DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER COURSE LEAD MENTORS

DATA ENGINEER COURSE FEE IN INDIA

Live Virtual

Instructor Led Live Online

110,000
60,900

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
34,900

  • IABAC® & JAINx® Certification
  • One year access to Self Learning
  • 10 Capstone Projects
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Classroom

In - Person Classroom Training

110,000
65,900

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Classroom Training
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

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UPCOMING DATA ENGINEER ONLINE CLASSES IN INDIA

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

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WHY DATAMITES FOR DATA ENGINEER TRAINING

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN INDIA

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools and platforms
• Core concepts of Data engineering

MODULE 2: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 3: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data wrangling with Pandas
• Managing large data sets with Pandas
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 4: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data Ingestion through Pandas methods
• Hands-on: Ingestion, Transform Data and Load data

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Iterators
• Generator functions
• Lambda functions
• Map, reduce, filter functions

MODULE 5: PYTHON NUMPY PACKAGE

• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations

MODULE 6: PYTHON PANDAS PACKAGE

• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis

MODULE 1 : OVERVIEW OF STATISTICS 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

MODULE 1: DATA ENGINEERING INTRODUCTION

• What is Data Engineering?
• Data Engineering scope
• Data Ecosystem, Tools, and platforms
• Core concepts of Data engineering

MODULE 2: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 3: DATA SOURCES AND DATA IMPORT

• Types of data sources
• Databases: SQL and Document DBs
• Connecting to various data sources
• Importing data with SQL
• Managing Big data

MODULE 4: DATA PROCESSING

• Python NumPy Package Introduction
• Array data structure, Operations
• Python Pandas package introduction
• Data structures: Series and DataFrame
• Importing data into Pandas DataFrame
• Data processing with Pandas

MODULE 5: DOCKER AND KUBERNETES FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping, and managing images)
• Publishing containers to DockerHub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 6: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 7: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : AWS DATA SERVICES INTRODUCTION 

  • AWS Overview and Account Setup
  • AWS IAM Users, Roles and Policies
  • AWS Lamdba overview
  • AWS Glue overview
  • AWS Kinesis overview
  • AWS Dynamodb overview
  • AWS Anthena overview
  • AWS Redshift overview

MODULE 2 : DATA INGESTION USING AWS LAMDBA 

  • Setup AWS Lamdba  local development env
  • Deploy project to Lamdba console
  • Data pipeline setup with Lamdba
  • Validating data files incrementally
  • Deploying Lamdba function

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

  • AWS Kinesis overview and setup
  • Data Streams with AWS Kinesis
  • Data Ingesting from AWS S3 using AWS Kinesis

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

  • AWS Redshift Overview
  • Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
  • Develop Applications using AWS Redshift cluster
  • AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

  • Azure Synapse setup
  • Understanding Data control flow with ADF
  • Data Pipelines with Azure Synapse
  • Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

  • Create Azure storage account
  • Connect App to Azure Storage
  • Azure Blog Storage

MODULE 7: AZURE DATA FACTORY

  • Azure Data Factory Introduction
  • Data transformation with Data Factory
  • Data Wrangling with Data Factory

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

  • Hands-on Project Case-study
  • Setup Project Development Env
  • Organization of Data Sources
  • AZURE/AWS services for Data Ingestion
  • Data Extraction Transformation  

MODULE 1: DATA WAREHOUSE FOUNDATION

• Data Warehouse Introduction
• Database vs Data Warehouse
• Data Warehouse Architecture
• ETL (Extract, Transform, and Load)
• ETL vs ELT
• Star Schema and Snowflake Schema
• Data Mart Concepts
• Data Warehouse vs Data Mart — Know the Difference
• Data Lake Introduction
• Data Lake Architecture
• Data Warehouse vs Data Lake

MODULE 2: DOCKER FOUNDATION

• Docker Introduction
• Docker Vs. regular VM
• Hands-on: Running our first container
• Common commands (Running, editing, stopping and managing images)
• Publishing containers to Docker Hub
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 3: KUBERNETES CONTAINER ORCHESTRATION

• Kubernetes Introduction
• Setting up Kubernetes Clusters
• Kubernetes Orchestration of Containers
• Build Docker on Kubernetes Cluster

MODULE 4: DATA ORCHESTRATION WITH APACHE AIRFLOW

• Data Orchestration Overview
• Apache Airflow Introduction
• Airflow Architecture
• Setting up Airflow
• TAG and DAG
• Creating Airflow Workflow
• Airflow Modular Structure
• Executing Airflow

