As its top priority, the Spark Technology Center contributes to the Apache Spark™ open-source community. How? The statistics below offer a picture:
Since the launch of the Spark Technology Center last year, we’ve trained ~400,000 data scientists via in-person and online instruction, including free classes available through BigDataUniversity.com.
26 members of the Spark Technology team contributed to the Spark 1.6.x release. For Spark 2.0, that number is 31 — and counting.
Our Spark-specific JIRAs represent almost 25,000 lines of code.That’s 479 Spark JIRAs overall (from 2015 to May 27, 2016) and nearly 500 cumulative code contributions. Add in documentation contributions — and the number is over 620. Some specifics:
110 contributions to Spark 1.6 and earlier.
369 contributions just to Spark 2.0.
12,900 lines of code contributed to Spark SQL — 10,600 of which were for Spark 2.0.
6,100 lines of code contributed to PySpark — 5,100 to Spark 2.0.
2,900 lines of code contributed to MLlib — 2,500 to Spark 2.0.
Check out our full dashboard of metrics at http://jiras.spark.tc.
Many of you reading this post will know that the Spark Technology Center contributed SystemML to open source. (The github is here. Let’s look at some updates about SystemML from the past 9 months:
In November 2015, Apache accepted SystemML as an official incubator.
Since we open-sourced SystemML in August of 2015, we’ve seen 859 contributions to the project. Those included:
A build-out of the Spark backend
Performance improvements and general hardening of the full engine stack
Additional language operators
Usability with Scala Spark & PySpark notebooks for data science
Additional algorithms, including as recent experiments with deep learning
Since open-sourcing SystemML, we’ve offered two major releases, the most recent was under the Apache brand — and we’re currently in the process of releasing the next major release (0.10).
We hope you’ll check out what we’re up to — and join us for the ride.