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 the design, construction, and management of infrastructure and systems for collecting, storing, processing, and analyzing large volumes of data. The aim is to ensure data availability, reliability, and accessibility for informed decision-making.
The timeframe varies, but generally, it takes six months to two years to acquire the necessary skills and experience for a data engineering career.
a. In-depth knowledge of data engineering concepts and tools.
b. Hands-on experience with industry-standard technologies.
c. Enhanced job prospects and earning potential.
d. Strong foundation for career progression in data-related roles.
a. Basic understanding of math, statistics, and programming.
b. Familiarity with databases and SQL.
c. Proficiency in a programming language like Python or Java.
d. Knowledge of data manipulation and analysis techniques.
a. Build a strong foundation in mathematics, statistics, and programming.
b. Develop proficiency in data manipulation, database management, and integration.
c. Gain expertise in big data technologies like Hadoop, Spark, and cloud platforms.
d. Create a portfolio showcasing data engineering projects.
e. Seek internships or entry-level positions in organizations requiring data engineering skills.
f. Stay updated on emerging technologies and industry trends.
Costs vary but generally range from 40,000 INR to 1,00,000 INR. Research different providers for specific course costs.
Job opportunities include roles like Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, and Cloud Data Engineer across various industries.
Skills include proficiency in Python, Java, SQL, knowledge of big data technologies, data modeling, cloud platform familiarity, and problem-solving abilities.
The average salary for Data Engineers in Bangalore varies based on factors like experience and industry but averages around 11,00,000 per year in India, according to Glassdoor.
Datamites is considered one of the best institutes, offering a comprehensive curriculum, industry projects, and experienced instructors for a strong foundation in data engineering.
For data engineering training in BTM, consider enrolling in the comprehensive DataMites® program, available both online and in-person. This training equips you with essential skills in data engineering, preparing you for real-world applications.
The Data Engineer Course at DataMites® in BTM 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 looking to transition into data engineering roles.
The duration of the DataMites Data Engineer Course in BTM is approximately 6 months, encompassing more than 150 learning hours. This time investment ensures a comprehensive exploration of the course material.
Opting for online data engineer training from DataMites® provides you with the flexibility to learn at your own pace and convenience. Additionally, you gain access to industry-expert instructors, hands-on assignments, real-world projects, interactive learning materials, and the chance to network with a global community of learners.
The DataMites® training program in BTM covers a broad spectrum of topics, including data integration, modeling, ETL processes, data warehousing, big data technologies, and cloud platforms. The curriculum includes hands-on projects and real-world case studies to enhance practical skills and understanding.
The cost of the DataMites Data Engineer Training in BTM varies based on factors such as the chosen learning mode and additional services. Typically, the course fee ranges from INR 26,548 to INR 68,000, making it a valuable investment in your education and career development.
Yes, DataMites® offers classroom training for Data Engineer courses in BTM. This allows students to experience in-person learning, fostering direct interactions with instructors and peers. Moreover, offline training options are available on demand.
The Flexi-Pass offered by DataMites® provides learners with the flexibility to access recorded sessions of their courses. This feature allows individuals to revisit or catch up on missed classes, ensuring a convenient and comprehensive learning experience.
Upon successfully completing the Data Engineer training from DataMites®, you will be awarded industry-recognized certifications, including those from the International Association of Business Analytics Certifications (IABAC). These certifications not only validate your acquired skills and knowledge in data engineering but also carry the prestige of IABAC accreditation.
The instructor for the Data Engineer Course at DataMites® in BTM is a qualified professional with substantial experience and expertise in data engineering and related fields. DataMites® ensures that their instructors have practical industry experience and in-depth knowledge of the subject matter.
DataMites provides classroom training at multiple locations in Bangalore, including Kudlu Gate, Marathahalli, and BTM. These strategically chosen venues offer accessible and convenient options for learners seeking in-person instruction.
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.