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
Data engineering involves designing, constructing, and managing infrastructure and systems for collecting, storing, processing, and analyzing large volumes of data. Its focus is on ensuring data availability, reliability, and accessibility for informed decision-making.
a. Establish a strong foundation in mathematics, statistics, and programming.
b. Develop proficiency in data manipulation, database management, and data integration.
c. Acquire expertise in big data technologies like Hadoop, Spark, and cloud platforms.
d. Build a portfolio showcasing data engineering projects.
e. Seek internships or entry-level positions in organizations requiring data engineering skills.
f. Stay informed about emerging technologies and industry trends.
The timeframe varies, but it generally takes six months to two years to gain the necessary skills and experience for a career as a data engineer.
a. In-depth knowledge of data engineering concepts, tools, and techniques.
b. Hands-on experience with industry-standard data engineering technologies.
c. Enhanced job prospects and increased earning potential.
d. A strong foundation for career progression in data-related roles.
a. Basic understanding of mathematics, statistics, and programming.
b. Familiarity with databases and SQL.
c. Proficiency in at least one programming language (Python or Java).
d. Knowledge of data manipulation and analysis techniques.
e. Check specific course requirements or recommendations.
Costs can vary, ranging from 40,000 INR to 1,00,000 INR. Research different providers for specific course fees.
DataMites is widely regarded as one of the best institutes for data engineering training. It offers a comprehensive curriculum, industry-relevant projects, and experienced instructors, focusing on practical learning and industry connections.
Job opportunities include roles such as Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, or Cloud Data Engineer across various industries.
a. Proficiency in Python, Java, or Scala.
b. Strong knowledge of SQL and database management systems.
c. Understanding of big data technologies (Hadoop, Spark, NoSQL).
d. Data modeling and architecture design skills.
e. Familiarity with cloud platforms (AWS, Azure, Google Cloud).
f. Experience in data pipeline development, data integration, and ETL processes.
g. Problem-solving and analytical thinking.
h. Effective communication and collaboration skills.
The average salary for Data Engineers in Bangalore varies based on factors such as experience, skills, industry, and organization. However, Glassdoor reports an average salary of ₹11,00,000 per year in India.
The course duration is approximately 6 months, encompassing over 150 learning hours. This time investment ensures a comprehensive exploration of the course material.
For data engineering training in Marathahalli, consider enrolling in the comprehensive program offered by DataMites®, available both online and in-person. This training equips you with essential skills in data engineering, preparing you for real-world applications.
DataMites offers classroom training at diverse Bangalore locations, namely Kudlu Gate, Marathahalli, and BTM. These well-situated venues cater to a broad audience, ensuring ease of access for learners from various parts of the city.
The training program covers a broad spectrum, including data integration, modeling, ETL processes, data warehousing, big data technologies, and cloud platforms. Hands-on projects and real-world case studies enhance practical skills and understanding.
The course is designed for individuals with a foundational understanding of mathematics, statistics, and programming. It is suitable for aspiring data engineers, IT professionals, software engineers, and those transitioning into data engineering roles.
Opting for online data engineer training provides flexibility, access to industry-expert instructors, hands-on assignments, real-world projects, and the chance to network with a global community of learners.
Yes, DataMites® offers classroom training, allowing in-person learning and fostering direct interactions with instructors and peers. Offline training options are available on demand.
The Flexi-Pass provides flexibility to access recorded sessions, allowing individuals to revisit or catch up on missed classes, ensuring a convenient and comprehensive learning experience.
Upon completion, you will receive industry-recognized certifications, including those from the International Association of Business Analytics Certifications (IABAC). These certifications validate skills and carry the prestige of IABAC accreditation, enhancing employment prospects in data engineering.
Instructors are qualified professionals with substantial experience in data engineering. DataMites® ensures their practical industry experience and in-depth knowledge.
The cost varies based on factors such as the learning mode and additional services. Typically, the course fee ranges from INR 26,548 to INR 68,000, making it a valuable investment in education and career development.
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.