Big Data Certification Training Course in Gurgaon

Build & automate Big Data Pipelines using Sqoop, Hive, HDFS, Oozie & implement business transformation using PySpark that is processing tool used to build ETL jobs to configure Data Pipelines & perform all the business rules & validation across the pipeline using PySpark rich library. It combines Data warehousing, ETL Processing capability using Spark Engine to bring end-to-end (E2E) ETL solution from Data ingestion to hydration to transformation using Spark Executor.

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Why Join DevOps Foundation Certification Course

Learn at your own will

Train using realtime course materials using online portals & trainer experience to get a personalized teaching experience.

Practical experience

Active interaction in sessions guided by leading professionals from the industry

Realtime Industries Project exercises

Gain professionals insights through leading industry experts across domains

Personalized Q&A support

24/7 Q&A support designed to address training needs

Organizational Hiring Insights

Big Data via Hadoop Demand

46%

Global Employment Opportunities

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Big Data (Sqoop, Flume, Hive & Spark) Skills Hiring

36% in IT

Big Data & Hadoop Certification Course Overview

Shape your carrer in building & automating Big Data Pipelines using Sqoop, Hive, HDFS, Oozie & implement business transformation using PySpark that is processing tool used to build ETL jobs to configure Data Pipelines & perform all the business rules & validation across the pipeline using PySpark rich library. It combines Data warehousing, ETL Processing capability using Spark Engine to bring end-to-end (E2E) ETL solution from Data ingestion to hydration to transformation using Spark Executor.

Key Features

Benefit from ongoing access to all self-paced videos and archived session recordings
Success Aimers supports you in gaining visibility among leading employers
Industry-paced training with realtime scenarios using HDFS, Hive & PySpark scripts/templates for automating Big Data Pipelines.
Real-World industry scenarios with projects implementation support
Live Virtual classes heading by top industry experts alogn with project implementation
Q&A support sessions
Job Interview preparation & use cases

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Corporate Training

Enterprise training

HDFS NameNode & DataNodes

Big Data Ecosystem - ETL Analytics Pipeline

About Big Data & Hadoop Foundation Certification Course

Explain Big Data & Hadoop Engineers?

Big Data & Hadoop Engineers build Data Pipeline & Infrastructure using Big Data tools like, Sqoop, Flume, Hive, Spark & others while writing ETL templates. Sqoop & Flume automate the Data ingestion & transformation cycle that integrates with Spark to implement business transformations on Hybrid Cloud platforms (AWS, Azure, GCP & others).This training will provide hands-on training & covers HDFS, Sqoop, Spark, Hive, Oozie & other modules to build Data Ingestion & ELT & ETL workflows.

Role of Big Data & Hadoop Engineer?

Responsibilities include:

Big Data & Hadoop Engineers use Visual Studio & others IDE’s to write ETL & Spark scripts to build Data Pipelines.
Big Data Engineers manages the end-to-end Data life cycle using HDFS workflow and Spark templates.
Develop and Design ETL workflows that automate Data Pipeline securely & seamlessly
Success Aimers helps aspiring Big Data professionals to build, deploy, manage data pipelines using cloud environments & build ETL templates effectively & seamlessly.
Design, Build & Deploying ETL scripts within cloud infrastructure securely & seamlessly.

Who should opt for Big Data Engineer course?

Big Data Engineer course accelerates/boost career in Big Data & Cloud organizations.

Big Data Engineers – Big Data Engineers manages the end-to-end Data lifecycle from design, build & deploy using Hive workflow and Spark templates.
Big Data Engineers – Implementing Big Data Pipelines using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others)
Big Data Developers – Build, Design & Automate Big Data Pipelines via Big Data workflows using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others)
Big Data Architect – Leading Data initiative within enterprise.
Big Data & Cloud Engineers – Deploying Big Data Application using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others) across environments seamlessly and effectively.

Prerequisites of Big Data Engineer Course?

Prerequisites required for the Big Data Engineer Certification Course

High School Diploma or a undergraduate degree
Python + JSON/YAML scripting language
IT Foundational Knowledge along with DevOps and cloud infrastructure skills
Knowledge of Cloud Computing Platforms like AWS, AZURE and GCP will be an added advantage.

