CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN DEHRADUN

Live Virtual

Instructor Led Live Online

110,000
62,423

  • 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
35,773

  • 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
67,548

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

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UPCOMING DATA ENGINEER ONLINE CLASSES IN DEHRADUN

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 DEHRADUN

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 DEHRADUN

The data engineering market is experiencing significant growth, projected to reach $146.2 billion by 2028, with a remarkable CAGR of 20.8%. This surge can be attributed to the exponential growth of data-driven technologies and the increasing adoption of cloud computing and AI. As organizations strive to leverage data for insights and competitive advantage, the demand for skilled data engineers is soaring. This presents immense opportunities for professionals in this field to contribute to cutting-edge projects and shape the future of data-driven innovation.

DataMites presents a comprehensive Data Engineer Course in Dehradun, designed to equip participants with extensive knowledge and practical skills. This 6-month program spans over 150 learning hours and includes interactive live online training for more than 50 hours. Engaging in 10 capstone projects and a real-world client project, participants gain valuable hands-on experience in data engineering. The course further provides a 365-day Flexi Pass and Cloud Lab access, enabling continuous learning. For those preferring in-person learning, DataMites also offers offline courses on demand in Dehradun.

10 Reasons to Choose DataMites for Data Engineer Training in Dehradun:

  • Guidance of Ashok Veda and Expert Faculty: Learn from industry experts under the leadership of Ashok Veda, a renowned figure in the data engineering field.

  • Comprehensive Course Curriculum: Gain in-depth knowledge through a meticulously crafted and up-to-date curriculum covering all essential aspects of data engineering.

  • Global Certification: Earn globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, enhancing your professional credibility.

  • Flexible Learning Options: Choose between online or offline courses based on your preferences and convenience.

  • Real-world Projects: Apply your skills to work on projects using real-world datasets, gaining practical experience.

  • Internship Opportunities: Avail data engineer internship programs to put your skills into practice and gain valuable industry exposure.

  • Placement Assistance and Job References: Receive dedicated support for data engineer course with placements and valuable job references to jumpstart your data engineering career.

  • Hardcopy Learning Materials and Books: Access high-quality hardcopy learning materials and books for better understanding and future reference.

  • DataMites Exclusive Learning Community: Join a vibrant community of like-minded learners and professionals, fostering collaboration and networking opportunities.

  • Affordable Pricing and Scholarships: Benefit from affordable pricing options and explore scholarship opportunities to make the course accessible to a wider audience.

DataMites offers a recognized Data Engineer Certification in Dehradun, validating your expertise in the field of data engineering. Dehradun, situated in the picturesque state of Uttarakhand, India, provides a serene environment surrounded by verdant hills. The city is renowned for its esteemed educational institutions, including top engineering colleges and technology-driven companies. Embrace the opportunity to learn data engineering in this captivating city and unlock a promising career in the ever-growing field of data engineering.

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

ABOUT DATA ENGINEER COURSE IN DEHRADUN

Data engineering can be defined as the practice of designing, developing, and managing systems and processes that enable the acquisition, storage, organization, processing, and delivery of data. Its primary focus is on creating and maintaining the infrastructure and architecture required for efficient and reliable data processing. Data engineering also involves ensuring data quality, integration, and accessibility. By facilitating these aspects, data engineering supports data-driven decision-making and enables the implementation of various data-intensive applications and analytics projects.

The average timeline for becoming a data engineer can differ based on individual circumstances, including prior experience, educational background, learning dedication, and training intensity. Typically, it takes several months to a couple of years to develop the essential skills and knowledge required for a data engineer role. This timeframe involves gaining expertise in areas such as data modeling, database management, ETL processes, big data frameworks, data warehousing, and other pertinent technologies and tools.

Data Engineer Training in Dehradun is priced differently based on factors such as the training provider, course duration, and curriculum scope. Typically, the cost of data engineer training in Dehradun falls within the range of approximately 40,000 INR to INR 1,00,000. For accurate and detailed information regarding the specific costs of courses, it is advisable to conduct thorough research on various training providers operating in Dehradun.

Both data engineering and data analytics hold significant value in their respective domains. It is not a question of one field being more valuable than the other but rather depends on individual preferences, skills, and career goals. Data engineering focuses on building and managing data infrastructure, while data analytics involves analyzing and extracting insights from data. Both fields contribute to the overall data ecosystem, and the choice between them should align with your specific interests and career aspirations.

