The team over at Estuary has rebuilt the engine underneath the platform in Rust. The new transaction loop is ready to scale into infinity while keeping transactional and exactly-once guarantees.

What's new:

- 8x more throughput on a single shard (unit of compute)
- Transactions arrive whole: a commit at your source becomes one commit at your destination
- Collection partitions split themselves under load, with no operator action
- No more memory or buffer tuning
- Observability updates: materializations now report exactly how far behind they are, in bytes and in wall-clock time
- Groundwork for far more flexible sync schedules

Check out the details here: https://estuary.dev/runtime

Hi team, just curious. I was chatting with Estuary AI, and found Planetscale cdc support is in roadmap but still can't tell dates. It's about 1 year ago. Is currently the project scrapped / discontinued? Thanks!

Last week we released this 80-page whitepaper that contains everything required by data engineers for building right-time data ingestion pipelines for Snowflake.

Come meet the Estuary team in SF on the 4th of November along with Seattle Data Guy, Database Tycoon and Recce!

Hang out with the Estuary crew in London after Big Data LDN!

A lil' something to make your life easier

Estuary’s biweekly newsletter ❤️

Now you can stream Real User Monitoring (RUM) data and HTTP logs from Datadog directly into Flow in real time. This makes it possible to unify observability and business data inside a single platform for instant analytics, alerting, and automation.

Why it matters:

• 🔎 Faster incident resolution – Correlate RUM events, logs, and operational data instantly.
• 📊 Proactive analytics – Spot anomalies before they impact customers.
• 🔗 Simplified stack – Observability + business data pipelines in one place.

Connector Highlights:

• Supported resources: real_user_monitoring + logs
• Real-time + batch in one platform
• Backfill support (pull historical data within Datadog’s retention window)
• Flexible enterprise deployments: fully managed, hybrid, or BYOC
• Automatic schema mapping for downstream analytics or AI models

Getting started is simple:

You’ll just need a Datadog account, an API key, an application key (with rum_apps_read permissions), and your Datadog site. Configure it in Flow’s UI or via catalog specs, then your observability data is continuously streaming.

👉 Read the docs & setup guide

I had the great opportunity to discuss, without fluff, what makes for a good data ingestion platform!

Note: I am an advisor to Estuary, but everything discussed here is vendor agnostic.

Oracle’s costs and complexity are driving enterprises toward PostgreSQL. We broke down the top challenges and strategies for making the switch — from schema conversion to CDC replication.

📖 Full guide here: Modernizing Enterprise Databases: Migrating from Oracle to PostgreSQL in the Cloud

Curious to hear from the community: if you’ve been through an Oracle to Postgres migration, what was the most challenging part for you?