Spark Overview

Jen Underwood provides an overview of the Apache Spark project:

Spark provides a comprehensive framework to manage big data processing with a variety of data set types including text and graph data. It can also handle batch pipelines and real-time streaming data. Using Spark libraries, you can create big data analytics apps in Java, Scala, Clojure, and popular R and Python languages.

Spark brings analytics pros an improved MapReduce type query capability with more performant data processing in memory or on disk. It can be used with datasets that are larger than the aggregate memory in a cluster. Spark also has savvy lazy evaluation of big data queries which helps with workflow optimization and reuse of intermediate results in memory. TheSpark API is easy to learn.

One of my taglines is, Spark is not the future of Hadoop; Spark is the present of Hadoop.  If you want to get into this space, learn how to work with Spark.

Related Posts

Building TensorFlow Neural Networks On Spark With Keras

Jules Damji has an example of using the PyCharm IDE to use Keras to build TensorFlow neural network models on the Databricks MLflow library: Our example in the video is a simple Keras network, modified from Keras Model Examples, that creates a simple multi-layer binary classification model with a couple of hidden and dropout layers and […]

Read More

Hortonworks Data Platform 3.0 Released

Saumitra Buragohain, et al, announce the newest version of the Hortonworks Data Platform: Highlighted Apache Hive features include: Workload management for LLAP:  You can assign resource pools within LLAP pool and allocate resources on a per user or per group basis. This enables support for large multi-tenant deployments. ACID v2 and ACID on by default:  We are […]

Read More

Categories

October 2016
MTWTFSS
« Sep Nov »
 12
3456789
10111213141516
17181920212223
24252627282930
31