Kind of Job Placement/Offers after Big Data Engineer Certification Course?

Job Career Path in Infrastructure(Cloud) Automation using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others)

- - Big Data Engineer – Build, Design Develop & Deploying Spark scripts within cloud infrastructure using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others).
  - Big Data Engineer – Design, Developed and build ELT & ETL workflows to drive key business processes/decisions.
  - Big Data Architect – Leading Data initiative within enterprise.
  - Big Data Engineers – Implementing Data Pipelines using using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others)
  - Cloud and Big Data Engineers –
  - Build, Design Develop & Deploying Spark scripts within cloud infrastructure using Hadoop & Ecosystem Tools (Sqoop, Hive, Spark & others) across environments seamlessly and effectively.

Training Guidelines for Big Data & Hadoop Engineer Foundation Certification Course

Training Options	Weekdays (Mon-Fri)	Weekends (Sat-Sun)	Fast Track
Duration of Course	2 months	3 months	15 days
Hours / day	1-2 hours	2-3 hours	5 hours
Mode of Training	Offline / Online	Offline / Online	Offline / Online

Big Data & Hadoop Engineer Foundation Certification Course Curriculum

Start your carrer in building & automating Big Data Pipelines using Sqoop, Hive, HDFS, Oozie & implement business transformation using PySpark that is processing tool used to build ETL jobs to configure Data Pipelines & perform all the business rules & validation across the pipeline using PySpark rich library. It combines Data warehousing, ETL Processing capability using Spark Engine to bring end-to-end (E2E) ETL solution from Data ingestion to hydration to transformation using Spark Executor.

Course Content

Big Data & Hadoop Ecosystem

Apache Hadoop consists of Storage Layer (HDFS) that is used to store large volume of data in a distributed manner & top of that it consist of a Analytical Engine Map Reduce (MR) used for analytical purpose. Hadoop ecosystem consists of other tools like Sqoop, Hive, Pig, Hbase & other that is used to analyze the data stored inside HDFS. Sqoop is used to ingest data from structured sources into HDFS. Hive used Hive QL to analyze HDFS data using SQL style language. Flume is used to ingest realtime feeds into HDFS . HBase is a DBMS tool on top of HDFS. These components works together to handle large-volume of data storage processing & analysis in a distributed environment.

Course Details : Big Data & Hadoop Ecosystem

Understanding Big Data and Distributed Data Processing

Using Hadoop’s Core: HDFS & Map Reduce

HDFS: What it is, and how it works
Alternate MovieLens download location
Installing the MovieLens Dataset
Install the MovieLens dataset into HDFS using the command line
MapReduce: What it is, and how it works
How MapReduce distributes processing
MapReduce example: Break down movie ratings by rating score
Code up the ratings histogram MapReduce job and run it
Rank movies by their popularity

Setting up Cluster and doing hands on with Hadoop

Manage our HDFS Cluster

YARN explained
Yarn Schedulers
Demo: Capacity Scheduler
Yarn Sizing
Hive Query Optimizations
Join Strategies
Spark Optimizations
Name Node High Availability
Label based scheduling
Tez explained
Use Hive on Tez and measure the performance benefit
Apache MESOS explained
Apache Zookeeper explained
Simulating a failing master with Zookeeper
Oozie explained
Set up a simple Oozie workflow
Zeppelin Overview
Use Zeppelin to analyze movie ratings, part 1
Use Zeppelin to analyze movie ratings, part 2
Hue overview

Course Details - Apache Sqoop - Structured Data Ingestion into HDFS

Sqoop Architecture
Sqoop import
Sqoop Connect
Sqoop CodeGen
Join Table Import
Controlling Parallelism
Sqoop multiple Mappers
Sqoop Eval & change the file delimiter
Incremental Import
Password Protection
Using Last Modified
Import multiple File Formats
Import multiple tables
Handling null during import
Sqoop export
Sqoop Integration with Hive and HBase
Sqoop Performance Tuning

