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You're reading from Big Data Analytics with Hadoop 3

Product typeBook

Published inMay 2018

PublisherPackt

ISBN-139781788628846

Edition1st Edition

Tools

Hadoop

Concepts

Big Data

Author (1)

Sridhar Alla

Chapter 9. Stream Processing with Apache Flink

In this chapter, we will look at stream processing using Apache Flink and how the framework can be used to process data as soon as it arrives to build exciting real-time applications. We will start with the DataStream API and look at various operations that can be performed.

We will be looking at the following:

Data processing using the DataStream API
Transformations
Aggregations
Window
Physical partitioning
Rescaling
Data sinks
Event time and watermarks
Kafka connector
Twitter connector
Elasticsearch connector
Cassandra connector

Introduction to streaming execution model

Flink is an open source framework for distributed stream processing that:

Provides results that are accurate, even in the case of out-of-order or late-arriving data
Is stateful and fault tolerant, and can seamlessly recover from failures while maintaining an exactly-once application state
Performs on a large scale, running on thousands of nodes with very good throughput and latency characteristics

The following diagram is a generalized view of stream processing:

Many of Flink's features - state management, handling out-of-order data, flexible windowing – are essential for computing accurate results on unbounded datasets and are enabled by Flink's streaming execution model:

Flink guarantees exactly-once semantics for stateful computations. Stateful means that applications can maintain an aggregation or summary of data that has been processed over time, and Flink's checkpointing mechanism ensures exactly-once semantics for an application's state in the event...

Data processing using the DataStream API

It is crucial to have robust analytics in place to process real-time data. This is more important for domains that are data-driven. Flink enables you to do real-time analytics using its DataStream API. This streaming data processing API helps you cater to Internet of Things (IoT) applications and store, process, and analyze data in real time or near real time.

In the following sections, let's examine each of the elements related to the DataStream API:

Execution environment
Data sources
Transformations
Data sinks
Connectors

Execution environment

To write a Flink program, you need an execution environment. You can use an existing environment or create a new environment.

Based on your requirements, Flink allows you to use an existing Flink environment, create a local environment, or create a remote environment.

Use thegetExecutionEnvironment()command to accomplish different tasks based on your requirement:

To execute on a local environment in an IDE, it starts...

Summary

In this chapter, we learned about Flink's most powerful API, the DataStream API; how data sources, transformations, and sinks work together; and about various technology connectors, such as Elasticsearch, Cassandra, Kafka, RabbitMQ, and so on. In this chapter, we also discussed stream processing using Apache Flink.

In the next chapter, we will switch gears and look at one of the most exciting fields of visualizing data.

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Author (1)

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

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