Data Panda lifts data from your CRM, ERP, ecommerce and event systems into Materialize through Postgres CDC, MySQL CDC, Kafka or webhooks. The materialized views you define on top of those sources keep themselves up to date as writes come in, so dashboards, alerts and operational apps read the same SQL answer the rest of the warehouse will see at the next batch.
Materialize was founded in early 2019 by Arjun Narayan, Frank McSherry and Nikhil Benesch, with offices in New York and a distributed engineering team. The engine is built on Timely Dataflow and Differential Dataflow, two open-source frameworks Frank McSherry started during his time at Microsoft Research on the Naiad project. Differential Dataflow's contribution: when an input record changes, the system computes the minimum amount of work needed to update every view that depends on it, instead of recomputing the view from scratch.
Practically, you point Materialize at upstream sources (Kafka, Redpanda, Postgres or MySQL via logical replication, SQL Server, MongoDB, webhooks from Stripe or Segment) and write SQL views that join, aggregate and shape that data. The views are kept fresh in under a second as writes land upstream, and you can read them directly over the Postgres wire protocol, push them downstream into Kafka or Apache Iceberg, or expose them to a data app. Underneath, the platform splits storage and compute: the control plane (`environmentd`) handles SQL parsing and the catalog, while compute clusters (`clusterd`) run Timely Dataflow and persist their inputs and outputs in S3, so a cluster can be scaled or replaced without losing state. That architecture is what lets a real-time fraud check, a dynamic pricing rule or an operational alert read from a SQL view instead of from a hand-written stream-processing job.
BI tools and operational consoles read materialized views over the Postgres wire protocol and never wait for a refresh.
Fraud checks, pricing rules and SLA alerts read a SQL view that already reflects the latest write upstream.
Agents and chat assistants read Materialize to answer questions on data that changed seconds ago, not last night.
Web and mobile apps read materialized views directly and reflect user actions in the next render.
A list of concrete reports, automations and AI features we have built on Materialize data. Pick the one that matches your situation.
Why would we put Materialize in the stack if we already have Snowflake or BigQuery?
Snowflake and BigQuery are built for batch analytics on data that lands at warehouse cadence. Materialize fits the slice where the answer needs to reflect a write that happened five seconds ago: fraud checks, pricing, operational alerts, customer-facing live UIs. The two coexist; Materialize handles the latency-sensitive views, the warehouse handles the wider, slower reporting.
How is Materialize different from a Postgres materialized view?
A Postgres materialized view recomputes the whole query when you call REFRESH. Materialize keeps the same view continuously fresh by recomputing only the rows affected by each upstream change, using Differential Dataflow. The view stays sub-second behind the source instead of going stale between scheduled refreshes.
Do we still need Kafka if we use Materialize?
Often yes, but for fewer reasons. Kafka stays useful as the durable log that fans events out to multiple consumers. Materialize can ingest from Kafka and from Postgres or MySQL CDC directly, so a small stack can skip the Kafka layer for database changes and keep Kafka where you genuinely need a topic with multiple readers.
The CFO sees revenue, refunds and exposure on a view that already reflects the last hour, not the last close. Cash, AR and risk numbers stop being a daily artefact and become a current state the team can act on the same day.
Sales reads pipeline, account activity and product usage on a view that updates as the events happen. A renewal call doesn't start with a CSV from last week; the rep sees the customer's behaviour up to a few seconds ago.
Operations gets SLA breaches, stock movements and order anomalies as soon as the upstream system writes them. Alerts fire on the same SQL view a dashboard renders, so the on-call and the morning standup work from one number.
You keep the reporting tool you already have. We connect it to the warehouse where your Materialize data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
When the workload needs an answer that reflects a write from a few seconds ago. Operational dashboards, fraud and risk checks, dynamic pricing, SLA alerting and customer-facing live UIs all sit in that slice. Snowflake and BigQuery still own the wider reporting, governance and historical analytics; Materialize sits next to them and takes the latency-sensitive views off their plate.
Materialize is built on Differential Dataflow, the framework Frank McSherry started during the Naiad project at Microsoft Research. When an input record changes, the engine works out the minimum amount of recomputation needed to update every materialized view that depends on it, rather than re-running the SQL from scratch. The result: complex joins and aggregates stay fresh under a second as the source data moves.
Sources include Kafka and Redpanda (and managed variants like Confluent Cloud, Amazon MSK and WarpStream), Postgres, MySQL, SQL Server and MongoDB CDC, and webhooks from Stripe, Segment, HubSpot and Amazon EventBridge. Sinks include Kafka and Redpanda, Apache Iceberg and Amazon S3. On the read side, every view is queryable over the Postgres wire protocol, so existing BI tools and ORMs connect without a custom driver.
We review your Materialize setup and the systems around it. Together we pick the first thing worth building.