iiinova — where your digital self keeps evolving

Feldera @feldera.bsky.social · 29/09/2026

Fresh off our Series A announcement, our CEO and co-founder, Lalith Suresh, joins Taha Mubashir of Inovia Capital to talk about how data freshness turns a "directionally correct report" into a live command center you can actually build your business around.

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Feldera @feldera.bsky.social · 21/09/2026

We just announced our Series A fundraise! 🎉🚀 Read more here ➡️ www.feldera.com/blog/announc...

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Feldera @feldera.bsky.social · 16/09/2026

This Thursday Feldera’s co-founder and Chief Scientist Mihai Budiu will give a presentation at the San Francisco chapter of the "Papers We Love" meetup about Incremental Computation.

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Feldera @feldera.bsky.social · 13/08/2026

“It’s not a system improvement. It’s a fundamentally different way of doing things.”

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Reposted by Feldera

Kris Jenkins @krisajenkins.bsky.social · 08/07/2026

There’s a fascinating algorithm called DBSP that single-handedly managed to combine streaming and batch systems and solve a decades-old database efficiency problem. Lalith Suresh joined me to explain the core of how it works: 😊 youtu.be/CyvnH8OUCUA

youtu.be

Making Materialized Views Actually Fast with DBSP (with Lalith Suresh)

YouTube video by Developer Voices

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Feldera @feldera.bsky.social · 08/07/2026

How do customers convert 10,000+ lines of production Spark SQL into running Feldera pipelines in minutes? They hand it to their AI agents powered by our latest skill, Felderize.

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Feldera @feldera.bsky.social · 06/07/2026

Once you've experienced a True Incremental View Maintenance (IVM) engine, recomputing everything from scratch sounds absolutely absurd. Too slow and too expensive to justify. We set up a little match of our own: Databricks Incrementally Materialized Views vs. Feldera.

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Feldera @feldera.bsky.social · 06/07/2026

We just improved Feldera support for deletion vectors and column mapping in Delta Lake tables. What does this mean for you? You can now experience True Incremental View Maintenance without having to move your data.

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Feldera @feldera.bsky.social · 23/06/2026

80% of enterprise Feldera pipelines are now authored by agents. So we built them skills.

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Feldera @feldera.bsky.social · 19/06/2026

Watch what happens when we run a simple 1v1 comparison against ClickHouse. As we turn up the volume on the transaction records, things start to really cook. Just not for us. Feldera barely broke a sweat. Incremental view maintenance never degrades at scale.

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Feldera @feldera.bsky.social · 18/06/2026

Right now, somewhere, a data engineer is getting another message from stakeholders: “Why is the Gold layer late?” It is a familiar reminder that the traditional medallion architecture was not built for real-time freshness. It is being held back by legacy batch processing.

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Feldera @feldera.bsky.social · 17/06/2026

A true incremental compute engine's latency should be *flat* no matter how much your data grows (O(delta) vs O(size of data)). Let's see this in action.

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Feldera @feldera.bsky.social · 17/06/2026

3 seconds versus 226 seconds can mean you’re flagging fraud or writing it off as a loss.

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Feldera @feldera.bsky.social · 08/06/2026

Our co-founder and Chief Scientist Mihai Budiu is speaking at @hasgeek Rootconf’s special conference on Databases on Friday, June 12 at 9:40PM PT virtually or Saturday, June 13 at 10:10AM IST Bangalore.

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Feldera @feldera.bsky.social · 05/06/2026

⚡️ Shipped This Week ✨ New profiler UI: Our pipeline profiler UI got a major glow up. Now in one view see your dataflow graph, your SQL, and a tabbed panel of live metrics, logs, and triage suggestions. When something needs triage, everything you need to assess the situation is in one clean view.

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Feldera @feldera.bsky.social · 05/06/2026

Databricks Data + AI Summit is the one place where everyone in the room knows the medallion architecture intimately, and nobody talks about what it actually costs you to keep it fresh.

