Earlier this week, RStudio announced sparklyr, a new package that provides an interface between R and Apache Spark. We republish RStudio’s blog post below (see original) for your convenience.
Over the past couple of years we’ve heard time and time again that people want a native dplyr interface to Spark, so we built one! sparklyr also provides interfaces to Spark’s distributed machine learning algorithms and much more.
Today, Cloudera announced the availability of an Apache Spark 2.0 Beta release for users of the Cloudera platform.
Apache Spark 2.0 is tremendously exciting (read this post for more background) because (among other things):
- The Dataset API further enhances Spark’s claim as the best tool for data engineering by providing compile-time type safety along with the benefits of a query-optimization engine.
- The Structured Streaming API enables the modeling of streaming data as a continuous DataFrame and expresses operations on that data with a SQL-like API.
Can using simple statistical techniques in combination with big data help solve the Tamam Shud mystery?
Everyone loves a good real-life mystery. That’s why the three most popular TV shows of the 80s and 90s were Jack Palance’s reboot of Ripley’s Believe It or Not!, Unsolved Mysteries with Robert Stack, and Beyond Belief: Fact or Fiction hosted by Commander Riker.
Learn how the performance advantages of the Crypto cryptographic library will provide an upgrade for Spark shuffle encryption over the current approach.
When running a big data computing job, the data being processed may contain sensitive information that users don’t want anyone else to access. Encrypting that sensitive data is becoming more and more important, especially for enterprise users.
For Apache Spark, which is the emerging standard for big data processing,
Apache Hadoop is a proven platform for long-term storage and archiving of structured and unstructured data. Related ecosystem tools, such as Apache Flume and Apache Sqoop, allow users to easily ingest structured and semi-structured data without requiring the creation of custom code. Unstructured data, however, is a more challenging subset of data that typically lends itself to batch-ingestion methods. Although such methods are suitable for many use cases,