Spark and Spark streaming with Python

Apache Spark is a fast and general engine for large-scale data processing. It is 100x faster than Hadoop MapReduce in memory and 10x faster on disk. Apache Spark is designed to write applications quickly in Java, Scala or Python. You can use it interactively from the Scala and Python shells. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN. Access data in HDFS, Cassandra, HBase, Hive, and any Hadoop data source.

לחצו כאן BI לקורס פייתון עבור אנליסטים ואנשי

לקורס Spark with Java לחצו כאן

This course will teach you to create applications in Spark with the implementation of Python programming. It provides a clear comparison between Spark and Hadoop and covers techniques to increasing your application performance and enabling high-speed processing.

The module Spark Streaming will explain how easy to build scalable fault-tolerant streaming applications. It will let you to work with large scale streaming data using familiar batch processing abstractions.

In addition, we will cover MLlib, Apache Spark’s scalable machine learning library. We present an integrated view of data processing by highlighting the various components of these pipelines, including exploratory data analysis, feature extraction, supervised learning, and model evaluation. You will gain hands-on experience applying these principles using Spark

For more information about Apache Spark with Java & Spring click here