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CLOUD DATA ENGINEER

A Cloud Data Engineer designs, builds, and manages data infrastructure in cloud environments, focusing on efficient data storage, processing, and analysis for large datasets. 

Cloud Data Engineer Job Oriented and Certification Training Program

Become a Cloud Data Engineer in just 60 Days by mastering cloud-based data engineering tools and technologies through real-world case studies and hands-on projects!

  • Gain deep knowledge in Cloud Data Engineering through our Masters' Program, designed and delivered by highly experienced and certified faculty.
  • Work on real-time cloud projects and simulate enterprise data pipelines on platforms like AWS, Azure, and GCP.
  • Master industry tools such as Apache Spark, Airflow, Kafka, BigQuery, Redshift, Snowflake, and Databricks.
  • Build scalable ETL pipelines, automate workflows, manage large data lakes, and deploy analytics-ready cloud infrastructure.
  • Hands-on labs for data ingestion, transformation, storage, security, and streaming on major cloud platforms.
  • Receive 100% placement assistance upon successful completion of the course.
  • Earn a recognized certification through our in-house exam and use your skills confidently in top-demand roles.

Pathway to Becoming a Cloud Data Engineer

  • Foundational Knowledge: Master SQL, data structures, and core programming (Python/Java)
  • Cloud Familiarity: Choose a primary cloud provider (AWS, Azure, or GCP). Gain proficiency in data services and pipelines
  • Big Data & Pipelines: Learn tools like Hadoop, Spark, Kafka, NiFi, and Airflow
  • Projects & Certifications: Build hands-on projects—e.g., end-to-end pipelines, data lakes, streaming dashboards—and consider credentials such as GCP Professional Data Engineer, Azure DP-203, or AWS Data Analytics certifications
  • Security, Governance & Monitoring: Master compliance, security best practices, and cost efficiency
  • Continuous Learning: Stay current with trends in serverless, ML integration, AI-assisted pipelines, edge computing, and containerization (Kubernetes/Docker)

Career Outlook & Growth Potential

  • Job Demand: Rising demand across industries like e-commerce, healthcare, and finance as enterprises migrate to the cloud
  • Compensation: Data engineering professionals are among the more highly paid roles in tech, often reaching six figures USD annually (adjusted regionally)
  • Future Trends: Emphasis on serverless, orchestration, AI-augmented pipelines, and edge computing to stay competitive
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Essential Skills & Tools

These are the essential skills and tools we include in the cloud data engineer course so that we can do real time projects

DOMAIN

Skills & Technologies

Languages

SQL, Python (commonly used; sometimes Scala or Java)

Cloud Platforms 

AWS, Azure, GCP—focusing on data-specific components (e.g., Snowflake, ADF, Dataflow, Databricks)

Big Data & Streaming

Apache Spark, Hadoop, Kafka, Flink, Beam for processing large and real-time data

ETL/Orchestration

Airflow, NiFi, Luigi, Prefect; automation via IaC tools like Terraform, plus Git version control

Data Modeling & Storage

Relational/NoSQL databases, data lakes & warehousing, schema design

Cloud Security & Compliance

Encryption, IAM, zero-trust; monitoring and governance

Soft Skills

Problem-solving, system design, collaboration, adaptability, communication

Training Pattern Details

  • Get trained through an industry-grade Full Stack Deep Dive curriculum, covering essential Cloud platforms, data pipelines, and real-time processing frameworks used by top companies.
  • Receive class recordings and detailed notes after every session to ensure you never miss a concept and can revise anytime at your own pace.
  • Work on hands-on exercises and real-world case studies both in class and at home to build confidence in handling cloud-based data engineering tasks.
  • Access comprehensive topic-wise notes, e-books, and important documents—both in soft copy and hard copy formats—for deep reference and exam preparation.
  • Prepare for job interviews with mock interview sessions, resume building workshops, and expert guidance focused specifically on the cloud data engineering career path.
  • Get complete end-to-end project exposure, enabling you to understand how modern cloud data workflows are designed, built, deployed, and maintained in production.
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Who Should Enroll?

Eligibility/Qualification

  1. Aspiring IT Professionals:
    Whether you hold a degree in B.E, B.Tech, MCA, MBA, M.Sc, MS, ME, M.Tech, BCA, BSc, BCom, or BA, this course will pave your way to a high-demand career in Cloud Data Engineering.

  2. Current IT Experts:
    Developers, Data Analysts, Support Engineers, or DevOps professionals — take your skills to the next level with in-depth training in cloud-based data pipelines and modern data architecture.

  3. Students & Freshers:
    Graduates and Post-Graduates from all academic backgrounds are welcome to step into the exciting and futuristic world of cloud-driven data engineering.

Become a sought-after data professional with our Cloud Data Engineer Training Course. Sign up today and take the first step towards mastering Data Engineer.

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Cloud Data Engineer Training Course Outline

Pre Requisite

  • Operating System : Windows & Unix Basics
  • Database : DBMS Concepts, SQL
  • Programming Language : Python
  • Software Engineering : Basic Concepts

Cloud Data Engineer (Data Bricks & Spark)

