- Introduction to Big Data and Hadoop
- Introduction to Big Data
- Big Data Analytics
- What is Big Data?
- Four vs of Big Data
- Case Study Royal Bank of Scotland
- Challenges of Traditional System
- Distributed Systems
- Introduction to Hadoop
- Components of Hadoop Ecosystem Part One
- Components of Hadoop Ecosystem Part Two
- Components of Hadoop Ecosystem Part Three
- Commercial Hadoop Distributions
- Demo: Walkthrough of Simplilearn Cloudlab
- Key Takeaways
- Knowledge Check
- Home
- Simplilearn
- Courses
- Big Data Hadoop Certification Training
Big Data Hadoop Certification Training Course
The Big Data Hadoop Certification training course is specially designed to teach the core principles and concepts of the Big Data framework via Spark and Hadoop
Online
₹ 21,420
Quick facts
particular | details | ||
---|---|---|---|
Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Frequency of Classes
Weekends
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Course overview
The Big Data Hadoop Certification course is a meticulously designed training program which helps students get familiar with the Big Data ecosystem. Big Data Hadoop Certification Training Course by Simplilearn also empowers candidates to establish themselves as an expert in the tools and methodologies that are covered in the course, making them ready to face and solve the everyday Big Data problems of the industry.
The Big Data Hadoop Certification Training course imbues the candidates with an essential understanding of the Hadoop framework, Spark, and other Big data platforms, preparing the candidates for a long and successful career in the Big Data industry.
Along with the detailed understanding of the concepts, the Big Data Hadoop Certification course also equips the candidates with crucial skills via industry-based projects, which include problems from various sectors such as stock markets, sentiment analysis, insurance, and e-commerce.
Lastly, Big Data Hadoop Certification Training Course by Simplilearn also gets the students prepared for the (Cloudera) CCA 175 Spark and Hadoop Developer exam.
The highlights
- 52 hours of instructor-led training
- 22 hours of self-paced video
- 24/7 learners assistance
- Live classes by top instructors
- Flexible pricing options
Program offerings
- Self paced learning
- Instructor-led options
- Real-time industry insights
- Self paced video access
- Blended learning
- Corporate training
Course and certificate fees
Fees information
To pursue the course, students need to pay the necessary fee. The Big Data Hadoop Certification Training Course Fee has been mentioned in the table below
Fee Structure
Particulars | Course Fee |
Self-Paced Learning | ₹ 21,420 |
Corporate Training | Available |
certificate availability
certificate providing authority
Eligibility criteria
Certification Qualifying Detail
Students who opt for the online classroom need to attend the complete batch of Big Data Hadoop Certification Training Course by Simplilearn and also complete one project and simulation test with a minimum of 80% marks.
For online self learning courses, students need to complete 85% of the class, one project and one simulation test. They also need to score at least 80% marks.
What you will learn
After the completion of the Big Data Hadoop Certification course, candidates will become proficient in:
- Executing various operations on real-time streaming data in a short-time period
- Obtaining an almost instantaneous output
- The fundamentals of functional programming, Scala, operators in Scala, Scala REPL, Collections, and the various functions in Scala.
- The ability to develop Spark applications
- Learn how to create, pair, process, and perform other operations on spark RDD
- Working knowledge of Spark SQL
- Enabling Spark SQL to efficiently process data
- Understanding the Spark SQL architecture, implement dataFrame operations, and process dataFrames
Who it is for
The Big Data Hadoop Certification course is suitable for the following profiles:
- Professionals in an Analytics related field looking to upskill
- IT professionals looking for a transfer in the Analytical side of things
- Business Intelligence professionals looking for a platform to upgrade their Analytics skills
- Students currently pursuing their graduation/post-graduation in an IT or Computer science
Admission details
Filling the form
To apply for Big Data Hadoop Certification Training Course by Simplilearn, follow the steps below.
Step 1 - Visit https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training#Overview
Step 2 - Choose the type of training you prefer and click on Enrol Now
Step 3 - You will be redirected to a new page
Step 4 - At this stage, you need to apply any coupon if you have or click on the Proceed button.
Step 5 - Provide your name, email, and contact number and proceed
Step 6 - Pay the fee and you are ready to proceed with the course.
