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Should you use UUIDs as the primary key in your database? You might have heard they are terrible for performance, which is often true for traditional UUIDv4. However, the introduction of UUIDv7 fixes many of the previous issues of UUIDv4. Let’s therefore explore what they are and why it might be a good idea to use them.

Python developers often reach for Streamlit when they need to construct compelling web applications quickly. It provides a fast way to transform Python scripts into interactive applications without complex web frameworks. When paired with InfluxDB 3 Core, the leading time series database, engineers can build powerful real-time analytics dashboards entirely in Python.

We’ve been working on a number of items here at InfluxData to give you even more options for creating visualizations and dashboards for your time series data in InfluxDB 3.

Kafka is too expensive and complex for 80% of users. Most Kafka usage is small-data - ~60% of clusters are sub-1 MB/s, yet teams pay big-data prices. Diskless (KIP-1150), Tiered, and Iceberg topics give Apache Kafka multiple storage classes, but they’re advanced storage primitives. They are powerful in the hands of seasoned platform teams but too complex for beginners.

Companies today face growing pressure to manage and analyze massive flows of time series data, from IoT sensors to cloud-native infrastructure. Storing this information is relatively straightforward. The greater obstacle is keeping it useful and consistent while balancing a wide range of tools and modern technology platforms that continue to evolve.

In under a month, OpsHelm the continuous, enriched changelog for cloud infrastructure - migrated its streaming backbone from MSK and NATS to Aiven Diskless Kafka (BYOC on AWS). The switch eliminated cross-cloud networking fees, collapsed multiple storage layers into one, and cut total streaming costs by 5x (from >$50,000/year to <$10,000/year) while serving the team a single logical event bus that stretches across multiple regions and accounts.

The BigQuery Sink connector is a critical piece of Kafka infrastructure that allows you to offload your Kafka topic data into BigQuery in real time. It is the third most-used connector among Kafka users (after the Google Cloud Managed Service for Apache Kafka and the original WePay sink connector), but it's not without its fair share of plot twists. Here's the story of how this connector switched hands three times and we ultimately ended up helping to re-build it.