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Building Data Streaming Applications with Apache Kafka

practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with;

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Kafka in Action

Kafka in Action is a practical, hands-on guide to building Kafka-based data pipelines. Filled with real-world use cases and scenarios, this;

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Modern Data Engineering with Apache Spark

step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data;

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Apache Kafka Quick Start Guide

Apache Kafka 2.0 Key Features Solve practical large data and processing challenges with Kafka Tackle data processing challenges like late events;

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Streaming Architecture

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, you ll learn;

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Big Data Analytics

applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New;

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Apache Spark 2.x Cookbook

operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through;

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Beginning Apache Spark 2

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable;

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Apache Pulsar in Action

guide to building high-traffic applications with Pulsar, delivering extreme levels of speed and durability. about the technology;

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Apache Spark 2.x for Java Developers

text, using the Spark core Library. * Perform analytics on data from various data sources such as Kafka, and Flume using Spark Streaming;

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Apache Hadoop 3 Quick Start Guide

streaming using Apache Storm, and data analytics using Apache Spark. By the end of the book, you will be well versed with different configurations;

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Apache Spark 2

Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework Key;

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Learning Spark SQL

semi-structured data using real-world datasets and gain hands-on exposure to the issues and challenges of working with noisy and dirty real;

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Practical Apache Spark

Kafka Use Spark SQL, DataFrames, and Datasets to process data using traditional SQL queries Work with different machine learning concepts;

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Learning Apache Apex

-time data processing * Program Apache Apex streaming applications * This book shows you Apex integration with the open source Big Data;

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Beginning Apache Spark 3

data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications;

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Mastering Apache Storm

-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm. With real-world examples and clear;

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Apache Spark Quick Start Guide

Features Learn about the core concepts and the latest developments in Apache Spark Master writing efficient big data applications with Spark's;

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Streaming Data

with streaming or real-time data systems required. Perfect for developers or architects, this book is also written to be accessible to;

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Mastering Kafka Streams and ksqlDB Building realtime data systems by example

Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream;

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Pro Spark Streaming

data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing;

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Data Science on AWS

repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis;

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Heron Streaming

. Overall, this book aims at professionals who want to process streaming data based on Apache Heron. A basic knowledge of Java and Bash commands for;

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Hadoop Application Architectures

Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various;

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PySpark Cookbook

. In addition to this, you'll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You'll then;

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Learning Spark

data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming;

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Spark - The Definitive Guide

well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will;

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