An Introduction To Kafka

Kevin Feasel

2017-07-14

Hadoop

Prashant Sharma explains the basics of Apache Kafka:

Apache describes Kafka as a distributed streaming platform that lets us:

  1. Publish and subscribe to streams of records.

  2. Store streams of records in a fault-tolerant way.

  3. Process streams of records as they occur.

Kafka is probably the most generally interesting of the current Hadoop ecosystem, with Spark not too far behind.  By “generally interesting,” I mean in the sense that companies with no vested interest in Hadoop as a whole could still be excited by the prospect of Kafka.

Related Posts

Kafka Topic Reuse

Martin Kleppmann walks through the trade-offs of reusing Apache Kafka topics for different event types: The common wisdom (according to several conversations I’ve had, and according to a mailing list thread) seems to be: put all events of the same type in the same topic, and use different topics for different event types. That line of […]

Read More

Set Operations In Spark

Fisseha Berhane compares SparkSQL, DataFrames, and classic RDDs when performing certain set-based operations: In this fourth part, we will see set operators in Spark the RDD way, the DataFrame way and the SparkSQL way. Also, check out my other recent blog posts on Spark on Analyzing the Bible and the Quran using Spark and Spark […]

Read More

Categories

July 2017
MTWTFSS
« Jun Aug »
 12
3456789
10111213141516
17181920212223
24252627282930
31