build-server-protocol
Apache Spark
| build-server-protocol | Apache Spark | |
|---|---|---|
| 3 | 101 | |
| 425 | 38,414 | |
| 3.1% | 0.7% | |
| 8.0 | 10.0 | |
| 10 days ago | 7 days ago | |
| Java | Scala | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
build-server-protocol
- Build Server Protocol
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Build Server Protocol - LSP like protocol for "compile, run, test, debug and more"
https://build-server-protocol.github.io/ https://github.com/build-server-protocol/build-server-protocol
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
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.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
seed - Build tool for Scala projects
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Play - The Community Maintained High Velocity Web Framework For Java and Scala.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
sbt-dependency-graph - sbt plugin to create a dependency graph for your project [Moved to: https://github.com/sbt/sbt-dependency-graph]
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
sbt - sbt, the interactive build tool
Scalding - A Scala API for Cascading
Mill - Your shiny new Java/Scala build tool!
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
sbt-dependency-graph - sbt plugin to create a dependency graph for your project
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.