MODULE 5: DATA ENGINEERING PROJECT

• Setting Project Environment
• Data pipeline setup
• Hands-on: build scalable data pipelines

MODULE 1 : DATABASE INTRODUCTION 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort
• Hands-on Map Reduce task

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive
• Working with Spark SQL Query Language

MODULE 5: MACHINE LEARNING WITH SPARK ML

• Introduction to MLlib Various ML algorithms supported by Mlib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest

MODULE 6: KAFKA and Spark

• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
• Operations

DATA ENGINEER TRAINING COURSE REVIEWS

ABOUT DATA ENGINEER COURSE IN INDIA

A Data Engineering course in India unlocks vast career opportunities in managing, processing and optimizing big data for informed decision-making in diverse industries. According to a Market Data Forecast report, the data engineering market is projected to reach around $87.37 billion by 2025, demonstrating a robust Compound Annual Growth Rate (CAGR) of 17.6%. Additionally, the salary of a data engineer in India is INR 10,75,000 per year according to a Glassdoor report.

DataMites, a leading international training institute, offers a comprehensive 6-month data engineering course in India. Featuring a curriculum covering 150 dedicated learning hours, over 50 hours of interactive online training, and a blended learning approach, DataMites courses are accredited by IABAC®, ensuring top-notch quality. Upon successful completion, students receive a prestigious global IABAC® certification, complemented by a distinctive two-month live project mentoring component for practical guidance. Additionally, students enjoy unlimited access to a data engineer cloud lab, providing ample opportunities for hands-on practice.

Furthermore, DataMites presents a Certified Data Engineer in India, encompassing a broad spectrum of topics such as Python, Numpy, Pandas, data manipulation, statistics, fundamental principles of big data, and essential database skills.

DataMites provides key highlights for its data engineer course in India that include:

  1. Course Content: DataMites covers essential topics in data engineering, such as data modeling, ETL (Extract, Transform, Load) processes, data warehousing, and database management systems.

  2. Faculty and Instructors: Datamites provide industry-experience instructors who can significantly enhance the quality of the learning experience.

  3. Industry Recognition: DataMites holds 10 years of delivering excellence and has trained 50,000+ learners worldwide. DataMites data engineer course in India is widely recognised within the industry.

  4. Placement Assistance: DataMites is committed to aiding students in securing placements with leading IT companies, providing comprehensive assistance to enhance their prospects in the industry. From resume optimization to interview preparation, their support services are designed to empower students to enter top-tier IT organizations.

  5. Hands-On Experience: The DataMites program emphasizes practical learning, providing students with extensive hands-on experience, and ensuring they gain real-world skills essential for a successful career in data engineering.

  6. Flexible mode of learning  DataMites offers a flexible mode of learning such as online, blended mode and offline data engineer courses in India allowing students to tailor their study schedules to accommodate work or other commitments. 

  7. Accreditation Assurance: The DataMites data engineer training course in India is accredited, ensuring a high standard of education and recognized credentials that add value to your career in the dynamic field of data engineering.

The scope for Data Engineers in India holds great promise as industries increasingly adopt data-driven decision-making. With organizations leveraging the potential of big data, proficient Data Engineers will be instrumental in crafting, implementing, and enhancing data infrastructure. This trajectory is poised to generate abundant career prospects, offering a dynamic environment for Data Engineers to thrive and contribute significantly to the city's technological advancement. 

Enrol with DataMites to access enhanced learning opportunities in the domain for a more robust educational experience.

Datamites provides offline data analytics courses across multiple cities, including Bangalore, Kochi, Ahmedabad, Pune, Delhi, Mumbai, Hyderabad, Kolkata, Nagpur, Bhubaneswar, Chennai, and Vijayawada.

ABOUT DATAMITES DATA ENGINEER TRAINING IN INDIA

A data engineer is a professional responsible for designing, constructing, and maintaining the systems and architecture that enable the efficient processing and storage of large volumes of data.

Data engineers require skills in programming languages (e.g., Python, Java), database management, ETL (Extract, Transform, Load) processes, and knowledge of data modeling and architecture.

  • Mindtree. 
  • Databricks. 
  • Atos. 
  • Deloitte. 
  • Capgemini.

While many data engineers start their careers after completing a bachelor's degree, it's also feasible to enter the field by transitioning from another role within the data-related domain.

In the current year, the demand for jobs in the Data Science Domain is expected to further increase, emphasizing the growing significance of Data Engineering and MLOps. The need for certified data engineering skills remains crucial, given the plethora of new technology tools available in the market, ranging from open source to paid solutions, and spanning both on-premises and cloud-based platforms.

