Data Engineering

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Overview

Training in Data Engineering
Data Engineering involves designing, building, and maintaining systems for collecting, storing, and analyzing large volumes of data. It enables efficient data flow and access across organizations, supporting analytics, machine learning, and decision-making processes. Data engineers work with ETL pipelines, databases, big data tools, and cloud platforms to ensure data reliability and scalability.
Data Engineering
Data Engineering is the backbone of data-driven organizations, responsible for creating robust infrastructure and pipelines that collect, clean, transform, and store data from diverse sources. These systems ensure that analysts and data scientists have timely, accurate, and accessible data for reporting, predictive modeling, and business intelligence. Scalability and automation are core objectives.

Modern data engineering relies on cloud platforms (AWS, Azure, GCP), big data tools (Spark, Kafka, Hadoop), and orchestration frameworks (Airflow, Prefect) to handle complex, high-volume workloads. Data engineers also implement data governance, quality checks, and real-time processing capabilities, enabling organizations to make faster and more informed decisions from reliable data assets.

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    Who should take this training?
    The GCP Associate Cloud Engineer certification is designed for individuals who want to demonstrate their ability to deploy, manage, and maintain cloud projects on Google Cloud Platform. It’s ideal for entry-level cloud practitioners, system administrators, or developers aiming to validate their foundational cloud skills.
    • Aspiring Data Engineers who want to build a strong foundation in data pipelines, databases, and big data technologies.
    • Software Developers looking to transition into data-centric roles and understand large-scale data architecture and infrastructure.
    • Data Analysts and Data Scientists who want to gain hands-on knowledge in data wrangling, ETL processes, and cloud data tools.
    • IT Professionals and System Architects aiming to design and manage scalable data systems in enterprise environments.
    • Engineering Students and Fresh Graduates with interest in the data domain, seeking job-ready skills aligned with current industry requirements.
    What you’ll learn
    You will learn how to design scalable data architectures, build efficient ETL pipelines, manage databases, and leverage big data and cloud platforms. This course prepares you for real-world data engineering roles with practical skills and tools used across industries.
    • Build and automate end-to-end data pipelines
    • Master SQL, Python, and data modeling
    • Work with big data tools like Spark and Kafka
    • Deploy cloud-based data solutions (AWS, GCP, Azure)
    Exam Overview
    • Level: Associate
    • Length: 120 minutes to complete the exam
    • Cost: 125 USD.
    • Visit: https://cloud.google.com/certification/
    • Format: Multiple choice and Multiple select questions.
    • Delivery Method: Online proctored or at a test center via Kryterion.

    Program Highlights

    Earn a certificate and 36 Continuing Education Units from iGuroo
    Insights and case studies from renowned subject matter experts
    A great foundation towards a degree or certification in cloud computing and GCP
    Capstone presentation project to share with potential employers

    This Course Includes

    Hours of instructor led interactive classes
    0 +
    Hours of pre recorded sessions for reference
    0 +
    Practice question sets for certification preparation
    0
    Capstone project to hone your learnings
    0

    Weekly Program Planner

    Learners should expect to dedicate a minimum of 10-12 hours per week to the program

