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, building, and maintaining the infrastructure and systems to collect, process, and store vast amounts of data. It focuses on creating reliable, scalable, and efficient data pipelines to support data analytics, machine learning, and other data-driven applications.
While there is no specific educational qualification required, a bachelor's or master's degree in computer science, data science, or a related field is often preferred by employers for pursuing a career in data engineering.
Yes, coding is a prerequisite for data engineering. Proficiency in programming languages such as Python, SQL, and others is essential for data engineers to develop and maintain data pipelines, perform data transformations, and work with databases.
Python is a widely preferred programming language for data engineering due to its versatility, rich ecosystem of libraries, and ease of use in tasks like data manipulation, data integration, and building scalable data pipelines.
While data engineering involves some mathematical concepts, it is primarily focused on the design and implementation of data systems and processes. The level of mathematical complexity in data engineering tasks may vary depending on the specific project requirements.
Yes, data engineering is considered a promising career choice for the future. With the increasing volume and complexity of data generated by organizations, the demand for skilled data engineers who can efficiently handle and process this data is expected to grow significantly.
The eligibility requirements for enrolling in a Data Engineer Course in Kohima may vary depending on the training institute. Generally, a basic understanding of programming and databases, along with a passion for working with data, can be beneficial.
The cost associated with Data Engineer Training in Kohima can vary depending on factors such as the training provider, program duration, and delivery mode. The cost of data engineer training in Kohima typically ranges between 40,000 INR and 1,00,000 INR, depending on the specific training program and institute. It is recommended to research and compare different training options to determine the specific cost.
Upon completing Data Engineer Training, potential job prospects include roles such as Data Engineer, Database Administrator, ETL Developer, Data Integration Specialist, or Big Data Engineer. Job opportunities can be found in various industries that deal with large volumes of data, including technology, finance, healthcare, and e-commerce.
Essential skills for a successful data engineer include proficiency in programming languages (such as Python, SQL), data modeling, database management, ETL (Extract, Transform, Load) processes, knowledge of big data technologies, cloud platforms, problem-solving abilities, and strong communication skills for effective collaboration with cross-functional teams.
DataMites® offers comprehensive data engineer training that equips individuals with the skills and knowledge needed to excel in the field. Their training programs cover essential topics such as data modeling, ETL processes, big data technologies, cloud platforms, and more. With experienced instructors, practical hands-on exercises, and industry-relevant curriculum, DataMites® strives to empower students with the expertise required to tackle real-world data engineering challenges and succeed in this rapidly growing field.
The DataMites Certified Data Engineer Training program in Kohima covers a comprehensive curriculum that includes:
Data modeling and database design
ETL (Extract, Transform, Load) processes and tools
Data integration techniques and technologies
Data warehousing concepts and implementation
Big Data technologies like Hadoop, Spark, and Kafka
The cost of DataMites Data Engineer Training in Kohima falls within the range of INR 26,548 to INR 68,000, offering flexibility for learners to choose the option that suits their budget.
The Data Engineer Course at DataMites® in Kohima is open to professionals who have a background in IT, computer science, engineering, or a related field.
Individuals with a basic understanding of programming, databases, and data analysis are eligible to participate in the Data Engineer Course at DataMites® in Kohima.
Aspiring data engineers who have a keen interest in working with big data, data processing, and data infrastructure can enroll in the Data Engineer Course at DataMites® in Kohima.
Professionals who are looking to upskill or transition their career to data engineering can participate in the Data Engineer Course at DataMites® in Kohima.
Graduates and postgraduates who want to gain specialized knowledge and practical skills in data engineering can join the Data Engineer Course at DataMites® in Kohima.
The DataMites Data Engineer Course in Kohima offers flexible duration options, with online instructor-led training typically spanning 6 months and involving more than 150 learning hours.
There are several advantages of opting for online data engineer training from DataMites®:
Flexibility: Online training allows you to learn at your own pace and schedule, giving you the flexibility to balance your studies with other commitments.
Accessibility: You can access the training materials and resources from anywhere with an internet connection, eliminating the need for travel or relocation.
Interactive Learning: Online training often includes live instructor-led sessions, interactive exercises, and discussions, providing a dynamic learning experience.
Cost-effective: Online training is typically more affordable than in-person courses, as it eliminates expenses such as travel and accommodation.
Updated Course Material: Online training providers like DataMites® regularly update their course material to reflect the evolving field of data engineering, ensuring that you learn the most up-to-date practices and technologies.
Industry-relevant Content: DataMites® designs its online data engineer training programs to cover the latest industry trends, tools, and techniques, ensuring that you gain relevant skills and knowledge.
Support and Networking: Online training platforms often offer support from instructors and provide opportunities to connect with fellow learners, fostering a supportive learning community.
Self-paced Learning: Online data engineer training allows you to progress at your own speed, enabling you to spend more time on challenging topics and move quickly through familiar concepts.
DataMites®'s Flexi-Pass offers learners the opportunity to select from a range of courses and attend them at their convenience. It allows individuals to design their own learning path and gain knowledge in multiple areas.
Certainly! Upon successfully completing the Data Engineer training program from DataMites®, you will be awarded industry-recognized certifications from esteemed organizations like the International Association of Business Analytics Certifications (IABAC), Jain (Deemed-to-be University), and NASSCOM FutureSkills Prime. These certifications serve as a testament to your expertise in data engineering and can greatly enhance your professional profile and career prospects.
The Data Engineer Course in Kohima at DataMites® is conducted by a qualified and experienced instructor who has extensive knowledge and practical experience in the field of data engineering.
For learners in Kohima, DataMites® provides ON DEMAND classroom training for Data Engineer courses, giving students the chance to attend traditional in-class sessions and enhance their learning experience.
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.