AWS Data Engineering Training in Chennai



Want to learn an AWS Big Data Certification Training in Chennai? BTree Systems is the right place for you. AWS Data Engineering Training in Chennai provides you with AWS Big Data technologies such as Amazon EMR, Amazon Redshift, Amazon S3, as well as Spark. You will discover how to process data using Amazon EMR in conjunction with the broad Hadoop ecosystem, which includes Hive. Moreover, you'll explore how to create Big Data environments, interact with Amazon Redshift and Amazon Athena, and use best practices to establish secure and cost-efficient Big Data environments. With the AWS Big Data Engineer training, you will gain the professional and technical skill sets required to succeed as an AWS cloud specialist.

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Course crafted and taught LIVE by industry experts.

  • Cognizant
  • Deloitte
  • Freshwork
  • IBM
  • Hexaware Technologies
  • Infosys
  • Intel
  • TCS
  • Wipro

AWS Data Engineering Key Highlights

Real-Time Experts

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No Cost EMI

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Overview of AWS Data Engineering Course in Chennai

You become familiar with the AWS platform’s features, such as AWS Big Data storage, machine learning techniques, Kinesis analytics, cloud technology processes, and other tools, through this AWS Big Data certification course in Chennai. Through real-world projects and case studies, the complete course will help you obtain comprehensive knowledge and abilities on AWS.

Data engineering is the practice of developing large-scale data collection, storage, and analysis systems. It covers a wide range of topics and has uses in almost every business.

AWS Data Engineering focuses on overseeing several AWS services so that customers can receive an integrated solution that meets their needs. An AWS Engineer examines the customer’s requirements, their data’s quantity and quality, and the outcomes of their activities. For customers to use them and perform at their best, they also choose the greatest equipment and services.

Data engineers create systems that gather, handle and transform unprocessed data into information that data scientists and business analysts may use to evaluate it in several contexts. Their ultimate objective is to open up data so that businesses can utilize it to assess and improve their performance.

You can start or enhance your career in data engineering with the appropriate skills and knowledge. A bachelor’s degree in computer science or a closely related subject is common among data engineers. You may lay the groundwork for the information you’ll need in this rapidly changing sector by acquiring a degree. For the chance to expand your career and open doors to opportunities with possibly greater salaries, think about getting a master’s degree.

As data generation rates rise, there is a growing need for specialists in AWS Data Engineering and Data Analytics. There is a dearth of Certified Data Analytics Engineers, according to numerous polls and reports. AWS Certified Data Analytics and Certified Data Engineering with a real, hands-on cloud platform are required for this career.

The points listed below should be your main areas of attention to become an AWS Certified Data Analytics and Data Engineer, expert:

• To choose the best storage tool based on needs, be aware of the key distinctions and use cases between various storage services offered by AWS.

• Practice manually moving data between Amazon Redshift clusters and Amazon S3 with real-world examples.

• Learn how to query data from various tables in the data lake and data warehouse.

• Learn the AWS tools and the Data Integration process.

• QuickSight for analytics and BI dashboards, AWS Glue for ETL, and AWS Athena for storage querying.

• AWS Data Engineering knowledge can also be increased by studying the documentation, taking classes, and practicing more.

AWS training is provided by certified AWS cloud professionals at BTree Systems with more than 12 years of experience in the planning, design, and scalability of AWS applications. AWS Big Data training in Chennai provides substantial instruction with a variety of tools, including Sqoop, Hive, Scala, and Spark, which may be developed using Python, an exceptionally popular language in the industry.

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AWS Data Engineering Career Transition


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Hiring Partners

I’m very excited to share my experience at btree systems I’m a data analyst so I want to change my domain to AWS Data Engineering. I enrol an AWS data engineer course at btree and the trainers are very supportive, they use real-world projects.



Data Engineer


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Data Engineer

I work as a data scientist in a reputed company but I wanna change my career to the cloud, I recently see btree Instagram reels and reviews which impressed me, so I enrol my AWS data engineering course at btree systems.



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Data Scientist

I’m a data engineer I know about the cloud but not in-depth so I join btree systems for the AWS data engineering course. The course is a great and affordable fee, and all sessions are clarified by the trainers.



