Course Outline

Introduction

Overview of MapR Streams Architecture

MapR Stream Core Components

Understanding How Messages Are Managed in MapR Streams

Understanding Producers and Consumers

Developing a MapR Streams Application

  • Streams, Producer, Consumer
  • Using the Kafka Java API

Working with Properties and Options

Summary and Conclusion

Requirements

  • An understanding of Big Data concepts
  • An understanding of Hadoop concepts
  • Java programming experience
  • Comfortable using a Linux command line
  7 Hours
 

Need help picking the right course?
Call us +971 4871 6715

Testimonials

Recalling/reviewing keypoints of the topics discussed.

Paolo Angelo Gaton - SMS Global Technologies Inc.

-

Roxane Santiago - SMS Global Technologies Inc.

The lab exercises. Applying the theory from the first day in subsequent days.

- Dell

The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.

The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.

Related Courses

Apache Ignite for Developers

 14 hours

Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale, and availability. In this instructor-led, live training, participants will learn the principles behind persistent and pure

Apache Apex: Processing Big Data-in-Motion

 21 hours

Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live

Unified Batch and Stream Processing with Apache Beam

 14 hours

Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of

Building Kafka Solutions with Confluent

 14 hours

This instructor-led, live training (online or onsite) is aimed at engineers who wish to use Confluent (a distribution of Kafka) to build and manage a real-time data processing platform for their applications. By the end of this training,

A Practical Introduction to Stream Processing

 21 hours

Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user

Apache Kafka for Python Programmers

 7 hours

Apache Kafka is an open-source stream-processing platform that provides a fast, reliable, and low-latency platform for handling real-time data analytics. Apache Kafka can be integrated with available programming languages such as Python. This

Stream Processing with Kafka Streams

 7 hours

Kafka Streams is a client-side library for building applications and microservices whose data is passed to and from a Kafka messaging system. Traditionally, Apache Kafka has relied on Apache Spark or Apache Storm to process data between message

Samza for Stream Processing

 14 hours

Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing.  It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource

Tigon: Real-time Streaming for the Real World

 14 hours

Tigon is an open-source, real-time, low-latency, high-throughput, native YARN, stream processing framework that sits on top of HDFS and HBase for persistence. Tigon applications address use cases such as network intrusion detection and analytics,

Apache Flink Fundamentals

 28 hours

Apache Flink is an open-source framework for scalable stream and batch data processing. This instructor-led, live training introduces the principles and approaches behind distributed stream and batch data processing, and walks participants

Confluent KSQL

 7 hours

Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations. This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache

Apache NiFi for Administrators

 21 hours

Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a

Apache NiFi for Developers

 7 hours

Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a

Spark Streaming with Python and Kafka

 7 hours

Apache Spark Streaming is a scalable, open source stream processing system that allows users to process real-time data from supported sources. Spark Streaming enables fault-tolerant processing of data streams. This instructor-led, live

Apache Storm

 28 hours

Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing). "Storm is for