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AWS Data Engineering Training By Experts
- 1000+ Ratings
- Master Python for data manipulation and analysis
- Dive deep into SQL for efficient data querying and management
- Understand big data concepts and the Hadoop ecosystem
- Harness the power of Apache Spark with PySpark
- Explore Databricks for collaborative data engineering
- Get introduced to cloud computing with Google Cloud Platform
- Cloud Technologies – Using services like AWS and Google Cloud.
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- Live interactive sessions
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- Live Projects, Questions & Quizzes
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- Live Classes With Industry Expert Faculty
- 24/7 Support
Data Engineering Overview
What is Data Engineering?
Data engineering is all about collecting, cleaning, and organizing data so others (like data scientists and analysts) can use it. It’s the behind-the-scenes work that powers apps, websites, and business decisions. Imagine a giant library with millions of books scattered around. A data engineer’s job is like organizing those books into neat shelves, labeling them properly, and making them easy to find.
Why Learn Data Engineering ?
- Job Market Demand
- Career Advancement Opportunities
- Workday’s Role in HR and Finance
Data is the new oil. Businesses run on it. From tracking customer habits to improving services, every industry—healthcare, finance, education—needs solid data pipelines. Without skilled data engineers, all that raw information would be useless.
Building a Career in Data Engineering
- Enhancing Your Technical Skillset
- Enhanced Job Performance
- Increased Earning Potential
- Greater Job Satisfaction
Skills to Learn
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Python and SQL
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Cloud platforms
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Data pipeline tools
-
Data modeling
Certifications to Consider
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Google Cloud Data Engineer
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AWS Certified Data Analytics
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Databricks Data Engineer Associate
Salary Expectations and Job Roles
Entry-level roles start around $80K/year, with senior roles going well over $150K in high-demand areas.
Data Engineering Key Features
- Data engineers gather data from various sources—apps, websites, APIs, and even IoT devices
- A pipeline is like a factory line for data: it moves, transforms, and stores data
- Data engineers ensure the data is complete, accurate, and consistent.
- Comprehensive curriculum covering in-demand skills
- Hands-on experience with industry-standard tools
- Practical, job-ready skills for the data-driven world
Flexible batches for you
Instructor-led Python Spark Certification Training using PySpark live online Training Schedule
Who Should Learn Data Engineering?
- HR Professionals
- Business Analysts
- Consultants
- Any Degree Commpleted
- IT professionals
- Software Testers
- Career & Job Seekers
- Finance Professionals
- Aspiring data engineers
- Software developers looking to transition to data engineering
- Data analysts seeking to expand their technical skills
- Recent graduates in computer science or related fields
You don’t need to be a tech genius to start. If you’re curious, enjoy solving puzzles, and like working with systems, this path might be perfect for you. Even if you’re a recent graduate, a career changer, or just a lifelong learner—data engineering training opens doors.
Training Options
Online Training
- Live online training from Certified Trainers
- Your Flexible Time and if are you interested next batch also give access
- 24x7 learner assistance and support
Batch Starting From
- 22nd Mar, Weekdays Batch
Corporate Training
- For Corporate Training from Certified Trainers with 12+ years experts
- Your Flexible Time and provide daily 4 to 8 Hours
- 24x7 learner assistance and support
Our Training Benefits
- As your requirement online or ofline
Self Paced Training
- Provide Self Faced With Life Time Acces
- Also provide material, if need access
- 24x7 assistance and support if have any doubt
Best Price And Quality Job Oriented Videos
- Contact Our team for more details
Career Services
- Career oriented Online sessions
- Job Or Placement Assistance
- Mock Interview And Resume Preparation
- Resume and Linked in Profile
- Exclusive Access Popular Job Portal
- One on One and Group Batches
Detailed Data Engineering Course Syllabus
- Live Course
- Self-Paced
- Industry Experts
- Academic Faculty
Introduction to Data Engineering
1. What is Data Engineering?
- Definition and scope of data engineering
- Differences between data engineers, data scientists, and data analysts
- Key responsibilities of a data engineer
- Evolution of data engineering
- Partition Components
2. Data Engineering Lifecycle
Data generation and collection
- Sources of data (APIs, databases, logs, etc.)