Data Engineer


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Data Engineer

AWS Data Engineering Skills Covered

AWS Athena




AWS Glue




Sqoop and Oozie



AWS Flink

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AWS Data Engineering Course Fees




08:00 PM TO 11:00 PM IST (GMT +5:30)




08:00 PM TO 11:00 PM IST (GMT +5:30)




08:00 PM TO 11:00 PM IST (GMT +5:30)

₹ 42,000

₹ 37,800

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Curriculum for AWS Data Engineering Certification Course

Big Data Software Installation in Windows & Mac & Ubunth

  • IntelliJ
  • Pycharm
  • Anaconda
  • Hadoop 2.7.2
  • Spark 2.4.8
  • Kafka 2.4.0
  • Git
  • Sbt
  • Sql-workbench
  • Java 8
  • Scala 2.11.12
  • Putty
  • WinSCP

Introduction to Big Data and Hadoop

  • What is Bigdata?
  • What is Hadoop
  • What is Spark
  • What is Nosql databases
  • Difference between Hadoop, Spark
  • Common Bigdata problems
  • Hadoop Ecosystem

Scala basics

  • What is JVM
  • JVM languages
  • What is Java
  • What is Scala
  • Java Vs Scala
  • Scala Vs Python
  • Java Datatypes Vs Scala Datatypes
  • Class Vs Objects
  • Sbt vs Maven
  • Functions Vs Methods
  • Scala type hierarchy
  • IntelliJ sample Scala programs

Scala Important Concepts

  • If else expression
  • While
  • For loop
  • For - Yield importance
  • Case class importance
  • Array Vs List
  • Tuple Vs Set

Scala Functions

  • Create Sample Scala Functions
  • Anonymous Functions
  • Recursive function
  • Nested functions
  • Higher order functions
  • Map
  • Filter
  • Flatten
  • Flatmap
  • Foreach
  • Zip
  • Find
  • drop Vs dropWhile
  • foldRight Vs foldLeft

AWS Introduction EC2

  • Create Windows/mac/Linux servers
  • Create a sample website
  • Autoscaling
  • image


  • What is serverless computing?
  • Athena process json, csv data
  • Recommended approaches


  • store data,
  • Client mode submit s3 commands.
  • Get data from various sources and store
  • S3 bucket Policies

IAM(Identity and Access management)

  • Users
  • Groups
  • Roles
  • Custome policies


  • Load data from S3 process data
  • Sortkey, Distkey power
  • Redshift architecture
  • Get data from various sources


  • How to process csv, json data using Glue
  • Get Athena data using glue
  • Crawler, Job execute Pyspark and Scala spark
  • Glue architecture/internals
  • Advanced concepts & best practice


  • Create different databases
  • create sample tables and process
  • best practice/low cost
  • Practice oracle MySQL using rds.


  • Practice Py-spark, hive,
  • Create EMR (Elastic Map Reduce) cluster and process
  • EMR vs EC2
  • Hive internals sample programs
  • Sqoop import data from RDS store in s3

Hadoop Ecosystem HDFS

  • What is HDFS?
  • Hadoop architecture
  • How HDFS replicate data
  • Limitations in Hadoop


  • Namenode Importance
  • Datanode responsibilities
  • Secondary namenode
  • High Availability
  • Hdfs commands Handson
  • Hadoop 1.x Vs 2.x Vs 3.x
  • Daemons in Yarn
  • Node manager
  • Application master
  • Resource Manager
  • Yarn Commands
  • How Yarn allocates resources
  • Container
  • How spark /Mapreduce running in Yarn


  • Hive architecture
  • Sql Vs HQL
  • How to process CSV data
  • How to process Json data
  • Serdes
  • Partition
  • Bucketing
  • Orc vs Parquet importance
  • Limitation in Hive


  • Sqoop architecture
  • Import data from Oracle
  • Import data from MySQL
  • Import data from MsSql data
  • Shell script importance in Sqoop
  • Import data to Hive
  • Compression techniques (parquet, sequence, Avro)
  • Best practice