Module - 1: Data Bricks
Chapter 01: Introduction to Data Bricks
  • Basic Concepts
  • About UI
  • Data Lake and Delta Lake
  • Lake House Architecture
  • Login and Account Creation
  • Cluster Set Up Using Data Bricks
  • Overview of Data Bricks Workspace and Notebooks
  • Running Code in Python And SQL
  • Hands on with Delta Lake and Structured Streaming
Chapter 02: ETL
  • Modern ETL & ELT Process
  • Transformation
  • Data Loading Using Data Bricks
  • Basic Data Ingestion and Transformation
Chapter 03: Data Engineering with Data Bricks
  • Building ETL Pipelines using Lake Flow Connect
  • Using SQL and Python for Data Processing
  • Scheduling Jobs and Managing Workflows
  • Working with Apache Spark and Delta Lake
Module - 2: Spark
Chapter 01: Spark Introduction
  • Objective
  • Motivation for Spark
  • Processing Engine Concept
  • Spark Vs Map Reduce Processing
  • Advantages of IN_MEMORY Processing over DISK Based
  • Where to use Spark
  • ROI Comparison of Hadoop Processing over Spark Processing
  • Why Spark Processing is Faster than Map Reduce?
  • Spark Benefits
  • Architecture:
  • Hadoop Vs Spark Architectures
  • Spark Master
  • Spark Driver
  • Spark Worker Node
  • Spark Runtime Managers
    • Standalone
    • YARN
    • Apache Mesos
  • Start Spark Daemons
  • Spark Basics:
  • Creating Spark Context
  • Creating Spark Conf, Spark Session
  • File Operations in Spark Shell
  • Linking and Initializing Spark
  • Caching in Spark
  • Real time Examples of Spark
Chapter 02: Spark Core
  • Introduction to Resilient Distributed Datasets (RDD)
  • How to create a RDD
  • RDD Types
  • Core Features of RDD:
    • Lazily Evaluated
    • Immutable
    • Partitioned
  • RDD Operations
  • Different Transformations in RDD
  • Different Actions in RDD
  • Loading Data through RDD
  • Saving Data
  • Loading and Saving Data through different File Formats:
    • Text, CSV, TSV, JSON, PARQUET, ORC, Object files
    • As a Hadoop file
  • Key-Value Pair RDD operations
  • Spark Storage Persistence Levels
  • Running Spark in a Clustered Mode
  • Deploying Application with spark-submit
  • Cluster Management
  • Accumulators:
  • Introduction to Accumulators
  • Practical applicability of accumulators
  • Real time examples on Accumulators
  • Broadcast Variables:
  • Introduction to Broadcast variables
  • Practical applicability of Broadcast variables
  • Real time examples on Broadcast variables
Chapter 03: Spark SQL
  • Introduction
  • SQL Context
  • Hive Vs Spark SQL
  • Spark SQL support for Text Files, Parquet and JSON files
  • Data Frames
  • Data Sets
  • Data Frames vs Data Sets – Performance Optimization
  • Real Time Examples
  • Different File Formats Support in Spark SQL:
    • Text
    • JSON
    • CSV
    • ORC
    • TSV
    • Parquet
  • Integration with Spark SQL:
    • Data warehouse – Hive
    • RDBMS – SQL : MySQL
    • Non RDBMS – NOSQL : Cassandra
Chapter 04: Spark Processing – With Different Programming Languages
  • Scala:
  • Installing Scala
  • How to use “Spark-Shell”
  • Examples on Spark with Scala
  • Python:
  • Installing Python
  • How to use “PySpark”
  • Examples on Spark with Python
  • R:
  • Installing R
  • How to use “SparkR”
  • Examples on Spark with R Language
Chapter 05: Spark Streaming
  • Introduction to Spark Streaming
  • Architecture of Spark Streaming
  • Streamings: DStreams, SSC, Kafka, Flume
  • DStreams:
  • RDD vs Discretized Streams (DStreams)
  • DStream Operations:
    • Window Operations
    • Transform Operations
Chapter 06: Spark MLib (Optional)
  • Introduction to Machine Learning
  • Vector Class in MLib
  • Spark MLib Algorithms Introduction
  • Classification and Regression Algorithms
  • Naïve Bayes Classification Algorithm
  • Decision Trees Algorithm Overview
Chapter 07: Spark Project
  • Real Time Projects On Spark with Hadoop Integration
  • Proof Of Concepts (POCs)

Cloud Data Engineer (Snowflake)