Course Details - Apache Hive - Querying your Data Interactively – Analytical Tool on top of HDFS

Hive Architecture
Hive Metastore
Hive Datatypes (Array, Map & Struct)
Hive Table Types : Internal & External Tables
Hive Query Language (HQL)
Hive Optimization Techniques - (Partitioning (Static & Dynamic Partition), Hive Bucketing)
Hive Joins - (Map Join, Bucket Map, SMB(SortedBucketMap) ,Skew)
Hive File Formats (ORC+SEQUENCE+TEXT+AVRO+PARQUET)
CBO (Cost Based Optimization)
Vectorization
Indexing (Compact + Bitmap)
Hive Integration with TEZ & Spark
Hive SerDer (Custom + Inbuilt)
Hive integration NoSQL (HBase + MongoDB + Cassandra
Thrift API (Thrift Server)
UDF, UDTF & UDAF
Hive Multiple Delimiters
XML & JSON Data Loading HIVE.
Aggregation & Windowing Functions in Hive
Hive Connect with Tableau
Hive Normalization vs Denormalization
Implement SCD in Hive
Execute Hive Queries using a Script
Schema Evolution in Hive
MSCK Repair
Hive vs SQL
Case Studies - (Sentimental Analysis Hive, E-Commerce Data Analysis using Hive, Real Estate Data Analysis Aviation Data Analysis using Hive)

Course Details - Apache Phoenix, Drill & Presto - Querying your Data Interactively

Overview of Drill
Setting up Drill
Querying across multiple databases with Drill
Overview of Phoenix
Install Phoenix and query HBase with it
Integrate Phoenix with Pig
Overview of Presto
Install Presto, and query Hive with it
Query both Cassandra and Hive using Presto

Course Details - NoSQL Databases - Apache HBase & Cassandra

HBase Architecture
Master & Region Server
Hbase Regions
Scan over Snapshot
In-Memory Compaction Strategies
HBase and HDFS
HBase Configuration
HBase Shell
HBase Data Model - (Conceptual View, Physical View, Namespace, Table, Column Family, Joins, ACID)
HBase External API’s
HBase and Spark
HBase Snapshots
-----Apache Cassandra------
What is Cassandra
Features of Cassandra
Cassandra Architecture
Cassandra Architecture Components
Data replication
Simple strategy
Network Topology
Data Partition
Snitches
Gossip Protocol
Seed Nodes
Cassandra Data Model - (Features of Cassandra Data Model, Cassandra Data Model Rules, Indexes, Collections, DML Statements, Compound Key)
Cassandra Interfaces (CQL, Cqlsh, Cqlsh Shell Commands, CQL Data Definition, CQL Data Manipulation, ODBC Driver for Cassandra)
Cassandra Advance Architecture - (What is Partitioning?, Features of Partitioners, Types of Partitioners, Replication of Data, Replication strategy , Types of Common replication Strategies, Tunable Consistency, Read Consistency,Write Consistency, Hinted handoff,Time to Live (TTL) Tombstones, Monitoring the Cluster, Monitoring the NodeTool, Monitoring with OpsCenter)

Course Details - Apache NiFi - Building Realtime Streaming Workflows

Apache NiFi Basics
NiFi User Interface
Core NiFi Terminologies
More on Flow Files on NiFi
Types of Processors available in NiFi
Processor Configuration, Connection & Relationship in NiFi
Connection Queue & Back Pressure in NiFi
Hands-On with Apache NiFi - (Working with Attributes & Content in NiFi , Working with Expression Language in NiFi, More on Expression Language Functions in NiFi, Working with Processor Group (PG), Input Port & Output Port in NiFi, Working with Templates in NiFi, Working with Funnel in NiFi, Working with Controller Services in NiFi, Working with Variable Registry in NiFi)
Apache NiFi Advanced Concepts - (Flow File in NiFi, Flow File Expiration in NiFi, Data Provenance in NiFi)
Monitoring in NiFi (Monitoring in NiFi, Monitoring NiFi using Reporting Task, Remote Monitoring NiFi using Reporting Task)
NiFi Registry for Version Control - (Overview on NiFi Registry, Installation of NiFi Registry, Configuring NiFi and NiFi Registry to enable version control, Configuring NiFi Registry with multiple NiFi Instances, Configuring NiFi Registry to enable Git Persistence)
NiFi Cluster for Heavy Lifting - (Overview of NiFi Clustering, Limitation in NiFi Clustering, NiFi Cluster Configuration using Embedded Zookeeper, NiFi Cluster Configuration Steps, NiFi Cluster Configuration using External Zookeeper, NiFi Cluster Configuration Steps in Single Machine)
NiFi Custom Processor - (Overview of NiFi Clustering, Limitation in NiFi Clustering)
Logging in NiFi - (Logging in NiFi, Pipeline Monitoring, Cluster and Resource Monitoring)