  • Oozie architecture
  • Workflow importance in oozie
  • importance in oozie
  • Coordinator importance in oozie
  • Multiple actions in workflow
  • How to automate Sqoop & Hive applications using Oozie

NOSQL Database introduction

  • What is NOSQL?
  • Cap Theorem
  • Cassandra
  • Cassasndra Architecture
  • Cassandra installation in EMR
  • Keyspace & tables
  • Cassandra Limitation
  • Hbase
  • Hbase Architecture
  • Hbase commands
  • Hbase limitations
  • Phoenix
  • Phoenix Architecture
  • Process different type data

Apache Spark Training Spark Core

  • Why Spark why not Hadoop?
  • HDFS/Yarn importance in Spark
  • Spark architecture
  • Different types of APIs
  • RDD (Resilient Distributed Dataset)
  • Dataframe
  • Dataset
  • Where your using Spark?
  • Why spark faster than MapReduce?
  • Why /How spark process in Memory?
  • Why MapReduce Slow?

RDD Internals

  • Immutability
  • Laziness
  • Fault tolerance
  • SparkContext, SqlContext, SparkSession Internals
  • Create RDD different ways
  • Transformations
  • Action
  • Commonly used transformations & Actions
  • Narrow transformations
  • Wide transformations
  • Debugging transformations
  • Spark web UI

RDD Internals

  • Map
  • FlatMap
  • Filter
  • Distinct
  • ReduceByKey Vs GroupByKey
  • SortBy
  • Other Transformations & Actions
  • Spark-submit
  • Minimum 20 RDD use case programs

Spark SQL

  • Dataframe
  • Convert RDD to Dataframe
  • Python Dataframe
  • Spark dataframe Introduction
  • Dataframe reader
  • Dataframe Vs dataset
  • Process different type data
  • CSV
  • Json (complex)
  • XML
  • Avro
  • Orc
  • Text data
  • Parquet
  • Spark vs Hive
  • Spark process Hive data
  • Process Different Database data
  • Oracle
  • MySQL
  • MySQL data analysis
  • Sqoop Vs Spark
  • Data-migration Project
  • ETL project Vs Spark project
  • Process different NoSQL Database data
  • Spark integrate with HBase and Phoenix
  • Spark Cassandra Integration
  • Spark MongoDb integration

Kafka Internals

  • Kafka Architecture
  • Producer API
  • Consumer API
  • Write producer code to get data from sources (Scala, Python)
  • Write consumer code to get data from Kafka and flush data to sink.
  • Spark Kafka integration
  • Get data from web server and process data using spark
  • Spark Streaming end to end spark workflow
  • How to submit a project using AWS EMR, Azure, Databricks, Cloudera

Apache Nifi Introduction

  • Nifi Internals
  • Different Procedures
  • Import/export Templates
  • Get data from Rest API and process
  • Spark Kafka Nifi integration


  • Traditional Datawarehouse Vs Snowflake
  • Snowflake Architecture
  • Create cluster, warehouse
  • Process huge amount of data,
  • Stages (internal, external)
  • Get data from S3 process using snowflake.
  • Best practice


  • Flink Architecture
  • Flink Core
  • Dataset API
  • How to process CSV, JSON, Oracle data
  • Spark Vs Flink

“Accelerate Your Career Growth: Empowering You to Reach New Heights in AWS Data Engineering”

AWS Data Engineering Training Options

AWS Data Engineering Classroom Training

  • 50+ hours of live classroom training
  • Real-Time trainer assistance
  • Cutting-Edge on AWS tools
  • Non-Crowded training batches
  • Work on real-time projects
  • Flexible timings for sessions
Automation Anywhere live training

AWS Data Engineering online training

  • 50+ Hours of online Aws Training
  • 1:1 personalised assistance
  • Practical knowledge
  • Chat and discussion panel for assistance
  • Work on live projects with virtual assistance
  • 24/7 support through email, chat, and social media.

Certification of AWS Data Engineering Course

AWS Data Engineering Certification Course in Chennai can help you get started on your path to becoming an AWS cloud expert. An in-depth knowledge of Amazon Web Services is what you will learn in this course.

