Become a Data Engineer
Microsoft Azure | Data Lake | Synapse | Factory | Bricks
Fabric | Power BI | SQL | Python
Become a Data Engineer
Microsoft Azure | Data Lake | Synapse | Factory | Bricks
Fabric | Power BI | SQL | Python
Introduction to Microsoft Azure
Cloud Computing Fundamentals
• Understanding cloud computing models: IaaS, PaaS, SaaS.
• Benefits of cloud computing in data engineering.
Overview of Azure Services
• Key Azure services relevant to data engineering
• Azure Storage Accounts
• Azure Data Lake Storage (ADLS)
• Azure Synapse Analytics
• Azure Data Factory
• Azure Databricks
• Introduction to additional services like Azure Stream Analytics and Event Hubs.
Azure Data Lake Storage (ADLS)
Introduction to R and RStudio
• Overview of R Language
• What is R and its Applications in Data
• Data Lake Concepts
• Differences between data lakes and data warehouses.
• Use cases for data lakes in big data analytics.
Working with ADLS
• Setting up Azure Storage Accounts and ADLS Gen2.
• Managing data access and permissions.
• Interacting with ADLS using Azure Portal and Azure Storage Explorer.
Azure Synapse Analytics
Introduction to Azure Synapse
• Unified analytics platform overview.
• Synapse vs. traditional data warehousing solutions.
Navigating Synapse Studio
• Interface walkthrough.
• Workspace creation and management.
Data Integration with Synapse Pipelines
• Creating and configuring pipelines.
• Data ingestion from various sources.
• Transformations using Data Flows.
Analytics with Synapse SQL
• Writing and executing SQL queries.
• Understanding serverless vs. dedicated SQL pools.
• Basic query optimization techniques.
Azure Data Factory (ADF) Basics
Introduction to Azure Data Factory
• Role of ADF in ETL processes.
• Key components: pipelines, activities, datasets.
Building Pipelines in ADF
• Creating linked services and datasets.
• Implementing data movement and transformation activities.
• Monitoring and scheduling pipelines.
Azure Databricks Overview
Introduction to Azure Databricks
• What is Databricks and its role in data engineering.
• Key features and benefits.
Setting Up Azure Databricks
• Creating a Databricks workspace.
• Understanding clusters and notebooks.
Data Processing with Apache Spark
• Reading and writing data with Spark.
• Basic transformations and actions.
• Integrating Databricks with ADLS.
Data Security and Compliance in Azure
Security Fundamentals
• Role-Based Access Control (RBAC).
• Managing Azure Active Directory identities.
Compliance and Data Governance
• Understanding Azure policies.
• Data encryption at rest and in transit.
Introduction to Azure DevOps for Data Engineering
Version Control with Git
• Basics of Git and repository management.
• Integrating Azure Repos with data projects.
Continuous Integration/Continuous Deployment (CI/CD)
• Setting up pipelines in Azure DevOps.
• Automating deployments for data pipelines.
Overview of Fabric, Review, and Capstone Project Guidance
Overview of Fabric
Review of Key Concepts
• Recap of all services covered.
• How they integrate within data engineering workflows.
Capstone Project Overview
• Guidance on a comprehensive project combining multiple Azure services.
• Project planning and resource allocation.
Python
Introduction to Python
• Overview of Python and its Applications
• Why Python for Data Analysis?
• Setting Up Python Environment
• Introduction to Google Colab
Python Basics
• Python Syntax and Basics
• Variables, Data Types, and Operators
• Control Structures: if-else, loops
• Functions and Modules
• Defining and Calling Functions
• Importing and Using Modules
Data Structures and Handling
• Working with Lists, Tuples, and Dictionaries
• Introduction to NumPy
• Creating and Manipulating Arrays
• Array Operations and Broadcasting
Data Manipulation with Pandas
• Introduction to Pandas
• DataFrames and Series
• Data Loading, Cleaning, and Preparation
• Reading and Writing Data (CSV, Excel, etc.)
• Handling Missing Data
• Data Transformation and Filtering
Data Analysis and Exploration
• Exploratory Data Analysis (EDA)
• Descriptive Statistics
• Data Visualization with Matplotlib and Seaborn
• Plotting and Customizing Graphs
• Grouping and Aggregating Data
• Groupby() and aggregate functions
Working with Dates and Times
• Date and Time Data Types and Operations
• Resampling and Time Series Analysis
• Date Functionality in Pandas
SQL
Introduction to SQL Server
• Overview of SQL Server
• What is SQL Server?