Course Details - Apache Oozie - Schedule ETL Workflows (Hadoop & Spark)

What is Oozie
Oozie Features
Oozie Version
Oozie Workflows
Oozie Workflow Nodes
Oozie Coordinator Jobs
Running Oozie
Oozie Bundle Jobs

Cloudera Hadoop Distribution (CDP Administration)

Cloudera Distribution is an enterprise ready Hadoop platform consisting of all the hadoop ecosystem tools like Sqoop, Flume, Hive, Oozie, Spark & others. It consist of pre-built Kerberos Authentication for Hadoop users & also has pre-built Encryption capability for the Data security. Also this platform can be deployed on-prem or on-cloud & can be used for Analytics & AI.

Course Details : Cloudera Hadoop Distribution (CDP Administration)

Installation of Cloudera Manager and CDH

Deploying Virtual Machines on Amazon Web Service
Configuring Prerequisites for Hadoop Installation
Configuring Local Repository for Cloudera Manager and CDH
Installing and Configuring MySQL Database for Cloudera Manager
Installation and Configuration of Cloudera Manager
Installation and Configuration of CDH

CDP - Proof of Concept Installation

Deploying Master Virtual Machine in Azure Cloud
Configuring Prerequisites on Master Host
Configure Cloudera Manager Local Repository
Installing Cloudera Manager Server
Creating Cloudera Runtime Parcel Local Repository
Deploying Virtual Machines for Worker Hosts
Configure Prerequisites on Worker Hosts
Configure Prerequisites on Worker Hosts - Part 2
Deploying Cluster using Cloudera Manager - Part1
Deploying Cloudera Cluster using Cloudera Manager - Part 2

CDP - Adding New Host to existing Hadoop Cluster

Deploying new virtual machine on Amazon Web Service
Adding new host to existing Hadoop Cluster using Cloudera Manager
Configure Prerequisites on New host - Part 1
Configure Prerequisites on New host - Part 2
Adding new hosts to cluster using Cloudera Manager

Centralized Authentication Using Active Directory

CDP - Production Installation

Configure prerequisites on Master
Installing Cloudera Manager Server
Deploy Worker Nodes Using ARM template
Configure Prerequisites on Nodes
Install Cloudera Manager Agent on Hosts
Deploy CDP Cluster Using Cloudera Manager

Cloudera Manager Authentication using Active Directory

Prepare AWS AMI for Cloudera Installation
Install - Cloudera Data Hadoop (CDH) Quick Install
Cloudera Installation Phases and Paths
Cloudera Manager Introduction and Overview
Cloudera Parcels
Cloudera Repository Setup with Apache httpd
Cloudera Installation Path B with local repository - AMI Prepare
Cloudera Installation Path B - Manager Installation and Configuration
Cloudera Installation Path B - Agent and CDH Installation and Configuration
Add Cluster, Add Service and Delete Cluster life cycle

CDP - Validating Cluster

CDP - Adding Services

Adding Hive Service
Validating Hive Service
Creating ADLS Gen-2 and App ID
Connecting ADLS Gen-2 Storage with CDP Cluster
Adding HBase Service
Creating Tables in HBase
Adding Kafka Service
Adding Spark Service
Adding Solr service

