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 intricate process of designing, constructing, and overseeing the essential infrastructure and systems required for efficiently handling substantial data volumes. Its goal is to ensure data availability, reliability, and accessibility, facilitating well-informed decision-making.
a. Forge a robust foundation in mathematical principles, statistical methodologies, and advanced programming languages.
b. Master data manipulation, database administration, and seamless integration of complex datasets.
c. Attain proficiency in avant-garde big data technologies such as Hadoop, Spark, and diverse cloud platforms.
d. Craft a compelling portfolio showcasing diverse and impactful data engineering projects.
e. Embark on internships or secure entry-level positions with organizations placing a premium on cutting-edge data engineering capabilities.
f. Stay ahead of the curve by remaining attuned to emerging technologies and industry trends.
The journey towards becoming a proficient data engineer is uniquely variable, typically spanning from six months to two years. This timeframe depends on individual circumstances and the chosen educational pathway.
a. Attain a profound understanding of sophisticated data engineering concepts, tools, and methodologies.
b. Gain hands-on experience with industry-standard technologies, fortifying practical skills.
c. Experience a substantial upswing in job prospects, coupled with an augmented earning potential.
d. Cultivate a solid foundation, paving the way for sustained career progression within data-centric roles.
a. Demonstrate a foundational grasp of mathematical, statistical, and programming principles.
b. Showcase familiarity with databases, coupled with proficiency in SQL.
c. Exhibit proficiency in at least one programming language, be it Python, Java, or a comparable language.
d. Demonstrate knowledge of data manipulation techniques and analytical methodologies.
DataMites stands out for its comprehensive curriculum, hands-on industry projects, and seasoned instructors, providing an immersive and unparalleled learning experience.
Upon completing training, individuals unlock an array of opportunities, spanning roles such as Data Engineer, Data Analyst, Big Data Engineer, ETL Developer, Database Administrator, and Cloud Data Engineer across diverse industries.
Critical skills encompass proficiency in programming languages, mastery of SQL, a deep understanding of big data technologies, prowess in data modeling, familiarity with cloud platforms, and a robust combination of problem-solving and communication acumen.
Glassdoor averages the annual salary for Data Engineers in Hyderabad at INR ₹10,41986, underscoring the increasing importance of their pivotal role.
The costs associated with data engineering training in Madhapur typically range from 40,000 INR to 1,00,000 INR. This estimation is contingent on variables such as the institute, program duration, and the depth of instruction.
For comprehensive data engineering training in Madhapur, consider enrolling in the versatile DataMites® program, available both online and in-person, preparing you for real-world applications.
Covering data integration, ETL processes, data warehousing, big data technologies, and cloud platforms, the DataMites® program in Madhapur includes hands-on projects and case studies for practical skill enhancement.
Designed for those with foundational knowledge in math, statistics, and programming, the Data Engineer Course suits aspiring data engineers, IT professionals, and software engineers.
Lasting approximately 6 months with over 150 learning hours, the DataMites Data Engineer Course in Madhapur ensures a thorough exploration of the curriculum.
For individuals seeking data engineer courses in Hyderabad, DataMites provides classroom training in key location of Madhapur. These carefully selected venues prioritize convenience and accessibility for aspiring learners in the city.
Online training offers flexibility, expert instruction, hands-on projects, interactive materials, and networking opportunities for global community engagement.
The course fee, ranging from INR 26,548 to INR 68,000, varies based on learning mode and additional services, representing a valuable investment in education and career development.
Yes, DataMites® offers classroom training in Madhapur, providing in-person learning experiences with instructors and peers. Offline training is also available on demand.
Instructors are qualified professionals with substantial experience in data engineering, ensuring practical industry knowledge in the Data Engineer Course at DataMites®.
Flexi-Pass allows flexible access to recorded sessions, aiding in revisiting or catching up on missed classes for a comprehensive learning experience.
Upon completion, you'll receive industry-recognized certifications, including those from the International Association of Business Analytics Certifications (IABAC), enhancing your credibility and employment prospects in data engineering.
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.