Materialize VS Timeplus
Compare Materialize VS Timeplus and see what are their differences
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Materialize
Apache Flink
Apache Kafka
RisingWave
ClickHouse
OctoSQL
Amazon Kinesis
DeltaStream.io
Timeplus
Apache Flink
KSQL
RisingWave
Apache Spark
Azure Stream Analytics
Amazon Kinesis
Splunk
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Materialize TimeplusBased on our record, Materialize seems to be a lot more popular than Timeplus. While we know about 74 links to Materialize, we've tracked only 1 mention of Timeplus. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Did I miss in the article where OP reveals the magic database that actually does this? 3rd party solutions like https://readyset.io/ and https://materialize.com/ exist specifically because databases donโt actually have what we all want materialized views to be. - Source: Hacker News / 12 months ago
This triggered some associations for me. Strongest was Cells[0], a library for Common Lisp CLOS. The earliest reference I can find is 2002[1], making it over 20 years old. Second is incremental view maintenance systems like Feldera[2] or Materialize[3]. These use sophisticated theories (z-sets and differential dataflow) to apply efficient updates over sets of data, which generalizes the case of single variables.... - Source: Hacker News / about 1 year ago
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 / over 1 year 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 / over 1 year 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 / over 1 year ago
* Proton is more developer friendly To explore Proton yourself, visit the [Proton GitHub repo](https://github.com/timeplus-io/proton). - Source: Hacker News / over 2 years ago
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
KSQL - Confluent KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafkaยฎ.
RisingWave - RisingWave is a stream processing platform that utilizes SQL to enhance data analysis, offering improved insights on real-time data.
ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.