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 encompasses the strategic process of conceptualizing, constructing, and overseeing the intricate infrastructure and systems indispensable for the meticulous collection, storage, processing, and analysis of copious data volumes. The ultimate goal is to guarantee the seamless availability, dependability, and accessibility of data for discerning, data-driven decision-making.
a. Establish a robust foundational understanding of mathematics, statistics, and programming.
b. Cultivate proficiency in nuanced areas like data manipulation, database management, and the art of data integration.
c. Attain mastery in cutting-edge big data technologies, including but not limited to Hadoop, Spark, and various cloud platforms.
d. Curate an impressive portfolio spotlighting data engineering projects that showcase your competencies.
e. Actively seek out internships or entry-level positions with organizations placing a premium on data engineering expertise.
f. Stay abreast of emerging technologies and industry trends, fostering a continuous learning mindset.
The trajectory to becoming a data engineer is variable, generally spanning from six months to two years. This timeframe hinges on individual circumstances and the selected learning pathway.
a. Attain profound insights into the intricacies of data engineering concepts, tools, and methodologies.
b. Immerse yourself in practical, hands-on experiences utilizing industry-standard technologies.
c. Augment job prospects and elevate earning potential within the dynamic realm of data engineering.
d. Cultivate a robust foundation for sustained career growth within data-centric roles.
a. Establish a foundational grasp of mathematics, statistics, and programming paradigms.
b. Foster familiarity with databases and exhibit proficiency in SQL.
c. Demonstrate competency in at least one programming language, be it Python, Java, or equivalent.
d. Exhibit knowledge of data manipulation techniques and analytical methodologies.
DataMites emerges as a beacon among institutes for data engineering training, distinguished by its comprehensive curriculum, practical industry projects, and a faculty of seasoned instructors.
Following data engineering training, a diverse array of roles beckons, including positions such as Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, and Cloud Data Engineer across various industries.
Essential skills encompass mastery in programming languages, SQL prowess, proficiency in big data technologies, adept data modeling capabilities, familiarity with cloud platforms, and the finesse to navigate complex problem-solving, coupled with effective communication.
Investment in data engineering training in Vadapalani typically ranges between 40,000 INR to 1,00,000 INR. The specific cost varies contingent upon factors such as the institute, program duration, and the depth of instruction.
The average remuneration for Data Engineers in Chennai is contingent upon factors such as experience, skillset, industry, and the organizational milieu. On average, Data Engineers command an annual salary of ₹9,96269 in Chennai, as reported by Indeed.
Consider enrolling in the comprehensive DataMites® program, offering both online and in-person options for data engineering training in Vadapalani. This program equips you with essential skills for real-world applications.
The training covers 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 math, statistics, and programming. It suits aspiring data engineers, IT professionals, software engineers, and those transitioning into data engineering roles.
The course spans approximately 6 months, with over 150 learning hours, ensuring a comprehensive exploration of the material.
For those seeking data engineer courses in Chennai, DataMites conducts classroom training in key locations like Perungudi, Vadapalani, and Guindy. These varied options provide flexibility and convenience for prospective participants.
Online training offers flexibility, access to expert instructors, hands-on assignments, real-world projects, interactive materials, and networking opportunities.
The cost varies based on learning mode and additional services, typically ranging from INR 26,548 to INR 68,000.
Yes, DataMites® offers classroom training, providing in-person learning experiences and offline training options on demand.
Instructors are qualified professionals with practical industry experience and in-depth knowledge of data engineering.
The Flexi-Pass allows flexible access to recorded sessions, facilitating convenient learning experiences.
Upon completion, you'll receive industry-recognized certifications, including those from the International Association of Business Analytics Certifications (IABAC). These certifications validate your skills and carry the prestige of IABAC accreditation, boosting your credibility in the field.
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.