Through the curriculum created by the AWS Data Engineer trainers, you will not only learn about the storage and infrastructure components of AWS, but you will also get expertise in the design, planning, and scaling of applications within AWS.

Apart from certification, the skills you have acquired from our training with Live projects, case studies, and practice sessions can enhance your profile

Knowledge Hub with Additional Information of AWS Data Engineering

  • Newer patterns are emerging as data engineering soars to new heights. Here's a sneak preview of some potential futuristic trends that data engineers might find appealing in their upcoming endeavours:
  • • Each team will receive data engineering help.
  • • Standardization of real-time infrastructure will occur.
  • • Data engineers will be a part of the DevOps process.
  • • The use of product-based data engineering will increase.
  • • The number of data engineers who operate remotely will rise.
  • • Growth of self-service analytics using contemporary tools.
  • • A data analyst is responsible for making decisions that have an impact on the company’s current market. The task of building a platform on which data scientists and analysts can work falls to a data engineer.
  • • To summarise the data, a data analyst uses methodologies from descriptive analysis and static modeling. On the other side, a data engineer is in charge of creating and managing data pipelines.
  • • The data is examined by a data analyst, who then presents it to teams in an understandable format. To enhance sales or website visits, they may need to evaluate their current performance, make plans for the future, establish methods for doing so, and spot trends among different user groups.
  • • Data cleansing, analysis, and visualization are common duties that data analysts carry out that are similar to those carried out by data scientists. Data analysts, however, are more focused on communicating and analyzing data. A data engineer’s attitude frequently leans more toward constructing and optimizing.
  • • For data analysts, machine learning knowledge is not necessary. The knowledge of machine learning is not necessary for a data engineer, but the knowledge of core computing.
  • • With the aid of data analysts, conventional organizations can become data-driven enterprises. Data engineers make ensuring that information is acquired, transformed, saved, and made accessible to other users in a timely and correct manner. Software developers with experience in data engineering are more likely to be able to transition between and combine different technologies to achieve a shared goal.
  • • A data analyst makes sure that the pertinent data is available for a company by conducting a thorough study. DE to guarantee data accuracy and flexibility in response to shifting business requirements.
  • • SQL is the most important skill, regardless of whether you’re a data engineer or a data analyst. An excellent job option for someone with SQL and data analysis skills is data analysis.
  • Cloud Engineer
  • • An expert who makes plans for migrating and maintaining various business apps and services to the cloud is known as a cloud engineer. A cloud engineer evaluates an organization’s IT infrastructure. A cloud engineer’s responsibility is to provide direction and support to businesses wanting to migrate important business processes and applications to different types of clouds.
  • • These cloud categories can include but are not limited to, public, private, community, and hybrid.
  • • Engineers who specialize in cloud computing deploy engineering applications using a variety of cloud computing paradigms. Platform-based computing (PaaS), Infrastructure as a Service (IaaS), Serverless computing, and Software as a Service are some of these (SaaS).
  • • The ability to move workloads between the cloud and on-premises is only one of many benefits of employing cloud computing. In terms of cloud computing, cloud engineering offers businesses the tools and processes they need to employ cloud-based services for business goals.
  • Data Engineer
  • • An information technology specialist known as a “data engineer” analyses, improves and creates algorithms using data to achieve business goals and objectives. Data engineers may assist businesses in growing when it comes to managing resources like money, infrastructure, and staff.
  • • Engineering applications are used in this discipline to gather, examine, and create algorithms from various data sets to acquire fresh perspectives on the business. It is impossible to exaggerate the value of data engineering in the IT sector. Data engineering uses data that can be used effectively to achieve organizational goals.
  • • The need for data engineers with the right combination of skills to manage sizable and complex datasets and databases is constant. Additionally, it enables a company to see all of its data sets in a way that is simpler to understand.
  • AWS Sysops Administrator salaries in India range from 2.7 Lakhs to 9.8 Lakhs per year, with an annual income of 5.1 Lakhs being the average. With less than one year of experience to seven years of experience, the AWS Sysops Administrator’s pay in India ranges from 2.7 Lakhs to 9.8 Lakhs, with an average yearly wage of 5.1 Lakhs.

