This new open source complement to HDFS and Apache HBase is designed to fill gaps in Hadoop’s storage layer that have given rise to stitched-together, hybrid architectures.
The set of data storage and processing technologies that define the Apache Hadoop ecosystem are expansive and ever-improving, covering a very diverse set of customer use cases used in mission-critical enterprise applications. At Cloudera, we’re constantly pushing the boundaries of what’s possible with Hadoop—making it faster,
Proper configuration of your Python environment is a critical pre-condition for using Apache Spark’s Python API.
One of the most enticing aspects of Apache Spark for data scientists is the API it provides in non-JVM languages for Python (via PySpark) and for R (via SparkR). There are a few reasons that these language bindings have generated a lot of excitement: Most data scientists think writing Java or Scala is a drag,
Thanks to Holden Karau (@holdenkarau), Software Engineer at Alpine Data Labs (also a Spark contributor and book author), for providing the following post about her work on new base classes for testing Apache Spark programs.
Testing in the world of Apache Spark has often involved a lot of hand-rolled artisanal code, which frankly is a good way to ensure that developers write as few tests as possible. I’ve been doing some work with Spark Testing Base (also available on Spark Packages) to try and make testing Spark jobs as easy as “normal”
Apache Spark continues to be a major theme in the Strata + Hadoop World conference series; here are highlights at NYC next week.
Strata + Hadoop World NYC 2015 (Sept. 29-Oct. 1; if you haven’t registered yet, a 20% discount is still available) is a learning bonanza for many reasons, but this year the focus on Apache Spark and its growing importance in the Apache Hadoop ecosystem is notable.
The super-active Apache Spark community is exerting a strong gravitational pull within the Apache Hadoop ecosystem. I recently had that opportunity to ask Cloudera’s Apache Spark committers (Sean Owen, Imran Rashid [PMC], Sandy Ryza, and Marcelo Vanzin) for their perspectives about how the Spark community has worked and is working together, and the work to be done via the One Platform initiative to make the Spark stack enterprise-ready.