Instructor Led Live Online
Self Learning + Live Mentoring
In - Person Classroom Training
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.
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
MODULE 2 : HARNESSING DATA
MODULE 3 : EXPLORATORY DATA ANALYSIS
MODULE 4 : HYPOTHESIS TESTING
MODULE 5 : CORRELATION AND REGRESSION
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
MODULE 2 : DATA INGESTION USING AWS LAMDBA
MODULE 3 : DATA PIPELINE WITH AWS KINESIS
MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT
MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE
MODULE 6 : STORAGE IN AZURE
MODULE 7: AZURE DATA FACTORY
MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS
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
MODULE 2 : SQL BASICS
MODULE 3 : DATA TYPES AND CONSTRAINTS
MODULE 4 : DATABASES AND TABLES (MySQL)
MODULE 5 : SQL JOINS
MODULE 6 : SQL COMMANDS AND CLAUSES
MODULE 7 : DOCUMENT DB/NO-SQL DB
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
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.
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.
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
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: -
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.