Thanks to Sam Shuster, Software Engineer at Edmunds.com, for the guest post below about his company’s use case for Spark Streaming, SparkOnHBase, and Morphlines.
Every year, the Super Bowl brings parties, food and hopefully a great game to appease everyone’s football appetites until the fall. With any event that brings in around 114 million viewers with larger numbers each year, Americans have also grown accustomed to commercials with production budgets on par with television shows and with entertainment value that tries to rival even the game itself.
A Hive-on-Spark beta is now available via CDH parcel. Give it a try!
The Hive-on-Spark project (HIVE-7292) is one of the most watched projects in Apache Hive history. It has attracted developers from across the ecosystem, including from organizations such as Intel, MapR, IBM, and Cloudera, and gained critical help from the Spark community.
Many anxious users have inquired about its availability in the last few months.
Cloudera and Google are collaborating to bring Google Cloud Dataflow to Apache Spark users (and vice-versa). This new project is now incubating in Cloudera Labs!
“The future is already here—it’s just not evenly distributed.” —William Gibson
For the past decade, a lot of the future has been concentrated at Google’s headquarters in Mountain View. Because of the scale of its operations, Google usually bumped up against the limitations of the current state-of-the-art before anyone else,
As we progressively move from MapReduce to Spark, we shouldn’t have to give up good HBase integration. Hence the newest Cloudera Labs project, SparkOnHBase!
[Ed. Note: In Aug. 2015, SparkOnHBase was committed to the Apache HBase trunk in the form of a new HBase-Spark module.]
Apache Spark is making a huge impact across our industry, changing the way we think about batch processing and stream processing.
Cloudera Labs contains ecosystem innovations that one day may bring developers more functionality or productivity in CDH.
Since its inception, one of the defining characteristics of Apache Hadoop has been its ability to evolve/reinvent and thrive at the same time. For example, two years ago, nobody could have predicted that the formative MapReduce engine, one of the cornerstones of “original” Hadoop, would be marginalized or even replaced. Yet today, that appears to be happening via Apache Spark,