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,
Learn how to build an Impala table around data that comes from non-Impala, or even non-SQL, sources.
As data pipelines start to include more aspects such as NoSQL or loosely specified schemas, you might encounter situations where you have data files (particularly in Apache Parquet format) where you do not know the precise table definition. This tutorial shows how you can build an Impala table around data that comes from non-Impala or even non-SQL sources,
Cloudera Director 1.5 introduces a new plugin architecture to enable support for additional cloud providers. If you want to implement a plugin to add integration with a cloud provider that is not supported out-of-the-box, or to extend one of the existing plugins, these details will get you started.
As discussed in our previous blog post, the Cloudera Director Service Provider Interface (Cloudera Director SPI) defines a Java interface and packaging standards for Cloudera Director plugins.
Our thanks to Karthik Vadla and Abhi Basu, Big Data Solutions engineers at Intel, for permission to re-publish the following (which was originally available here).
Data science is not a new discipline. However, with the growth of big data and adoption of big data technologies, the request for better quality data has grown exponentially. Today data science is applied to every facet of life—product validation through fault prediction,
Thanks to Wuheng Luo, a Hadoop and big data architect at Sears Holdings, for the guest post below about Pig job-level performance tuning
Many factors can affect Apache Pig job performance in Apache Hadoop, including hardware, network I/O, cluster settings, code logic, and algorithm. Although the sysadmin team is responsible for monitoring many of these factors, there are other issues that MapReduce job owners or data application developers can help diagnose,