Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Qubole
Snowflake
HortonWorks Data Platform
Databricks
Materialize
Apache Flink
Apache Kafka
RisingWave
ClickHouse
OctoSQL
Amazon Kinesis
Timeplus
Amazon EMR MaterializeAmazon EMR is recommended for data engineers, data scientists, and IT professionals who need to manage and process large datasets in a scalable, efficient, and cost-effective manner. It is especially suitable for businesses that are already using AWS services and want to leverage a tightly integrated ecosystem. Additionally, it is a good choice for organizations that require rapid and flexible data analysis capabilities provided by frameworks such as Hadoop, Spark, HBase, and Presto.
Based on our record, Materialize should be more popular than Amazon EMR. It has been mentiond 74 times since March 2021. 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.
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: over 3 years ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: about 4 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: over 4 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / almost 5 years ago
Check out https://aws.amazon.com/emr/. Source: over 4 years ago
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 / about 1 year 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 / almost 2 years 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 / almost 2 years ago
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost
RisingWave - RisingWave is a stream processing platform that utilizes SQL to enhance data analysis, offering improved insights on real-time data.
