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Bluesky provides a public firehose that we can stream into Materialize, through which we can observe live social behavior and trends.

AI agents need a scalable data layer that stays in sync with reality. Create real-time digital twins and expose them through model context protocol (MCP) to make your data agent-ready.

And for the first time, you can build always-correct, always-current operational data products on top of SQL Server.

Explore how Materialize engineers diagnosed and resolved a rare concurrency bug in Rust's unbounded channels that led to undefined behavior through double-free memory errors.

Data products provide context and relations for data assets. Learn how tools like Materialize can help you deploy them for AI development.

Live, versioned data products at your models' fingertips with automatic MCP endpoints.

Comprehensive privacy policy details for Materialize users, focusing on data use & protection.

Materialize now exposes source maps in its catalog, so you can build your own debugging queries that attribute performance characteristics to high-level operators.

Learn how Materialize brings order to views over independent, continually changing, transactional data sources

Discover how real-time structured data elevates Retrieval-Augmented Generation (RAG) systems. Learn how Materialize ensures fresh, consistent views of operational data to power context-aware, actionable AI.

Replica expiration is a new feature in Materialize that limits the resource requirements of temporal filters.

Learn how to simplify microservices data integration by using a shared database without sacrificing performance or scalability. Discover how materialized views can enable consistent, efficient data access across services.

Discover how Materialize engineered its self-managed product to support flexible deployments, improve architecture, and meet diverse customer needs, all while refining its managed cloud service.

Get early access to Materialize Self-Managed and run it within your private infrastructure. Meet governance, compliance needs, and deploy in any cloud with Materialize's real-time data transformation capabilities.

Discover how Materialize empowers intelligent agents to collaborate in real-time, ensuring cost-effective and efficient orchestration for autonomous systems. Transform the future of AI-powered ecosystems with fresh, consistent, and actionable insights.

Explore how Materialize overcomes key microservices challenges like data silos, network fan-out, and reconvergence issues. Learn how database-level transformations unlock real-time, consistent, and efficient operations in microservices architectures.

Four Takeaways from AWS re:Invent 2024

A new way to run Materialize in the cloud for organizations with unique operational requirements. Join the Early Access program today!

To showcase the power of an ODS, we’ve developed a demo for an e-commerce company, based on a dynamic pricing use case.

Here's a step-by-step walkthrough of how to use the Materialize Emulator.

Let's explore why many teams rely on PostgreSQL for analytics, the challenges they face, and how Materialize solves these problems.

In this guide, we’ll show you how to migrate your existing PostgreSQL dbt project to Materialize with minimal SQL tweaks.

Read about how we give back to the open source community through our Community Sponsorship Program.

In this tutorial, we’ll connect Oracle CDC to Materialize in just a few minutes using Estuary Flow’s Dekaf.

In this post, we’ll explore the difficulties of cache invalidation, how Materialize and Redis address them, and when this solution is most effective.

Materialize can respond faster than your primary database, with results that are at least as fresh as your primary would provide.

In the following blog, we’ll show you how to create real-time alerts using Materialize’s integration with Novu.

Materialize costs 1/20th what Aurora PostgreSQL read replicas cost, when you have non-trivial business logic.

Operational data stores maintained real-time data, and allowed access to denormalized data across databases. But why don't you see that pattern much any more?

IVMRs can deliver 1000x performance for read-heavy workloads, without losing freshness, and do so at a fraction of the price of a traditional replica.

Materialize outperforms Aurora for complex queries over relatively small data volumes. Here are the benchmarks.

There are many different methods for OLTP offload, and in the following blog, we will examine the most popular options.

Read the following blog to learn about OLTP vs. OLAP, problems with complex OLTP workloads, and the case for OLTP offload.

A breakdown of how we built the Materialize Fivetran Destination with Fivetran's Partner SDK, and how this unlocks new workflows in Materialize.