Module - 1: Snowflake
Chapter 01: Data Warehouse Overview
  • What is Database?
  • What is Data Warehouse?
  • OLTP Vs OLAP
  • ETL Vs ELT
  • Data Warehouse Concepts
  • Data Warehouse Architecture
Chapter 02: Cloud Overview
  • Introduction to Cloud Data Warehouse
  • About Cloud
  • On Premises DWH Vs Cloud DWH
  • IAAS, PAAS, SAAS
  • Various Popular Cloud Data Warehouses
  • Supported Cloud Platforms: AWS, GCP, Azure
Chapter 03: Introduction to Snowflake
  • What is Snowflake?
  • Snowflake Features and Architecture
  • ETL in Snowflake
  • Virtual Warehouse, Editions, Regions
  • Create Free Trial Account
  • Warehouse, Databases, Schemas, Tables
  • Roles and Account Types
  • Snowflake UI Components and Worksheets
Chapter 04: Snowflake Pricing
  • Pricing Model Overview
  • Credits, Editions, and Storage Cost
Chapter 05: Resource Monitoring
  • Resource Monitor Overview and Properties
  • Suspension, Resumption, and UI Creation
Chapter 06: Micro Partitioning
  • Traditional vs Snowflake Partitioning
  • Structure and Query Processing
Chapter 07: Clustering in Snowflake
  • Clustering Keys, Metadata, Depth
  • Re-Clustering and Query History
Chapter 08: Query History & Caching
  • Query History and SQL Retrieval
  • Caching: Result, Metadata, Query Data
  • Performance & Cost Optimization
Chapter 09: Coding Part
  • Worksheets: Databases, Tables, Schemas
  • Parameters, SQL Execution, Result Area
Chapter 10: Load Data
  • Load/Unload Data into/from Snowflake
  • Web Interface & Stages
  • File Formats: CSV, JSON
  • COPY Command, Error Handling
Chapter 11: Table Categories
  • Understanding Snowflake Table Structures
  • Design & Storage Considerations
  • Permanent, Transient, Temporary Tables
  • Convert Between Table Types
  • External Tables & Multi-User Management
Chapter 12: Queries in Snowflake
  • Structured Data: CTE, Subqueries, Hierarchies
  • Functions, Joins, Sequences
  • Semi-Structured Data Handling (JSON, etc.)
  • Query Profiling, Result Caching
  • Data Estimation & Analysis Functions
Chapter 13: Cloning
  • What is Zero-Copy Cloning?
  • Clone: Databases, Schemas, Tables, Streams
  • DDL, DML & Retention Considerations
  • Access Control for Cloned Objects
Chapter 14: Time Travel & Fail Safe
  • Time Travel: Retention, Restore, Historical Query
  • Cloning Historical Objects
  • Fail Safe: View, Storage Cost, Use Cases
Chapter 15: Streams – CDC (Change Data Capture)
  • Introduction to Streams
  • INSERT, UPDATE, DELETE Capture
  • Streams with Transactions and Merges
Chapter 16: Views
  • Materialized vs Non-Materialized Views
  • When to Use, Advantages, Limitations
  • Secure Views: Introduction and Use Cases
Chapter 17: User Defined Functions (UDF)
  • What is a Function?
  • Function Categories
  • Using UDFs and UDTFs in Snowflake
Chapter 18: Procedure
  • What is a Procedure?
  • Create and Call Procedures
  • Exception Handling in Procedures
Chapter 19: Tasks – Scheduling Service
  • Introduction to Tasks and Task Tree
  • Standalone Task Creation & History
  • Accessing Procedures via Tasks
  • Scheduling Tasks with Time Zones
  • Automate Daily & Weekly Loads
Chapter 20: SnowSQL
  • Partner Connect & Tool Overview
  • Install, Configure, and Use SnowSQL (CLI)
  • Working with SnowSQL for Data Operations
Chapter 21: Virtual Warehouse
  • Creating and Managing Warehouses
  • Warehouse Sizes and Multi-Clustering
  • Scale-In, Scale-Out, Scale-Up, Scale-Down
  • Auto Scaling & Query Acceleration
Chapter 22: Secure Data Sharing
  • What is Data Sharing?
  • Providers and Consumers
  • Reader Accounts and Cross-Region Sharing
  • Secure Sharing with and without Snowflake Users
  • Privileges and Limitations
Chapter 23: Security – Dynamic Data Masking
  • Overview of Data Masking
  • Creating and Implementing Masking Policies
  • Practical Use Cases and Examples
Chapter 24: Sharing Data in Snowflake
  • Introduction to Secure Data Sharing
  • Grant Data Exchange Admin Privileges
  • Secure Shares & Business Critical to Non-Critical
  • Manage Reader Accounts & Sharing Across Clouds
  • Using Secure Objects to Control Data Access
Chapter 25: Managing Snowflake Account
  • Roles: Account Admin, Security Admin, System Admin
  • Public and Custom Roles
  • Data Security and Access Control
Chapter 26: Cloud Integration
  • Load from AWS Cloud: S3, IAM, COPY, Integration
  • Load from Azure Cloud: Storage, Identity, COPY
  • Snowpipe for Continuous Data Ingestion
  • Upload, Integrate and Automate Loads from Cloud
Module - 2: DBT(Data Build Tool)
Chapter 01: Introduction to DBT
  • What is DBT?
  • Why DBT is important in modern data stacks
  • Key concepts: Models, Sources, Seeds, Snapshots, Tests
  • Supported warehouses (Snowflake, BigQuery, Redshift, etc.)
  • DBT CLI vs DBT Cloud
Chapter 02: Setting Up DBT
  • Installing DBT using pip
  • Creating and initializing a DBT project
  • Connecting DBT to your data warehouse
  • DBT profile setup and configuration
  • Understanding DBT folder structure
Chapter 03: DBT Models
  • Creating and organizing models using SQL
  • Materializations: view, table, incremental, ephemeral
  • Using Jinja templating in DBT models
  • Ref and source functions
  • Best practices for writing reusable models
Chapter 04: Testing and Documentation
  • Creating custom and generic tests
  • Built-in tests: unique, not null, accepted values
  • Documenting your models and columns
  • Generating and hosting DBT documentation site
Chapter 05: DBT Sources, Seeds, and Snapshots
  • Declaring and using sources (raw tables)
  • Creating seed files (CSV-based datasets)
  • Creating and configuring snapshots
  • Managing slowly changing dimensions (SCD)
Chapter 06: DBT Deployment and Scheduling
  • DBT run and DBT build commands
  • Running models with tags and selectors
  • Using DBT Cloud for CI/CD pipelines
  • Scheduling jobs in DBT Cloud
  • Monitoring and debugging runs
Chapter 07: DBT Advanced Concepts
  • Creating reusable macros and custom Jinja logic
  • Version controlling DBT projects with Git
  • Using packages and modular development
  • Integrating DBT with Airflow, Prefect, or other orchestrators
  • Managing staging vs production environments

Cloud Data Engineer (Azure)

Module - 1: Common Azure Data Components
Chapter 1: Data Warehouse Overview
  • What is Database?
  • What is Data Warehouse?
  • OLTP Vs OLAP
  • ETL Vs ELT
  • Data Warehouse Concepts
  • DWH Life Cycle
  • DWH Approaches (INMON and KIMBALL)
  • Data Granularity, Data Movement Stages
Chapter 2: Cloud Computing Overview
  • Introduction to Cloud & Deployment Types
  • IaaS, PaaS, SaaS
  • Cloud vs On-Premise DWH
  • Popular Cloud Platforms: AWS, GCP, Azure
  • Advantages of Cloud Computing
Chapter 3: Azure Overview
  • Azure vs AWS Comparison
  • Core Azure Services
  • Azure Portal Tour
  • Resource Groups, Subscriptions
  • Regions and Availability Zones
Chapter 4: Azure Pricing & Free Tier
  • Azure Pricing Calculator
  • Free Trial Setup
  • Cost Management and Budgets
Chapter 5: Azure Storage Introduction
  • Types: Blob, Table, Queue, File
  • Storage Accounts and Containers
  • Hot, Cool, Archive Tiers
  • Security, Encryption, and SAS Keys
Chapter 6: Azure SQL Database
  • What is Azure SQL?
  • Provisioning SQL Databases
  • Single vs Elastic Pool
  • DTUs vs vCores
  • Connectivity and Migration
Chapter 7: Azure Data Warehouse
  • What is Synapse Analytics (DW)?
  • Architecture & Components
  • Polybase, Data Movement
  • Integration with ADF
Chapter 8: Azure Data Lake
  • Gen1 vs Gen2
  • Mounting to Databricks
  • Data Access Layers
  • Integration with ADF & Spark
Chapter 9: Azure Logic Apps
  • Introduction to Logic Apps
  • Triggers and Connectors
  • Data Integration with External Sources
Chapter 10: Azure Event Hub
  • Event Streams Introduction
  • Working with Producers and Consumers
  • Capture and Retention Policies
Chapter 11: Azure Synapse Analytics
  • What is Synapse?
  • Synapse Studio Interface
  • SQL Pools and Integration Runtimes
  • Data Integration with Pipelines
Chapter 12: Azure Key Vault
  • Storing Secrets Securely
  • Accessing Secrets via ADF
  • Permissions and RBAC
Chapter 13: Role-Based Access Control (RBAC)
  • Understanding Roles and Scopes
  • Creating Custom Roles
  • Managing Resource Access
Chapter 14: Azure Active Directory
  • What is AAD?
  • Authentication and Authorization
  • User, Group, and App Management
Chapter 15: Azure DevOps Introduction
  • Repos, Boards, Pipelines Overview
  • CI/CD for Data Projects
  • Git Integration with ADF
Module - 2: Azure Data Factory (ADF)
Chapter 16: Understanding Azure Data Factory
  • Introduction to ADF
  • About Azure Data Factory
  • Azure Data Factory vs SSIS
  • Parameters vs Variables
  • Monitor Manage
  • Autor and Deploy
Chapter 17: Azure Integration Runtime
  • What is Integration Runtime?
  • Different Types of Integration Runtimes
  • Configure different Integration Runtimes
  • Azure Bell Hosted Integration Runtime
Chapter 18: Azure Data Factory Components
  • Pipelines
  • Activities
  • Datasets
  • Linked Services
  • Triggers
Chapter 19: General Activities
  • Append Variable
  • Execute Pipeline
  • Execute SSIS Package
  • Get Metadata
  • Lookup
  • Stored Procedure
  • Set Variable
  • Copy Data
  • Move and Transform
  • Delete
  • Wait
Chapter 20: Working with Azure Data Factory
  • Integration with Azure SQL Database
  • Monitoring and Troubleshooting
  • Scheduling the Data Pipeline
  • Working with Parameters
  • Working with Incremental Data
  • Working with Bulk Copy
  • Working with Mapping Flows
Chapter 21: Iteration & Conditionals
  • Filter
  • For Each
  • If Condition
  • Until
Chapter 22: Data Flow
  • Source, Lookup, Derived Column
  • Alter Row, Conditional Split, Sink
  • ARM Templates for Deployment
  • Deploy Data Factory Pipelines (DEV, Test, Prod)
  • Version Control and GitHub Repository
  • Debugging, Monitoring & Error Logging
  • Lift and Shift SSIS Packages
Chapter 23: Transformations
  • Different Types of Transformations
  • Data Integration using ADF or Azure Synapse
  • Code-free Transformation at Scale
  • Data Pipeline to Import Poorly Formatted CSVs
  • Create Mapping Data Flows
  • Explore, Analyze, Clean, and Transform Data
Chapter 24: Data Load
  • Incremental Load with Blob Storage
  • Full Load
  • Slowly Changing Dimension (SCD) Implementation
  • Load Data into Data Bricks using SQL
Chapter 25: Data Pipeline Project Needs
  • Incremental Load on SQL Source and File System
  • Multiple Tables and Files in Single Pipeline
  • Call Pipelines within Pipelines
  • Dataflow Transformations in One Pipeline
  • Email Notification for Failures
  • Log Maintenance via Azure Log Analytics
  • Handle Error Data in ADF
Module - 3: Azure Data Bricks (ADB)
Chapter 26: Understanding Azure Data Bricks
  • Introduction to Data Bricks
  • Azure Data Bricks Main Concepts
  • Data Bricks Features
  • Azure Data Bricks Architecture
  • Create Data Bricks Free Account
  • Configure Data Bricks
Chapter 27: Data Bricks Components
  • Data Bricks Workspace
  • Data Bricks Clusters
  • Data Bricks Run Time
  • Data Bricks Run Jobs
  • Notebook
  • Driver and Worker
  • Workflows
  • DBFS
Chapter 28: Working with Azure Data Bricks
  • Data Bricks Integration with Azure Blob Storage
  • Big Data Analytics with Few Use Cases
  • Extract, Load and Transform (ETL)
  • Batch Processing
  • Azure Data Bricks as Unified Solution
  • Data Bricks File System
  • Reading Data From Blob Storage and Writing into Azure SQL
  • Reading Data From Data Lake Storage and Writing into Azure SQL
Chapter 29: Spark
  • Spark Overview
  • Processing Engine – Apache Spark
  • Spark Features
  • Spark Context
  • Data Frame
  • Spark SQL
Chapter 30: Microsoft Fabric
  • Fabric Introduction
  • Workspace
  • One Lake Catalog
  • Pipelines and Dataflow
  • Ingesting Data into One Lake

