CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN AIZAWL

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 ?

Enquire Now

UPCOMING DATA ENGINEER ONLINE CLASSES IN AIZAWL

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.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA ENGINEER COURSE

Why DataMites Infographic

SYLLABUS OF DATA ENGINEER CERTIFICATION IN AIZAWL

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 AIZAWL

Data engineering is the backbone of modern data-driven organizations, enabling them to extract actionable insights from vast amounts of data. According to a recent industry report, the global data engineering market is projected to reach a staggering $117.22 billion by 2027, growing at a CAGR of 20.1%. This exponential growth is driven by the increasing adoption of cloud computing, the Internet of Things (IoT), and artificial intelligence (AI). As organizations strive to unlock the true potential of data, the demand for skilled data engineers continues to soar.

DataMites offers a comprehensive Data Engineer Course in Aizawl, designed to equip participants with the necessary skills and knowledge to thrive in the field of data engineering. This 6-month course spans over 150+ learning hours, providing in-depth training and expertise. Participants will engage in 50+ hours of live online training, ensuring interactive and immersive learning experiences. The course includes 10 capstone projects and 1 client project, enabling learners to apply their knowledge to real-world scenarios. With a 365-day flexi pass and access to a cloud lab, students have the flexibility to practice and experiment with data engineering tools and technologies.

Here are 10 reasons to choose DataMites for Data Engineer Training in Aizawl:

Expert Faculty: DataMites boasts experienced faculty members, including renowned data expert Ashok Veda, who provide comprehensive guidance and mentorship throughout the course.

Comprehensive Course Curriculum: The course curriculum is designed to cover all essential concepts and skills required for data engineering, ensuring a well-rounded learning experience.

Global Certification: DataMites offers prestigious global certifications, including IABAC, NASSCOM FutureSkills Prime, and JainX, validating the expertise and competency of learners in the field of data engineering.

Flexible Learning: DataMites provides flexible learning options, allowing participants to choose from online data engineer training in Aizawl or data engineer offline courses in Aizawl according to their preferences and convenience.

Projects with Real-World Data: The training includes hands-on projects that involve working with real-world data, enabling learners to gain practical experience and develop problem-solving skills.

Internship Opportunity: DataMites provides data engineer course with internship in Aizawl to students, offering them a chance to apply their knowledge in a professional setting and gain valuable industry exposure.

Placement Assistance: The training program includes data engineer training with placement in Aizawl and job references, helping students connect with potential employers and enhance their career prospects.

Hardcopy Learning Materials and Books: Participants receive hardcopy learning materials and books, ensuring easy access to reference materials even after completing the course.

DataMites Exclusive Learning Community: DataMites fosters an exclusive learning community where learners can interact, collaborate, and share insights with fellow data enthusiasts, creating a supportive and engaging learning environment.

Affordable Pricing and Scholarships: DataMites strives to make quality education accessible by offering competitive pricing and scholarship opportunities to deserving candidates.

Aizawl, the capital city of Mizoram, is a vibrant and picturesque location situated in Northeast India. Known for its rich cultural heritage and natural beauty, Aizawl provides an inspiring environment for learning and growth. The city is home to educational institutions that promote excellence in various fields, including data engineering. By choosing DataMites for Data Engineer Training Course in Aizawl, students can leverage the expertise of industry professionals, gain practical skills, and explore promising career opportunities in the data engineering domain.

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

ABOUT DATA ENGINEER COURSE IN AIZAWL

The field of data engineering encompasses the design, development, and management of systems and processes that handle the lifecycle of data. It involves acquiring, storing, organizing, processing, and delivering data in an efficient and reliable manner. Data engineering is essential for establishing the infrastructure and architecture needed for effective data processing, ensuring data quality, integration, and accessibility. It plays a vital role in facilitating data-driven decision-making and supporting data-intensive applications and analytics initiatives.

 The duration required to transition into a data engineer role can vary depending on factors like prior experience, educational background, learning commitment, and training intensity. Generally, it takes several months to a few years to acquire the necessary skills and knowledge for working as a data engineer. This timeframe encompasses gaining proficiency in areas such as data modeling, database management, ETL processes, big data frameworks, data warehousing, and other relevant technologies and tools.

The cost of Data Engineer Training in Aizawl varies depending on factors like the training provider, course duration, and curriculum coverage. On average, the fees for data engineer training in Aizawl range from around 40,000 INR to INR 1,00,000. To obtain precise information about the costs associated with specific courses, it is recommended to conduct comprehensive research on different training providers in Aizawl.

Data engineering and data analytics are distinct fields with different advantages. It is not about one being inherently superior to the other, but rather about individual preferences, skills, and career objectives. Data engineering specializes in designing and managing data infrastructure, while data analytics involves analyzing and deriving insights from data. Both fields are valuable and essential components of the data ecosystem, and the choice between them depends on personal interests and career aspirations.