Companies will likely find data handling less challenging in the future as the data engineering role transitions toward pipeline and warehouse-centric accessibility. Over the next 5 years, automation is expected to shape the future of data engineering, transforming data into a valuable end product.

The demand for data engineers will persist as long as there is data to be processed. With the increasing reliance on data-driven decision-making across industries, skilled professionals in data engineering remain essential for optimizing information workflows and infrastructure.

The salary of a data engineer in India is INR 10,75,000 per year according to a Glassdoor report.

While AI may automate specific tasks in data engineering, it is improbable to completely replace data engineers. These professionals will remain crucial for crafting and sustaining data infrastructure, guaranteeing data quality, and tackling intricate data issues that demand human expertise and supervision.

While AI may automate specific tasks in data engineering, it is improbable to completely replace data engineers. These professionals will remain crucial for crafting and sustaining data infrastructure, guaranteeing data quality, and tackling intricate data issues that demand human expertise and supervision.

The domain of data engineering is broad and ever-changing, covering a variety of technologies and practices essential for efficient data processing. This includes the utilization of Big Data technologies like the Hadoop ecosystem and Apache Spark to handle large datasets effectively.

The job market in India demonstrates a substantial and increasing demand for data engineers. With a rising acknowledgment of the importance of data-driven insights, companies are actively pursuing proficient data engineers to construct and oversee the necessary infrastructure for optimal data utilization.

A robust basis in mathematics, specifically in linear algebra, probability theory, and statistics, is crucial for individuals aspiring to be big data engineers. These mathematical principles play a vital role in comprehending the algorithms and methodologies applied in the processing and analysis of big data.

Typically, Data Engineers possess a degree in Computer Science, Software Engineering, or a related discipline, coupled with proficiency in database systems, distributed computing, and big data technologies. Additionally, they might hold certifications in cloud platforms or data engineering tools.

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FAQ'S OF DATA ENGINEER TRAINING IN INDIA

The complexity of the Data Engineer Course can vary based on individual backgrounds and prior knowledge. DataMites ensures a robust training experience with practical projects for effective learning.

The Data Engineer Course is open to professionals aspiring to build a career in data engineering. Having a basic understanding of programming and databases can be beneficial.

DataMites offers online Data Engineer Training in India, providing flexibility to accommodate diverse learning preferences.

The Data Engineer Course spans six months, delivering a comprehensive curriculum that includes over 150 hours of learning. DataMites ensures a thorough training experience with hands-on exposure to data engineering. Whether you opt for an intensive program or an extended course, the objective is to equip you with essential skills to excel in the field of data engineering.

Possessing a postgraduate degree is not mandatory for participating in Data Engineer Training. A bachelor's degree or equivalent relevant work experience is deemed sufficient.

DataMites' pricing structure reflects the quality and value of the training provided. The Data Engineer Training Fee in India varies, ranging approximately from INR 35,773 to INR 110,000 tailored to accommodate different program choices and meet individual learning objectives and preferences.

Indeed. The offline training option provides flexibility, allowing for in-person learning from various location such as Bangalore, Chennai, Hyderabad, Pune, Mumbai etc leveraging DataMites' specialized knowledge.

In DataMites®, experienced data engineering professionals are designated as instructors for the Data Engineer Training in India. Committed to providing effective guidance, they ensure ongoing support throughout your learning journey.

DataMites® offers a variety of training methods, including self-paced online learning and instructor-led online classes. You have the flexibility to choose the approach that best suits your preferred learning style.

As an integral part of the Data Engineer Course in India, DataMites® provides valuable internship opportunities, facilitating practical skill development for a successful data engineering career. The dedicated team assists in securing internships that align with your training and career aspirations.

The duration for obtaining IABAC certification depends on your training program and the exam schedule. DataMites will guide you through this process.

Certainly! We regularly organize assistance sessions and dedicated doubt resolution sessions, ensuring you receive the necessary support to fully grasp the course content.

DataMites® offers multiple payment options, including Cash

  • Net Banking
  • Check
  • Debit Card
  • Credit Card
  • PayPal
  • Visa
  • Mastercard
  • American Express

DataMites® provides recorded sessions in case you miss a class, allowing you to review the material and stay on track with the course.

DataMites provides the Flexi-Pass option, granting participants a 3-month window to access training sessions. This feature ensures an extended period of support and guidance, allowing for the resolution of doubts and the opportunity to review concepts as required.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

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