Apache Kafka Quick Start Guide

Leverage Apache Kafka 2.0 to simplify real-time data processing for distributed applications

Raúl Estrada

• Publisher: Packt Publishing
• Year: 2018
• ISBN:
  9781788992251
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Process large volumes of data in real-time while building high performance and robust data stream processing pipeline using the latest Apache Kafka 2.0

Key Features

• Solve practical large data and processing challenges with Kafka
• Tackle data processing challenges like late events, windowing, and watermarking
• Understand real-time streaming applications processing using Schema registry, Kafka connect, Kafka streams, and KSQL

Book Description

Apache Kafka is a great open source platform for handling your real-time data pipeline to ensure high-speed filtering and pattern matching on the fly. In this book, you will learn how to use Apache Kafka for efficient processing of distributed applications and will get familiar with solving everyday problems in fast data and processing pipelines.

This book focuses on programming rather than the configuration management of Kafka clusters or DevOps. It starts off with the installation and setting up the development environment, before quickly moving on to performing fundamental messaging operations such as validation and enrichment.

Here you will learn about message composition with pure Kafka API and Kafka Streams. You will look into the transformation of messages in different formats, such asext, binary, XML, JSON, and AVRO. Next, you will learn how to expose the schemas contained in Kafka with the Schema Registry. You will then learn how to work with all relevant connectors with Kafka Connect. While working with Kafka Streams, you will perform various interesting operations on streams, such as windowing, joins, and aggregations. Finally, through KSQL, you will learn how to retrieve, insert, modify, and delete data streams, and how to manipulate watermarks and windows.

What you will learn

• How to validate data with Kafka
• Add information to existing data flows
• Generate new information through message composition
• Perform data validation and versioning with the Schema Registry
• How to perform message Serialization and Deserialization
• How to perform message Serialization and Deserialization
• Process data streams with Kafka Streams
• Understand the duality between tables and streams with KSQL

Who this book is for

This book is for developers who want to quickly master the practical concepts behind Apache Kafka. The audience need not have come across Apache Kafka previously; however, a familiarity of Java or any JVM language will be helpful in understanding the code in this book.

Accessing and Retrieving Data

In this chapter, we will cover the following recipes:

• Viewing and analyzing M functions in the Query Editor
• Establishing and managing connections to data sources
• Building source queries for DirectQuery models
• Importing data to Power BI Desktop models
• Applying multiple filtering conditions
• Choosing columns and column names
• Transforming and cleansing source data
• Creating custom and conditional columns
• Integrating multiple queries
• Choosing column data types
• Visualizing the M library

Introduction

Power BI Desktop contains a very rich set of data source connectors and transformation capabilities that support the integration and enhancement of source data. These features are all driven by a powerful functional language and query engine, M, which leverages source system resources when possible and can greatly extend the scope and robustness of the data retrieval process beyond what's possible via the standard query editor interface alone. As with almost all BI projects, the design and development of the data access and retrieval process has great implications for the analytical value, scalability, and sustainability of the overall Power BI solution.

In this chapter, we dive into Power BI Desktop's Get Data experience and walk through the process of establishing and managing data source connections and queries. Examples are provided of using the Query Editor interface and the M language directly to construct and refine queries to meet common data transformation and cleansing needs. In practice and as per the examples, a combination of both tools is recommended to aid the query development process.

A full explanation of the M language and its implementation in Power BI is outside the scope of this book, but additional resources and documentation are included in the There's more... and See also sections of each recipe.

Viewing and analyzing M functions

Every time you click on a button to connect to any of Power BI Desktop's supported data sources or apply any transformation to a data source object, such as changing a column's data type, one or multiple M expressions are created reflecting your choices. These M expressions are automatically written to dedicated M documents and, if saved, are stored within the Power BI Desktop file as Queries. M is a functional programming language like F#, and it's important that Power BI developers become familiar with analyzing and later writing and enhancing the M code that supports their queries.

Getting ready

1. Build a query through the user interface that connects to the AdventureWorksDW2016CTP3 SQL Server database on the ATLAS server and retrieves the DimGeography table, filtered by United States for English.
2. Click on Get Data from the Home tab of the ribbon, select SQL Server from the list of database sources, and provide the server and database names.
   • For the Data Connectivity mode, select Import.

Figure 1: The SQL Server Get Data dialog

A navigation window will appear, with the different objects and schemas of the database. Select the DimGeography table from the Navigation window and click on Edit.

3. In the Query Editor window, select the EnglishCountryRegionName column and then filter on United States from its dropdown.

Figure 2: Filtering for United States only in the Query Editor

At this point, a preview of the filtered table is exposed in the Query Editor and the Query Settings pane displays the previous steps.

Figure 3: The Query Settings pane in the Query Editor

How to do it...

Formula Bar

1. With the Formula Bar visible in the Query Editor, click on the Source step under Applied Steps in the Query Settings pane.
   • You should see the following formula expression:

Figure 4: The SQL.Database() function created for the Source step

2. Click on the Navigation step to expose the following expression:

Figure 5: The metadata record created for the Navigation step

• The navigation expression (2) references the source expression (1)
• The Formula Bar in the...

Table of contents

• Title Page
• Copyright
• Credits
• Foreword
• About the Author
• About the Reviewers
• www.PacktPub.com
• Customer Feedback
• Preface
• Configuring Power BI Development Tools
• Accessing and Retrieving Data
• Building a Power BI Data Model
• Authoring Power BI Reports
• Creating Power BI Dashboards
• Getting Serious with Date Intelligence
• Parameterizing Power BI Solutions
• Implementing Dynamic User-Based Visibility in Power BI
• Applying Advanced Analytics and Custom Visuals
• Developing Solutions for System Monitoring and Administration
• Enhancing and Optimizing Existing Power BI Solutions
• Deploying and Distributing Power BI Content
• Integrating Power BI with Other Applications