    According to Glassdoor, the average annual salary of these experts in India is roughly 711,000, with the potential to climb to 1,170,000 depending on position and experience.

    In India, the beginning salary for an AWS Data Engineer is approximately 4.4 Lakhs (or 36.7k) per year. AWS Data Engineers must have at least two years of experience. An entry-level AWS Data Engineer makes an average income of 7.1 Lakhs annually with fewer than three years of experience. While an experienced AWS Data Engineer with 10-20 years of experience earns an average pay of 23.2 Lakhs per year, a mid-career AWS Data Engineer with 4–9 years of experience makes an average salary of 11.5 Lakhs annually.

  • • A data analyst's job is to analyze and aggregate various datasets to help a company comprehend the trends in the data and make better business decisions. The data analyst works with well-structured and modelled data to comprehend present situations and to highlight recent patterns from the data, whereas a data scientist constructs models that make future forecasts or find non-obvious patterns in data.
  • • A data analyst may provide answers to issues like which food item performed the best over the course of the most recent month in various geographic locations or which medical procedure produced the best results for patients of various ages. An organization can use these insights to improve future decisions.
  • • In our example, the data analyst may use complicated queries to connect portions of data from other datasets (such as an orders database or web server logs) to acquire new insights. For instance, the data analyst might provide a report indicating which alternative goods customers most frequently peruse before making a particular purchase. To get further useful insights, the data analyst may also leverage sophisticated machine learning models created by data scientists.
  • • The data analyst is like a skillful pilot, utilizing their experience to bring customers to their goal, whilst the data engineer is like a civil engineer building infrastructure and the data scientist is like a data scientist developing means of transportation.
  • • A data scientist's job is to use artificial intelligence and machine learning to extract complicated insights from varied datasets and create predictions. The data scientist will combine a variety of abilities to assist an organization in leveraging data to solve complicated problems and make wise decisions. These abilities will include computer science, statistics, analytics, and arithmetic.
  • • To create and train sophisticated machine learning models that can find patterns in data and forecast future trends, data scientists must have a thorough understanding of the raw data they will be working with. In our hypothetical situation, the data scientist might create a machine-learning model using historical sales data that has been connected with daily weather data for the reporting period. Then, based on the anticipated weather forecast, they can construct and train this model to assist business users in making predictions about the likely top-selling categories for upcoming days.
  • • The data scientist is developing the automobiles, planes, and other modes of transportation used to travel into and out of the development, whereas the data engineer is like a civil engineer building infrastructure for new development. Data scientists build machine learning models that let business analysts and data consumers derive fresh conclusions and forecasts from data.
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    FAQ for AWS Data Engineering Course

    BTree Systems makes recordings of every Big Data with AWS Certification course class available for review before the next session. You have access to all or any classes for 90 days with Flexi-pass from BTree Systems, giving you the freedom to choose sessions whenever it’s most convenient for you.

    We always recommend that students meet with the trainer before beginning the course. Before paying fees, BTree Systems offers a free sample class or a discussion session with trainers. We only consider you to enrol in classes if you are happy with the mentorship provided by the instructor.

    No, the placement team at BTree Systems offers technical training, industry projects, case studies, resume preparation, and mock interviews to help increase the likelihood of being hired.

    Yes, you will need some experience with coding, such as Python, since the Big Data with AWS Certification course incorporates software and IT sectors.

    We provide various modes of training like:

    Classroom training

    One and one training

    Fast track training

    Live instructor LED online training

    Customized training

    Yes, we provide career guidance camps for freshers and working (IT or Non-IT).

    Call us at 044-4560 5237, and we’ll get back to you as quickly as we can with further details on the deals and discounts.

    The Real-Time Implementation methodology underpins the entire AWS Data Engineer training program. Through hackathons and lab sessions, you acquire hands-on experience working on projects for the industry, which will help you develop a portfolio of projects.

    Yes, we give students lifetime access to the pupil portal’s study materials, videos, and top MNC interview questions.

    You will receive BTree Systems globally recognized course completion certification.

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