Christian Posta

Check out these System Instructions for Gemini 3 Pro that improved performance on agentic benchmarks by up to ~5%. For complex agents, enforcing behaviors like persistence, risk assessment, and proactive planning drives better reliability in multi-step workflows. Resources 🔗  SI Template: https://goo.gle/4rBTBMi Docs: https://goo.gle/4oAYN0d

165

9 Comments

ClickHouse doesn't have built-in schema migration support. In 2026. For a database used in production by thousands of companies. https://lnkd.in/gCU-4Fry That's not a niche edge case. Schema changes are one of the most common operations in any production database. And in ClickHouse, what looks like a simple column addition can trigger a full table rewrite when you're streaming hundreds of megabytes per second. Tinybird's own migration story is telling. Their first approach to handling materialized view changes was, in their words, "bulletproof but brutally inefficient." Touch just one materialized view and you'd migrate terabytes of upstream data. For a 14TB Kafka table, that meant days-long deployments. Every database has tradeoffs. ClickHouse is genuinely fast for narrow analytical queries. But if your workload evolves (and it will), the operational cost of that evolution is worth understanding before you're 14TB deep. What's the most painful schema migration you've dealt with?

7

New blog post: A Fork in the Road: Deciding Kafka's Diskless Future. Kafka is getting serious about S3 and finds itself at an architectural crossroads that will shape its next decade. Several new KIPs (1150, 1176, 1183) aim to reduce replication costs across cloud availability zones, but the implications go far beyond networking cost. It’s a mistake to think of S3 as simply a cheaper disk or a networking cheat. Building on object storage opens the door to operational benefits such as elastic, stateless compute — something many modern analytics systems already exploit. In my latest post, I outline two competing future paths: 🔹 Evolutionary path: Reuse large parts of existing Kafka components to reduce code changes and long-term maintenance. 🔹 Revolutionary path: Separate stateless and stateful layers to realize the full operational benefits of disaggregated storage. The post examines the trade-offs of each path, how the current KIPs map to these two paths and poses a broader question: what should Kafka become? And what will keep it relevant in the decade ahead? https://lnkd.in/d4E2BAe4

149

12 Comments

The Apache Software Foundation's new top-level projects include Apache #HertzBeat, a real-time observability system featuring in-built #MCP servers. https://lnkd.in/ea8rZ_PU #OpenSource #Apache #SDK

1

PostgreSQL 18 brings big upgrades to constraints! 🔒 Gülçin Yıldırım Jelínek Xata.io breaks down what’s new—from temporal PK/FKs to NOT ENFORCED constraints and better partition support—and how to use them in real systems. https://lnkd.in/d_xZ6HD2 #PostgreSQL #P2D2 #PG18 #DataIntegrity

36

Learn how to implement the Maximum Marginal Relevance (MMR) algorithm with Elasticsearch and Python, which includes code examples for vector search reranking: https://gag.gl/q0iEvL #ElasticSearchLabs

2

Build your own Snowflake Cortex chatbot — in under 40 minutes. In this step-by-step demo, Duan Uys shows how to automate every layer of a governed Cortex chatbot using WhereScape RED and Snowflake. ✅ ELT generation via templates ✅ Cortex-ready table prep ✅ Streamlit deployment & orchestration ✅ Built-in governance—no manual coding Watch the full recording + download the sample code. 👉 https://ow.ly/O44550XpZhn #Snowflake #Cortex #DataAutomation #DataEngineering #ELT #WhereScape

4

Very exciting Akka news: New Akka deployment options: elasticity on any infrastructure. The Akka "magic" of elastic clustering and the reliability of multi-region replication. All these massively powerful capabilities are implemented with a simple and elegant SDK. https://lnkd.in/ewmDDVXp

6