In this post, we'll build a recipe for a generic live data source using standard SQL primitives and some Materialize magic.

Under-resourced small data teams can now leverage a SaaS solution with streaming data and SQL support to build real-time applications.

Small data teams can't wait to build real-time data architectures. Find out why, and how they're approaching the problem.

Learn how replacing the legacy materialized view with a new element is transformational for your data stack.

Here's how we developed frictionless private networking for Kafka by using librdkafka.

Or why clear consistency guarantees are how to stay sane when programming distributed systems.

Export a snapshot of your data to Amazon S3 object storage as an intermediary to sink data to a broader set of systems downstream

Materialize is now partners with Snowflake. Celebrate with us next week at Snowflake Data Cloud Summit.

We reduced memory requirements for many users by nearly 2x, resulting in significant cost-savings.

The following blog will show you we keep our customers and developers happy with our rigorous QA process, including our tools and testing methods.

This blog will provide an overview of the different data architectures lenders use to power real-time loan underwriting.

In this blog, we'll examine the loan underwriting process, including the current landscape, credit modeling, and the move toward big data and SQL.

New names, new sizes, plus spill-to-disk capabilities

Decrease your data warehouse costs by sinking precomputed results and leveraging real-time analytics.

Here's how to save money on your data warehouse bill with normalized data models and data mesh principles.

With Materialize, teams can lower the cost of their data warehouse bill and implement new use cases.

Materialize welcomes new CEO Nate Stewart, who previously served on the Materialize board and comes to us from Cockroach Labs.

An in-depth breakdown of how we architected and built a native MySQL CDC source

Combining Redpanda Serverless with Materialize makes developing streaming data apps easier than ever before.

Access the freshest data in MySQL to power your operational workflows

In this blog, we’ll explain the different roles of analytical and operational data warehouses in building real-time fraud detection systems.

Get complete visibility into your usage trends and billing history to manage your spend effectively

Now in Private Preview, Query History lets you monitor your SQL query performance to detect potential bottlenecks

Data freshness is essential for real-time business use cases. Here's how an operational data warehouse powers your business processes with fresh data.

Learn how recursive SQL provides an elegant solution for a fundamental use case in economics - stable matching.

Learn how an operational data warehouse enables organizations to use their freshest data for day-to-day decision-making

Discover the essentials of real-time analytics databases, their benefits, and how they compare to traditional databases for better operational decision-making.

The Materialize team participated in Advent of Code 2023 and took a bold approach in using SQL to solve each puzzle. Check it out.

See how Materialize supports operational work with responsiveness.

Learn how we built an in-browser SQL shell that empowers Materialize users to interact with their databases

Recently, I've felt the pain of long Rust compile times at Materialize, and so was motived to improve them a bit. Here's how I did it.

Integrate Materialize with VS Code for schema exploration, SQL validation & query execution, all within your IDE for efficient development.

At the heart of freshness in Materialize is autonomous proactive work, done in response to the arrival of data rather than waiting for a user command.

Today Materialize customers can create webhook sources, making it much easier to pipe in events from a long tail of SaaS platforms, services, and tools.

Materialize's consistency guarantees are key for confidence in data warehouses. Understand the benefits & see real-world tests in action.

Take a guided tour through Materialize's three pillars of product value, and see how we think about providing value for your operational workloads.

We've built Materialize as a new kind of data warehouse, optimized to handle operational data work with the same familiar process from analytical warehouses.

Comprehensive RBAC for Materialize users ensures secure, production-grade environment management & access control.

Materialize has a subtly different cost model that is a huge advantage for operational workloads that need fresh data.

An illustration of the unexpectedly high downstream cost of clever optimizations to change data capture.

Data Warehouses are great for many things but often misused for operational workloads.

Materialize & Confluent partnership offers SQL on Kafka capabilities for efficient data team integration.

Support for recursive SQL queries in Materialize is now available.

A framework for understanding why and when to shift a workload from traditional cloud data warehouses to Materialize.