• Installation and Setup
• SQL Server Management Studio (SSMS) Interface
Basics of SQL
• Introduction to SQL
• SQL Syntax and Structure
• Data Types
• Basic SQL Commands
• SELECT, FROM, WHERE
• INSERT, UPDATE, DELETE
• Filtering and Sorting Data
• WHERE Clause
• ORDER BY Clause
Advanced SQL Queries
• Aggregate Functions
• COUNT, SUM, AVG, MIN, MAX
• Grouping Data
• GROUP BY Clause
• HAVING Clause
• Joining Tables
• INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
Data Manipulation and Transformation
• Subqueries and Nested Queries
• Common Table Expressions (CTEs)
• Window Functions
• ROW_NUMBER(), RANK(), DENSE_RANK(), NTILE()
• Data Transformation Techniques
SQL Server Advanced Topics
• Indexing and Performance Tuning
• Creating and Managing Indexes
• Query Optimization
• Stored Procedures and Functions
• Creating and Executing Stored Procedures
• User-Defined Functions
Data Analysis and Reporting
• Basic Data Analysis Techniques
• Descriptive Statistics
• Using SQL for Data Analysis
• Exploratory Data Analysis (EDA)
• Generating Reports
• Creating Simple Reports in SSMS
• Exporting Data to Excel
Power BI
Introduction to Power BI Desktop
• Overview of Power BI components: Desktop, Service, and Mobile
• Key features and benefits of Power BI Desktop
• Understanding the Power BI interface and workflow
Data Transformation with Power Query
• Importing data from Excel, databases, CSV files, and cloud services
• Handling multiple data sources
• Removing duplicates and filtering data
• Pivoting and unpivoting data
• Splitting and merging columns
• Conditional columns
• Replacing and transforming values
• Grouping and aggregating data
• Merging and appending queries
Data Modeling in Power BI
• Creating a Data Model
• Defining relationships between tables
• Star schema and snowflake schema in Power BI
• Difference between calculated columns and measures
• Creating calculated columns for custom data
• Optimizing Data Models
• Hiding and sorting columns
• Managing relationships and model performance
• Implementing hierarchical structures (drill-downs)
DAX (Data Analysis Expressions) Functions
• Introduction to DAX
• Syntax and structure of DAX
• Understanding row context and filter context
• Working with important DAX functions
Data Visualization and Reporting
• Choosing the right visual for your data
• Overview of visualization types: bar charts, line charts, pie charts, maps, etc.
• Building dynamic and interactive visuals
• Using slicers and filters to add interactivity
• Adding tooltips and custom visuals for enhanced reporting
Report Sharing and Collaboration
• Publishing reports to the cloud (Power BI Service)
• Sharing reports via email, Teams, and embedding in websites
• Setting up automatic data refreshes
• Scheduling reports and dashboards to update regularly
Become a Data Engineer with AIN GenX
• Data is the backbone of modern decision-making, and at AIN GenX, we empower you to master this critical domain. Our "Become a Data Engineer" training program equips you with the skills and tools needed to excel in this high-demand field. Here's why aspiring data engineers choose us:
Industry-Relevant Curriculum
• Our comprehensive program covers essential tools and technologies, including Azure, Data Lake, Synapse Analytics, Data Factory, DevOps, Databricks, Fabric, Power BI, SQL, and Python. Learn how to design, build, and manage robust data pipelines and architectures that power today's businesses.
Hands-On Projects
• Get real-world experience through practical projects like building data pipelines, designing data lakes, and creating insightful dashboards. Apply your knowledge to solve real business challenges using cutting-edge data engineering practices.
Expert-Led Sessions
• Learn from seasoned industry professionals who bring years of expertise in data engineering and cloud technologies. Gain both theoretical insights and practical know-how to thrive in real-world scenarios.
Career-Oriented Focus
• Our training is designed to prepare you for high-growth roles in the data engineering field. Develop job-ready skills, build an impressive project portfolio, and boost your confidence with interview readiness support.
