CERTIFIED DATA ENGINEER CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ENGINEER LEAD MENTORS

DATA ENGINEER COURSE FEES IN LUCKNOW

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 LUCKNOW

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 LUCKNOW

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 LUCKNOW

In today's data-driven landscape, data engineering has emerged as a vital component for organizations across industries. A survey conducted by Deloitte revealed that an overwhelming 73% of organizations view data engineering as critical for their business success. This highlights the growing recognition of the role data engineering plays in unlocking the full potential of data and driving informed decision-making. As businesses strive to harness the power of data, the demand for skilled data engineers continues to rise, offering promising career opportunities in this dynamic field.

Embark on a transformative journey into the world of data engineering with DataMites' comprehensive Data Engineer Course in Lucknow. Over a span of 6 months, immerse yourself in 150+ learning hours dedicated to mastering the intricacies of data engineering. Benefit from 50+ hours of live online training, led by industry experts, where you'll delve into the latest tools and techniques. Engage in 10 capstone projects and a client project to apply your knowledge to real-world scenarios. With a 365 Days Flexi Pass and access to Cloud Lab, you can continue honing your skills at your own pace. DataMites also offers offline courses on demand for learners seeking in-person training in Lucknow.

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

  • Ashok Veda and Faculty: Learn from Ashok Veda, a distinguished expert, and a team of experienced faculty members who bring their industry knowledge and expertise to the classroom.

  • Comprehensive Course Curriculum: Gain comprehensive knowledge through a well-structured curriculum designed to cover all essential aspects of data engineering.

  • Global Certification: Earn prestigious certifications from IABAC, NASSCOM FutureSkills Prime, and JainX, recognized globally and valued by industry professionals.

  • Flexible Learning: Choose from flexible learning options such as online data engineer course in Lucknow or ON DEMAND data engineer offline courses in Lucknow to suit your schedule and learning preferences.

  • Real-World Projects: Work on projects that involve real-world data, allowing you to gain practical experience and develop valuable skills.

  • Internship Opportunity: Get the chance to participate in data engineer internships, enabling you to apply your knowledge in a real working environment.

  • Placement Assistance and Job References: Receive dedicated support for job data engineer course with placements, including assistance and valuable references to help you kickstart your career.

  • Hardcopy Learning Materials and Books: Access high-quality hardcopy learning materials and books, providing you with comprehensive study resources.

  • DataMites Exclusive Learning Community: Engage with a vibrant learning community of fellow data enthusiasts, fostering collaboration and networking opportunities.

  • Affordable Pricing and Scholarships: Explore affordable pricing options and scholarship opportunities to make the course accessible to a wide range of learners.

DataMites offers prestigious Data Engineer Certification in Lucknow, validating your expertise in data engineering and opening doors to exciting career opportunities. Situated in the state of Uttar Pradesh, Lucknow is renowned for its cultural heritage, historical significance, and educational institutions. The city's vibrant atmosphere and emerging IT sector make it an ideal location for aspiring data engineers. By enrolling in DataMites' Data Engineer Training Course in Lucknow, you can tap into the city's potential while gaining valuable insights and knowledge in the field of data engineering.

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

ABOUT DATA ENGINEER COURSE IN LUCKNOW

Data engineering encompasses the creation, management, and optimization of systems and processes to handle large volumes of data. It involves building efficient data pipelines, ensuring data quality, and supporting data-driven decision-making.

Data Engineer Training provides several benefits, including a comprehensive understanding of data engineering concepts, practical skills in developing and managing data pipelines, improved career prospects in the expanding field of data engineering, and staying updated with industry trends and technologies.

Prerequisites for enrolling in a Data Engineer Course in Lucknow may vary depending on the specific course and training provider. Generally, having a basic understanding of programming, databases, and data concepts is beneficial. Some courses may recommend a background in computer science or related fields.

The price of Data Engineer Training in Lucknow can differ based on various factors including the training institute, program duration, delivery method (online or classroom), and additional features. Generally, the fees for data engineer training in Lucknow typically range from 40,000 INR to INR 1,00,000.

