gatling-jdbc
Apache Spark
| gatling-jdbc | Apache Spark | |
|---|---|---|
| 1 | 101 | |
| 20 | 38,414 | |
| - | 0.6% | |
| 0.0 | 10.0 | |
| almost 3 years ago | about 1 hour ago | |
| Scala | 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.
gatling-jdbc
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Performance testing framework
Depending on how adventurous you're feeling, you can extend Gatling's functionality pretty easily. Here's an example I found where someone does this for JDBC. In particular you might want to take a look at JdbcInsertAction and JdbcInsertionActionBuilder, and then the log function here.
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?
ScalikeJDBC - A tidy SQL-based DB access library for Scala developers. This library naturally wraps JDBC APIs and provides you easy-to-use APIs.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Gatling - Modern Load Testing as Code
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
doobie - Functional JDBC layer for Scala.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Quill - Compile-time Language Integrated Queries for Scala
Scalding - A Scala API for Cascading
Anorm - The Anorm database library
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
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.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing