Big Data Certification Training
Every business whether running on a small or a large scale is on a look for valuable data and insights. Seeing the growing needs of every organization and demand for professionals, it’s vital for every professional working in this field to boost their career with program certification of big data. Whether you are a starter or need a certification in hand, we will prepare you and certify you in multiple domains of this program. So, join the team of professionals and become the big data expert with us.
Course Features
- Start: 17 November, 2020
- Course Duration: 1.5 Month
- Seats Available: 13
- Time: Two Slots Available
- Class: Weekdays & Weekends
Curriculum
- Cluster
- Hadoop Cluster
- Data Storage on HDFS
- Block
- Design principles of Block
- Configuration of Block
- Replication
- Design principles of Replication
- HDFS Architecture
- HDFS interaction
- Configuration files in Hadoop Installation
- Different versions of Hadoop
- LFS vs HDFS
- HDFS commands/Scripts practice
- What is Map Reduce
- Processing Daemons of Hadoop
- Input Split
- Input Split vs Block
- Map Reduce Life Cycle
- Data types in MR
- Phases of MR
- Program model of MR
- File formats in MR
- Combiner in MR
- Partitioner in MR
- Compression Techniques in MR
- Map Reduce Job Chaining
- Introduction to Apache Pig
- Map Reduce Vs Pig
- SQL Vs Pig
- Different data types in Pig
- Execution modes in Pig
- Execution Mechanism
- Transformations/Built in operators in Pig
- Different Pig Latin scripts
- Custom data types
- UDFs in Pig
- XML Processing in Pig
- Practice of Pig Latin Programs
- Introduction to Sqoop
- Key observations of Sqoop
- MySQL client interaction in cluster
- Connection to RDBMS using Sqoop
- Sqoop Import
- Sqoop Export
- Different Sqoop Commands
- Different flavors of Imports
- Sqoop-Hive Imports
- Sqoop Incremental Load
- Limitations in Incremental Load
- File formats in Sqoop
- Hive Introduction
- Need of Apache HIVE in Hadoop
- Hive Architecture
- Meta Store in Hive
- Hive Query Language (Hive QL)
- Configuring Hive with MySQL MetaStore
- SQL VS HQL
- Partitions in Hive
- Buckets in Hive
- Partitioning Vs Bucketing/li>
- File formats in Hive
- Collection Data Types in HIVE
- Conditional Functions in HIVE
- DATE Functions in HIVE
- User Defined Functions in HIVE
- Need of UDFs in HIVE/li>
- Hive Serializer/Deserializer - SerDe
- Semi Structured Data Processing Using Hive
- Hive–HBase Integration
- Latest Hive version features/li>
- Hive integration with Java
- Different optimization techniques in Hive
- HBase introduction
- HBase Data modeling Elements
- HBase Architecture
- Bulk Loading
- Different Clients
- HBase Hive integration
- HBase integration with Java
- HDFS Vs HBase
- HBase Vs RDBMS
- HBase Vs NoSQL
- Flume Introduction
- Flume Architecture
- Flume Master, Flume Collector and Flume Agent
- Flume Configurations
- Flume use cases
- Oozie Introduction
- Oozie Architecture
- Oozie Configuration Files
- Oozie Job Submission
- YARN Introduction
- Difference between Map Reduce & YARN
- YARN Architecture
- YARN Process flow
- YARN Web UI
- Need of NoSQL Databases
- Relational VS Non-Relational Databases
- Introduction to MongoDB
- Features of MongoDB
- MongoDB Basic operations
- Java integration with MongoDB
- Introduction to Cassandra
- Features of Cassandra
- Basic Operation using Cassandra
- Java integration with Cassandra
- Cloudera
- Hortonworks
- MapR
- Apache
- Introduction to Spark
- Motivation for Spark
- Spark Vs Map Reduce Processing
- Architecture of Spark
- Creating Spark Context
- File Operations in Spark Shell
- Introduction to Spark Components
- Spark Core
- Spark SQL
- Spark Streaming
- Spark MLLib
- Spark Core
- Resilient Distributed Dataset(RDD)
- Different Operation on RDDs
- Transformation in RDD
- Actions in RDD
- Different examples on Actions
- Running Spark in a Clustered Mode
- Deploying Application with spark-submit
- Cluster Management
- Spark SQL
- Introduction to Spark SQL
- The SQL Context
- Hive Vs Spark SQL
- Introduction to Data Frames (DF)
- Examples on Spark SQL
- Different File Formats Processing through Spark SQL
- Introduction to Scala
- Scala Vs Java
- Scala Basics
- Scala Data types
- Scala Packages
- Runtime environment of Scala
- Different IDE Support for Scala
- Scala REPL
- Writing Scala Scripts
- Compiling the Scala Programs
- Different IDEs for Scala
- Data types in Scala
- Type Casting in Scala
- Conditional Statements in Scala
- Functional Programing in Scala
- Scala Environment Set up
- Scala Collections
- Spark Streaming using Scala
- Overview of Kafka/CDC/MapR Streaming
- Overview of Apache Drill
- Overview of MapR DB
- Overview of Cloud (OSE/OSO)
- Overview of DevOps (Jenkins)
- Overview of Crontab/TWS
FAQ's
Massive amount of Data is produced everyday by users and businesses. Data may be structured, unstructured and semi structured. Simple tools are not capable enough to store and analyze the data which has the properties of high volume, variety and velocity. Big Data Technologies are used to examine and analyze the data for gaining insights and patterns that can help in making strategic business decisions.
Extensive usage of Big Data Analytics leads to increased demand for the Big Data professionals, which is why Big Data Analytics has become the most sought after skill set for the organizations of all sizes.
Any professional working in the IT industry or a fresh graduate or a job seeker may enroll for Big Data course to upskill themselves and be a part of the fastest growing industry.
Yes, basic understanding of Core Java and SQL will be helpful but is not mandatory. However we cover the fundamentals and prerequisites before we begin the program.
Yes, you can attend the demo session on any weekdays or weekends. We also run webinars every weekend, you can attend any of them according to your convenience.
Yes, we have numerous tie ups with the organizations ranging from MNCs to the start ups. Organizations would approach us as and when the vacancies exist. Our placement cell would help you out with your resume preparation, mock interview questions and interview scheduling.
Don’t worry. We compensate your missed class with any of the multiple batches that we run on weekdays and weekends. You can attend the class based on your convenience. We can also get you the access to the recorded sessions, just in case if you are unable to attend the classroom training.
Our Student Got Placement In
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Student Reviews
Arun Kumar
Excellent course!
One of the best institutes for bigdata training. They are providing best hands on experience, Training is given by India's Top Trainers who had very good experience, I gained a lot of practical knowledge and I can definitely say that skillridge is the best institute for bigdata.
Vikas
Excellent course!
Well organized sessions. Trainer was well versed with all the modules. Overall experience was excellent.
Dharani Kumar
Excellent course!
Skillridge was recommended by a friend of mine. Course was finished on time and the training was really exceeded my expectations. Faculty has suggested me to go for the certifications such as Cloudera and Hortonworks. I cleared both.