CDP - Configure High Availability

CDP - Active Directory Integration and Kerberos

Deploying Windows Server
Installing and Configuring Active Directory Service
Linux Server Active Directory Integration
Creating Users and Validating
Connecting Hosts With Active Directory
Enabling Kerberos using Active Directory

HDFS Basic Shell Commands

HDFS High Availability HA - Concept, Setup, Configure,Test,Verify, Remove

HDFS Manage - Balancer, Maintenance, Quota Management, Canary Test

HDFS Checkpoint, Understand, Manage, Work with Edits, FSIMage, Roll Edits

HDFS Advanced - Snapshot, WebHDFS, Federation, Recovery, httpFS, Edge Node

HDFS Snapshot
HDFS Snapshot Policy
HDFS Edge Node
HDFS WebHDFS
HDFS httpFS
HDFS FSCK Utility
HDFS Recovery
HDFS Federation
HDFS - Home Directory

Cloudera Manage- Commission, Decommission, Client Configuration, Host Template

LDAP - Install, Configure OS, phpLDAPAdmin Client

YARN - Components, Submit MR

Resource Manager - Scheduler Types, Dynamic Resource Pool, High Availability

YARN - Types of Resource Manager Summary
YARN - Resource Manager - Static Service Pool
YARN - Resource Manager - FIFO - First In First Out Scheduler
YARN - Resource Manager - Fair Scheduler
YARN - Resource Manager - Capacity scheduler
YARN - Resource Manager - Dynamic Resource Pool Configuration
YARN - Resource Manager - High Availability

Apache Zookeeper - Install, Configure

Apache Hive - Manage, SetUp HA, Beeline, WebHCat, HCatalog, Warehouse dirconfig

Hive Introduction
Hive Installation and Configuration
Hive Data set Preparation for Demo
Hive Client Demo - Hive Shell - Beeline Shell Demo
Hive Client - Experience Hive Query with HUE - Query, Visualize and Analyze
Hive High Availability -HA
Hive WebHCat and HCatalog
Hive Warehouse Directory and Metastore DB Configuration

Apache Oozie - Install Configure

Hadoop User Interface (HUE) - SetUp, Install, LDAP Integration, Extended ACL

Sentry - Install, Configure, Role based Authentication, LDAP Integration

Sentry Introduction and Role Based Authorization Concepts
Sentry Installation and Configuration
HUE Security Module Configuration and Integration with Sentry for Authorization
Hive Table Authorization with Sentry - Practical Scenario
Cloudera Manager - OpenLDAP Integration for Authentication
Installing Sentry Service
Sentry Configuration
Creating Sentry Admin Role in Hive
Hive authorization using Sentry

Impala - Install, Configure, Compare with Hive, Architecture

Kerberos - Install, Configure, Verify, Secure

Kerberos Introduction, Architecture and Authentication Process
Kerberos Prepare Server and Client for Setup and Configuration
Kerberos - Configure Cloudera to Kerberize the Cluster
Kerberos - Working with Keytab and Service Ticket
Enabling Kerberos using Active Directory as Kerberos Server

Upgrading Cloudera Manager

CDH Upgrade

Rack Awareness

High Availability

Access Control List (ACL)

Installing and Configuring Hadoop Ecosystem Components

Sqoop - Data Ingestion

HBase - Hadoop Base - NoSQL Database

Flume - Single and Multi Agent Data Ingestion

HDFS Encryption

Apache Spark Installation, Configuration and Administration

Apache Kafka Installation, Configuration and Administration

Hadoop Benchmarking

Memory Management - Container JVM, Role,Node Memory and Performance Management

HDFS Redaction,IO, Compression, distcp, file copy

Reports, Charts and Dashboard

Cloudera 6

Apache Spark & its related Ecosystem

PySpark is a data processing tool that is used to build ETL jobs to configure Data Pipelines & perform all the business rules & validation across the pipeline using PySpark rich library. It combines Data warehousing, ETL Processing capability using Spark Engine to bring end-to-end (E2E) ETL solution from Data ingestion to hydration to transformation using Spark Executor.

