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You're reading from  Big Data Analytics with Hadoop 3

Product typeBook
Published inMay 2018
PublisherPackt
ISBN-139781788628846
Edition1st Edition
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Sridhar Alla
Sridhar Alla
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Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.
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Interoperability with streaming platforms (Apache Kafka)


Spark Streaming integrates well with Apache Kafka, currently the most popular messaging platform. This integration has several approaches, and the mechanism has improved over time with regards to performance and reliability.

There are three main approaches:

  • Receiver-based approach
  • Direct Stream approach
  • Structured Streaming

Receiver-based

The first integration between Spark and Kafka is the receiver-based integration. In the receiver-based approach, the driver starts the receivers on the executors, which then pull data using a high-level API from the Kafka brokers. Since the events are being pulled from the Kafka brokers, the receivers update the offsets into Zookeeper, which is also used by the Kafka cluster. The important aspect here is the use of the write ahead log (WAL), which is what the receiver writes to as it collects data from Kafka. If there is a problem and the executors and receivers have to restart or are lost, the WAL can...

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Big Data Analytics with Hadoop 3
Published in: May 2018Publisher: PacktISBN-13: 9781788628846

Author (1)

author image
Sridhar Alla

Sridhar?Alla?is the co-founder and CTO of Blue Whale Consulting and is expert at helping companies (big and small) define their vision for systems and capabilities that will allow them to establish a strategic execution plan to deal with the ever-growing data collected to support analytics and product teams. He has very experienced at dealing with all aspects of data collection, security, governance, and processing as part of end-to-end big data analytics and machine learning initiatives (including predictive modeling, deep learning, and ML automation). Sridhar?is a published book author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit.? He also has several patents filed with the US PTO on large-scale computing and distributed systems.? He has over 18 years' experience writing code in Scala, Java, C, C++, Python, R, and Go, and has extensive hands-on knowledge of Spark, Flink, TensorFlow, Keras, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing, and high-performance computing. Sridhar lives with his wife and daughter in New Jersey and in his spare time loves blogging and coaching organizations on next-generation advancements in technology and their alignment with business goals.
Read more about Sridhar Alla