The syllabus
Introduction to Bigdata and Hadoop
Hadoop Architecture Distributed Storage (HDFS) and YARN
- Hadoop Architecture Distributed Storage (HDFS) and YARN
- What is HDFS
- Need for HDFS
- Regular File System vs HDFS
- Characteristics of HDFS
- HDFS Architecture and Components
- High Availability Cluster Implementations
- HDFS Component File System Namespace
- Data Block Split
- Data Replication Topology
- HDFS Command Line
- Demo: Common HDFS Commands
- Practice Project: HDFS Command Line
- Yarn Introduction
- Yarn Use Case
- Yarn and its Architecture
- Resource Manager
- How Resource Manager Operates
- Application Master
- How Yarn Runs an Application
- Tools for Yarn Developers
- Demo: Walkthrough of Cluster Part One
- Demo: Walkthrough of Cluster Part Two
- Key Takeaways
- Knowledge Check
- Practice Project: Hadoop Architecture, distributed Storage (HDFS) and Yarn
Data Ingestion into Big Data Systems and ETL
- Data Ingestion Into Big Data Systems and Etl
- Data Ingestion Overview Part One
- Data Ingestion Overview Part Two
- Apache Sqoop
- Sqoop and Its Uses
- Sqoop Processing
- Sqoop Import Process
- Sqoop Connectors
- Demo: Importing and Exporting Data from MySQL to HDFS
- Practice Project: Apache Sqoop
- Apache Flume
- Flume Model
- Scalability in Flume
- Components in Flume’s Architecture
- Configuring Flume Components
- Demo: Ingest Twitter Data
- Apache Kafka
- Aggregating User Activity Using Kafka
- Kafka Data Model
- Partitions
- Apache Kafka Architecture
- Demo: Setup Kafka Cluster
- Producer Side API Example
- Consumer Side API
- Consumer Side API Example
- Kafka Connect
- Demo: Creating Sample Kafka Data Pipeline Using Producer and Consumer
- Key Takeaways
- Knowledge Check
- Practice Project: Data Ingestion Into Big Data Systems and ETL
Distributed Processing MapReduce Framework and Pig
- Distributed Processing Mapreduce Framework and Pig
- Distributed Processing in Mapreduce
- Word Count Example
- Map Execution Phases
- Map Execution Distributed Two Node Environment
- Mapreduce Jobs
- Hadoop Mapreduce Job Work Interaction
- Setting Up the Environment for Mapreduce Development
- Set of Classes
- Creating a New Project
- Advanced Mapreduce
- Data Types in Hadoop
- Output formats in Mapreduce
- Using Distributed Cache
- Joins in Mapreduce
- Replicated Join
- Introduction to Pig
- Components of Pig
- Pig Data Model
- Pig Interactive Modes
- Pig Operations
- Various Relations Performed by Developers
- Demo: Analyzing Web Log Data Using Mapreduce
- Demo: Analyzing Sales Data and Solving Kpis Using Pig
- Practice Project: Apache Pig
- Demo: Wordcount
- Key Takeaways
- Knowledge Check
- Practice Project: Distributed Processing - Mapreduce Framework and Pig
Apache Hive
- Apache Hive
- Hive SQL over Hadoop Mapreduce
- Hive Architecture
- Interfaces to Run Hive Queries
- Running Beeline from Command Line
- Hive Metastore
- Hive DDL and DML
- Creating New Table
- Data Types
- Validation of Data
- File Format Types
- Data Serialization
- Hive Table and Avro Schema
- Hive Optimization Partitioning Bucketing and Sampling
- Non-Partitioned Table
- Data Insertion
- Dynamic Partitioning in Hive
- Bucketing
- What Do Buckets Do?
- Hive Analytics UDF and UDAF
- Other Functions of Hive
- Demo: Real-time Analysis and Data Filtration
- Demo: Real-World Problem
- Demo: Data Representation and Import Using Hive
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Hive
NoSQL Databases HBase
- NoSQL Databases HBase
- NoSQL Introduction
- Demo: Yarn Tuning
- Hbase Overview
- Hbase Architecture
- Data Model
- Connecting to HBase
- Practice Project: HBase Shell
- Key Takeaways
- Knowledge Check
- Practice Project: NoSQL Databases - HBase
Basics of Functional Programming and Scala
- Basics of Functional Programming and Scala
- Introduction to Scala
- Demo: Scala Installation
- Functional Programming
- Programming With Scala
- Demo: Basic Literals and Arithmetic Programming
- Demo: Logical Operators
- Type Inference Classes Objects and Functions in Scala
- Demo: Type Inference Functions Anonymous Function and Class
- Collections
- Types of Collections
- Demo: Five Types of Collections
- Demo: Operations on List
- Scala REPL
- Demo: Features of Scala REPL
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Hive
Apache Spark Next-Generation Big Data Framework
- Apache Spark Next-Generation Big Data Framework
- History of Spark
- Limitations of Mapreduce in Hadoop
- Introduction to Apache Spark
- Components of Spark
- Application of In-memory Processing
- Hadoop Ecosystem vs Spark
- Advantages of Spark
- Spark Architecture
- Spark Cluster in Real World
- Demo: Running a Scala Programs in Spark Shell
- Demo: Setting Up Execution Environment in IDE
- Demo: Spark Web UI
- Key Takeaways
- Knowledge Check
- Practice Project: Apache Spark Next-Generation Big Data Framework
Spark Core Processing RDD
- Introduction to Spark RDD
- RDD in Spark
- Creating Spark RDD
- Pair RDD
- RDD Operations
- Demo: Spark Transformation Detailed Exploration Using Scala Examples
- Demo: Spark Action Detailed Exploration Using Scala
- Caching and Persistence
- Storage Levels
- Lineage and DAG
- Need for DAG
- Debugging in Spark
- Partitioning in Spark
- Scheduling in Spark
- Shuffling in Spark
- Sort Shuffle
- Aggregating Data With Paired RDD
- Demo: Spark Application With Data Written Back to HDFS and Spark UI
- Demo: Changing Spark Application Parameters
- Demo: Handling Different File Formats
- Demo: Spark RDD With Real-world Application
- Demo: Optimizing Spark Jobs
- Key Takeaways
- Knowledge Check
- Practice Project: Spark Core Processing RDD
Spark SQL Processing DataFrames
- Spark SQL Processing DataFrames
- Spark SQL Introduction
- Spark SQL Architecture
- Dataframes
- Demo: Handling Various Data Formats
- Demo: Implement Various Dataframe Operations
- Demo: UDF and UDAF
- Interoperating With RDDs
- Demo: Process Dataframe Using SQL Query
- RDD vs Dataframe vs Dataset
- Practice Project: Processing Dataframes
- Key Takeaways
- Knowledge Check
- Practice Project: Spark SQL - Processing Dataframes
Spark MLib Modelling BigData with Spark
- Spark Mlib Modeling Big Data With Spark
- Role of Data Scientist and Data Analyst in Big Data
- Analytics in Spark
- Machine Learning
- Supervised Learning
- Demo: Classification of Linear SVM
- Demo: Linear Regression With Real World Case Studies
- Unsupervised Learning
- Demo: Unsupervised Clustering K-means
- Reinforcement Learning
- Semi-supervised Learning
- Overview of Mlib
- Mlib Pipelines
- Key Takeaways
- Knowledge Check
- Practice Project: Spark Mlib - Modelling Big data With Spark
Stream Processing Frameworks and Spark Streaming
- Streaming Overview
- Real-time Processing of Big Data
- Data Processing Architectures
- Demo: Real-time Data Processing
- Spark Streaming
- Demo: Writing Spark Streaming Application
- Introduction to DStreams
- Transformations on DStreams
- Design Patterns for Using Foreachrdd
- State Operations
- Windowing Operations
- Join Operations Stream-dataset Join
- Demo: Windowing of Real-time Data Processing
- Streaming Sources
- Demo: Processing Twitter Streaming Data
- Structured Spark Streaming
- Use Case Banking Transactions
- Structured Streaming Architecture Model and Its Components
- Output Sinks
- Structured Streaming APIs
- Constructing Columns in Structured Streaming
- Windowed Operations on Event-time
- Use Cases
- Demo: Streaming Pipeline
- Practice Project: Spark Streaming
- Key Takeaways
- Knowledge Check
- Practice Project: Stream Processing Frameworks and Spark Streaming
Spark GraphX
- Spark GraphX
- Introduction to Graph
- GraphX in Spark
- GraphX Operators
- Join Operators
- GraphX Parallel System
- Algorithms in Spark
- Pregel API
- Use Case of GraphX
- Demo: GraphX Vertex Predicate
- Demo: Page Rank Algorithm
- Key Takeaways
- Knowledge Check
- Practice Project: Spark GraphX
- Project Assistance
Evaluation process
Candidates need to clear the CCA175 - Spark and Hadoop certification exam offered by Cloudera to receive the Big Data Hadoop Certification. Candidates can take maximum three attempts to pass the exam. The fee for the certification exam is USD 295.
FAQs
Candidates can expect to finish the Big Data Hadoop certification course in roughly 74 hours.
A candidate must wait for 30 calendar days starting the day after they failed an attempt before retaking the CCA175 Hadoop certification exam.
A candidate can appear for a maximum of three times for the CCA175 Hadoop certification exam.
On passing the CCA175 Hadoop certification exam, Candidates will receive an email with the certificate in a digital format attached, accompanied by a license number.