Sparklyr, an LF AI Foundation Incubation Project, has released version 1.3.0! Sparklyr is an R Language package that lets you analyze data in Apache Spark, the well-known engine for big data processing, while using familiar tools in R. The R Language is widely used by data scientists and statisticians around the world and is known for its advanced features in statistical computing and graphics.
In version 1.3.0, sparklyr adds a variety of improvements; highlights include:
- Now supports seamless integration of Spark higher-order functions with R (similar to how dplyr allows R users to compose clear and concise data-manipulation verbs instead of long SQL queries)
- After seeing popular demand for Apache Avro functionalities in sparklyr, spark_read_avro, spark_write_avro, sdf_from_avro, and sdf_to_avro methods are implemented to make working with Apache Avro simpler for sparklyr users (context: Apache Avro is a popular data serialization format that combines flexibility of JSON schema definition with efficiency of binary serialization of data columns)
- It is now also possible to run user-defined R serialization and deserialization procedures on Spark worker nodes through sparklyr
- As usual, creating new features wasn’t the only focus for the sparklyr 1.3 release. There were also a number of crucial bug fixes (as outlined in https://github.com/sparklyr/sparklyr/pull/2550)
The power of open source projects is the aggregate contributions originating from different community members and organizations that collectively help drive the advancement of the projects and their roadmaps. The sparklyr community is a great example of this process and was instrumental in producing this release. The sparklyr team wanted to give a special THANK YOU to the following community members for their contributions via pull requests (listed in chronological order):
- Hossein Falaki (Databricks)
- Samuel Macedo (IFPE)
- Yitao Li (RStudio)
- Andy Zhang (Databricks)
- Javier Luraschi (RStudio)
- Neal Richardson (RStudio)
- Jozef Hajnala (Individual)
Contributions take many forms, roadmap input for sparklyr 1.3 from Javier Luraschi ([#2434 and #2552). And great insight from @mattpollock and @benmwhite on several issues (#1773, #2514). Truly a great team effort for this release!
To learn more about the sparklyr 1.3.0 release, check out the full release notes. Want to get involved with sparklyr? Be sure to join the sparklyr-Announce and sparklyr Technical-Discuss mailing lists to join the community and stay connected on the latest updates.
Congratulations to the sparklyr team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.
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