CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN AGARTALA

Live Virtual

Instructor Led Live Online

110,000
63,945

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Live Online Training
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

55,000
36,645

  • IABAC® & JAINx® Certification
  • One year access to Self Learning
  • 10 Capstone Projects
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Classroom

In - Person Classroom Training

110,000
69,195

  • IABAC® & JAINx® Certification
  • 6-Month | 150+ Learning Hours
  • 50+Hour Classroom Training
  • 10 Capstone & 1 Client Project
  • Cloud Lab Access
  • Internship + Job Assistance

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

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UPCOMING DATA ENGINEER ONLINE CLASSES IN AGARTALA

BEST CERTIFIED DATA ENGINEER CERTIFICATIONS

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.

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WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN AGARTALA

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 

  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's  Minimum Sample Size
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Biased Random Sampling Methods
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Confidence Interval
  • Parametric Hypothesis Testing Methods
  • Hypothesis Testing Errors : Type I And Type Ii
  • One Sample T-test
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test

MODULE 5 : CORRELATION AND REGRESSION 

  • Correlation Introduction
  • Direct/Positive Correlation
  • Indirect/Negative Correlation
  • Regression
  • Choosing Right Method

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 

  • AWS Overview and Account Setup
  • AWS IAM Users, Roles and Policies
  • AWS Lamdba overview
  • AWS Glue overview
  • AWS Kinesis overview
  • AWS Dynamodb overview
  • AWS Anthena overview
  • AWS Redshift overview

MODULE 2 : DATA INGESTION USING AWS LAMDBA 

  • Setup AWS Lamdba  local development env
  • Deploy project to Lamdba console
  • Data pipeline setup with Lamdba
  • Validating data files incrementally
  • Deploying Lamdba function

MODULE 3 : DATA PIPELINE WITH AWS KINESIS 

  • AWS Kinesis overview and setup
  • Data Streams with AWS Kinesis
  • Data Ingesting from AWS S3 using AWS Kinesis

MODULE 4 : DATA WAREHOUSE WITH AWS REDSHIFT 

  • AWS Redshift Overview
  • Analyze data using AWS Redshift from warehouses, data lakes and operations DBs
  • Develop Applications using AWS Redshift cluster
  • AWS Redshift federated Queries and Spectrum

MODULE 5 : DATA PIPELINE WITH AZURE SYNAPSE 

  • Azure Synapse setup
  • Understanding Data control flow with ADF
  • Data Pipelines with Azure Synapse
  • Prepare and transform data with Azure Synapse Analytics

MODULE 6 : STORAGE IN AZURE 

  • Create Azure storage account
  • Connect App to Azure Storage
  • Azure Blog Storage

MODULE 7: AZURE DATA FACTORY

  • Azure Data Factory Introduction
  • Data transformation with Data Factory
  • Data Wrangling with Data Factory

MODULE 8 : DATA ENG PROJECT WITH AZURE/AWS

  • Hands-on Project Case-study
  • Setup Project Development Env
  • Organization of Data Sources
  • AZURE/AWS services for Data Ingestion
  • Data Extraction Transformation  

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 

  • DATABASE Overview
  • Key concepts of database management
  • CRUD Operations
  • Relational Database Management System
  • RDBMS vs No-SQL (Document DB)

MODULE 2 : SQL BASICS 

  • Introduction to Databases
  • Introduction to SQL
  • SQL Commands
  • MY SQL  workbench installation
  • Comments
  • import and export dataset

MODULE 3 : DATA TYPES AND CONSTRAINTS 

  • Numeric, Character, date time data type
  • Primary key, Foreign key, Not null
  • Unique, Check, default, Auto increment

MODULE 4 : DATABASES AND TABLES (MySQL) 

  • Create database
  • Delete database
  • Show and use databases
  • Create table, Rename table
  • Delete table, Delete  table records
  • Create new table from existing data types
  • Insert into, Update records
  • Alter table

MODULE 5 : SQL JOINS 

  • Inner join
  • Outer join
  • Left join
  • Right join
  • Cross join
  • Self join

MODULE 6 : SQL COMMANDS AND CLAUSES 

  • Select, Select distinct
  • Aliases, Where clause
  • Relational operators, Logical
  • Between, Order by, In
  • Like, Limit, null/not null, group by
  • Having, Sub queries

MODULE 7 : DOCUMENT DB/NO-SQL DB

  • Introduction of Document DB
  • Document DB vs SQL DB
  • Popular Document DBs
  • MongoDB basics
  • Data format and Key methods
  • MongoDB data management

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 ENGINEER TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ENGINEER TRAINING IN AGARTALA

Step into the realm of data engineering, where creativity meets technical prowess to unravel the mysteries hidden within the vast sea of data. Data engineers are the magicians who transform raw information into actionable insights, empowering businesses to make informed decisions. The data engineering market is on a trajectory of rapid expansion, with analysts projecting a market size of $117.22 billion by 2027. This remarkable growth is driven by the ever-increasing need for data-driven strategies in a world where information is power.