Cloud Data Engineer (GCP)

Module - 1: Cloud & GCP Overview
Chapter 1: About Cloud Computing
  • Introduction to Cloud Computing
  • Roles and Responsibilities of Cloud Data Engineer
  • Overview of Cloud Platforms
  • About Cloud
  • On Premises DWH Vs Cloud DWH
  • IaaS, PaaS, SaaS
  • Various Popular Cloud Data Warehouses
  • Advantages of Cloud Computing
  • Types of Cloud Deployments
Chapter 2: Entering to GCP Cloud
  • Cloud Platforms
  • About Google Cloud Platform
  • Analytics Services on GCP
  • GCP Project, Credits & Billing
Chapter 3: Account Creation & Access
  • Setup GCP for Individual Account
  • Access GCP services with Google Cloud Shell
  • Access GCP services with Google Cloud SDK
Module - 2: GCP Components
Chapter 4: Google Big Query [DWH Setup]
  • Introduction to Google BigQuery
  • Overview of CRUD Operations
  • Merge/Upsert Operations
  • UI and Command Table Creation
  • Loading Data from Files
  • Execution Plan of BigQuery
  • Partitioned & Clustered Tables
  • External Tables & Queries
  • Integration with Python, Pandas, PostgreSQL
  • Views & Materialized Views
  • Advanced SQL in BigQuery
Chapter 5: Google Cloud Storage (GCS) [Datalake Setup]
  • GCS UI and gsutil Commands
  • File Handling with Python
  • Pandas-based Processing in GCS
  • Validation of Files using Python & gsutil
Chapter 6: GCP Cloud SQL [PostgreSQL Setup]
  • Setup Cloud SQL with PostgreSQL
  • DB Operations with Python and Pandas
Chapter 7: Google Cloud Composer [Data Pipeline Orchestration]
  • Airflow Overview and Architecture
  • DAG Setup and Deployment
  • Dataproc Workflow Integration
  • Run Jobs with gcloud
  • Schedule and Monitor Data Pipelines
Chapter 8: Google BigTable
  • Introduction to BigTable
  • Integration with PySpark
Module - 3: Big Data Processing
Chapter 9: GCP Dataproc
  • Cluster Setup for Development
  • HDFS Commands & gsutil Basics
  • Local and GCS File Handling in HDFS
  • PySpark/Spark SQL CLI Usage
  • ETL Pipeline Creation and Validation
  • Dataproc Job Management via gcloud
Chapter 10: Data Bricks on GCP
  • Setup Data Bricks on GCP
  • Data Bricks CLI and Architecture
  • ELT Pipeline using Jobs & Workflows
  • DBFS Operations with Spark SQL
  • Execution Monitoring of Data Pipelines
Chapter 11: Spark on Google Dataproc and BigQuery
  • BigQuery Connector Review
  • Spark App Submission: CLI, Client, Cluster Modes
  • Write to BigQuery Tables from Spark
  • Deploy & Run Apps as Dataproc Jobs
  • Review Jobs and Applications in Dataproc UI

Cloud Data Engineer (AWS)

Chapter - 01: Introduction to SQL
  • Introduction AWS
  • Understanding Cloud Computing
  • Evolution of AWS
  • AWS Global Infrastructure Overview
  • AWS Services Overview
  • AWS Free Tier and Account Setup
Chapter - 02: Amazon S3 (Simple Storage Service)
  • Introduction to Object Storage
  • Creating S3 Buckets
  • Managing Objects in S3
  • Versioning and Lifecycle Policies
  • Cross-Region Replication
  • Transfer Acceleration
  • S3 Security and Encryption
  • S3 Access Control Policies
Chapter - 03: AWS Lambda
  • Introduction to Serverless Computing
  • Creating and Deploying Lambda Functions
  • Event Sources and Triggers
  • Lambda Function Monitoring and Logging
  • Using Layers and Libraries with Lambda
  • Scaling and Concurrency
  • Lambda Security Best Practices
Chapter - 04: IAM (Identity and Access Management)
  • Introduction to IAM
  • Users, Groups, and Roles
  • IAM Policies and Permissions
  • Multi-Factor Authentication (MFA)
  • IAM Best Practices and Security
Chapter - 05: Amazon Cloud Watch
  • Monitoring AWS Resources with CloudWatch
  • CloudWatch Metrics and Alarms
  • CloudWatch Logs
  • CloudWatch Events
  • CloudWatch Dashboards
  • Application Insights
Chapter - 06: Amazon EC2 (Elastic Compute Cloud)
  • Launching EC2 Instances
  • EC2 Instance Types and Pricing
  • AMIs and Snapshots
  • Security Groups and Key Pairs
  • Elastic IP Addresses
  • Auto Scaling and Load Balancing
  • EC2 Placement Groups
Chapter - 07: Amazon SNS (Simple Notification Service)
  • Introduction to SNS
  • Creating Topics and Subscriptions
  • Sending Messages and Notifications
  • SNS Message Filtering
  • SNS Mobile Push Notifications
  • SNS Security Best Practices
Chapter - 08: Amazon SQS (Simple Queue Service)
  • Overview of SQS
  • Creating Queues
  • Sending and Receiving Messages
  • Message Visibility Timeout
  • Dead Letter Queues (DLQ)
  • SQS FIFO Queues
Chapter - 09: Amazon Event Bridge (Formerly Cloud Watch Events)
  • Introduction to EventBridge
  • Creating Rules and Targets
  • Event Patterns and Filters
  • EventBridge Scheduler
  • EventBridge Event Buses
  • Custom Event Sources
Chapter - 10: Amazon Kinesis
  • Introduction to Kinesis
  • Kinesis Data Streams
  • Kinesis Data Firehose
  • Kinesis Data Analytics
  • Kinesis Data Streams Scaling and Shading
  • Kinesis Security Best Practices
Chapter - 11: Amazon DynamoDB
  • Introduction to DynamoDB
  • Creating Tables and Indexes
  • Query and Scan Operations
  • DynamoDB Streams and Triggers
  • Provisioned Capacity vs. On-Demand Capacity
  • DynamoDB Best Practices
Chapter - 12: AWS Step Functions
  • Introduction to IAM
  • Overview of Step Functions
  • Creating State Machines
  • Step Function Workflow Execution
  • Error Handling and Retrying
  • Step Function Integration with Lambda
  • Step Function State Visualization
Chapter - 13: Amazon EMR (Elastic MapReduce)
  • Introduction to EMR
  • Launching EMR Clusters
  • EMR Core Components (Hadoop, Spark, Hive)
  • EMR Security Configuration
  • EMR Auto Scaling and Instance Groups
  • EMR Monitoring and Logging
Chapter - 14: AWS Glue
  • Introduction to Glue
  • Data Catalog and Crawlers
  • ETL Jobs with Glue
  • Glue Data Transformation and Mapping
  • Glue Security and Encryption
  • Glue Best Practice
Chapter - 15: Amazon RDS (Relational Database Service)
  • Introduction to RDS
  • Launching RDS Instances (MySQL, PostgreSQL, etc.)
  • RDS Multi-AZ Deployments
  • RDS Read Replicas
  • Backups and Snapshots
  • RDS Security Groups and Parameter Groups
Chapter - 16: Amazon Athena
  • Introduction to IAM
  • Introduction to Athena
  • Querying Data in S3 with Athena
  • Working with Athena Databases and Tables
  • Partitioning and Performance Tuning
  • Athena Security Best Practices
  • Athena Query Federation
Chapter - 17: Amazon Redshift
  • Introduction to Redshift
  • Creating Redshift Clusters
  • Data Loading and Querying
  • Redshift Spectrum
  • Redshift Performance Optimization
  • Redshift Security Best Practices

Cloud Data Engineer (Big Data Hadoop)

Chapter-01: Big data Concepts
  • Introduction to Big data
  • Characteristics of Big data
  • Relation between Big Data and Hadoop
  • Big Data Opportunities
  • Challenges with Big data
  • Hadoop – Big Data Solutions
  • Difference between Hadoop 1.X.X , Hadoop 2.X.X & 3.X.X Version
Chapter-02: Hadoop Eco System
  • Introduction to Eco System
  • Hadoop Architecture
  • OLAP Database Limitation
  • Uses of connected Components
  • Oozie vs Zoo Keeper
Chapter-03: HDFS - Data Storage
  • Introduction to HDFS
  • Apache HDFS Architecture
  • Cluster Environment
  • How the Data stored in HDFS ?
  • What is BLOCK ?
  • Replication Factor in HDFS
  • HDFS Commands
Chapter-04: Map Reduce-Data Processing
  • Introduction to Map Reduce
  • Difference between Traditional RDBMS and Map Reduce
  • Map Reduce essential is in Hadoop
  • Hadoop Processing Daemons
  • Input Split
  • Map Reduce Life Cycle
  • Map Reduce Programming Model
  • Map Reduce Terminologies
  • Combiner and Reducer
  • Serialization vs De-Serialization
  • Compiling and Verifying Map-Reduce Program
  • Word Count Example
Chapter-05: Yarn - Memory Management
  • What is YARN?
  • Difference between Map Reduce & YARN
  • When to use YARN
  • YARN Process Flow
  • YARN Architecture
    -Resource Manager
    -Application Master
    -Node Manager
  • YARN Web UI
  • Different Configuration Files for YARN
Chapter-06: Zoo Keeper - Distributed Application
  • What is Zoo Keeper ?
  • Why Required ?
  • Zoo Keeper Architecture
  • Advantages and Disadvantages
  • Apache Zoo Keeper Application
  • Workflow
  • CLI
  • API
Chapter-07: Oozie - Job Scheduling
  • Oozie Introduction
  • Oozie Architecture
  • Oozie Configuration Files
  • Oozie Job Submission
    -Workflow .xml
    – Coordinator.xml
Chapter-08: Hive - Relational Data Base/Data Warehouse
  • Introduction to Hive
  • Hive Architecture
    -Driver
    -Compiler
    -Executor(Semantic Analyser)
  • Need of Apache Hive in Hadoop
  • Collection Data Type
  • Work on Hive Database
  • Hive Shell
  • Meta Store
  • Hive Table Operation
  • Column Operation
  • HIVEQL SELECT
  •  Views and Index
  • Built-in Operators
  • Buit-in Functions
  • HIVEQL Joins
  • Sub Queries
  • Partitioning and Bucketing
  • Internal vs External Table
  • Hive Serializer/Deserializer
  • Semi Structured Data Processing Using Hive
  • Compressing and Migrating Hive Tables
  • Dynamic substation of Hive and Different ways of running Hive
  • ACID in HIVE
  • How to enable Update in HIVE
  • Log Analysis on Hive
Chapter-09: Impala - Non Relational Database
  • Introduction to Impala
  • General Impala Commands
  • Query Processing
  • Impala Table operation
  • Work on Table Data
  • User Permission
Chapter-10: Kafka - Stream Processing
  • Introduction to Kafka
  • Installation of Kafka
  • Difference between MQ Vs Kafka
  • Basic Operation using Kafka
Chapter-11: SQOOP - Import/Export (Optional)
  • Introduction to SQOOP
  • SQOOP Import and Export
  • SQOOP Job
  • Connect to Relational Database using SQOOP
  • SQOOP Commands
  • Code Gen
  • Eval
  • Working in Database
  • Table Operation
Chapter-12: Pig - ETL (Optional )
  • Introduction to Pig
  • Pig Data Type
  • Pig Execution
  • Grunt Shell
  • Pig Latin
  • Operators
  • Grouping
  • Join
  • Combining
  • Spliting
  • Filtering & Sorting
  • Buit-in Functions
  • UDF
  • Pig Scripting

