Designing and Building Big Data Applications training, manila,philippines

Designing and Building Big Data Applications training, Manila,Philippines

Designing And Building Big Data Applications Course Description

Duration: 4.00 days (32 hours), 

This four day training 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.

Intended Audience For This Designing And Building Big Data Applications Course

  • » This course is best suited to developers, engineers, architects, data scientist who want to use use Hadoop and related tools to solve real-world problems.
  • Designing And Building Big Data Applications Course Objectives

    • » Creating a data set with Kite SDK
    • » Developing custom Flume components for data ingestion
    • » Managing a multi-stage workflow with Oozie
    • » Analyzing data set with Pig
    • » Analyzing data with Crunch
    • » Writing user-defined functions for Hive and Impala
    • » Transforming data with Morphlines
    • » Indexing data with Cloudera Search
    • Designing And Building Big Data Applications Course Outline

        1. Application Architecture
          1. Scenario Explanation
          2. Understanding the Development Environment
          3. Identifying and Collecting Input Data
          4. Selecting Tools for Data Processing and Analysis
          5. Presenting Results to the Use
        2. Defining and Using Data Sets
          1. Metadata Management
          2. What is Apache Avro?
          3. Avro Schemas
          4. Avro Schema Evolution
          5. Selecting a File Format
          6. Performance Considerations
        3. Using the Kite SDK Data Module
          1. What is the Kite SDK?
          2. Fundamental Data Module Concepts
          3. Creating New Data Sets Using the Kite SDK
          4. Loading, Accessing, and Deleting a Data Set
        4. Importing Relational Data with Apache Sqoop
          1. What is Apache Sqoop?
          2. Basic Imports
          3. Limiting Results
          4. Improving Sqoop–s Performance
          5. Sqoop 2
        5. Capturing Data with Apache Flume
          1. What is Apache Flume?
          2. Basic Flume Architecture
          3. Flume Sources
          4. Flume Sinks
          5. Flume Configuration
          6. Logging Application Events to Hadoop
        6. Developing Custom Flume Components
          1. Flume Data Flow and Common Extension Points
          2. Custom Flume Sources
          3. Developing a Flume Pollable Source
          4. Developing a Flume Event-Driven Source
          5. Custom Flume Interceptors
          6. Developing a Header-Modifying Flume Interceptor
          7. Developing a Filtering Flume Interceptor
          8. Writing Avro Objects with a Custom Flume Interceptor
        7. Managing Workflows with Apache Oozie
          1. The Need for Workflow Management
          2. What is Apache Oozie?
          3. Defining an Oozie Workflow
          4. Validation, Packaging, and Deployment
          5. Running and Tracking Workflows Using the CLI
          6. Hue UI for Oozie
        8. Analyze data set with Pig
          1. What is Apache Pig?
          2. Pig’s Features
          3. Basic Data Analysis with Pig
          4. Filtering and Sorting Data
          5. Commonly-Used Functions
          6. Processing Complex Data with Pig
          7. Techniques for Combining Data Sets
          8. Pig Troubleshooting and Optimization
        9. Processing Data Pipelines with Apache Crunch
          1. What is Apache Crunch?
          2. Understanding the Crunch Pipeline
          3. Comparing Crunch to Java MapReduce
          4. Working with Crunch Projects
          5. Reading and Writing Data in Crunch
          6. Data Collection API Functions
          7. Utility Classes in the Crunch API
        10. Working with Tables in Apache Hive
          1. What is Apache Hive?
          2. Accessing Hive
          3. Basic Query Syntax
          4. Creating and Populating Hive Tables
          5. How Hive Reads Data
          6. Using the RegexSerDe in Hive
        11. Developing User-Defined Functions
          1. What are User-Defined Functions?
          2. Implementing a User-Defined Function
          3. Deploying Custom Libraries in Hive
          4. Registering a User-Defined Function in Hive
        12. Executing Interactive Queries with Impala
          1. What is Impala?
          2. Comparing Hive to Impala
          3. Running Queries in Impala
          4. Support for User-Defined Functions
          5. Data and Metadata Management
        13. Understanding Cloudera Search
          1. What is Cloudera Search?
          2. Search Architecture
          3. Supported Document Formats
        14. Indexing Data with Cloudera Search
          1. Collection and Schema Management
          2. Morphlines
          3. Indexing Data in Batch Mode
          4. Indexing Data in Near Real Time
        15. Presenting Results to Users
          1. Solr Query Syntax
          2. Building a Search UI with Hue
          3. Accessing Impala through JDBC
          4. Powering a Custom Web Application with Impala and Search