Certified Data Engineering & Pipelines

Master Airflow, Spark, and Data Lakes to build & deploy robust ETL pipelines on AWS & GCP Cloud.
👥 34 students

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Course Overview
- This certified program immerses participants in modern data engineering, focusing on designing, building, and deploying scalable, robust ETL pipelines that drive business intelligence and operational efficiency.
- Gain comprehensive, hands-on expertise with industry-leading tools like Apache Airflow for sophisticated workflow orchestration and Apache Spark for distributed, high-performance data processing across vast datasets.
- Learn to architect, implement, and manage resilient Data Lakes, optimizing them for diverse data types, complex analytics queries, and cost-efficiency on leading cloud platforms: AWS and Google Cloud Platform.
- The comprehensive curriculum emphasizes practical application, ensuring you can confidently design, develop, and deploy end-to-end data solutions specifically tailored for enterprise-level environments.
- With a strictly limited cohort of just 34 students, this course guarantees a highly interactive, personalized learning journey, thoroughly preparing you for real-world data engineering challenges and certified proficiency.
Requirements / Prerequisites
- Foundational Programming Skills: Proficiency in Python, encompassing basic data structures, algorithms, and fundamental programming constructs, is highly recommended as it underpins much of data engineering scripting and tooling.
- Basic SQL Proficiency: A solid working knowledge of SQL is essential for data manipulation; this includes familiarity with common query constructs such as joins, aggregations, subqueries, and DML (Data Manipulation Language) operations.
- Core Data Concepts: A fundamental understanding of data concepts such as different database types (relational vs. NoSQL), basic data warehousing principles, data modeling, and various data formats. Prior extensive data engineering experience is not strictly required.
- Cloud Computing Basics (Beneficial): While not mandatory, a preliminary understanding of cloud computing fundamentals (e.g., IaaS, PaaS, SaaS, compute, storage, networking services) on either AWS or GCP will significantly facilitate a smoother learning curve, as the course heavily leverages cloud services.
- Problem-Solving Aptitude: An inherent curiosity, strong logical reasoning, and a keen analytical mindset are crucial for engaging effectively with the complex technical and architectural challenges involved in large-scale data systems.
- Technical Setup: A reliable, high-speed internet connection and a personal computer capable of supporting modern development tools, cloud console access, and virtual environments are necessary for all hands-on labs and project work.
Skills Covered / Tools Used
- Advanced ETL Pipeline Design: Master modern methodologies for conceptualizing, designing, and implementing highly fault-tolerant, scalable, and idempotent Extract, Transform, Load (ETL) processes, including robust data quality checks, error handling, and recovery strategies.
- Apache Airflow Orchestration: Gain comprehensive expertise in scheduling, managing complex task dependencies, backfilling historical data, and meticulously monitoring the health and performance of intricate data workflows using Airflow’s powerful DAGs (Directed Acyclic Graphs), custom operators, and sensors.
- Apache Spark for Big Data Processing: Develop strong, practical capabilities in distributed data processing using Apache Spark, covering Spark SQL, DataFrames, RDDs, and leveraging its immense power for both batch and real-time processing of massive datasets.
- Data Lake Architecture & Implementation: Understand and practically implement robust Data Lake principles, including efficient data ingestion strategies, optimizing storage formats (e.g., Parquet, ORC), effective data cataloging (e.g., AWS Glue Catalog, Google Data Catalog), and managing the data lifecycle on AWS S3 and GCP Cloud Storage.
- Cloud Platform Proficiency (AWS & GCP): Acquire extensive hands-on experience deploying, configuring, and managing critical data engineering components and services across both Amazon Web Services (AWS) and Google Cloud Platform (GCP). This includes services like AWS EMR, Glue, Lambda, Athena, Redshift Spectrum, and GCP Dataproc, Dataflow, Cloud Functions, BigQuery.
- Containerization with Docker: Effectively utilize Docker for creating consistent and reproducible development environments, packaging data engineering applications, and ensuring seamless deployment across various environments.
- Version Control with Git: Apply industry best practices for collaborative development, robust code management, branching strategies, and version control using Git, which is essential for any modern data project team.
- Monitoring, Logging, & Alerting: Implement comprehensive strategies for observing data pipeline performance, proactively troubleshooting issues, setting up effective logging mechanisms, and configuring critical alerts to ensure continuous operational excellence and reliability.
Benefits / Outcomes
- Certified Professional Expertise: Attain a highly recognized certification that rigorously validates your advanced skills and practical mastery in data engineering, significantly boosting your professional credibility and market value.
- Comprehensive Job-Ready Portfolio: Develop and curate a compelling portfolio of practical, real-world data pipeline projects, explicitly showcasing your ability to design, implement, and deploy robust ETL and data processing solutions on leading cloud platforms.
- Expanded High-Demand Career Opportunities: Position yourself uniquely for a wide array of high-demand roles such as Data Engineer, Senior ETL Developer, Cloud Data Engineer, Big Data Engineer, or Data Platform Engineer across various industries and organizational sizes.
- Dual-Cloud Adaptability & Versatility: Acquire a highly versatile and in-demand skill set applicable to both Amazon Web Services (AWS) and Google Cloud Platform (GCP) ecosystems, making you an exceptionally adaptable and valuable asset in any multi-cloud or hybrid-cloud environment.
- Engineered Optimized Data Solutions: Learn to engineer data pipelines that are not only fully functional but also meticulously optimized for peak performance, cost-efficiency, scalability, maintainability, and security, directly translating into tangible business value.
- Extensive Practical, Hands-on Experience: Immerse yourself in extensive hands-on labs, real-world case studies, and a culminating capstone project, ensuring you can immediately apply your newly acquired knowledge and skills in a professional, enterprise-level setting.
- Networking & Collaborative Growth: Benefit immensely from focused interaction within a small, dedicated cohort of 34 peers and direct engagement with expert instructors, fostering a dynamic collaborative learning environment and significantly expanding your professional network.
- Mastery of Core Technologies: Achieve deep, actionable proficiency in critical technologies such as Apache Airflow for orchestration, Apache Spark for distributed processing, and Data Lake architectures for scalable data storage and management, enabling you to confidently tackle diverse and challenging data infrastructure problems.
PROS
- Dual-Cloud Advantage: Offers comprehensive, hands-on training across both AWS and GCP, uniquely distinguishing it from many single-platform focused courses.
- Practical Project Focus: Strong emphasis on building real-world, end-to-end projects ensures practical application and the creation of a tangible, job-ready portfolio.
- Personalized Learning: The limited class size of 34 students guarantees personalized attention and fosters a highly interactive and supportive educational experience.
- Current & Relevant Curriculum: The program directly addresses the most sought-after technologies and best practices essential for contemporary data engineering roles.
- Industry Certification: Provides a valuable certification that formally validates your expertise, significantly enhancing your professional credibility and career prospects.
CONS
- Significant Time Investment: Mastering such a broad and complex set of industry-leading technologies and multiple cloud platforms necessitates a substantial commitment of time and focused effort.