The general salary range for Data Engineers in Dehradun varies based on factors such as experience, skills, and the scale of the organization. Typically, Data Engineers in Dehradun can anticipate salaries falling within the range of approximately INR 4,00,000 to INR 10,00,000 per year.

Qualification requirements for enrolling in a Data Engineer Course in Dehradun can vary depending on the specific program and training provider. However, having a background in computer science, engineering, mathematics, or a related field is generally beneficial. Some courses may also specify prerequisites in areas like programming, database management, or statistics.

Yes, Data Science and Data Engineering are separate domains with distinct focuses. Data Science involves extracting insights and building predictive models from data, while Data Engineering primarily deals with tasks related to data collection, storage, processing, and managing data infrastructure. While they collaborate closely, they are considered as distinct fields within the broader realm of data-related disciplines.

No, DevOps and data engineering are not interchangeable terms. While there may be some overlap in concepts, they represent separate fields. DevOps focuses on fostering collaboration between software development and operations teams, whereas data engineering centers around managing and processing data infrastructure to enable data-driven operations and analytics. Although related, they have distinct objectives and responsibilities.

Yes, it is possible for individuals with no prior experience to secure entry-level Data Engineer job positions. While experience can be advantageous, individuals can increase their chances by acquiring relevant skills through training programs, showcasing practical projects, and obtaining data engineer certifications that demonstrate their knowledge and capabilities in data engineering.

The curriculum of a data engineer course usually encompasses key subjects such as database management, data modeling, ETL (Extract, Transform, Load) processes, big data processing frameworks, data warehousing, data governance, and data integration. Additionally, practical hands-on projects may be incorporated to enhance proficiency in the industry's prevalent tools and technologies.

FAQ’S OF DATA ENGINEER COURSE IN DEHRADUN

If you are seeking data engineering training in Dehradun, one option is to enroll in the Data Engineer Course provided by DataMites. DataMites is a well-established institute known for its comprehensive training programs in data engineering. With highly experienced instructors and a curriculum designed to meet industry requirements, DataMites can equip you with the essential skills and knowledge needed to excel in the field.

The Data Engineer Course at DataMites® in Dehradun is open to individuals who meet the eligibility criteria, which typically includes having a background in computer science, engineering, mathematics, or a related field.

The DataMites Certified Data Engineer Training program conducted in Dehradun incorporates key components essential for a thorough understanding of data engineering. These components usually encompass data modeling, database management, ETL processes, big data frameworks, data warehousing, data governance, and data integration. The program also emphasizes practical projects and hands-on exercises to reinforce learning outcomes.

The average timeframe for completing the DataMites Data Engineer Course in Dehradun varies based on the learning mode chosen. Typically, for online instructor-led training, the course duration is around 6 months, requiring more than 150 learning hours. However, the timeframe may vary for self-paced learning options.

To obtain a course completion certificate from DataMites®, participants need to fulfill the specified requirements of the Data Engineer training program. Once the requirements are met, DataMites® will issue a certificate, certifying the individual's completion of the course. This certificate serves as a testament to their expertise and successful completion of the training program.

Yes, DataMites® in Dehradun offers Data Engineer Courses that come with placement assistance. They are committed to helping participants secure relevant job placements in the field of data engineering. For specific details regarding the extent of placement support provided, it is recommended to contact DataMites® directly.

DataMites® offers the Flexi-Pass concept, which grants learners the flexibility to participate in multiple batches of the same course within a designated timeframe. This enables learners to revisit course content, reinforce their understanding of concepts, and enhance their learning. The Flexi-Pass allows individuals to delve deeper into the subject matter and solidify their knowledge.

Yes, individuals who successfully complete Data Engineer training from DataMites® are granted various certifications. DataMites® has established partnerships with esteemed organizations, including the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations ensure that the training programs meet industry benchmarks and bestow certifications that are highly regarded, serving as a testament to the individuals' data engineering skills.

The documentation requirements for training sessions at DataMites® can vary depending on the course and program. As a general guideline, it is recommended to bring a valid identification proof, such as a government-issued ID card, to the training session. To obtain precise details about any additional papers or documents necessary, participants should refer to the communication received from DataMites®.

At DataMites®, there is a procedure in place to address situations where participants miss a session during Data Engineer Training in . Generally, they offer solutions such as access to recorded sessions or the option to attend makeup sessions. These provisions enable participants to cover any missed content and maintain continuity in 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: -

  • 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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