When determining the best institute for data engineering training, factors such as the curriculum, faculty knowledge, industry affiliations, feedback from alumni, and available training modes play a crucial role. Among the top institutes, DataMites stands out as a reputable choice for data engineering training. With its extensive curriculum, practical projects aligned with industry standards, and seasoned instructors, the institute offers a solid grounding in data engineering principles, tools, and methodologies.

A strong educational foundation in computer science, information technology, or related fields is typically required for a career in data engineering. While a bachelor's degree is usually the minimum requirement, some positions may prefer or require a master's degree for advanced or research-focused roles.

While a postgraduate degree is not necessarily mandatory for Data Engineer Training, it can be advantageous for individuals seeking advanced knowledge and research skills in data engineering. However, a bachelor's degree in computer science, information technology, or a related field is often sufficient to start a career in data engineering.

No, DevOps and data engineering are not interchangeable terms. DevOps refers to a set of practices that combines software development and IT operations to achieve faster and more reliable software delivery. Data engineering, on the other hand, focuses specifically on the management and processing of data to support data-driven decision-making and analytics. While there may be some overlapping skills and concepts, they are distinct disciplines within the field of technology.

After completing Data Engineer Training, individuals can pursue various career options, including roles such as Data Engineer, Data Architect, ETL Developer, Data Warehouse Manager, Big Data Engineer, Database Administrator, or Cloud Data Engineer. These roles involve designing and managing data infrastructure, developing data pipelines, and ensuring efficient data processing and storage.

Yes, it is possible for individuals without an IT background to transition into a career as a data engineer. While having a strong foundation in IT can be advantageous, it is not a strict requirement. Non-IT professionals can acquire the necessary skills and knowledge through targeted education, training, and hands-on experience in data engineering.

FAQ’S OF DATA ENGINEER COURSE IN LUCKNOW

To acquire training in data engineering in Lucknow, individuals have the opportunity to enroll in reputable institutes such as DataMites®. These institutes offer comprehensive data engineering courses through online or classroom modes, providing hands-on training, practical projects, and expert guidance to develop data engineering skills.

The DataMites Certified Data Engineer Training in Lucknow covers a wide range of topics, including data engineering concepts, tools, and technologies such as Hadoop, Spark, and SQL. The training program includes interactive sessions, practical assignments, and real-world case studies to enhance understanding and proficiency in data engineering.

The cost of DataMites Data Engineer Training in Lucknow varies, ranging from approximately INR 26,548 to INR 68,000.

Yes, upon successful completion of the Data Engineer training from DataMites®, you will receive certifications. DataMites offers globally recognized certifications from organizations such as IABAC, NASSCOM FutureSkills Prime, and JainX in collaboration with Jain (Deemed-to-be) University.

=The duration of the DataMites Data Engineer Course in Lucknow can vary depending on the chosen learning mode. Typically, for online instructor-led training, the course duration is around 6 months with 150+ learning hours, while the duration may differ for self-paced learning options.

The eligibility criteria for enrolling in the Data Engineer Course in Lucknow at DataMites® may vary. Generally, individuals with a background in computer science, mathematics, or related fields, as well as professionals aspiring to work in data engineering roles, are eligible to enroll.

The specific documents required for the training session at DataMites may vary based on the course and program. Typically, participants are advised to bring a valid ID proof, such as a government-issued ID card, and any specific documents mentioned in the communication received from DataMites.

The duration to become certified by the IABAC (International Association of Business Analytics Certifications) can vary depending on the specific certification and individual preparation. It typically requires rigorous study, practical experience, and successfully passing the certification examination.

DataMites® accepts various payment methods for their training programs, including online payment through credit cards, debit cards, net banking, and other digital payment options.

Yes, upon successful completion of the Data Engineer Course from DataMites®, you will receive a Data Engineer Course Completion Certificate. This certificate acknowledges your successful completion of the course and serves as evidence of your proficiency in data engineering concepts and techniques.

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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