Ssk Systems in its mission to provide cost effective innovative training services in latest software technologies has started online training service which is accessible to one and all globally. Our online software training program is designed to provide rich learning experience for students through our Live Interactive Environment which is accessible from the comfort of the home by internet. Our customized solutions focus on each of our clients specific needs.
Big Data and Hadoop:
In today's world, properly leveraged data can give organizations of all types a competitive advantage. Companies now handle vast amounts of data on a daily basis and there is unparalleled demand for professionals in this space. Learn how to extract useful information from data and increase the ROI of a business by taking up our wide range of Big Data Analytics Courses.
Big Data Hadoop and Spark Developer
Course preview:
| - Challenges for processing big data | - Fault tolerance |
| - Technologies support big data | - Setting up the CDH |
| - The Motivation For Hadoop | - Map Reduce Story |
| - History of Hadoop | - Map Reduce Architecture |
| - Use cases of Hadoop | - How Map Reduce works |
| - RDBMS vs Hadoop | - Developing Map Reduce |
| - When to use and when not to use Hadoop | - Map Reduce Programming Model |
| - Ecosystem tour | - Different phases of Map Reduce Algorithm |
| - Vendor comparison | - Different Data types in Map Reduce |
| - How to Write a basic Map Reduce Program | |
| - Features of HDFS | |
| - 5 daemons of Hadoop | - Driver Code |
| - Name Node and its functionality | - Mapper |
| - Data Node and its functionality | - Reducer |
| - Secondary Name Node and its functionality | - Creating Input and Output Formats in Map |
| Reduce Jobs | |
| - Job Tracker and its functionality | - Text Input Format |
| - Task Tracker and its functionality | - Key Value Input Format |
| - Data Storage in HDFS | |
| - Sequence File Input Format | |
| - Introduction about Blocks | |
| - Data localization in Map Reduce | |
| - Data replication | |
| - Combiner (Mini Reducer) and Partitioner | |
| - Accessing HDFS through CLI | |
| (Command Line Interface) | - Hadoop I/O |
| - Distributed cache |
| - Basics of Hive, Hive architecture | - Introduction to Apache Pig |
| - Working with Hive and Impala, Hive vs RDBMS, HiveQL and the shell, | - SQL vs. Apache Pig |
| - Managing tables (external vs managed) | - Different data types in Pig |
| - Data types and schemas | - Modes of Execution in Pig |
| - Partitions and buckets | - Grunt shell |
| - Introduction to Impala | - Loading data |
| - Impala Architecture | - Exploring Pig |
| - Hive vs Impala | |
| - Exploring Impala |
| - HBase Architecture and | - Introduction to Sqoop | - Introduction to Flume |
| schema design | ||
| - Sqoop Architecture | - Flume Architecture | |
| - HBase vs. RDBMS | ||
| - Sqoop Syntax | - Flume Data Flow | |
| - HMaster and Region Servers | ||
| - Database connection | - Configuration | |
| - Column Families and Regions | ||
| - Importing & Exporting data | - Introduction to Oozie | |
| - Write pipeline | ||
| - Oozie Workflow | ||
| - Read pipeline | ||
| - Property file, Coordinator & Bundle | ||
| - HBase commands |
| -Introduction to Apache Spark | -DataFrames and DataSets |
| -Apache Spark Framework | -DataFrame Operations |
| -Playing with RDD’s | -Creating & Saving DataFrames from Data Sources |
| -Using Spark Shell | -Transformations & Actions |
| -Writing Spark Applications | -Caching & Persisting |
| -Spark SQL |
Interview Preparation
Interested Candidates please respond to sales@ssksystems.com and contact us at 925-262-9383.
Thanks & Regards
Satya,
Ssk Systems ,
925-262-9383.
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