Embark on a transformative journey with the DataMites Data Engineer Course in Agartala. This comprehensive 6-month program is designed to equip participants with the essential skills and knowledge in data engineering. With over 150 learning hours, including 50+ hours of live online training, participants will delve into the intricacies of data engineering. The course emphasizes practical application through 10 capstone projects and 1 client project, allowing participants to gain hands-on experience in real-world scenarios. Additionally, participants receive a 365-day Flexi Pass, granting them access to course materials and a Cloud Lab for practice and experimentation. For those who prefer offline learning, DataMites also offers Data Engineer Offline Courses On Demand in Agartala, catering to diverse learning preferences.

Discover the top 10 reasons to choose DataMites for Data Engineer Training in Agartala

  1. Led by renowned industry expert Ashok Veda and a team of experienced faculty members, the program offers a comprehensive course curriculum that covers all crucial aspects of data engineering. 

  2. Graduates receive globally recognized certifications, including IABAC, NASSCOM FutureSkills Prime, and JainX, ensuring their skills are recognized worldwide. 

  3. The program offers flexible learning options, including online data engineer training in Agartala and ON DEMAND data engineer training in Agartala allowing participants to tailor their learning journey to their convenience. 

  4. Participants engage in projects with real-world data, gaining practical experience and honing their problem-solving abilities. A data engineer internship, data engineer training with placement assistance, and job references further enhance career prospects. 

  5. Participants also receive hardcopy learning materials and books to support their learning journey. 

  6. By joining DataMites, participants become part of an exclusive learning community, fostering collaboration and knowledge sharing. 

  7. The program is priced affordably, and scholarships are available to make it accessible to aspiring data engineers.

Agartala, the capital city of Tripura, is a captivating destination nestled in the northeastern part of India. Known for its rich history, culture, and natural beauty, Agartala offers a unique blend of tradition and modernity. The city's connectivity is well-established, with an international airport and a robust road network linking it to other major cities. The warm and hospitable nature of the locals adds to the charm of Agartala, making it an ideal place to pursue your Data Engineer Certification. Immerse yourself in the cultural heritage of Agartala while embarking on a transformative learning experience.

Along with the data engineer courses, DataMites also provides artificial intelligence, mlops, data analyst, python training, data mining, AI expert, data science, deep learning, IoT, r programming, data analytics, tableau and machine learning courses in Agartala.

ABOUT DATA ENGINEER COURSE IN AGARTALA

The field of data engineering involves the design, development, and management of systems and processes for handling and analyzing large volumes of data. Data engineers build and maintain data pipelines, databases, and infrastructure, ensuring data quality, reliability, and accessibility for organizations to gain insights and make informed decisions based on data.

The educational qualifications required for a career in data engineering typically include a bachelor's or master's degree in computer science, information technology, data science, or a related field. However, practical experience and relevant certifications can also be valuable.

Yes, data engineering is considered an IT-related occupation as it involves working with data infrastructure, databases, programming languages, and related technologies to ensure effective data management and analysis.

The eligibility criteria for enrolling in a Data Engineer Course in Agartala may vary depending on the training provider. Generally, a basic understanding of computer programming, databases, and data processing concepts is beneficial.

The cost of Data Engineer Training in Agartala can vary depending on factors such as the training provider, course duration, and delivery mode (online or classroom). The cost of data engineering training in Agartala typically ranges from 40,000 INR to 1,00,000 INR, depending on factors such as the institute, program duration, and level of instruction. To find accurate information about training costs, it is recommended to research and compare various training providers in Agartala.

After completing Data Engineer Training, job prospects can include roles such as Data Engineer, Database Administrator, ETL Developer, Data Integration Specialist, or Big Data Engineer. Opportunities can be found in various industries that deal with large volumes of data, such as technology, finance, healthcare, and e-commerce.

A certified data engineer course typically covers a comprehensive curriculum that includes topics like data modeling, database design, data integration, ETL processes, data warehousing, big data technologies, cloud platforms, and data governance. It aims to provide the necessary skills and knowledge to work as a professional data engineer.