The typical salary bracket for Data Engineers in Aizawl can vary depending on factors like experience, skills, and the size of the organization. On average, Data Engineers in Aizawl can expect salaries ranging from approximately INR 4,00,000 to INR 10,00,000 per year.

The prerequisites for enrolling in a Data Engineer Course in Aizawl may differ based on the training provider and program. However, having a background in computer science, engineering, mathematics, or a related field is typically advantageous. Some courses may also require prerequisites in programming, database management, or statistics.

Yes, there is a clear distinction between Data Science and Data Engineering as fields. Data Science primarily revolves around extracting insights and building predictive models from data, while Data Engineering is more focused on tasks such as data collection, storage, processing, and managing data infrastructure. Although closely related, they have distinct roles and responsibilities in the overall data ecosystem.

Yes, individuals without any prior experience can still secure entry-level Data Engineer job roles. While experience can be beneficial, they can enhance their prospects by acquiring relevant skills through training programs, showcasing practical projects, or obtaining data engineer certifications that validate their knowledge and aptitude in data engineering. Demonstrating a strong skill set and a willingness to learn can help individuals secure opportunities in the field.

A typical data engineer course curriculum covers important topics like database management, data modeling, ETL (Extract, Transform, Load) processes, big data processing frameworks, data warehousing, data governance, and data integration. Practical projects are often included to foster hands-on experience and proficiency in utilizing the relevant tools and technologies commonly used in the industry.

No, DevOps and data engineering are not synonymous. While they share some similarities, they are distinct fields. DevOps emphasizes collaboration between software development and operations teams to enhance software development processes, while data engineering primarily concentrates on managing and processing data infrastructure to support data-driven operations and analytics.

FAQ’S OF DATA ENGINEER COURSE IN AIZAWL

If you are looking for data engineering course in Aizawl, you can consider enrolling in the Data Engineer Course offered by DataMites. DataMites is a renowned institute that offers comprehensive training programs in data engineering. With experienced instructors and a curriculum aligned with industry standards, DataMites can provide you with the necessary skills and knowledge to succeed in the field.

The requirements for enrolling in the Data Engineer Course at DataMites® in Aizawl can vary depending on the specific program. Typically, individuals with a background in computer science, engineering, mathematics, or related fields are eligible to apply.

The DataMites Certified Data Engineer Training program in Aizawl comprises essential components that encompass a comprehensive understanding of data engineering concepts, tools, and technologies. These components typically include topics like data modeling, database management, ETL processes, big data frameworks, data warehousing, data governance, and data integration. The program often incorporates practical projects and hands-on exercises to enhance the learning experience.

The duration of the DataMites Data Engineer Course in Aizawl can be flexible and depends on the selected learning mode. For online instructor-led training, the typical duration is approximately 6 months, involving more than 150 learning hours. However, the duration may differ for self-paced learning alternatives.

DataMites® has a certification process in place to verify the completion of their courses. Upon meeting the necessary requirements of the Data Engineer training program, participants are awarded a certificate by DataMites®. This certification serves as evidence of their proficiency and successful completion of the course.

Yes, DataMites® provides Data Engineer Courses in Aizawl with placement assistance. They strive to support participants in finding suitable job opportunities in the data engineering field. For more detailed information about the specific placement assistance services offered, it is advisable to reach out to DataMites® directly.

The Flexi-Pass option at DataMites® provides learners with the freedom to attend multiple batches of the same course during a specified duration. This unique feature allows learners to revisit course materials, review concepts, and strengthen their understanding of the subject matter. The Flexi-Pass empowers individuals to enrich their learning experience and gain a more comprehensive grasp of the course content.

Yes, upon completion of Data Engineer training at DataMites®, participants are eligible to receive multiple certifications. DataMites® has affiliations with prestigious organizations such as the International Association of Business Analytics Certifications (IABAC), NASSCOM FutureSkills Prime, and Jain (Deemed-to-be University). These affiliations ensure that the training programs adhere to industry standards and provide certifications that are widely recognized, validating the participants' proficiency in data engineering.

The required documentation for training sessions at DataMites® may vary based on the particular course and program. Typically, it is advisable to bring valid identification proof, such as a government-issued ID card. Additionally, participants should refer to the communication received from DataMites® to identify any specific documents mentioned for the training session.

DataMites® has established a policy to handle missed sessions during Data Engineer training. Typically, they provide alternatives such as access to recorded sessions or opportunities to attend makeup sessions. These options ensure that participants can catch up on any missed content and continue their learning journey effectively.

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.

View more

DATA ENGINEER PROJECTS

DATA ENGINEER JOB INTERVIEW QUESTIONS

OTHER DATA ENGINEER TRAINING CITIES IN INDIA

Global CERTIFIED DATA ENGINEER COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog