GitKraken
SourceTree
GitHub Desktop
SmartGit
TortoiseGit
Tower
Fork
Git Extensions
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
GitKraken Amazon EMRBased on our record, Amazon EMR should be more popular than GitKraken. It has been mentiond 10 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.
The Git CLI is terrifying and awful. It's far too easy to clobber your own work -- and that of others -- when the whole point of it was to prevent that. While you still need to really deeply understand several git concepts to use it, GitKraken[0] is the best GUI tool I've used in daily practice. It integrates well with git hosts and has an attractive and mostly comprehensible interface. Accordingly, it isn't free... - Source: Hacker News / over 1 year ago
I like GitKraken partially because it was originally loosely based on the look/feel of Guitar Hero. Source: about 2 years ago
This experience was also invaluable because I had a walking fountain of knowledge sitting next to me and was really cool about answering my questions and pointing out all code style errors in countless PR reviews. I cannot count the amount of times he had to explain me the whole rebase workflow. What really helped me improve my Git knowledge was GitKraken and other similar tools. - Source: dev.to / about 2 years 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: over 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: about 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: about 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
SourceTree - Mac and Windows client for Mercurial and Git.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
GitHub Desktop - GitHub Desktop is a seamless way to contribute to projects on GitHub and GitHub Enterprise.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
SmartGit - SmartGit is a front-end for the distributed version control system Git and runs on Windows, Mac OS...
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost