CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN AMRITSAR

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

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UPCOMING DATA ENGINEER ONLINE CLASSES IN AMRITSAR

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 AMRITSAR

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 AMRITSAR

Data engineering serves as the foundation for today's data-centric organizations, empowering them to derive meaningful and actionable insights from massive datasets. Recent market research forecasts a remarkable growth trajectory for the global data engineering market, with a projected value of $117.22 billion by 2027 and an impressive compound annual growth rate (CAGR) of 20.1%. This surge is propelled by the widespread adoption of transformative technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI). As businesses increasingly recognize the value of data, the demand for proficient data engineers remains high, creating abundant opportunities in this rapidly expanding field.

Embark on an exciting journey into the world of data engineering with DataMites' Data Engineer Course in Amritsar. This comprehensive course spans over 6 months, offering an immersive learning experience of 150+ hours. With our 50+ hours of live online training, you will dive deep into the core concepts of data engineering, mastering the skills required to design and build robust data pipelines. The course includes 10 capstone projects and a real-world client project, allowing you to apply your knowledge to practical scenarios. With our 365 Days Flexi Pass and Cloud Lab, you can access the course materials and practice in a cloud-based lab environment at your own pace. Additionally, we also offer Data Engineer Offline Courses On Demand in Amritsar, catering to those who prefer offline learning options.

10 reasons to choose DataMites for Data Engineer Training in Amritsar:

Ashok Veda and Faculty: Learn from industry experts and experienced faculty, including the renowned data scientist Ashok Veda, who will guide you throughout the course.

Comprehensive Course Curriculum: Our course curriculum covers all essential aspects of data engineering, ensuring you gain a solid foundation in the field.

Global Certification: Earn globally recognized certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, validating your skills and expertise.

Flexible Learning: Enjoy the flexibility of learning with options for online data engineer courses in Amritsar, self-paced study, and the freedom to access course materials anytime, anywhere.

Projects with Real-World Data: Gain practical experience by working on projects that involve real-world datasets, preparing you for real-life data engineering challenges.

Internship Opportunity: Get the chance to apply your knowledge through a data engineer course with internship in Amritsar, gaining industry exposure and hands-on experience.

Placement Assistance and Job References: Benefit from our data engineer training with placement in Amritsar and job references, helping you in your career transition as a data engineer.

Hardcopy Learning Materials and Books: Access hardcopy learning materials and books that complement your online learning experience.

DataMites Exclusive Learning Community: Join our exclusive learning community, where you can connect and collaborate with fellow learners, industry professionals, and experts.

Affordable Pricing and Scholarships: We offer competitive pricing for our courses, and eligible candidates can avail scholarships to support their learning journey.

Amritsar, located in the state of Punjab, India, is a city rich in history, culture, and spirituality. It is most famously known as the home of the iconic Golden Temple, a revered Sikh pilgrimage site. Amritsar is also a thriving commercial and educational center, with a growing IT sector and a focus on technology-driven industries. As a student in Amritsar, you will experience a unique blend of tradition and progress, with opportunities to explore the city's heritage while pursuing your education and career goals.

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

ABOUT DATA ENGINEER COURSE IN AMRITSAR

Defining data engineering involves understanding its purpose as the discipline focused on designing, developing, and managing systems and processes to efficiently handle and analyze vast volumes of data. Data engineering aims to establish robust data pipelines, ensure the integrity and quality of data, and support the utilization of data for making informed decisions.

To enter the field of data engineering in Amritsar, individuals can consider the following measures:

  • Build a strong understanding of mathematics, statistics, and programming.

  • Develop proficiency in programming languages such as Python or SQL.

  • Acquire expertise in database management systems and data manipulation techniques.

  • Familiarize themselves with big data technologies like Hadoop and Spark.

  • Gain practical experience and enhance their skills through project work and real-world applications.

Participating in data engineer training comes with numerous perks, such as:

  • Acquiring in-demand skills and expertise in the field of data engineering.

  • Enhancing employment prospects across different industries.

  • Gaining practical experience by working on hands-on projects.

  • Staying updated with the latest industry trends and advancements.

Yes, data engineering is poised for a favorable future. With the ever-increasing volume and complexity of data, organizations across various industries will rely on data engineers to effectively manage and optimize their data infrastructure. The continuous advancements in technology and the expanding data landscape ensure a promising future for data engineering professionals.

The prerequisites for enrolling in a data engineer course in Amritsar can vary depending on the specific course and provider. However, it is generally recommended to have a foundational understanding of mathematics, statistics, and programming. Familiarity with databases, SQL, and programming languages like Python or Java can also be advantageous for a smoother learning experience.

The prerequisites for enrolling in a data engineer course in Amritsar may vary depending on the specific program and institute. However, a basic understanding of mathematics, statistics, and programming concepts is typically beneficial. Knowledge of databases, SQL, and programming languages such as Python or Java can also be advantageous.

The average cost of data engineer training in Amritsar can vary depending on factors such as the institution providing the training, the duration of the program, and the mode of delivery (online or classroom). On average, the fees for data engineer training in Amritsar typically range from around 40,000 INR to 1,00,000 INR.

DataMites is widely considered a top choice for data engineer training. Their offerings include a comprehensive curriculum, industry-relevant projects, and experienced instructors who ensure that students gain the essential skills and knowledge in data engineering.

Upon completing data engineer training, individuals gain access to a wide range of employment options. They can pursue roles such as Data Engineer, Database Administrator, ETL Developer, Big Data Engineer, and Cloud Data Engineer in industries like technology, finance, healthcare, and e-commerce.

While prior experience can be advantageous, it is not always a requirement to secure data engineer job positions. Some entry-level roles or junior positions may be accessible to individuals without extensive experience. Engaging in internships or hands-on projects can serve as valuable experiences to demonstrate skills and competence in data engineering.

Data engineer training is considered an asset due to its ability to provide individuals with essential skills and knowledge. It covers vital concepts, tools, and techniques necessary for constructing data pipelines, managing databases, and facilitating efficient data processing and analysis. This expertise is highly valued in today's data-driven world, where organizations depend on data for strategic decision-making and achieving their objectives.

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FAQ’S OF DATA ENGINEER COURSE IN AMRITSAR

Individuals can access data engineering training in Amritsar by considering enrollment at DataMites. DataMites offers comprehensive courses that cover the essential concepts, tools, and techniques of data engineering. Through hands-on experience, practical projects, and guidance from experienced instructors, DataMites ensures individuals in Amritsar acquire the necessary skills in data engineering.

The DataMites Certified Data Engineer Training in Amritsar includes an extensive curriculum covering a wide range of topics. These include data engineering concepts, tools, and technologies such as Hadoop, Spark, SQL, and data pipeline development. Practical exercises and hands-on projects are incorporated to help you gain practical skills in these areas.

The Data Engineer Course at DataMites® in Amritsar is open to individuals who meet certain criteria. Generally, those with a background in computer science, mathematics, or related fields, as well as professionals looking to pursue careers in data engineering, are eligible to enroll.

The duration required to complete the DataMites Data Engineer Course in Amritsar may vary depending on the learning mode. Typically, online instructor-led training takes around 6 months, comprising over 150 learning hours. However, self-paced learning options may have different timeframes.

Certainly, DataMites® provides classroom training sessions for Data Engineer Courses in Amritsar, along with their online training alternatives. This gives individuals the choice to opt for classroom-based learning, depending on their preferences and convenience.

The Data Engineer Course in Amritsar at DataMites® is led by highly skilled instructors who have expertise in data engineering concepts, tools, and industry practices. These instructors offer valuable guidance, mentorship, and support throughout the training program.

The training for the Data Engineer Course in Amritsar at DataMites® is delivered by highly qualified instructors who specialize in data engineering. These instructors possess extensive knowledge in data engineering concepts, tools, and industry practices, ensuring participants receive comprehensive training and support.

At DataMites®, you can avail yourself of various training options for data engineering courses, including instructor-led online training, classroom training, and self-paced learning. This provides the freedom to choose the training method that suits your learning style and schedule.

Absolutely, DataMites® provides a provision for paying the course fee in installments. Recognizing that learners may have financial limitations, this option allows individuals to manage the course fee more conveniently while benefiting from data engineering training.

Yes, individuals undertaking Data Engineer Courses at DataMites® in Amritsar can expect placement assistance. DataMites® provides comprehensive support in terms of career guidance, resume building, interview preparation, and helping participants connect with potential employers for job placements.

DataMites® accepts a variety of payment methods for their training programs. You can make payments conveniently through online channels using credit cards, debit cards, net banking, and other digital payment options. This flexibility enables learners to choose the payment method that suits their preferences and ensures a smooth and secure transaction process.

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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