DBeaver
DataGrip
MySQL Workbench
HeidiSQL
phpMyAdmin
Navicat
DbVisualizer
Sequel Pro
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
DBeaver Amazon EMRBased on our record, DBeaver should be more popular than Amazon EMR. It has been mentiond 93 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.
It’s cool to show a demo and talk about the infrastructure with cute diagrams, but I always want to prove, even if just to myself, that things work as expected. So I thought a good way to test it would be to try connecting directly to both databases using my database client, DBeaver. - Source: dev.to / about 2 months ago
List of db clients I have bookmarked https://dbeaver.io/. - Source: Hacker News / about 2 months ago
As a great alternative to DBeaver, DBGate provides a variety of tools to manage your databases. Besides in built-in support charts and a query builder, you can use Javascript to query data. It even supports NoSQL drivers and native script builders. Give it a try if your project demands simplicity over in-depth features for SQL databases. - Source: dev.to / 3 months ago
This isn’t meant to be an exhaustive list. There are many other database management software packages out there, including MySQL Workbench, DBeaver, and pgAdmin. We’ve chosen these database tools because they cover the most common database systems and use cases, but if you find they aren’t meeting your needs, be sure to explore further or consider building your own database user interface — it’s easier than you... - Source: dev.to / 4 months ago
Tools like TablePlus, DBeaver, or HeidiSQL provide visual query building interfaces. While not performance analysis tools per se, they can help you build and understand complex queries more easily. - Source: dev.to / 7 months 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
DataGrip - Tool for SQL and databases
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
MySQL Workbench - MySQL Workbench is a unified visual tool for database architects, developers, and DBAs.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
HeidiSQL - HeidiSQL is a powerful and easy client for MySQL, MariaDB, Microsoft SQL Server and PostgreSQL. Open source and entirely free to use.
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost