Cloudera - Cloudera Hadoop Developer offered by Koenig Solutions
- Private Institute
Cloudera Hadoop Developer at Koenig Solutions Overview
Duration | 32 hours |
Total fee | ₹96,600 |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Credential | Certificate |
Future job roles | CRUD, .Net, CSR, Credit risk, Senior Software Developer |
Cloudera Hadoop Developer at Koenig Solutions Highlights
- Leverage Hive, Oozie, Pig, Flume, Sqoop, and ecosystem projects
- Courseware approved by Cloudera, Certified Trainers
Cloudera Hadoop Developer at Koenig Solutions Course details
- Developers and Engineers who have programming experience with basic familiarity of SQL and Linux commands
- Knowledge of Java recommended to complete the hands-on exercises
- Comprehend internals of HDFS and MapReduce
- Learn how to write MapReduce code
- Comprehend Hadoop debugging, development, and execution of workflows and algorithms
- Leverage Hive, Oozie, Pig, Flume, Sqoop, and other Hadoop ecosystem projects
- Create custom components such as InputFormats and Writable Comparables to administer complex data types
- Write and execute joins to link data sets in MapReduce
- Comprehend Advanced Hadoop API topics
- Hadoop Developer certification will let students create robust data processing applications using Apache Hadoop. After completing this course, students will be able to comprehend workflow execution and working with APIs by executing joins and writing MapReduce code. This course will offer the most excellent practice environment for the real-world issues faced by Hadoop developers. Hadoop developers are among the world's most in-demand and highly-compensated technical roles. According to a McKinsey report, US alone will deal with shortage of nearly 190,000 data scientists and 1.5 million data analysts and Big Data managers by 2018
Cloudera Hadoop Developer at Koenig Solutions Curriculum
Introduction
Introduction to Hadoop and the Hadoop Ecosystem
Problems with Traditional Large-scale Systems
Hadoop!
The Hadoop EcoSystem Hadoop Architecture and HDFS
Distributed Processing on a Cluster
Storage: HDFS Architecture
Storage: Using HDFS
Resource Management: YARN Architecture
Resource Management: Working with YARN Importing Relational Data with Apache Sqoop
Sqoop Overview
Basic Imports and Exports
Limiting Results
Improving Sqoop?s Performance
Sqoop 2 Introduction to Impala and Hive
Introduction to Impala and Hive
Why Use Impala and Hive?
Comparing Hive to Traditional Databases
Hive Use Cases Modeling and Managing Data with Impala and Hive
Data Storage Overview
Creating Databases and Tables
Loading Data into Tables
HCatalog
Impala Metadata Caching Data Formats
Selecting a File Format
Hadoop Tool Support for File Formats
Avro Schemas
Using Avro with Hive and Sqoop
Avro Schema Evolution
Compression Data Partitioning
Partitioning Overview
Partitioning in Impala and Hive Capturing Data with Apache Flume
What is Apache Flume?
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration Spark Basics
What is Apache Spark?
Using the Spark Shell
RDDs (Resilient Distributed Datasets)
Functional Programming in Spark Working with RDDs in Spark
A Closer Look at RDDs
Key-Value Pair RDDs
MapReduce
Other Pair RDD Operations Writing and Deploying Spark Applications
Spark Applications vs. Spark Shell
Creating the SparkContext
Building a Spark Application (Scala and Java)
Running a Spark Application
The Spark Application Web UI
Configuring Spark Properties
Logging Course Outline: Developer Training for Spark and Hadoop I Parallel Programming with Spark
Review: Spark on a Cluster
RDD Partitions
Partitioning of File-based RDDs
HDFS and Data Locality
Executing Parallel Operations
Stages and Tasks Spark Caching and Persistence
RDD Lineage
Caching Overview
Distributed Persistence Common Patterns in Spark Data Processing
Common Spark Use Cases
Iterative Algorithms in Spark
Graph Processing and Analysis
Machine Learning
Example: k-means Preview: Spark SQL
Spark SQL and the SQL Context
Creating DataFrames
Transforming and Querying DataFrames
Saving DataFrames
Comparing Spark SQL with Impala Conclusion
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Cloudera Hadoop Developer at Koenig Solutions Contact Information
Koenig Solutions Pvt Ltd, Plot # 22, IT Park,Sahashdhara Road, Dehradun, (India)
Dehradun ( Uttarakhand)