Cloudera Designing & Building Big Data Applications Training (CDBBDA)
Cloudera Designing & Building Big Data Applications Training Course Hands-on
Cloudera University’s four-day Cloudera Designing & Building Big Data Applications Training course for designing and building Big Data applications prepares you to analyze and solve real-world problems using Apache Hadoop and associated tools in the enterprise data hub (EDH).
You will work through the entire process of designing and building solutions, including ingesting data, determining the appropriate file format for storage, processing the stored data, and presenting the results to the end-user in an easy-to-digest form. Go beyond MapReduce to use additional elements of the EDH and develop converged applications that are highly relevant to the business.
With onsite Training, courses can be scheduled on a date that is convenient for you, and because they can be scheduled at your location, you don’t incur travel costs and students won’t be away from home. Onsite classes can also be tailored to meet your needs. You might shorten a 5-day class into a 3-day class, or combine portions of several related courses into a single course, or have the instructor vary the emphasis of topics depending on your staff’s and site’s requirements.
Duration: 4 days
Recommend (but not required) an understanding of the concept presented in Cloudera Administrator Training for Apache Hadoop (CATAH)
Practical work experience and knowledge of Java programming. Experience with SQL is helpful.
What You Will Learn:
Creating a data set with the Kite SDK
Developing custom Flume components for data ingestion
Managing a multi-stage workflow with Oozie
Analyzing data with Crunch
Writing user-defined functions for Hive and Impala
Transforming data with Morphlines
Indexing data with Cloudera Search
Module 1: Application Architecture
Understanding the Development Environment
Identifying and Collecting Input Data
Selecting Tools for Data Processing and Analysis
Presenting Results to the User
Module 2: Defining and Using Data Sets
What is Apache Avro?
Avro Schema Evolution
Selecting a File Format
Module 3: Using the Kite SDK Data Module
What is the Kite SDK?
Fundamental Data Module Concepts
Creating New Data Sets Using the Kite SDK
Loading, Accessing, and Deleting a Data Set
Module 4: Importing Relational Data with Apache Sqoop
What is Apache Sqoop?
Improving Sqoop’s Performance
Module 5: Capturing Data with Apache Flume
What is Apache Flume?
Basic Flume Architecture
Logging Application Events to Hadoop
Module 6: Developing Custom Flume Components
Flume Data Flow and Common Extension Points
Custom Flume Sources
Developing a Flume Pollable Source
Developing a Flume Event-Driven Source
Custom Flume Interceptors
Developing a Header-Modifying Flume Interceptor
Developing a Filtering Flume Interceptor
Writing Avro Objects with a Custom Flume Interceptor
Module 7: Managing Workflows with Apache Oozie
The Need for Workflow Management
What is Apache Oozie?
Defining an Oozie Workflow
Validation, Packaging, and Deployment
Running and Tracking Workflows Using the CLI
Hue UI for Oozie
Module 8: Processing Data Pipelines with Apache Crunch
What is Apache Crunch?
Understanding the Crunch Pipeline
Comparing Crunch to Java MapReduce
Working with Crunch Projects
Reading and Writing Data in Crunch
Data Collection API
Utility Classes in the Crunch API
Module 9: Working with Tables in Apache Hive
What is Apache Hive?
Basic Query Syntax
Creating and Populating Hive Tables
How Hive Reads Data
Using the RegexSerDe in Hive
Module 10: Developing User-Defined Functions
What are User-Defined Functions?
Implementing a User-Defined Function
Deploying Custom Libraries in Hive
Registering a User-Defined Function in Hive
Module 11: Executing Interactive Queries with Impala
What is Impala?
Comparing Hive to Impala
Running Queries in Impala
Support for User-Defined Functions
Data and Metadata Management
Module 12: Understanding Cloudera Search
What is Cloudera Search?
Supported Document Formats
Module 13: Indexing Data with Cloudera Search
Collection and Schema Management
Indexing Data in Batch Mode
Indexing Data in Near Real Time
Module 14: Presenting Results to Users
Solr Query Syntax
Building a Search UI with Hue
Accessing Impala through JDBC
Powering a Custom Web Application with Impala and Search
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