Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Confluent
Grails
Mercurial SCM
Git
GitHub Desktop
Atlassian Bitbucket Server
Azure DevOps
Perforce
Apache Subversion
GitKraken
Apache Flink Mercurial SCMNo Mercurial SCM videos yet. You could help us improve this page by suggesting one.
Based on our record, Apache Flink seems to be a lot more popular than Mercurial SCM. While we know about 29 links to Apache Flink, we've tracked only 2 mentions of Mercurial SCM. 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’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / 9 days ago
You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 23 days ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 2 months ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 4 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago
Many people have asked me to write a blog post on my preference of Mercurial over Git and so far I've refused and will continue doing so for the foreseeable future. - Source: dev.to / 3 months ago
Mercurial Paris Conference 2023 is a professional and technical conference around mercurial scm, a free, distributed source control management tool. Source: over 1 year ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Git - Git is a free and open source version control system designed to handle everything from small to very large projects with speed and efficiency. It is easy to learn and lightweight with lighting fast performance that outclasses competitors.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
GitHub Desktop - GitHub Desktop is a seamless way to contribute to projects on GitHub and GitHub Enterprise.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
Atlassian Bitbucket Server - Atlassian Bitbucket Server is a scalable collaborative Git solution.