Major updates to PostgreSQL streaming replication allow for real-time & incrementally updated materialized views with Materialize.

If you're already familiar with stream processors you may wonder: When is it better to use Materialize vs a Stream Processor? And why?

Materialize maintains an official Terraform Provider you can use to manage your clusters, replicas, connections and secrets as code.

Master Materialize for enhanced scale, performance & power with key internal insights. A guide for aspiring power-users.

Four questions, and their answers, to explain ACID transactions and how they are handled within Materialize.

Explore strategies for unleashing real-time dbt, from materializing views to leveraging micro-batches and incrementally maintained views.

Materialize aims to be usable by anyone who knows SQL, but for those interested in going deeper and understanding the architecture powering Materialize, this post is for you!

If you are familiar with materialized views and indexes from other databases, this article will help you apply that understanding to Materialize.

Let's build (in Python) the Differential Dataflow framework at the heart of Materialize, and explain what it's doing along the way.

If you're familiar with data warehouses, this article will help you understand Materialize Clusters in relation to well-known components in Snowflake.

Understand how to optimize joins with indexes and late materialization.

Understanding recursion in Materialize & its significance in differential dataflow for SQL updates.

Materialize is written in Rust. Why did we make that decision and how has it turned out for the project?

Let's demonstrate the unique features of Materialize by building the core functionality of a customer data platform.

As an operational data store, Materialize is fundamentally different on the inside, but it's compatible with PostgreSQL in a few important ways.

Today, we’re excited to announce a product that we feel is transformational: a persistent, scalable, cloud-native Materialize.

Insights on how indexes impact scaling in databases & their evolution in streaming-first data warehouses.

Real-time SQL monitoring & data quality tests with dbt & Materialize for continuous insights as data evolves.

Using dbt to manage and document a streaming analytics workflow from a message broker to Metabase.

The key to Materialize's ability to separate compute from storage and scale horizontally without sacrificing consistency is a concept called virtual time.

What is a Data Application? How do they help our customers? What new challenges do we face when building Data Apps? Here's our perspective.

Connect headless BI tool Cube.js to the read-side of Materialize to get Rest/GraphQL API's, Authentication, metrics modelling, and more out of the box.

Materialize's new cloud architecture enhances scalability & performance by breaking thematerialized binary into separate services.

Materialize provides a real-time feature store that updates dimensions with new data instantly & maintains speed & accuracy.

Real-time SQL query & view update subscriptions are made simple with Materialize's SUBSCRIBE feature.

Comprehensive updates in Materialize Vol. 2: AWS roles, PostgreSQL enhancements, Schema Registry SSL, & more for streamlined data management.

Comprehensive guide on using PostgreSQL's write-ahead log as a data source for Materialize, with technical insights & benefits.

In this article, we will talk about one of the ways we approach the testing of the SQL engine of the product at Materialize. We hope to cover other modules and interesting angles in the future.

Materialize Cloud integrates with Tailscale, offering secure & easy connection of clusters to private networks using WireGuard protocol.

Stay updated with Materialize: Kafka source metadata, protobuf & schema registry integration, time bucketing, Metabase, cloud metrics & monitoring enhancements.

Enhance your data workflows with Redpanda & Materialize for faster & more efficient streaming analytics. Get insights on integration & usage.

Materialize raises another round of funding to help build a cloud-native streaming data warehouse.

Change Data Capture (CDC) is finally gaining widespread adoption as a architectural primitive. Why now?

Materialize Cloud, now in open beta, offers real-time data warehousing for immediate insights & action on live data.

Materialize's Release 0.9 introduces an Operational Data Warehouse optimized for real-time data actions & cloud efficiency.

Materialize & Datalot collaborate on cutting-edge real-time application development, leveraging streaming data for immediate insights & action.

Comprehensive insights & updates on Materialize's Release 0.8, enhancing real-time data warehousing capabilities for immediate action.