    1.1 Overview of Data Engineering
    • Role and ResponsibilitiesIntroduce the function of a data engineer, including responsibilities such as building pipelines, managing data lakes, and supporting analytics teams. Discuss the typical tech stack and career outlook.
    • Data Engineering EcosystemExplore the tools and platforms commonly used in data engineering, including relational and NoSQL databases, distributed systems, and cloud providers.
    1.2 Python Programming for Data Engineers
    • Python BasicsCover core concepts such as data types, loops, functions, file handling, and error management. Emphasis on scripts used in automation and data processing.
    • Data Structures in PythonLearn about dictionaries, lists, sets, and tuples with practical examples relevant to handling and processing structured and semi-structured data.
    • Working with APIs and JSONDemonstrate API consumption and processing JSON data in Python. Use cases for integrating third-party services and fetching datasets from REST endpoints.
    2.1 Fundamentals of Databases
    • Database Design Concepts: Discuss relational database design, including normalization, keys, indexes, and schema creation using ER diagrams.
    • PostgreSQL and MySQL Basics: Introduction to two major relational databases. Setup, create tables, insert data, and basic administrative tasks.
    2.2 Structured Query Language (SQL)
    • CRUD Operations: Master Create, Read, Update, and Delete operations using SQL for managing transactional data.
    • Advanced SQL Queries: Work with joins, subqueries, set operations, and window functions to handle complex analytical queries.
    • Performance Tuning: Cover query optimization strategies, use of indexes, and explain plans to improve SQL performance.
    3.1 Data Warehousing Concepts
    • Data Warehouse Architecture: Understand star and snowflake schemas, OLAP vs OLTP, and data warehouse lifecycle management.
    • Modern Data Warehouses: Introduction to tools like Amazon Redshift, Snowflake, and Google BigQuery. Explore use cases and performance comparison.
    3.2 Building ETL Pipelines
    • ETL Process Design: Design and implement ETL pipelines including extraction, transformation, and loading techniques using Python and SQL.
    • Workflow Orchestration: Use tools like Apache Airflow to manage scheduling, dependency handling, retries, and logging in ETL workflows.
    4.1 Introduction to Hadoop and Spark
    • HDFS and MapReduce: Cover the basics of Hadoop Distributed File System and the MapReduce programming model for batch processing large datasets.
    • Apache Spark Overview: Discuss Spark components: Core, SQL, Streaming, MLlib, and GraphX. Explore distributed processing capabilities and performance benefits.
    4.2 Spark Programming with PySpark
    • RDDs and DataFrames: Learn how to use Resilient Distributed Datasets and DataFrames for parallelized data manipulation in PySpark.
    • Transformations and Actions: Dive into common Spark operations and building data pipelines with lazy evaluation and execution plans.
    5.1 Cloud Platforms Overview
    • Introduction to AWS, Azure, and GCP: Compare major cloud providers with emphasis on storage, compute, and database offerings for data engineers.
    • Cloud Data Services: Deep dive into services like AWS Glue, Azure Data Factory, GCP Dataflow and how they support ETL and orchestration.
    5.2 Cloud Storage and Compute
    • Data Lake Architecture: Design data lakes using Amazon S3, Azure Blob Storage, and Google Cloud Storage, and integrate with data processing tools.
    • Serverless Computing: Explore usage of AWS Lambda, Azure Functions, and Google Cloud Functions for real-time data processing workflows.
    6.1 Data Governance and Quality
    • Data Catalogs and Lineage: Implement metadata management with tools like AWS Glue Data Catalog, Apache Atlas, and Amundsen for transparency and traceability.
    • Data Quality Frameworks: Design data validation and error detection mechanisms using tools like Great Expectations and Deequ.
    6.2 CI/CD and Streaming Data
    • CI/CD for Data Pipelines: Setup continuous integration and deployment for data workflows using Git, Jenkins, Docker, and Terraform.
    • Real-Time Processing: Build streaming pipelines using Kafka, Apache Flink, or Spark Structured Streaming for event-driven applications.

    Testimonials

    ABOUT CLOUD COMPUTING

    The AWS Certified Solutions Architect – Associate certification has been a game-changer for my career. It provided me with in-depth knowledge of AWS services, cloud architecture, and best practices for building scalable, secure, and cost-efficient systems. The hands-on labs and practice exams were invaluable in reinforcing key concepts. Since earning the certification, I’ve gained confidence in designing cloud solutions and have opened up new opportunities for career growth. I highly recommend this certification to anyone looking to specialize in cloud architecture.

    Ashwini Gourukanti

    SEO Executive

    The AWS Certified Solutions Architect – Associate course was my introduction to AWS. It helped me understand cloud platforms and how to effectively leverage cloud services. The course covers the core concepts to help understand services and their features. I could understand how to use these services whilst adhering to AWS best practices for building secure, scalable, and cost-efficient solutions. I gained hands-on experience with key AWS services such as EC2, S3, RDS, and VPC. By the end of the course, I felt well-prepared to tackle cloud projects, improve existing systems, and achieve AWS certification.

     

    I highly recommend this course to anyone looking to start or advance their journey in cloud computing with AWS!

    Jumana Bagwala

    Software Engineer, AWS Certified

    As a Software Engineer, I wanted to expand my expertise in cloud architecture, so I pursued the AWS Certified Solutions Architect – Associate certification. The certification process was challenging but incredibly rewarding. It provided a deep dive into AWS services and taught me how to design scalable, secure, and cost-efficient solutions for real-world scenarios. The hands-on labs were particularly helpful, allowing me to apply theoretical knowledge to practical problems.

     

    Since earning the certification, I’ve been able to design more robust cloud architectures, optimize our infrastructure, and contribute to the team’s growth. It has definitely boosted my confidence and positioned me for new opportunities, both in my current role and in future projects. I highly recommend this certification to anyone serious about advancing their cloud expertise and becoming an effective AWS solutions architect.

    Manikanta Baswa

    Software Engineer

    Data Engineering