Big Data training and certification program
For Big Data workers, the Certified Big Data Foundation Specialist (CBDFS) designation is a widely recognised credential. Being a CBDFS highlights your experience working in a cloud environment and shows that you have the necessary abilities and knowledge. Businesses that use CBDFS will have professionals on staff who can help them exploit the commercial prospects that cloud computing is generating. The certification given to people who pass the exam is called the Certified Big Data Foundation Specialist (CBDFS) Certification. After completing our e-course, you’ll not only be familiar with the basics of big data, but you’ll also be introduced. This useful information can be used as a springboard for an organization’s Big Data journey.The Big Data Foundation E-Course is provided to you via our eLearning platform, giving you the flexibility to access it whenever you like, whether at home or at work. It includes an online course and an eBook study guide.
Curriculum
Understanding Big Data and Hadoop
- Introduction to Big Data & Big Data Challenges
- Limitations & Solutions of Big Data Architecture
- Hadoop & its Features
- Hadoop Ecosystem
- Hadoop 2.x Core Components
- Hadoop Storage: HDFS (Hadoop Distributed File System)
- Hadoop Processing: MapReduce Framework
- Different Hadoop Distributions
Hadoop Architecture and HDFS
- Hadoop 2.x Cluster Architecture
- Federation and High Availability Architecture
- Typical Production Hadoop Cluster
- Hadoop Cluster Modes
- Common Hadoop Shell Commands
- Hadoop 2.x Configuration Files
- Single Node Cluster & Multi-Node Cluster set up
- Basic Hadoop Administration
Hadoop MapReduce Framework
- Traditional way vs MapReduce way
- Why MapReduce
- YARN Components
- YARN Architecture
- YARN MapReduce Application Execution Flow
- YARN Workflow
- Anatomy of MapReduce Program
- Input Splits, Relation between Input Splits and HDFS Blocks
- MapReduce: Combiner & Partitioner
- Demo of Health Care Dataset
- Demo of Weather Dataset
Advanced Hadoop MapReduce
- Counters
- Distributed Cache
- MRunit
- Reduce Join
- Custom Input Format
- Sequence Input Format
- XML file Parsing using MapReduce.
Apache Pig
- Introduction to Apache Pig
- MapReduce vs Pig
- Pig Components & Pig Execution
- Pig Data Types & Data Models in Pig
- Pig Latin Programs
- Shell and Utility Commands
- Pig UDF & Pig Streaming
- Testing Pig scripts with Punit
- Aviation use-case in PIG
- Pig Demo of Healthcare Dataset
Apache Hive
- Introduction to Apache Hive
- Hive vs Pig
- Hive Architecture and Components
- Hive Metastore
- Limitations of Hive
- Comparison with Traditional Database
- Hive Data Types and Data Models
- Hive Partition
- Hive Bucketing
- Hive Tables (Managed Tables and External Tables)
- Importing Data
- Querying Data & Managing Outputs
- Hive Script & Hive UDF
- Retail use case in Hive
- Hive Demo on Healthcare Dataset
Advanced Apache Hive and HBase
- Hive QL: Joining Tables, Dynamic Partitioning
- Custom MapReduce Scripts
- Hive Indexes and views
- Hive Query Optimizers
- Hive Thrift Server
- Hive UDF
- HBase v/s RDBMS
- HBase Components
- HBase Architecture
- HBase Run Modes
- HBase Configuration
- HBase Cluster Deployment
Advanced Apache HBase
- HBase Data Model
- HBase Shell
- HBase Client API
- Hive Data Loading Techniques
- Apache Zookeeper Introduction
- ZooKeeper Data Model
- Zookeeper Service
- HBase Bulk Loading
- Getting and Inserting Data
- HBase Filters
Processing Distributed Data with Apache Spark
- What is Spark
- Spark Ecosystem
- Spark Components
- What is Scala
- Why Scala
- SparkContext
- Spark RDD
Oozie and Hadoop Project
- Oozie
- Oozie Components
- Oozie Workflow
- Scheduling Jobs with Oozie Scheduler
- Demo of Oozie Workflow
- Oozie Coordinator
- Oozie Commands
- Oozie Web Console
- Oozie for MapReduce
- Combining flow of MapReduce Jobs
- Hive in Oozie
- Hadoop Project Demo
- Hadoop Talend Integration