Efficiently maintain joins with shared arrangements & reduce resource usage with Materialize's innovative approach.

Differential dataflow uses simple linear operators:map,filter,flat_map and complex:explode and temporal filter operators. But, with some thinking, we can generalize them all to a restricted form of join.

Debezium and Materialize can be used as powerful tools for joining high-volume streams of data from Kafka and tables from databases.

Build a real-time A/B testing stack with Segment, Kinesis and Materialize.

Let's demonstrate how to manage streaming SQL in Materialize with dbt by porting the classic dbt jaffle-shop demo scenario to the world of streaming.

Comprehensive insights & updates in Materialize's Release 0.7, enhancing real-time data warehouse capabilities.

Insight into SQL subquery optimization & how Materialize's approach differs from other databases, enhancing query performance.

Efficient SQL data transformations & real-time analytics with dbt + Materialize: a powerful operational data warehouse combo.

Temporal filters give you a powerful SQL primitive for defining time-windowed computations over temporal data.

Let's build a python application to demonstrate how developers can create real-time, event-driven experiences for their users, powered by Materialize.

Comprehensive guide on slicing temporal aggregates with Materialize for real-time data analysis & actionable insights.

How to write algorithms in differential dataflow, using Conway's Game of Life as an example.

Materialize's Release 0.6 enhances cloud data warehousing with real-time streaming capabilities for immediate action on live data.

Comprehensive guide to implementing joins in Materialize, covering binary to delta joins for efficient streaming systems.

In principle, it is possible to use Kafka as a database. But in doing so you will confront every hard problem that database management systems have faced for decades

Real-time apps for Boston Transit with live data are easy to set up using Materialize; see two examples you can run at home.

Materialize secures Series B funding to enhance its Operational Data Warehouse with real-time streaming capabilities for immediate data action.

Materialize 0.5 operational data warehouse offers real-time action on live data for efficient & immediate insights.

An in-depth look at Materialize, the Operational Data Warehouse with streaming capabilities for real-time data action.

Comprehensive guide to using Materialize'sLATERAL join for efficient query patterns in incremental view maintenance engines.

Here we set the context for and propose a change data capture protocol: a means of writing down and reading back changes to data.

Discover how to reduce database query costs with Materialized Views. This guide will walk you through the benefits, creation process, and impact on database efficiency.

An explanation of our rationale for why Materialize chose not to use RocksDB as its underlying storage engine.

Comprehensive guide to implementing robust reductions in Materialize, ensuring efficient & real-time data processing.

Materialize 0.4 introduces an Operational Data Warehouse with real-time streaming capabilities for immediate data action & analysis.

Real-time data streaming directly to your browser with Materialize's latest one-day project; understand the technical journey & outcomes.

Understand why eventual consistency isn't suitable for streaming systems & the systematic errors it can cause with Materialize's insights.

Harness the power of Rust for data-intensive tasks with Materialize, offering real-time insights & performance benefits.

Arjun Narayan introduces the CMU DB group to streaming databases, the problems they solve, and specific architectural decisions in Materialize.

Materialize 0.3, an Operational Data Warehouse with cloud & streaming capabilities, optimizes real-time data action.

Insights on how Differential Dataflow manages & limits memory use for processing unbounded data streams, ensuring efficiency.

Understand the necessary consistency guarantees for a streaming data platform & how they ensure accurate data views.

Comprehensive guide to implementing upserts in differential dataflow with Materialize for real-time data warehouse optimization & efficiency.

Experience real-time data analysis with Materialize on NYC taxi data, showcasing a practical application of streaming SQL.

Materialize's approach to data processing & view maintenance offers real-time insights for immediate action on live data.

Materialize Beta offers insights on a cloud data warehouse with real-time streaming capabilities for immediate action on current data.

Materialize offers a streaming data warehouse for real-time analytics & interoperability with millisecond latency, revolutionizing data handling.