- Data ingestion techniques
Data storage and warehousing
- Types of data storage (relational, NoSQL, data lakes)
- Data modeling concepts
Data processing and transformation
- Batch vs. stream processing
- ETL vs. ELT
Data analysis and visualization
- Role of data engineers in supporting analytics
- Data quality and governance
Data Engineering Tools and Technologies Overview
Databases and data warehouses
- Relational databases (MySQL, PostgreSQL)
- NoSQL databases (MongoDB, Cassandra)
- Data warehouses (Snowflake, Amazon Redshift)
- Introduction to .dat and .dml files
- Partition Components
Data warehouses (Snowflake, Amazon Redshift)
- Hadoop ecosystem
- Apache Spark
- Distributed file systems (HDFS)
Cloud platforms
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure
ETL/ELT tools
- ETL/ELT tools
- Talend
AWS Data Engineering and Data Ingestion
Introduction to AWS Services
- Data Engineering Fundamentals
- AWS Global Infrastructure
- Properties of Data
- Basics of ETL
- Data Ingestion
- Modern data workflows
- Data Ingestion Patterns and Services
- Streaming vs. batch data ingestion
- Replayability of data ingestion pipelines
- Stateful and stateless data transactions
- Reading data from streaming sources
- Configuring Ingestion Options
- Batch Ingestion
- Consuming data APIs
- Calling a Lambda function from Amazon Kinesis
- Streaming Data Distribution
Data Transformation and Processing
Introduction
- Data Transformation
- Overview of ETL pipelines
- Business requirements for ETL
- Data characteristics: volume, velocity, and variety
- ETL Pipeline Implementation
- Apache Spark for data processing
- Data Sources Connection
- Integrating data from multiple sources
- Optimizing ETL Pipelines
- Optimizing container usage
- Cost optimization strategies
- Data transformation services
- Data format transformation
- Troubleshooting
- Making Data Available
- Creating data APIs
Orchestrating Data Pipelines and Programming Concepts
Introduction
- Data Pipeline Orchestration
- Integrating AWS services for ETL pipelines
- Event-driven architecture
- Configuring AWS services for data pipelines
- Serverless Workflows
- Building Data Pipelines
- Use Orchestration Services
- Programming Concepts for Data Pipelines
- SQL queries for data transformations
- Infrastructure as code (AWS CDK, AWS CloudFormation)
- Using AWS SAM for serverless deployments
- Mounting storage volumes
Data Storage Solutions
Introduction
- Storage Platforms
- Storage Services
- Configurations for Performance Demands
- Data Storage Formats
- Common data storage formats
- Choosing the right format for specific use cases
- Aligning Data Storage with Data Migration Requirements
- How to select storage solutions that meet migration needs
- Determining the Appropriate Storage Solution for Access Patterns
Data Cataloging and Lifecycle Management
Introduction
- Steps to create a data catalog
- AWS Glue Data Catalog
- Apache Hive metastore
- Data Classification
- Business and Technical Requirements
- Metadata
- Data Catalogs
- Metadata components
- Role of data catalogs in data management
- Lifecycle Management of Data
- Storage solutions for hot and cold data
- Data retention policies and legal requirements
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Frequently Asked Questions
Do I need a computer science degree to become a data engineer?
- No, but a background in programming and databases helps a lot.
Which is the best programming language for data engineering?
- Python is most commonly used, but SQL is equally important.
Can I become a data engineer without experience?
- Yes—start with online courses, build projects, and create a portfolio.
How long does it take to learn data engineering?
- With dedication, 4 to 6 months is enough to learn the basics and start applying for jobs.
Is data engineering a good career in 2025?
- Absolutely! Demand is growing and companies are investing more in data infrastructure than ever before.
6. Why is Certification Important?
- Workday certification is an acknowledgment of your skills and expertise in using the platform. Certified professionals are often more attractive to potential employers and can command higher salaries.