Supportive Learning Environment
• With interactive live sessions, Q&A opportunities, and dedicated mentorship, AIN GenX ensures that every learner gets the personalized guidance needed for success.
Transform Your Career with AIN GenX!
• Step into the future with confidence. Join our "Become a Data Engineer" program and gain the skills to become a vital part of the data-driven economy.
Artificial intelligence and big data are shaping the future. AIN GenX is excited to invite you to our transformative "Become a Data Engineer" training program, designed to equip you with essential tools like Azure, Data Lake, Synapse, Data Factory, DevOps, Databricks, Fabric, Power BI, SQL, and Python.
• Educational Background
Preferred: A bachelor’s degree in IT, computer science, engineering, or a related field is advantageous. These disciplines provide a strong foundation in data concepts.
Non-Specific: Passionate individuals from non-technical backgrounds eager to enter the data engineering field are welcome, provided they are ready to invest in learning.
• Basic Programming Knowledge
Familiarity with any programming language (such as Python, Java, or SQL) is beneficial.
Beginners with a willingness to learn coding will find this training approachable, as foundational programming concepts are included.
• Interest in Data and Technology
A passion for working with large datasets, data pipelines, and modern tools.
Enthusiasm for exploring solutions using Azure, Power BI, and other platforms in the data engineering ecosystem.
• Technical Comfort and Computer Literacy
Familiarity with basic computer tools and operations.
Prior exposure to data platforms like Excel or SQL is a plus, but not mandatory—our program starts from the basics.
High Demand and Career Opportunities
• Data engineering is the backbone of the modern data-driven world. Industries across technology, healthcare, finance, retail, and more are actively seeking skilled data engineers to manage, transform, and analyze vast amounts of data. Unlock diverse and lucrative career opportunities with this high-demand expertise.
Competitive Salary and Perks
• Data engineers rank among the top earners in the tech sector. With in-demand skills in Azure, Synapse, Data Lake, Data Factory, and more, you’ll position yourself for attractive salaries, growth opportunities, and exciting perks.
Master Cutting-Edge Tools and Technologies
• This training equips you with hands-on experience in industry-leading tools and platforms, including Azure Data Services, Synapse Analytics, Power BI, SQL, Python, and Microsoft Fabric. Develop robust, scalable solutions for real-world challenges.
Build Impactful, Data-Driven Solutions
• As a data engineer, you’ll design and implement systems that power decision-making processes, predictive analytics, and AI-driven solutions. Shape industries by creating smarter workflows and scalable infrastructures.
Versatile Skills for Endless Opportunities
• The knowledge you gain as a data engineer is highly transferable across industries. Whether it’s optimizing data pipelines, integrating systems, or supporting AI models, your skills will remain relevant and in demand.
Enhance Your Problem-Solving Abilities
• Data engineering sharpens your analytical and problem-solving skills. Learn to design efficient data architectures, ensure data quality, and troubleshoot complex systems.
Real-World Projects for Practical Experience
• At AIN GenX, our hands-on projects simulate real-world scenarios, ensuring you graduate with the confidence and expertise to excel in your role.
Stay Ahead in a Dynamic Field
• Data engineering is an evolving domain. Continuous learning through our training ensures you remain competitive and adapt to emerging trends and technologies like AI integration, cloud innovations, and advanced analytics.
Advance to Leadership Roles
• This training opens doors to advanced roles such as Data Architect, Cloud Engineer, and AI Data Strategist. Lead teams, drive innovation, and specialize in groundbreaking technologies.
Empower a Data-Driven World
• Contribute to the global shift toward intelligent systems and data-driven decision-making. Your work will play a critical role in building efficient, future-ready solutions.
Data Engineering is a cornerstone in today's data-driven world, powering analytics, machine learning, and AI solutions. Here's why learning Data Engineering with AIN GenX opens doors to transformative career opportunities:
Booming Career Opportunities
Data Engineering is among the most sought-after professions, essential in industries like finance, healthcare, retail, and technology. As organizations expand their data ecosystems, the demand for skilled Data Engineers continues to surge globally.
Versatile Industry Applications
Data Engineers enable critical applications such as real-time analytics, predictive modeling, and scalable data processing systems. From building cloud data warehouses to integrating Power BI dashboards, their work is vital across sectors.