We will be at Databricks Data + AI Summit. June 15-18, 2026

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Feldera @feldera.bsky.social · 04/06/2026

Just because your data keeps growing doesn’t mean your compute bill needs to. Batch is inherently wasteful, recomputing everything on every run. The result: your cloud bill scales with the size of your data, even if only a few rows changed.

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Feldera @feldera.bsky.social · 29/05/2026

May newsletter is out! Fun fact: we just shipped our 300th release. 🎉 We're celebrating by shipping even more great stuff. Take a look. www.linkedin.com/pulse/may-ed...

linkedin.com

May Edition 2026

🎉 We just hit our 300th release! We were founded in May 2023 with the deep belief that rerunning batch jobs from scratch when 99.9% of the data is unchanged was a fundamentally broken model.

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Feldera @feldera.bsky.social · 27/05/2026

How do you give AI agents live, actionable signals for dynamic access control? Swipe to find out 👉

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Feldera @feldera.bsky.social · 26/05/2026

AI agents are only as good as the information they see, and only as fast as they can access it.

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Feldera @feldera.bsky.social · 22/05/2026

⚡️ Shipped This Week. A few highlights from this week:

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Feldera @feldera.bsky.social · 15/05/2026

⚡️ Shipped This Week The teams rebuilding their data infrastructure aren't waiting for a better time. Every week this engine ships what they need, and the list keeps growing.

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Feldera @feldera.bsky.social · 08/05/2026

⚡️ Shipped This Week We keep tuning our engine to be faster and easier to observe. A couple of highlights:

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Feldera @feldera.bsky.social · 01/05/2026

⚡️ Shipped This Week We ship big and we ship small. This week was about the details. The kind that makes the engine easier to debug, observe, and run efficiently in production. A few highlights from this week:

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Feldera @feldera.bsky.social · 29/04/2026

April was a month of momentum. The team shipped across every layer of the stack: core engine performance, connector resilience, more SQL functions, and the OSS community showed up in a big way. Read the full newsletter here: www.linkedin.com/pulse/april-...

linkedin.com

April Edition 2026

April was a month of momentum. The team shipped across every layer of the stack: core engine performance, connector resilience, more SQL functions, and the OSS community showed up in a big way.

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Feldera @feldera.bsky.social · 27/04/2026

A great contribution from our OSS community! @flak153 shipped a Postgres CDC input connector for Feldera. Built with crash-safe replication, snapshot and streaming support, and fault tolerance baked in. Postgres pipelines can now read historical data and live changes in a single connector.

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Feldera @feldera.bsky.social · 24/04/2026

⚡️Shipped This Week More SQL functions. Pipelines got more observable. Memory usage went down. And the Feldera community keeps showing up. Here are some highlights from this week:

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Feldera @feldera.bsky.social · 22/04/2026

Feldera is SOC 2 Type 2 compliant ✅ As a bring-your-own-cloud platform, your data always stays in your infrastructure. SOC 2 Type 2 is our commitment to holding ourselves to the highest security standards. Independently audited by @PrescientSecurity. Full details: trust.feldera.com

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Feldera @feldera.bsky.social · 20/04/2026

What are the software patterns that make agents useful?

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Feldera @feldera.bsky.social · 17/04/2026

⚡️Shipped This Week Every week we move fast and we build in the open. This week the Feldera team and a community contributor shipped features that make your pipelines more powerful, more resilient, and easier to operate at scale. Here are some highlights from this week:

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Feldera @feldera.bsky.social · 16/04/2026

Your agents are ready to make decisions in real time. Is your data platform ready to provide live data? Feldera is. Try it now: github.com/feldera/feld... Tell us about your experience with agentic real-time decision-making.