Job Placed

- Congratulation Ankita -
TCS
TCSpune
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TIAA
TIAApune
- Congratulation Manideep -
Dell
DellHyderabad
- Congratulation Disha -
Accenture
AccentureMumbai
- Congratulation Rashmi -
Infosys
InfosysPune
- Congratulation Vivek -
Infosys
Infosys Hyderabad
- Congratulation Surya -
TCS
TCSHyderabad
- Congratulation Mansi - -
Tech Mahindra
Tech MahindraPune
- Congratulation Ravi -
Persistent
PersistentBangalore
- Congratulation Vamsi -
Cognizant
Cognizant Hyderabad
- Congratulation Nidhi -
Xavient
Xavient Noida
- Congratulation Karthik -
Wipro
WiproPune
- Congratulation Yosef -
Diyotta
DiyottaHyderabad
ongratulation Subhashree -
Altius Data Solution
Altius Data SolutionPune
- Congratulation Shruti -
3K Technologies
3K Technologies, Bangalore
Congratulation Vinod -
TCS
TCSChennai
- Congratulation Ashok -
Infosys
InfosysHyderabad
- Congratulation Ipsy -
Sears Holding
Sears HoldingPune
- Congratulation Joshna -
Ness
NessHyderabad
- Congratulation Rasheed -
Infosys
InfosysHyderabad
- Congratulation Manoj -
Syntel
SyntelPune
- Congratulation Sara Yasmeen -
Diyotta
DiyottaHyderabad
- Congratulation Smita -
Capgemini
CapgeminiPune
- Congratulation Neha -
IBM
IBMPune
- Congratulation Saikumar -
HCL
HCLHyderabad
- Congratulation Kirti -
IBM
IBM Pune
- Congratulation Tulsi -
Accenture
AccentureChennai
- Congratulation Diksha -
SLK Global
SLK GlobalBangalore
- Congratulation Diksha -
7dxperts
7dxpertsChennai
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Appreciations through WhatsApp