Mastery of Advanced Tools and Technologies
Our training covers industry-relevant technologies, including:
• Microsoft Azure: Cloud computing essentials for modern data solutions.
• Data Lake & Synapse Analytics: Managing and querying vast datasets effortlessly.
• Data Factory & Databricks: Orchestrating data pipelines and leveraging big data analytics.
• Fabric & DevOps: Ensuring streamlined development and deployment workflows.
• SQL, Python & Power BI: Core competencies for database management, data manipulation, and business intelligence.
High Earning Potential
Data Engineers rank among the highest-paid IT professionals. With expertise in cutting-edge tools, you can unlock lucrative roles that offer continuous growth and learning.
Gateway to Advanced Data Solutions
This training prepares you for emerging technologies like machine learning, real-time data streaming, and cloud-based analytics, ensuring your skills remain at the forefront of innovation.
Global Demand and Remote Opportunities
The global shift to digital and remote operations has amplified the need for Data Engineers. This role offers flexibility to work for top organizations or freelance globally.
Entrepreneurial and Innovative Growth
Armed with Data Engineering expertise, you can create your own data solutions, build startups, or consult businesses, making a mark in this dynamic field.
Strategic Impact on Organizations
Data Engineers are instrumental in designing and maintaining the infrastructure that fuels data-driven strategies, making them indispensable in driving organizational success.
Lifelong Learning in a Dynamic Domain
With the rapid evolution of technologies like Azure Synapse, AI-integrated Fabric, and cloud-native platforms, Data Engineers enjoy continuous learning opportunities, staying ahead in a competitive landscape.
What is Data Engineering, and why is it important?
Data Engineering involves designing, building, and managing systems for collecting, storing, and analyzing data efficiently. It's crucial because organizations rely on well-structured and accessible data to power decision-making, analytics, and AI solutions.
What are the prerequisites for a Data Engineering course?
Basic knowledge of:
SQL and databases.
Programming (preferably Python).
Data concepts like ETL (Extract, Transform, Load) processes.
Cloud fundamentals.
Who should enroll in this course?
Aspiring Data Engineers.
Software developers transitioning to data roles.
Analysts or BI professionals looking to enhance their skills.
IT professionals interested in cloud and big data technologies.
What career opportunities are available after completing this course?
Our course covers industry-leading tools and platforms, including:
Microsoft Azure (Azure Data Lake, Synapse, Data Factory).
Databricks (for big data processing).
SQL and Python (core for querying and scripting).
Power BI (for data visualization).
Fabric and DevOps (for deployment and monitoring).
What tools and technologies are essential for AI engineering?
Graduates can explore roles such as:
Data Engineer.
Cloud Data Architect.
Big Data Developer.
Analytics Engineer.
ETL Developer.
These roles are in high demand across industries like tech, healthcare, e-commerce, finance, and more.
How is Data Engineering different from Data Science?
Data Engineers focus on building the infrastructure and pipelines that Data Scientists use to analyze data. While Data Scientists perform predictive analytics and build models, Data Engineers ensure the data is clean, accessible, and reliable.
Is certification available upon completion of the training?
Successful participants will get the AIN GenX and Skill Development Council Karachi certificates.
Instructor
Irfan Bakaly
Data Analyst
25+ years of experience in Data Analysis
Noor Surani
Tech Entrepreneur
25+ years of experience in Data Analysis
Amynah Reimoo
Data Analyst
2+ years of experience in Data Analysis
Haris Jafri
Data Scientist
2+ years of experience in Data Science
Sohail Ahmed
Data Scientist
5+ years of experience
in Data Science
Who this course is for:
Students and Fresh Graduates, IT and Software Professionals, Data Enthusiasts, Career Switchers, Entrepreneurs and Innovators, Anyone Curious About Data
Duration
● 3 Months (55 Hours)
Classes
● Online via Zoom
Schedule
- Days: Sat & Sun only
- Timing: 06:30 to 09:00 PM (PST)
Starting From
● Sunday, 08 December, 2024
Course Fee
● Online
Rs. 14,000/- (one time payment)
Account Details
Bank: Habib Bank Limited
Account Title: AIN GenX
Account No: 5910-70000512-03
IBAN No: PK08 HABB 0059 1070 0005 1203
Secure a Verified Certificate from AIN GenX
Apply for Skill Development Council Certificate
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