Get ready for the next major evolution in database performance! 🚀 Apache Cassandra 6 reshapes the core behavior of the database. From broader transaction support to smarter metadata coordination, Cassandra is now much easier to manage. Whether you are a developer looking for stronger correctness or an operator seeking smoother maintenance, we've got you covered. Watch the latest video by Developer Advocate Mariah McLaughlin and explore the top features driving massive improvements: https://bit.ly/3R3Klmk #ApacheCassandra #Database #TechNews #NetAppInstaclustr

Migrating large binary document data from a legacy system → Kafka → PostgreSQL looked straightforward… until we realized many records contained unexpectedly large BLOB payloads 😅 Initially, the architecture looked simple: Legacy System → Kafka → PostgreSQL But pushing binary payloads directly through Kafka quickly created problems: • broker memory pressure • high network bandwidth usage • slow consumer rebalancing • replication delays across brokers • consumer lag spikes during replay • longer migration windows than expected At peak load, the pipeline wasn’t CPU-bound — it was bottlenecked by data transfer time. So we redesigned the flow. What worked better for us: • Read BLOBs in chunks using streaming • Upload files directly to object storage • Publish only metadata + object references through Kafka using Avro • Sync metadata into PostgreSQL through consumers The architecture became: → Legacy System → Object Storage → Kafka (metadata only) → PostgreSQL This drastically reduced #Kafka payload sizes, stabilized consumers, and significantly improved replay and migration times. Big takeaway from this migration: #Kafka is excellent for events and references. Not for transporting massive legacy binary payloads 😄 #SystemDesign #Kafka #PostgreSQL #DataEngineering #DistributedSystems #LegacyModernization

Your CDC pipeline is quiet. Your primary database is running out of disk. That is what a stalled replication slot can do in Postgres. The downstream consumer stops acknowledging progress, WAL files stay pinned, and the primary keeps accumulating logs until storage becomes the outage. This is the operational reality of logical decoding, the Postgres feature that turns WAL into row-level change events for systems like Debezium, Kafka pipelines, audit streams, and cross-database replication. The new monograph covers: - What logical decoding is, and why it differs from physical replication - How the ReorderBuffer turns interleaved WAL records into committed transactions in commit-LSN order - Why historical catalog snapshots matter when schema changes happen after the original row change - How output plugins like pgoutput, wal2json, and decoderbufs serialize decoded changes - Why REPLICA IDENTITY controls what UPDATE and DELETE events can actually contain What breaks in production is rarely the decoding code. It is the slot lifecycle no one owns: who created it, who consumes from it, who notices when it stalls. Full monograph + the pgoutput vs wal2json vs decoderbufs decision guide on Primitives. Link in first comment ↓ #Databases #Postgres #CDC #DataEngineering #SRE

Maximize the potential of your PostgreSQL 17 database by leveraging the advanced capabilities of pgvector, now available on FoundryDB. This powerful combination enables the deployment of highly efficient HNSW and IVFFlat indexing methods for vector similarity searches, simplifying complex data analysis tasks. To explore the full range of features and benefits, visit https://foundrydb.com and discover how to optimize your data management operations.

Most system design concepts feel complex until you see the right diagram. This simple sketch explains the core of Apache Kafka 👇 At the center is a broker (Kafka server) It stores all the data and serves producers and consumers Now the flow: • A producer sends data • Data goes into a topic (like a category) • Each topic is split into partitions • Inside each partition, messages are stored in order • Every message gets an offset (like an array index) • A consumer reads data using these offsets The most important idea here 👇 A partition is not a queue It’s an append-only log Data is always written at the end, never changed in between And because of this: ✔ Order is maintained inside a partition ✔ You can replay old data anytime ✔ Multiple consumers can read the same data ✔ Easy scaling by adding more partitions Think of Kafka as a distributed log, not just a messaging system Once this clicks, everything starts making sense Still learning system design 🚀 #Kafka #SystemDesign #DistributedSystems #Backend

Ever wonder how databases keep data even when pods restart in Kubernetes? The answer is Stateful Sets. Stateful Sets in Kubernetes are used for applications that need to maintain identity and data even if pods restart. Unlike regular Deployments, Stateful Sets give each pod: - a stable name - persistent storage - predictable startup and shutdown order This is important for stateful applications like databases, Kafka, and Elasticsearch where losing identity or storage could cause issues. Without Stateful Sets, replacing a pod could mean losing data or breaking communication between nodes. A really interesting part of Kubernetes that shows not every workload is meant to be fully stateless.

Databases are bad at answering a simple question: “What’s new?” PostgreSQL will happily query your entire dataset. But if you want to know what changed since you last checked, you can’t do this through one of the standard APIs—you have to drop down to the low-level logical replication protocol. Entire ecosystems exist just to bridge this gap. Tools like Debezium solve it well—but at a cost: you end up running and operating additional distributed systems like Kafka and Kafka Connect. That’s why I’m excited about our new #Postgres input connector. It taps directly into the replication protocol—no extra services required, which means a simpler architecture with fewer moving parts. And the best part is that it wasn’t built in-house. This connector was contributed by an OSS community member. Huge thanks to Mohammed Ali for this great contribution 🙌 https://lnkd.in/g7XUWmcS

Three things landed at MariaDB this year that change the architecture conversation: 1. Codership acquisition. Galera, the cluster tech behind most production deployments, is now in-house with the original team. 2. MariaDB Exa, an HTAP engine built with Exasol. One system for OLTP and OLAP. 3. GridGain integration for in-memory and real-time workloads. That's HA, columnar analytics and in-memory on one platform, with a real migration path off Oracle and MySQL Enterprise. On May 14 at 14:00 EAT, Sechpoint and MariaDB are walking through the platform, the roadmap, and migration architectures we're seeing in production. Built for CTOs, DBAs, architects and technical leaders. Registration link below 👇 https://shorturl.at/trmyC Chetan Rao Natasha Rinaldi Ashleigh Auld #MariaDB #DatabaseModernisation #Oracle #MySQL #AI #RAG #Migrations #AfricaTech

New major release, promised gains in throughput, latency, and concurrency handling… But what happens when tested under fully reproducible, controlled conditions? We put Cassandra 5 through the same YCSB protocol as Cassandra 4: identical infrastructure, identical workload, across five cluster sizes. Explore the full open database ranking and see how Cassandra 5 stacks up. Read the full article 👉 https://lnkd.in/daH4AyPp

Next‑Gen Ingestion Pipelines: Oracle Database 26ai to Kafka (Kraft mode)  Introduction Data ingestion from Oracle Database 26ai into Apache Kafka (KRaft mode) relies on a streamlined, connector‑based architecture that captures relational changes or table snapshots from Oracle and publishes them into Kafka topics without requiring ZooKeeper. Oracle 26ai provides modernized JDBC drivers, JSON-native views, and enhanced metadata capabilities that simplify integration with Kafka Connect. Full post: https://wp.me/p40sos-1jy