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Enrolled Student Feedback

"Hi I'm Koushik, I had enrolled in Azure Data Factory Course in Digital IT Hub Institute. My mentor has excellent Skills towards teaching . Although coming from a non IT background, I was able to finish the course with good understanding of the basic concepts. Sir made it easy by giving real-time scenarios, study material and guiding us with Interview questions. !"
Koushik RaoAzure Data Factory
"I'm M Manosha. In my experience it is the Best institute for Azure Data Factory. They give good guidance and support to all students and help them achieve placement. I had a great experience here and recommend this Digital IT Hub Institute to all students for a great career. !"
ManoshaAzure Data Factory
"I have completed my Unix/Linux Shell Scripting class with SQL. it was good to enhanced my knowledge and carrier. My mentor was Prakash sir. he is very positive person helps in interview preparation Personally, I'm Alisha Patil, thank you prakash sir. !"
Alisha Patil Unix/Linux Shell Scripting
"I thoroughly enjoyed the training classes in Digital IT Hub Institution and found it really satisfying in training Institute. I'm P Avinash Roy, i very much impressed with the trainer he was very professional and supportive. All doubts were cleared in precise manner. !"
Avinash Roy Unix/Linux Shell Scripting
"Training was excellent with good interaction. I'm sulochana. I have done Selenium Automation and sir have good Knowledge sharing Recording facility is excellent. Many thanks to Digital IT Hub Institution . !"
T SulochanaSelenium Automation
"I'm Shital Singh, Training in this institution was great experience and Trainer explained every single point and every small things in details which was very understanding it was great experience with Digital IT Hub Institution Thanks Thanks a lot. . !"
Shital SinghSelenium Automation
'No words to give me gratitude to Digital IT Hub Institution, I'm Deekshit and I did Bigdata Hadoop Course here they did a lot of hard work to get my skills improved. this is being known for its genuineness and they are very concerned for each and every individuals technical and overall growth. I wish all the very best to Prakash Sir & team.!"
P Deekshit Bigdata Hadoop Course
'I'm Padma Gowtham Actually I want to share my views about this course and Institute, This Bigdata Hadoop and Pyspark Training was really good as well as the trainer. Institute ensured to cover the most important topics of the content and trainer was excellent. I would highly recommend Digital IT Hub Institution. !"
Padma Gowtham Bigdata Hadoop and Pyspark
'My Name is Ritesh Jaiswal. It's a good institute in Hyderabad for beginners to start your career in IT. It improves not only your technical skills but also helps to improve your personality. All the faculties of Python are supportive and friendly which gives a good environment for learning. !"
Ritesh Jaiswal Python
'This institute is better way for Career, each and every student will start your career in best way in Python Course. Teacher staff is very good. Specially Prakash sir solve query and issue at every time. It's good for every student to take knowledge and experience. I'm Sapna Singh. Thanks to all the team. !"
Sapna SinghPython
'Hi, I'm P BalaSwamy, I have recently completed Snowflake from Digital IT Hub Institution and Sir taught us in a very simple and easy to understand manner. Overall it was a great experience to be trained in Digital IT Hub by Prakash Sir. Thank You. !"
P BalaSwamySnowflake
'Hi I'm Prasanth Kumar. This is the Great place to learn even people with NON-IT background can easily learn here. I joined Snowflake Course from Digital IT Hub Institution. Great learning experience with trainer. He makes easy what you learn or want to learn everything. !"
Prasanth KumarPython
'I'm Swathi Verma. I done course on Data Science. It was great experience for me. Trainer has great teaching skills. He taken all chapters with plenty of examples as well as given the assignments. Other trainers also guide for your future. Other staff is also helpful. Loved the experience. !"
Swathi VermaData Science
'Digital IT Hub technology is really very good institute with very professional trainers. I took Data Science. They help us to gain more skills and tought with 100% practical knowledge to apply in professional world. I'm Malika Singh from Mumbai. I'm sure that It is one of best institute in Hyderabad. Thank you Digital IT Hub Institution for best learning! !"
Malik SinghData Science
'I Vishal Kumar. I did SQL/PLSQL Course here. Teaching staff are very Co-operative and Supportive. Training was excellent with good interaction. Recording facility is excellent for revising and they provide Interview Preparation. !"
Vishal KumarSQL/PLSQL
'It has been great experiences. I have joined in this Institute for Microsoft Power BI. I would like to say that my experience has been amazing till now. I'm Suresh Bujji. eaching is easy to understand and Concepts ate tought very clearly and precisely. !"
Suresh Bujji Microsoft Power BI
'Digital IT Hub Institution is one of the best Microsoft Power BI Training Institute Center in Hyderabad if you want to make best as Technology expert. I'm Shahil Hameed. I am sure that you will enjoy with there teaching. Thanks to all staff of Digital IT Hub Institution. They give us better knowledge of technology. !"
Shahil Hameed Microsoft Power BI
'No.1 Institute in Hyderabad for Web Service Testing. I'm Ranjith Patil. My sister got placed in 2015. So I did in 2018. Definitely the best Institute. Please join here. Recommended with full guarantee. !"
Ranjith Patil Web Service Testing
'I'm Srikanth Sharma I did Web Service Testing here. Training was really good as well as the trainer. Institute ensured to cover the most important topics of the content and trainer was excellent. I would highly recommend Digital IT Hub Institution. !"
Srikanth Sharma Web Service
'I'm Priyadarshini. I did Devops in this Institute. This is one of the best Institute for Devops. Faculty and teaching staff are very friendly. Thank you sir for teaching us. !"
PriyadarshiniDevops
'Great place to learn Devops Training here. I'm Manonita Pattnaik. I joined here, explored and got placed ! Digital IT Hub Institute has having friendly environment and staff. Focused on Student's problem solving. Thanks a lot Digital IT Hub Institute. !"
Manonita PattnaikDevops
'Basically I am from Non IT background. I have recently completed my Informatica Power Center Course in Digital IT Hub Institute. Here the management gives free space to understand concepts even we are non IT background. My trainer Mr Prakash has unique way of teaching which helped me learn all concepts. I got help in interview preparation also.!"
Sridevi PatilInformatica Power Center
'I'm Sai Kumar Pattval. Really excited to share my feedback with Digital IT Hub Institution. It is a good Organization. I have successfully completed the course "Informatica Power Center" under supervision of trainer Mr Prakash. The quality of teaching is of good level. I am satisfied with the content of the course and excited for the next step of journey in my career. A big thanks to Prakash sir for help and support. Thanks Digital It Hub team for wonderful co-operation. !"
Sai Kumar PattvalInformatica Power Center
'It's a good institute for Salesforce Training. Teaching quality is amazing and overall experience is good. Helpful for gaining new knowledge and they provide good Interview question and also helps in resume preparing. Thank you so much Digital IT hub. I'm Prasad Varma and very happy to share my feelings. !"
Prasad Varma Salesforce
'It's a place where you will get well trained and placed within a shorter period of time. Great place to start your journey in Software Industry. I joined to learn Salesforce in this institution and I got good placement too. Thanks a lot. I'm Chandana. !"
ChandanaSalesforce
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FAQ'S

What if I miss a class?

You can cover from that Class Recording and ask doubt in Live Session.

What if I miss more classes due to some reason?

We will arrange backup classes for you or you can attend our next batch.

How much is the Course fee?

You can contact our team and we will get back to you with the course fee details.

What are the modes of Training?

We offer Online as well as Offline (Limited) with One-to-One or Batch.

What about live Projects?

We provide Live Projects during the Course in a Real Time Scenarios based practical manner.

Will I get a free demo?

Yes, we can schedule 1-2 free demo class.

Will you provide Class Recordings, Materials,Exercises, etc.?

Yes, we provide All Class Recordings, Materials, Notes, Exercises, etc.

Will I get a Course Completion Certificate?

Yes of course, Our Institute is Govt. registered and we give Course Completion Certificate.

What about the Trainer/Instructor?

Our Trainers/Instructors are having more and Well Experience in respective Course & IT Job Fields.

Why learn from Digital Hub Tech?

We provide in Deep Drive Advance Level Training to get multiple Job Offers with High Packages to settle the Long Career.

OUR COURSES

We Offer Various Courses. Here Are the Courses From Digital Hub Tech.

ETL

  • ETL TESTING
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Unix/Linux

  • UNIX SHELL SCRIPT
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DevOps

  • AWS
  • AZURE

Database

  • SQL
  • PL/SQL
  • DBA

Digital Marketing

  • SEO
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  • SMA

Cloud Data Engineer

  • AWS
  • GCP
  • AZURE
  • SNOWFLAKE
  • BIG DATA HADOOP
  • SPARK

Automation Testing

  • API
  • RPA
  • TOSCA
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  • PERFOMANCE

Full Stack Program

  • UI
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  • PYTHON
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Scrum/Agile

  • SCRUM MASTER
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Salesforce

  • ADMIN
  • DEVELOPMENT

Mulesoft

  • ADMIN
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Servicenow

  • ADMIN
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Programming Languages

  • C
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Software Testing

  • Manual Testing
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SAS

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