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 refers to the field that focuses on the design, development, and maintenance of systems and processes involved in handling large volumes of data. Data engineers build and manage data infrastructure to ensure data quality and availability for analysis and decision-making.
The benefits of undergoing Data Engineer Training include gaining in-depth knowledge of data engineering concepts, acquiring practical skills in data pipeline development and management, improving employability in the growing field of data engineering, and staying updated with industry practices and technologies.
The prerequisites for enrolling in a Data Engineer Course in Rourkela may vary, but a basic understanding of programming, databases, and data concepts is beneficial. Some courses may require a background in computer science or related fields.
The fees for Data Engineer Training in Rourkela vary depending on the institute, course duration, and curriculum. It is recommended to check with specific training providers for detailed fee information and any available scholarships or discounts.
The best institute for Data Engineer Training in Rourkela depends on individual preferences and specific requirements. It is advisable to research and compare different institutes based on curriculum, faculty expertise, industry reputation, alumni reviews, and placement opportunities.
After completing Data Engineer Training, individuals can pursue job roles such as Data Engineer, Data Architect, ETL Developer, Data Warehouse Manager, Big Data Engineer, Database Administrator, or Cloud Data Engineer. These roles involve designing and managing data infrastructure, developing data pipelines, and ensuring efficient data processing and storage.
A bachelor's degree in computer science, information technology, or a related field is typically required for a data engineering career. However, some positions may require a master's degree or higher level of education, depending on the organization and complexity of the data engineering tasks.
While a postgraduate (PG) degree is not mandatory for Data Engineer Training, it can be beneficial for gaining a deeper understanding of advanced data engineering concepts and research methodologies. However, individuals with a bachelor's degree and practical experience can also have successful careers in data engineering.
Essential skills for a data engineer include proficiency in programming languages like Python, SQL, or Scala, knowledge of database systems and data modeling, familiarity with big data technologies such as Hadoop and Spark, experience with data integration and ETL processes, understanding of cloud platforms and data warehousing, and strong problem-solving and analytical abilities.
The choice of language for data engineering depends on specific requirements and the technology stack being used. However, Python is widely preferred in the data engineering field due to its versatility, extensive libraries for data manipulation and analysis, and ease of use. SQL is also essential for working with relational databases and querying data. Other languages like Scala and Java are commonly used in big data processing frameworks like Apache Spark.
Data Analytics offers promising career opportunities in roles such as data analysts, data scientists, business analysts, and data engineers. Skilled professionals are in high demand across diverse industries.
Being a data analyst can be challenging as it involves working with complex datasets, applying analytical techniques, and staying updated with emerging technologies. Strong analytical and problem-solving skills are essential.
Data engineering can be defined as the discipline that involves designing, constructing, and maintaining the systems, processes, and infrastructure necessary for the efficient and reliable collection, storage, transformation, and analysis of large volumes of data. Data engineers play a crucial role in developing and managing the pipelines that extract, transform, and load data from various sources into data warehouses or data lakes, ensuring data quality, integrity, and availability for further analysis and decision-making.
Data Engineer Training offers several benefits, including gaining comprehensive knowledge of data engineering concepts, acquiring practical skills in data pipeline development and management, enhancing career prospects in the growing field of data engineering, and staying updated with industry practices and technologies.
A career in data engineering typically requires a strong educational background in computer science, information technology, or a related field. A bachelor's degree is often the minimum requirement, but some positions may prefer or require a master's degree, especially for more advanced or research-oriented roles.
The requirements for joining a Data Engineer Course in Rourkela typically include a bachelor's degree in a related field (such as computer science or engineering), basic programming skills, knowledge of data and databases, familiarity with big data technologies, and a basic understanding of mathematics and statistics.
The cost of Data Engineer Training in Rourkela can vary due to factors such as the institute selected, program duration, delivery mode (online or classroom), and any additional features included. On average, the fees for data engineer training in Rourkela usually range from 40,000 INR to INR 1,00,000.
With the exponential growth of data and the increasing importance of data-driven decision-making in organizations across industries, the demand for skilled data engineers is expected to continue rising. Data engineering plays a critical role in managing and processing large volumes of data efficiently, ensuring data quality and availability for analysis. As businesses seek to harness the power of data to gain insights and drive innovation, data engineers will be in high demand to design and maintain robust data infrastructure, develop efficient data pipelines, and implement scalable solutions.
After completing Data Engineer Training, individuals can pursue various career options, including roles such as Data Engineer, Data Architect, ETL Developer, Data Warehouse Manager, Big Data Engineer, Database Administrator, or Cloud Data Engineer. These roles involve designing and managing data infrastructure, developing data pipelines, and ensuring efficient data processing and storage.
No, DevOps and data engineering are not interchangeable terms. DevOps is a set of practices that combines software development and IT operations to improve collaboration and efficiency, while data engineering focuses specifically on the management and processing of data within an organization. Although there may be some overlap in skills and responsibilities, they are distinct disciplines within the technology industry.
The best institute for data engineering training depends on several factors, such as the curriculum, faculty proficiency, industry relationships, alumni testimonials, and available training formats. DataMites is widely regarded as an exceptional institute for data engineering training. Its well-structured curriculum, practical projects aligned with industry requirements, and experienced instructors contribute to a comprehensive learning experience, enabling students to grasp essential data engineering concepts, tools, and techniques.
While a postgraduate degree is not always mandatory for Data Engineer Training, it can provide a deeper understanding of advanced data engineering concepts and research methodologies. However, a bachelor's degree and relevant practical experience can also be sufficient to pursue a successful career 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.