Both data engineering and data science are distinct fields with their own focus areas. Data engineering primarily deals with building and managing data infrastructure and pipelines, while data science focuses on extracting insights and building predictive models from data. The choice between the two depends on individual interests and career goals.

Data engineering is currently in high demand as organizations across industries are increasingly relying on data-driven decision-making and need professionals to manage and process their data effectively.

The key skills necessary for a successful data engineer include proficiency in programming languages (such as Python, SQL), data modeling and database management, ETL (Extract, Transform, Load) processes, knowledge of big data technologies, familiarity with cloud platforms, problem-solving abilities, and strong communication skills for effective collaboration with cross-functional teams.

FAQ’S OF DATA ENGINEER COURSE IN AGARTALA

DataMites offers comprehensive data engineer training programs designed to equip aspiring professionals with the skills and knowledge required to excel in the field of data engineering. Their training covers essential concepts like data integration, ETL processes, data modeling, and big data technologies, providing hands-on experience through practical exercises and real-world projects. With a focus on industry relevance and experienced faculty, DataMites® aims to empower individuals with the expertise to build robust data pipelines and drive data-driven insights for businesses.

  • Graduates: The course is open to individuals who have completed a bachelor's degree in any discipline.
  • IT Professionals: IT professionals with a background in programming, database management, or related fields can also enroll in the course.
  • Business Analysts: Business analysts looking to enhance their data engineering skills and expand their career opportunities are eligible to join the course.
  • Data Professionals: Data professionals, such as data analysts or data scientists, who wish to specialize in data engineering can participate in the course.
  • Working Professionals: Working professionals from any industry who are interested in transitioning to a data engineering role or upskilling in data engineering are eligible to enroll in the course.

The DataMites Certified Data Engineer Training program in Agartala covers essential concepts in data engineering, including data ingestion, data transformation, and data integration.

  • The curriculum includes hands-on training in big data technologies such as Hadoop, Spark, and Hive, as well as data processing frameworks like Apache Kafka and Apache Nifi.

  • Participants will learn about data warehousing, data modeling, and database management systems, along with data governance and data quality assurance.

  • The program also covers advanced topics like cloud computing, real-time data streaming, and machine learning for data engineering applications.

  • Additionally, the curriculum includes practical exercises and projects to provide students with real-world experience in implementing data engineering solutions.

The duration of the DataMites Data Engineer Course in Agartala varies depending on the learning mode, with online instructor-led training typically lasting for 6 months and involving more than 150 learning hours.

Opting for online data engineer training from DataMites® offers several advantages:

  • Flexibility: Online training allows you to learn at your own pace and schedule, giving you the flexibility to balance your studies with other commitments.
  • Convenience: 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 platforms often include interactive elements such as live webinars, discussion forums, and virtual labs, enhancing the learning experience.
  • Experienced Instructors: DataMites® ensures that their online training programs are conducted by experienced instructors who provide guidance and support throughout the course.
  • Cost-Effective: Online training programs often have lower fees compared to traditional classroom-based training, making it a more cost-effective option.
  • Updated Content: Online training programs are regularly updated to reflect the latest industry trends and technologies, ensuring that you receive relevant and up-to-date knowledge.
  • Continuous Access: With online training, you typically have access to course materials and resources even after the completion of the training, allowing you to revisit and reinforce your learning as needed.

The fee for Data Engineer Training at DataMites in Agartala varies between INR 26,548 and INR 68,000, providing options to accommodate different budget considerations.

DataMites® caters to the needs of students in Agartala by offering ON DEMAND offline Data Engineer training, allowing them to attend in-person classes and engage in hands-on learning with expert trainers.

The Data Engineer Course in Agartala at DataMites® is facilitated by an experienced instructor who has a deep understanding of data engineering concepts and practices. They are committed to providing high-quality training and guiding you towards a successful career in data engineering.

Certainly! After successfully completing the Data Engineer training program offered by DataMites®, you will receive industry-recognized certifications from leading organizations like the International Association of Business Analytics Certifications (IABAC), Jain (Deemed-to-be University), and NASSCOM FutureSkills Prime. These certifications validate your proficiency in data engineering and increase your credibility among employers and peers in the industry.

The Flexi-Pass introduced by DataMites® allows learners to attend courses of their choice at their own pace and convenience. It provides the freedom to explore various subjects and acquire knowledge across different domains.

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: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

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.

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