Course Details : Apache Spark & its related Ecosystem

Introduction to Apache Spark (Spark Installation, Components & Architecture)

What is Apache Spark
Apache Spark System Architecture
Apache Spark Components & Architecture
Spark Platform and Development Environment
What is Databricks Cloud
Create your Databricks Free Account
Spark Installation
Spark Main Components
Spark & its features
Deployment Modes of Spark
Introduction to PySpark Shell
Submitting PySpark Jobs
Utilizing Spark Web UI

Getting started with Spark Programming

Starting Point - Spark Session
Introduction to Spark Session
Spark Object and Spark Submit Part 1
Spark Object and Spark Submit Part 2
Spark Object and Spark Submit Part 3
DataFrame – A view to Structured data
DataFrame Transformations and Actions
DataFrame Concepts
Exploring DataFrame Transformations
Creating Spark DataFrame

Spark Cluster Execution Architecture

Full Architecture
YARN As Spark Cluster Manager
JVMs across Clusters
Commonly Used Terms in Execution Frame
Narrow and Wide Transformations
DAG Scheduler Part 1
DAG Scheduler Part 2
DAG Scheduler Part 3
Task Scheduler

Spark Shared Variables

Spark SQL Introduction & Architecture

Spark SQL Architecture Part 1
Spark SQL Architecture Part 2
Spark SQL Architecture Part 3
Spark SQL Architecture Part 4
Spark SQL Architecture Part 5
Spark SQL Architecture Part 6
Spark SQL Architecture Part 7
Spark SQL Architecture Part 8
Working with SQL Context and Schema RDDs
User Defined Functions (UDFs) in Spark SQL
Data Frames, Datasets, and Interoperability with RDDs
Loading Data from Different Sources
Integration of Spark with Hive for Data Warehousing

Spark Session Features

Introduction to Spark Session
Spark Object and Spark Submit Part 1
Spark Object and Spark Submit Part 2
Spark Object and Spark Submit Part 3
Version and Range
createDataFrame
sql
Table
sparkContext
udf
read-csv
read-text
read-orc and parquet
read-json
read-avro
read-hive
read-jdbc
Catalog
newSession and stop

DataFrame Fundamentals

Introduction to DataFrame
DataFrame Features - Distributed
DataFrame Features - Lazy Evaluation
DataFrame Features - Immutability
DataFrame Features - Other Features
Organization

DataFrame ETL

Introduction to Transformations and Extraction
DataFrame APIs Introduction Extraction
DataFrame APIs Selection
DataFrame APIs Filter or Where
DataFrame APIs Sorting
DataFrame APIs Set
DataFrame APIs Join
DataFrame APIs Aggregation
DataFrame APIs GroupBy
DataFrame APIs Window Part 1
DataFrame APIs Window Part 2
DataFrame APIs Sampling Functions
DataFrame APIs Other Aggregate Functions
DataFrame Built-in Functions Introduction
DataFrame Built-in Functions - New Column Functions
DataFrame Built-in Functions - Column Encryption
DataFrame Built-in Functions - String Functions
DataFrame Built-in Functions - RegExp Functions
DataFrame Built-in Functions - Date Functions
DataFrame Built-in Functions - Null Functions
DataFrame Built-in Functions - Collection Functions
DataFrame Built-in Functions - na Functions
DataFrame Built-in Functions - Math and Statistics Functions
DataFrame Built-in Functions - Explode and Flatten Functions
DataFrame Built-in Functions - Formatting Functions
DataFrame Built-in Functions - Json Functions
Need of Repartition and Coalesce
How to Repartition a DataFrame
How to Coalesce a DataFrame
Repartition Vs Coalesce Method of a DataFrame
DataFrame Extraction Introduction
DataFrame Extraction - csv
DataFrame Extraction - text
DataFrame Extraction - Parquet
DataFrame Extraction - orc json
DataFrame Extraction - avro
DataFrame Extraction - hive
DataFrame Extraction - jdbc

