Based on our record, PostgreSQL should be more popular than Amazon EMR. It has been mentiond 15 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.
I’m on MacOS and erlang.org, elixir-lang.org, and postgresql.org all suggest installation via Homebrew, which is a very popular package manager for MacOS. - Source: dev.to / about 2 months ago
According to the documentation, crate sqlx is implemented in Rust, and it's database agnostic: it supports PostgreSQL, MySQL, SQLite, and MSSQL. - Source: dev.to / 8 months ago
Solution is just downloading and installilng pgAdmin from official pgAdmin homepage version, not the one that is included in the postgresql.org package. Source: 10 months ago
SQL immediately stands out here because it was designed for making relational algebra, the other side of the Entity-Relationship model, accessible. There are likely more people who know SQL than any programming language (for IaC) or data format you could choose to represent your cloud infrastructure. Many non-programmers know it, as well, such as data scientists, business analysts, accountants, etc, and there is... - Source: dev.to / about 1 year ago
Vapor[0] based on Swift. Advantage of this is that you don't have to evaluate multiple frameworks for Swift and suffer paralysis by analysis. All the Swift community is behind one framework. The next is Actix[1] based on Rust. There are many frameworks in Rust and most of them have not reached 1.0 And which framework will survive becomes a question. Other not so well-known is Wt[2] based on C++. This actually is... - Source: Hacker News / over 1 year ago
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: about 1 year 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: almost 2 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: almost 2 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 / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
MySQL - The world's most popular open source database
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
SQLite - SQLite Home Page
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost