Real-time Analytics
Materialize offers real-time stream processing and materialized views, which allow users to get instant results from their data without the need for batch processing. This is particularly useful for applications that require immediate insights.
SQL Support
Materialize supports SQL, making it easy for users familiar with SQL databases to adopt the platform without needing to learn a new language or framework.
Consistency
Materialize maintains strict consistency for its materialized views, ensuring that users always get accurate and up-to-date information from their streams.
Integration with Kafka
It integrates smoothly with Kafka, allowing for easy handling of streaming data and simplifying the process of working with real-time data feeds.
It's hard to write something that is both accessible and well-motivated. The best uses of category theory is when the morphisms are far more exotic than "regular functions". E.g. It would be nice to describe a circuit of live queries (like https://materialize.com/ stuff) with proper caching, joins, etc. Figuring this out is a bit of an open problem. Haskell's standard library's Monad and stuff are watered down to... - Source: Hacker News / 2 months ago
> [...] `https://materialize.com/` to solve their memory issues [...] Disclaimer: I work at Materialize Recently there have been major improvements in Materialize's memory usage as well as using disk to swap out some data. I find it pretty easy to hook up to Postgres/MySQL/Kafka instances: https://materialize.com/blog/materialize-emulator/. - Source: Hacker News / 3 months ago
I agree. So many disparate solutions. The streaming sql primitives are by themselves good enough (e.g. `tumble`, `hop` or `session` windows), but the infrastructural components are always rough in real life use cases. Crossing fingers for solutions like `https://github.com/feldera/feldera` to solve their memory issues, or `https://clickhouse.com/docs/en/materialized-view` to solve reliable streaming consumption.... - Source: Hacker News / 3 months ago
Or the related Materialize stuff https://materialize.com/. - Source: Hacker News / 4 months ago
The original post makes so much more sense in this context! One of the "holy grails" in my mind is making CQRS and dataflow programming as easy to learn and maintain as existing imperative programming languages - and easy to weave into real-time UX. There are so many backend endpoints in the wild that do a bunch of things in a loop, many of which will require I/O or calls to slow external endpoints, transform the... - Source: Hacker News / 4 months ago
This looks extremely cool. This is basically incremental view maintenance in databases, a problem that almost everybody (I think) has when using SQL databases and wanting to do some derived views for more performant access patterns. Importantly, they seem to support a wide breath of SQL operators, and it's open-source! There's already a bunch of tools in this area: 1. Materialize[0], which afaik is more... - Source: Hacker News / 4 months ago
Materialize | https://materialize.com/ | Staff Security Engineer 200k | $200- 230k NYC (HQ) or United States We are looking for a Staff Security Engineer on our Cloud team who will own the security of our infrastructure and product. (5+) years of experience as a security-focused engineer. https://boards.greenhouse.io/materialize/jobs/5220351004. - Source: Hacker News / 7 months ago
To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize,... - Source: dev.to / about 1 year ago
Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views. https://github.com/timelydataflow/differential-dataflow. - Source: Hacker News / over 1 year ago
Materialize | Full-Time | NYC Office or Remote | https://materialize.com Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that... - Source: Hacker News / over 1 year ago
Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/ You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. That is Materialize, the only true SQL... - Source: Hacker News / over 1 year ago
Materialize | NY, NY | https://materialize.com/ The Cloud Database for Fast-Changing Data. We put a streaming engine in a database, so your team can build real-time data products without the cost, complexity, and development time of stream processing. Cloud team openings: https://grnh.se/0ad6ab6b4us Senior PM openings: https://grnh.se/415c267f4us. - Source: Hacker News / almost 2 years ago
Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database. - Source: dev.to / almost 2 years ago
The relational model (and generally working at the level of sets/collections, instead of the level of individual values/objects) actually makes it easier to have this kind of incremental computation in a consistent way, I think. There's a bunch of work being done on making relational systems work this way. Some interesting reading: -... - Source: Hacker News / almost 2 years ago
If you want to keep in your Python/SQL area of expertise and by all means I don't mean to promote not learning a new language, but just as an FYI. There are some non-Java/Scala tools between streaming databases like risingwave and materialize, streaming platforms like fluvio and redpanda, and stream processors like bytewax and faust. Source: almost 2 years ago
AFAIK there is a lot of overlap between OLAP databases and time series databases. Timescale](https://legacy-docs.timescale.com/v1.7/introduction/architecture) gains a lot of its performance via the "Hypertable" abstraction which is fairly similar to something like Parquet partitioning/bucketing. In terms of performance I don't know if there is a huge gap either for non optimized use cases. The [Clickhouse] team... Source: about 2 years ago
Imo this is a matter of schema design. You shouldn't have to send the entire object in the event, just the delta. If you are using an event based schema, ideally you should be able to generate the current state by iterating over all the events and combining the deltas. An OLAP database/warehouse/lakehouse can be very efficient at this depending on how the data is partitioned. You could consider a Materialized... Source: about 2 years ago
Projects like https://readyset.io/, https://materialize.com/, https://github.com/mit-pdos/noria can keep your materialized views up to date as the underlying base tables change. - Source: Hacker News / about 2 years ago
Even though these technical stacks are listed by category, some fields actually overlap. For example, although Materialize is classified as a stream processor, it makes sense to treat as a serving layer because it is essentially a streaming database, and the same is true for ksqlDB. - Source: dev.to / about 2 years ago
Postgres Logical Replication (the ability to listen to the write-ahead-log) is an extremely useful tool that opens up a lot of use cases which has been put to great effect by companies like Materialize[0] and Redhat with Debezium[1]. Recently there was a discussion here on 'push-based output patterns'[2] where I saw it was not immediately obvious how to implement this pattern with existing tooling. This project... - Source: Hacker News / about 2 years ago
> I'm not sure if differential dataflow can provide this. Yes, it can, but you will have to write the views yourself, in Rust. Materialize (https://materialize.com) exists, though, and compiles SQL to differential-dataflow programs, in order to provide exactly what you're asking for. (I work for Materialize). - Source: Hacker News / over 2 years ago
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