DataFrame Transformations

Adding, Removing, and Renaming Columns
DataFrame Column Expressions
Filtering and removing duplicates
Sorting, Limiting and Collecting
Transforming Unstructured data
Transforming data with LLM

Working with different Data Types

Working with Nulls
Working with Numbers
Working with Strings
Working with Date
Working with Timestamps
Handling Time Zone Information
Working with Complex Data Types
Working with JSON data
Working with Variant Type

Joins in Spark DataFrame

Aggregation in Spark

UDF and Unit Testing

Spark Execution Model

Execution Methods - How to Run Spark Programs?
Spark Distributed Processing Model - How your program runs?
Spark Execution Modes and Cluster Managers
Summarizing Spark Execution Models - When to use What?
Working with PySpark Shell - Demo
Installing Multi-Node Spark Cluster - Demo
Working with Notebooks in Cluster - Demo
Working with Spark Submit - Demo

Spark Programming Model and Developer Experience

Creating Spark Project Build Configuration
Configuring Spark Project Application Logs
Creating Spark Session
Configuring Spark Session
Data Frame Introduction
Data Frame Partitions and Executors
Spark Transformations and Actions
Spark Jobs Stages and Task
Understanding your Execution Plan

Spark Structured API Foundation

Spark Data Sources and Sinks

Spark Data Sources and Sinks
Spark DataFrameReader API
Reading CSV, JSON and Parquet files
Creating Spark DataFrame Schema
Spark DataFrameWriter API
Writing Your Data and Managing Layout
Spark Databases and Tables
Working with Spark SQL Tables

Spark DataFrame and Dataset Transformations

Introduction to Data Transformation
Working with DataFrame Rows
DataFrame Rows and Unit Testing
DataFrame Rows and Unstructured data
Working with DataFrame Columns
Misc Transformations

DataFrame Joins

Optimizing Data Frame Transformations

General Join Mechanisms - (Shuffled Join, Optimized Join, Broadcast Join,Caching & Checkpointing, Skew Joins)
Partitioning - (Repartition & Coalesce, Shuffle Partitions )
Performance Tuning Problems - (Optimizing Cluster Resource Allocation, Serialization Problem, Fixing Data Skew (Data Skewness) & Straggling Task)
Partitioning - (Repartition & Coalesce, Shuffle Partitions, Custom Partitioners Column Pruning )

Optimizing Apache Spark on Databricks (Performance & Optimization)

Five Most Common Problems with Spark Applications
Key Ingestion Concepts
Optimizing with Adaptive Query Execution & Dynamic Partition Pruning
Designing Clusters for High Performance
Join Strategies_01_Broadcast Join
Join Strategies_02_Shuffle Hash Join
Join Strategies_03_Shuffle Sort Merge Join
Join Strategies_04_Cartesian Product Join
Join Strategies_05_Broadcast Nested Loop Join
Join Strategies_06_Prioritize different Join strategy
Driver Configurations
Executor Configurations Part 1
Executor Configurations Part 2
Configurations in spark-submit
Parallelism Configurations
Memory Management

Capstone Project

Project Scope and Background
Data Transformation Requirement
Setup your starter project
Test your starter project
Setup your source control and process
Creating your Project CI CD Pipeline
Develop Code
Write Test Cases
Working with Kafka integration
Estimating resources for your application

Capstone Project

Gain practical, real-world experience

Engineered by renowned industry specialists

Project 1

Design, Develop & Build Big Data through a Data Ingestion (ELT) pipeline with Hadoop Ecosystem Tools (Sqoop, Hive ,Spark & others)

Project Description : Data from multiple data sources will be ingested into HDFS using Sqoop & Spark Connectors using the Data Pipeline pipeline that containers raw, curated & derived zones. The Raw layer contains the hydrated data from the source which is then pushed to curated after Data cleansing & standardization & finally to derived zone for reporting purpose. The whole contains series of automated Spark jobs using Oozie scripts.
Also Oozie manages the end-to-end Data life cycle jobs automation using Oozie workflow and Job templates.

