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Our Blog
Ecosystem
Taking streaming analytics further faster with Redpanda + Materialize
We at Materialize are so excited that the Vectorized team is building Redpanda because it helps open up access to powerful streaming architectures pre...
News
Materialize raises a $60M Series C, bringing total funding to over $100M
We’re excited to share the news that we have raised $60 million in Series C funding, led by our newest partner, Redpoint Ventures, and joined by our...
Ecosystem
Change Data Capture is having a moment. Why?
Change Data Capture (CDC) concepts have been around for 20+ years, but recently we’ve seen a step-change increase in discussion, companies, and tool...
Product
Materialize Cloud Enters Open Beta
Since the very beginning the Materialize team has been focused on delivering a new approach to real-time data processing building on Timely Dataflow. ...
Release
Release: 0.9
We’re excited to announce the release of Materialize v0.9! This version of Materialize has been in development for nearly two months, and is focused...
Case Study
Materialize & Datalot: Real-time Application Development
About Datalot Founded in 2009, Datalot provides digital marketing and analytics solutions for insurance policy sales at scale. Their SaaS product prov...
Release
Release: 0.8
Materialize v0.8 was released on 9 June 2021 with significant improvements and features, including temporal filters, upserts, PubNub sources, S3 sourc...
Deep-dive
Maintaining Joins using Few Resources
Today’s post is on a topic that a lot of folks have asked for, once they dive a bit into Materialize. One of our join implementation strategies ...
Deep-dive
Generalizing linear operators in differential dataflow
Differential dataflows contain many operators, some of which are very complicated, but many of which are relatively simple. The map operator applies a...
Deep-dive
Join Kafka with a Database using Debezium and Materialize
The Problem We need to provide (internal or end-user) access to a view of data that combines a fast-changing stream of events from Kafka with a table ...
Use Case
Real-time A/B test results with Segment, Kinesis, and Materialize
Introduction This is meant primarily to demonstrate how the Segment + Kinesis + Materialize stack can create new capabilities around querying, joining...
Ecosystem
dbt + Materialize demo: Running dbt’s jaffle_shop with Materialize
Introduction We’ve recently partnered with dbt and community members to release a beta Materialize dbt adapter that allows you to transform your...
Release
Release: 0.7
Materialize 0.7 was released on 08 February 2021 with significant improvements around getting data into Materialize. Key change: Source data from Amaz...
Ecosystem
Introducing: dbt + Materialize
Managing data is hard. Managing data pipelines is even harder. The meaning of individual tables or values in your data warehouse gets lost in translat...
Deep-dive
How Materialize and other databases optimize SQL subqueries
Subqueries are a SQL feature that allow writing queries nested inside a scalar expression in an outer query. Using subqueries is often the most natura...
Streaming
Streaming SQL: What is it, why is it useful?
Summary Streaming SQL is about taking the same declarative SQL used to write database queries, and instead running it on streams of fast-changing data...
Deep-dive
Temporal Filters: Enabling Windowed Queries in Materialize
Materialize provides a SQL interface to work with continually changing data. You write SQL queries as if against static data, and then as your data ch...
Deep-dive
A Simple and Efficient Real Time Application Powered by Materialize’s TAIL Command
Within the web development community, there has been a clear shift towards frameworks that implement incremental view maintenance and for good reason....
Deep-dive
Slicing up Temporal Aggregates in Materialize
Materialize computes and maintains SQL queries as your underlying data change. This makes it especially well-suited to tracking the current state of v...
Deep-dive
Life in Differential Dataflow
I’ve been working at Materialize for almost a year now, and I have really enjoyed learning about and using Differential Dataflow (hereafter just...
News
Release: 0.6
Materialize 0.6 makes it easier to consume streams and build streaming applications. We’ve also made a number of changes that improve our SQL compat...
Deep-dive
Joins in Materialize
This post is also available at my personal blog. Materialize allows you to maintain declarative, relational SQL queries over continually changing data...
Streaming
Kafka is not a Database
This post is co-authored by George Fraser, the CEO of Fivetran, and Arjun Narayan, the CEO of Materialize. This blog post is cross-posted on the Fivet...
