Spark MLLib is growing in popularity for machine-learning model development due to its elegance and usability. In this post, you’ll learn why.
Spark MLLib is a library for performing machine-learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take a couple lines of code and leverage hundreds of machines. MLlib greatly simplifies the model development process.
In this post,
New testing results show a significant difference between the analytic database performance of Impala compared to batch and procedural development engines, as well as Impala running all 99 TPC-DS-derived queries in the benchmark workload.
2015 was an exciting year for Apache Impala (incubating). Cloudera’s Impala team significantly improved Impala’s scale and stability, which enabled many customers to deploy Impala clusters with hundreds of nodes, run millions of queries,
Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity.
There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you’ll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name).
The 0.2.0 release of the spark-ts package includes includes a fleshed-out Java API, among other things.
The spark-ts library, which was initially developed by Cloudera’s Data Science team, enables analysis of datasets comprising millions of time series, each with millions of measurements. It runs atop Apache Spark, and exposes Scala, Java, and Python APIs. Check out this recent post for a closer look at the library and how to use it.
Spark Dataflow from Cloudera Labs is now part of Google’s New Dataflow SDK, which will be proposed to the Apache Incubator.
Spark Dataflow is an experimental implementation of Google’s Dataflow programming model that runs on Apache Spark. The initial implementation was written by Josh Wills, and entered Cloudera Labs exactly a year ago. Since then, we’ve seen a number of contributions to the project, culminating in the recent addition of an implementation of streaming (running on Spark Streaming) by Amit Sela from PayPal.