Project 2

Automated Ingestion Framework Pipeline (Hadoop Platform)

The whole Data Ingestion pipeline will be automated through Hadoop & Spark jobs where in it deploys the Hadoop components like Sqoop, Hive, Flume, HDFS & Spark tables into Data storage layer before triggering the Data flow through the pipeline. Data will be extracted from the source like contact centers & others & this whole pipeline is realtime pipeline that triggers whenever data arrives from the source into Kafka using Kafka source and sink connecters that triggers the deployment process.

100+

Hours of content

23+

Live Sessions

Software Tools

Request More Information

Tools Covered

Big Data Hadoop Certification Course

After completion of this training program you will be able to launch your carrer in the world of Big Data being certified as Big Data Certified Professional.

With the Big Data Certification in-hand you can boost your profile on Linked, Meta, Twitter & other platform to boost your visibility

DevOps Skills to Highlight on Your Resume

Hadoop
HDFS
Hive
Sqoop
Oozie
Presto
Drill
Cloudera Impala

Career Outcomes

Our Alumni in Top Companies

Career help alongside Big Data Hadoop Course

What our Learners are saying in their Testimonials

Learning to build big data pipeline using automation frameworks like Hadoop & Spark that gave me real-world skills in creating data pipelines.

A course that connects the Big Data Pipelines Automation frameworks in real enterprise workflows

The hands-on projects taught me how to automate data pipeline with Hadoop and manages the end-to-end Data life cycle using ELT/ETL workflow and Spark templates.

This course gave me a deep dive into automating the Data Pipeline across Clouds using Hadoop & Spark frameworks.

he workflow-oriented teaching and use of Hadoop tools use, and Implementing Data Pipelines using Hadoop & Spark tools made this one of the most valuable certifications I’ve earned

Big Data Hadoop Course FAQs

Why should you choose to pursue a Big Data & Hadoop course with Success Aimers?

Success Aimers teaching strategy follow a methodology where in we believe in realtime job scenarios that covers industry use-cases & this will help in building the carrer in the field of Big Data & Hadoop also delivers training with help of leading industry experts that helps students to confidently answers questions confidently & excel projects as well while working in a real-world

What is the time frame to become competent as a Big Data & Hadoop engineer?

To become a successful Big Data & Hadoop Engineer required 1-2 years of consistent learning with dedicated 3-4 hours on daily basis.
But with Success Aimers with the help of leading industry experts & specialized trainers you able to achieve that degree of mastery in 6 months or one year or so and it’s because our curriculum & labs we had formed with hands-on projects.

Will skipping a session prevent me from completing the course?

Missing a live session doesn’t impact your training because we have the live recorded session that’s students can refer later.

What industries lead in Big Data & Hadoop implementation?

Manufacturing
Financial Services
Healthcare
E-commerce
Telecommunications
BFSI (Banking, Finance & Insurance)
“Travel Industry

Does Success Aimers offer corporate training solutions?

At Success Aimers, we have tied up with 500 + Corporate Partners to support their talent development through online training. Our corporate training programme delivers training based on industry use-cases & focused on ever-evolving tech space.

How is the Success Aimers Big Data & Hadoop Certification Course reviewed by learners?

Our Big Data & Hadoop Engineer Course features a well-designed curriculum frameworks focused on delivering training based on industry needs & aligned on ever-changing evolving needs of today’s workforce due to Data & AI. Also our training curriculum has been reviewed by alumi & praises the thoroguh content & real along practical use-cases that we covered during the training. Our program helps working professionals to upgrade their skills & help them grow further in their roles…

Can I attend a demo session before I enroll?

Yes, we offer one-to-one discussion before the training and also schedule one demo session to have a gist of trainer teaching style & also the students have questions around training programme placements & job growth after training completion.

What batch size do you consider for the course?

On an average we keep 5-10 students in a batch to have a interactive session & this way trainer can focus on each individual instead of having a large group

Do you offer learning content as part of the program?

Students are provided with training content wherein the trainer share the Code Snippets, PPT Materials along with recordings of all the batches

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Big Data & Hadoop Certification Course Overview

Big Data & Hadoop Engineer Foundation Certification Course Curriculum

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