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Feldera @feldera.bsky.social · 14/04/2026

How do you give AI agents live, actionable signals? Stay tuned. 👀

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Feldera @feldera.bsky.social · 10/04/2026

Every week our compute engine gets more reliable, more observable, and more predictable at scale. Your pipelines should just work at any size, for as long as you need them to. Here are a few highlights from this week:

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Feldera @feldera.bsky.social · 08/04/2026

We love seeing contributions to our OSS community! Raki Rahman knows the dbt ecosystem well and he chose to build a dbt adapter for Feldera, complete with integration tests, a demo video, a python SDK to be released on PyPI, and he even reached out to the dbt docs team to get it listed officially.

github.com

feat(py): A dbt adapter for Feldera (not a Feldera adapter, but a dbt adapter, that is wrapping Feldera) by mdrakiburrahman · Pull Request #5950 · feldera/feldera

A new dbt adapter for Feldera. Small demo Notes Not for this PR, but we need to open a PR in the dbt repo to get this adapter docs exposed in dbt's public docs, sample PR: Onboarding dbt-fa...

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Feldera @feldera.bsky.social · 06/04/2026

What would it mean for your business if all your batch jobs completed instantly? How many pipelines are you maintaining just to keep data fresh? What new use cases would you unlock if complex analytical queries returned answers the moment a user submits a form, makes a payment, or visits a branch?

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Feldera @feldera.bsky.social · 06/04/2026

Most data pipelines do 10x more work than the problem requires. Every day we ship features and improvements to Feldera that fix that. Here are a few highlights from this week:

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Feldera @feldera.bsky.social · 02/04/2026

We're obsessed with performance at Feldera, and that obsession shows up in the tools we build. We recently reduced one customer's backfill time for 8 billion records from 20 hours → 4 hours. Profiling was critical to getting there.

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Feldera @feldera.bsky.social · 31/03/2026

March was a month of depth. We went deep on engine performance, observability, and reliability. Read the full newsletter here: www.linkedin.com/pulse/march-...

linkedin.com

March Edition 2026

March was a month of depth, we went deep on engine performance, observability, and reliability. Here’s what shipped.

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Feldera @feldera.bsky.social · 27/03/2026

When two joins share the same source and key, our SQL compiler was building two separate indexes over identical data. We found the pattern and fixed it.

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Feldera @feldera.bsky.social · 19/03/2026

Batch jobs cost you more than you think. As your database grows, traditional batch jobs slow down because each run reprocesses everything from scratch.

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Feldera @feldera.bsky.social · 09/03/2026

Most databases can maintain a simple aggregate view incrementally. Few can do it when that aggregate feeds into another query.

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Feldera @feldera.bsky.social · 02/03/2026

What happens when a SQL table has 700+ nullable columns? At first glance: nothing unusual. But when that table turns into a Rust struct with hundreds of optional fields, something odd happens. The data looks small in memory but it yet suddenly takes twice the space on disk.

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Feldera @feldera.bsky.social · 26/02/2026

This month we were busy shipping 157 changes to production including new features and improvements that make your pipelines faster, smarter, and leaner. Highlights in this edition include: ⭐ Star Join Operator: Multi-way joins in a single pass. Faster star-schema queries with less storage.

Star join operator: multi-way joins in a single pass. Faster star-schema queries with less storage.

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Feldera @feldera.bsky.social · 20/02/2026

As Feldera has matured, so has the engine underneath it. Last year, to support large backfills, the engine began automatically split/accumulated large outputs using our storage layer. We realized recently that this unlocked something exciting.

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Feldera @feldera.bsky.social · 12/02/2026

It's time to build AI-era products that were impossible before. Batch processing recalculates everything, even when 99.9% of your data didn’t change. Feldera fixes that w/ incremental compute. Bring your existing SQL and get millisecond freshness instead of hours-long (or days-long) batch jobs.

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Feldera @feldera.bsky.social · 05/02/2026

This is a query plan for a real customer’s production SQL. - 61 input tables -> 33 output views - 217 joins - 27 aggregations - 218 projections and filters Most systems would either do an expensive full recomputation or flat out fail.

Query plan for a complex, real-world SQL program

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