgres to #OpenSearch with a bit of SQL, truly Serverless style on Decodable. Never done this sort of thing before, surely a fair bit of work, but might be useful for folks out there? Curious about your thoughts 🤓.

📘 New blog post from MYYL. We’ve shared our insights on optimizing solutions for a challenging problem - finding unexecuted subtasks, inspired by a hard-level #LeetCode problem (1767). The post discusses #PostgreSQL approaches, with a focus on efficiency and avoiding unnecessary recursion. It also highlights the need to be careful with the use of cross join, as it can be a ‘hammer nail’ approach if not used judiciously. We invite you to read and learn more about our journey through various approaches, striking a balance between readability and efficiency, and the role of adaptability and continuous learning in data engineering. 🚀🔍 #DataEngineering #Optimization #PostgreSQL #LeetCode #LeetCodeHard https://lnkd.in/gpc4eNKY

🚀 Day 50 of my full stack journey: SQL conquered! 🎉 Today marks a milestone as I wrap up my deep dive into SQL. From complex queries and joins to database optimization, I’ve gained invaluable skills in managing and manipulating data. This half-century mark celebrates persistence and the thrill of turning raw data into actionable insights. Every challenge faced and problem solved has strengthened my foundation in backend development. Excited to continue this adventure, bridging the gap between dynamic front-ends and powerful databases. Onward to new heights! #100DaysOfCode #FullStackJourney #SQLMastery #MilestoneAchieved

Playing Around with pgvector – It’s Pretty Cool! 🎉 https://lnkd.in/dzqJJgFV Lately, I’ve been working with these crazy 512-dimensional arrays (thanks, embeddings 😅), and I stumbled upon pgvector—a PostgreSQL extension that makes handling this kind of data a breeze. Here’s the deal: pgvector lets you store and search through high-dimensional vectors (like embeddings) right in your Postgres database. No extra tools or hacks needed. You can run vector similarity searches (cosine, inner product, L2 distance—take your pick) super efficiently. It’s perfect for stuff like recommendation systems, semantic search, and anything involving ML embeddings. Perform similarity searches directly within PostgreSQL, saving time and simplifying architecture. For me, it’s been a lifesaver. Instead of juggling a separate vector database, I just keep everything in Postgres. And the best part? It’s fast, simple, and integrates seamlessly. If you’re dealing with embeddings or thinking about how to handle vector data, definitely check out pgvector. It’s worth it! Anyone else using it or curious about how it works? Let’s chat below! ⬇️ #PostgreSQL #pgvector #MachineLearning #DeveloperFun