Use Case
Live Maintained Views on Boston Transit to Run at Home
Materialize can be used to quickly build scalable backends for real-time apps! In this blog post, we describe two apps that you can try out at home th...
News
Materialize Raises a Series B
Today we announced that we raised a $32M Series B round of funding led by Kleiner Perkins. This follows a $8.5m Series A last year led by Lightspeed V...
News
Release: Materialize 0.5
We recently released Materialize 0.5! Here’s what’s new and improved. What’s changed in Materialize 0.5 Version 0.5 includes a number of impro...
Product
Materialize under the Hood
Today we will take a bit of a tour of the moving parts that make up Materialize. This tour isn’t meant to be exhaustive, but rather to show off ...
Deep-dive
Lateral Joins and Demand-Driven Queries
In today’s post we are going to show off Materialize’s LATERAL join (courtesy @benesch), and how you can use it to implement some pretty n...
Streaming
Change Data Capture (part 1)
At Materialize we traffic in computation over data that change. As a consequence, it is important to have a way to write down and read back changes to...
Use Case
Why Use a Materialized View?
TL;DR: Querying materialized views, unlike querying tables or logical views, can reduce query costs by maintaining results in memory that are only upd...
Deep-dive
Why not RocksDB for streaming storage?
A roadmap for a storage engine for Materialize...
Deep-dive
Robust Reductions in Materialize
Materialize is an incremental view maintenance engine, one which takes your SQL queries expressed as views and continually maintains them as your data...
News
Release: Materialize 0.4
We’re proud to announce that we’ve just released Materialize version 0.4. Here is a quick overview of the main features. What’s changed in M...
Deep-dive
Streaming TAIL to the Browser – A One Day Project
Last week concluded up my first week at Materialize, with Friday being my first Skunkworks Friday. Skunkworks Friday is a Materialize sponsored day of...
Deep-dive
Eventual Consistency isn’t for Streaming
Streaming systems consume inputs and produce outputs asyncronously: the output of a system at any moment may not reflect all of the inputs seen so far...
Deep-dive
Rust for Data-Intensive Computation
I have some thoughts on the use of Rust for data-intensive computations. Specifically, I’ve found several of Rust’s key idioms line up ver...
Deep-dive
Materialize: Roadmap to Building a Streaming Database on Timely Dataflow
How do you build a streaming database from scratch?...
Deep-dive
CMU DB Talk: Building Materialize
This is an edited transcript and video of a talk that I gave at Carnegie Mellon’s Database Group Seminar on June 1st, 2020, hosted by Andy Pavlo. Yo...
News
Release: Materialize 0.3
We recently announced Materialize, a real-time streaming SQL database that powers production applications. The latest release of Materialize, version ...
Deep-dive
Managing memory with differential dataflow
Self-compacting dataflows Those of you familiar with dataflow processing are likely also familiar with the constant attendant anxiety: won’t my ...
Deep-dive
What consistency guarantees should you expect from your streaming data platform?
In-order reliable message delivery is not enough. Showing views over streams of data requires thinking through additional consistency semantics to del...
Deep-dive
Upserts in Differential Dataflow
“Upserts” are a common way to express streams of changing data, especially in relational settings with primary keys. However, they aren...
Deep-dive
What’s inside Materialize? An architecture overview
Materialize lets you ask questions about your streaming data and get fresh answers with incredibly low latency. Since announcing Materialize’s v0.1,...
Deep-dive
Taking Materialize for a spin on NYC taxi data
Trying out Materialize This post will also be available at my personal blog. We all at Materialize are working from home, and while this is all a bit ...
Deep-dive
View Maintenance: A New Approach to Data Processing
It’s with a great deal of excitement, and some trepidation, that we are now able to show off what we have been working on at Materialize. The Ma...
News
Materialize Beta: The Details
We recently announced Materialize, the Streaming Data Warehouse. Our first beta release of Materialize, version 0.1, is available now. As a streaming ...
News
Introducing Materialize: the Streaming Data Warehouse
Databases, and data infrastructure generally, have made substantial progress over the years. We now have access to cloud-native infrastructure that al...
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