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

Databricks Runtime 5.2 Released

Nakul Jamadagni announces Databricks Runtime 5.2: Delta Time TravelTime Travel, released as an Experimental feature, adds the ability to query a snapshot of a table using a timestamp string or a version, using SQL syntax as well as DataFrameReader options for timestamp expressions.Sample codeSELECT count() FROM events TIMESTAMP AS OF timestamp_expressionSELECT count() FROM events VERSION AS OF version Time travel looks a bit like temporal tables in SQL Server.

Read More

Kafka And The Differing Aims Of Data Professionals

Kai Waehner argues that there is an impedence mismatch between data engineers, data scientists, and ML production engineers: Data scientists love Python, period. Therefore, the majority of machine learning/deep learning frameworks focus on Python APIs. Both the stablest and most cutting edge APIs, as well as the majority of examples and tutorials use Python APIs. […]

Read More

Categories

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