Big Data and Hadoop Training in Bangalore by myTectra
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Mytectra is offering a comprehensive Big Data and Hadoop course with options for weekend, regular, and fast track training over a duration of up to 5 weekends. The course covers various aspects of Hadoop, including architecture, data loading techniques, the MapReduce framework, and advanced topics like Hive and HBase. Participants can expect instructor-led classes with provided study materials, and can contact Mytectra for further information and enrollment.
Big Data and Hadoop Training in Bangalore by myTectra
- 1. Course Details: Course Code –MYT1285-Big Data and Hadoop Course duration: Week End - 5 Week Ends Regular – 1 Month Fast Track – 2 Weeks Training mode: Instructor led Class training | Online Live Training Study material : Provided in the form of PDFs, PPTs & Word docs / On-Line materials Weekend, weekday, Super Fast track, fast track & normal training can be provided Contact: +91 90191 91856 Email: info@mytectra.com Web: www.mytectra.com About myTectra: “myTectra a global learning solutions company helps transform people and organization to gain real, lasting benefits “ myTectra provides training across the technologies and functions using time tested delivery mode such as class room training , virtual training , send me a trainer , trainer connect .We are now one of the largest online training company and expanding our class room training across multiple cities. Join myTectra! Explore world of possibilities in learning and acquiring professional Skills across domains and technologies irrespective of what kind of functional or technical background your are from..
- 2. Chapter 1: Hadoop Architecture What is Big Data Hadoop Architecture Hadoop ecosystem components Hadoop Storage: HDFS Hadoop Processing: MapReduce Framework Hadoop Server Roles: NameNode Secondary NameNode and DataNode Anatomy of File Write and Read. Chapter 2: Hadoop Cluster Configuration and Data Loading Hadoop Cluster Architecture Hadoop Cluster Configuration files Hadoop Cluster Modes Multi-Node Hadoop Cluster A Typical Production Hadoop Cluster MapReduce Job execution Common Hadoop Shell commands Data Loading Techniques: FLUME, SQOOP, Hadoop Copy Commands Hadoop Project: Data Loading. Chapter 3: Hadoop MapReduce framework Hadoop Data Types Hadoop MapReduce paradigm Map and Reduce tasks MapReduce Execution Framework Partitioners and Combiners Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs) Output Formats (TextOutput, BinaryOutPut, Multiple Output) Hadoop Project: MapReduce Programming.
- 3. Chapter 4: Advance MapReduce Counters Distributed Cache Mrunit Reduce Join Custom Input Format Sequence Input Format Hadoop Project: Advance MapReduce programming and error handling. Chapter 5: Pig and Pig Latin Installing and Running Pig Grunt Pig's Data Model Pig Latin Developing & Testing Pig Latin Scripts Writing Evaluation Filter Load & Store Functions Hadoop Project: Pig Scripting. Chapter 6: Hive and HiveQL Hive Architecture and Installation Comparison with Traditional Database HiveQL: Data Types Operators and Functions Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables) Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
- 4. Chapter 7: Advance Hive, NoSQL Databases and HBase Hive: -Data manipulation with Hive - User Defined Functions -Appending Data into existing Hive Table -Custom Map/Reduce in Hive Hadoop Project: Hive Scripting HBase: -Introduction to Hbase -Client API's and their features -Available Client -HBase Architecture -MapReduce Integration. Chapter 14: Advance HBase and ZooKeeper HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, States. Chapter 15: Hadoop 2.0, MRv2 and YARN - Schedulers:Fair and Capacity, Hadoop 2.0 New Features: NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN, Upgrade your existing MRv1 code to MRv2, Programming in YARN framework. Chapter 16: Hadoop Project Environment and Apache Oozie Topics - Some of the data sets on which you may work as a part of the project work: