Multicloud Data Engineering
GCP • Microsoft Azure • Apache Spark • PySpark • BigQuery • Synapse • Airflow • Databricks
Master end-to-end data pipelines across Google Cloud Platform (GCP) and Microsoft Azure using SQL, Python, Apache Spark, PySpark, BigQuery, Azure Synapse Analytics, ADLS Gen2, Cloud Dataflow, and Databricks with medallion architecture.
What You Will Master in 12 Weeks
Master end-to-end data pipelines across Google Cloud Platform (GCP) and Microsoft Azure using SQL, Python, Apache Spark, PySpark, BigQuery, Azure Synapse Analytics, ADLS Gen2, Cloud Dataflow, and Databricks with medallion architecture.
Program Highlights
Daily & Weekly Learning Routine
- Daily 90-minute structured classes with live interactive demonstrations
- Daily hands-on lab pipeline building on cloud sandboxes
- Weekly twice Show & Tell architecture defense sessions
- Weekly doubt-clearing and personalized pipeline code reviews
- Monthly mini projects and final multi-cloud capstone defense
Technologies & Core Competencies
Targeted industry stack covered through live coding, architecture blueprints, and dedicated sandbox labs.
Core Competencies Covered
Enterprise Tools & Platforms
Comprehensive 12-Week Curriculum
Exact, comprehensive weekly topics taken directly from official CES program specifications. Every week features hands-on assignments and code reviews.
Programming & Data Foundations
Mastering foundational data engineering tools, Linux shell environments, Python data structures, and serialization formats
- Linux command-line essentials: file systems, bash scripting, permissions, and cron jobs
- Python data engineering foundations: iterators, generators, custom modules, and virtual environments
- Data serialization formats: CSV, JSON, Parquet, Avro, and ORC column-oriented storage
- Interacting with REST APIs, handling paginated JSON responses, and web scraping basics
- Git version control for data workflows and collaborative repository patterns
Automated Data Extraction & Multi-Format Serialization Utility
Advanced SQL & Dimensional Data Modeling
Writing high-performance SQL transformations and designing analytical data warehouses
- Advanced analytical SQL: window functions (ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD)
- Common Table Expressions (CTEs), recursive queries, and complex aggregations
- Dimensional modeling: Kimball methodology, Star schema, Snowflake schema, Fact vs Dimension tables
- Slowly Changing Dimensions (SCD Type 1, Type 2, Type 3) implementation
- Database indexing strategies: B-Tree, Bitmap, clustering, and execution plan analysis
Financial Analytics Schema Design with SCD Type 2 History Tracking
Distributed Data Processing with Apache Spark
Understanding distributed compute architecture and big data processing with Apache Spark
- Big data challenges and distributed computing foundations
- Apache Spark architecture: Driver, Cluster Manager, Executors, and Tasks
- Resilient Distributed Datasets (RDDs): lineage graph, transformations, and actions
- Spark SQL and DataFrame API: schemas, column expressions, filtering, and grouping
- Lazy evaluation, catalyst optimizer, and physical execution plans
Large-Scale Log File Analysis Engine using Spark Core and DataFrames
PySpark Transformations, Joins & Optimization
Building production PySpark pipelines and tuning memory and shuffle performance
- Advanced PySpark DataFrame operations: user-defined functions (UDFs) and built-in functions
- Handling corrupt, malformed, and missing data gracefully in PySpark
- Join strategies: shuffle hash join, sort-merge join, and broadcast joins
- Performance optimization: repartition vs coalesce, caching, persistence, and skew handling
- Connecting PySpark to cloud object storage (GCS and Azure Blob/ADLS)
Optimized Customer Telemetry ETL Pipeline with Broadcast Joins and Partition Tuning
Google Cloud Platform: Storage & Serverless BigQuery
Architecting cloud data warehouses on GCP using Cloud Storage and BigQuery
- Google Cloud Platform architecture: projects, IAM roles, service accounts, and resource hierarchy
- Google Cloud Storage (GCS): buckets, storage classes, lifecycle policies, and security
- Google BigQuery architecture: compute-storage separation, slots, and capacitor columnar format
- BigQuery table design: time-unit partitioning, range partitioning, and multi-column clustering
- BigQuery performance tuning, query execution plans, bi-engine caching, and cost governance
Multi-Terabyte Analytical Data Warehouse on BigQuery with Partitioning & Clustering
GCP Ingestion & Streaming Pipelines
Building real-time and managed processing pipelines with Pub/Sub, Dataflow, and Dataproc
- Google Cloud Pub/Sub: topics, subscriptions, push vs pull delivery, and dead-letter topics
- Google Cloud Dataflow: unified batch and streaming data processing with Apache Beam
- Windowing concepts: fixed windows, sliding windows, sessions, and watermarks
- Managed Hadoop & Spark with Google Cloud Dataproc: ephemeral clusters and job submissions
- Serverless Cloud Functions for event-driven data ingestion triggers
Streaming IoT Sensor Anomaly Detection Pipeline with Pub/Sub and Beam Dataflow
Microsoft Azure: Storage, SQL & Synapse Analytics
Ingesting and organizing enterprise data lakes on Microsoft Azure
- Microsoft Azure foundation: subscriptions, resource groups, and Azure RBAC security
- Azure Data Lake Storage (ADLS) Gen2: hierarchical namespace, ACLs, and blob lifecycle
- Azure SQL Database: serverless compute, elastic pools, and hybrid transactional processing
- Azure Synapse Analytics overview: dedicated SQL pools, serverless SQL pools, and Spark pools
- Querying data lake files directly with serverless Synapse SQL
Enterprise Azure Data Lake Ingestion with ADLS Gen2 and Serverless Synapse SQL
Azure Data Factory (ADF) & Pipeline Orchestration
Building automated enterprise ETL/ELT pipelines using Azure Data Factory
- Azure Data Factory architecture: linked services, datasets, pipelines, and activities
- Copy Data activity: source-to-sink mapping, partition loading, and schema drift handling
- Mapping Data Flows: visually designing scalable transformations in ADF
- Pipeline parameters, variables, control flow (ForEach, IfCondition, Until), and triggers
- Monitoring pipeline runs, error alerting with Azure Monitor, and Git integration
Automated Enterprise Ingestion Pipeline with Dynamic Parameters and Failure Alerts in ADF
Lakehouse Architecture with Databricks & Delta Lake
Building high-performance lakehouses using Databricks, Delta Lake, and PySpark
- Databricks workspace, cluster types, runtimes, and notebook collaboration
- Delta Lake core technology: ACID transactions on object storage, write-ahead transaction log
- Medallion architecture implementation: Bronze (raw), Silver (cleansed), Gold (business-ready)
- Delta Lake features: schema enforcement, schema evolution, time travel, and vacuum
- Optimizing Delta tables: Z-Ordering, compacting files, and Delta Live Tables (DLT)
Production Medallion Lakehouse on Databricks with Time Travel and Delta ACID Tables
Multi-Cloud Data Governance, Security & Quality
Enforcing unified data governance, quality contracts, and security across GCP and Azure
- Multi-cloud governance challenges: unified catalogs with Databricks Unity Catalog and GCP Dataplex
- Data quality frameworks: Great Expectations, schema validation, and automated quality gates
- Security and privacy: column-level encryption, row-level security, and PII anonymization
- Cross-cloud data synchronization and replication strategies
- Cost optimization, budget alerts, and resource tagging across GCP and Azure
Automated Multi-Cloud Data Quality Audit & Masking Pipeline with Great Expectations
Orchestration with Apache Airflow & Cloud Infrastructure (IaC)
Orchestrating end-to-end multi-cloud workflows with Airflow and provisioning infrastructure with Terraform
- Apache Airflow architecture: Webserver, Scheduler, Metadata Database, and Workers (Celery/Kubernetes)
- Authoring Airflow Directed Acyclic Graphs (DAGs) in Python: tasks, operators, and dependencies
- Airflow sensors, hooks, XComs, connection secrets, and dynamic task mapping
- Google Cloud Composer (Managed Airflow) deployment and monitoring
- Infrastructure as Code (IaC) with Terraform: provisioning BigQuery, GCS, ADLS, and Synapse
Cloud Composer Airflow Multi-Cloud DAG Orchestrator with Automated Terraform Provisioning
Multi-Cloud Enterprise Capstone Pipeline & Defense
Delivering an end-to-end cross-cloud analytics product and conducting architectural defense
- Integrating streaming and batch multi-cloud pipelines (GCP BigQuery + Azure Synapse + Databricks)
- Building executive BI dashboards in Looker Studio and Power BI
- Production checklist: latency SLAs, disaster recovery, data backfilling, and incident runbooks
- Comprehensive technical mock interview covering GCP, Azure, Spark, SQL, and system design
- Live Show & Tell capstone presentation before industry data engineering leaders
Cross-Cloud Enterprise E-Commerce Analytics Lakehouse with Real-Time Ingestion and BI
Industry Projects & Capstone Deliverables
Graduation requires building and presenting an enterprise portfolio project following the standard software development lifecycle.
Multi-Cloud E-Commerce Analytics Lakehouse
End-to-end medallion pipeline ingesting streaming orders via Pub/Sub, transforming data in Databricks with PySpark, and syncing curated models to BigQuery and Synapse.
Real-Time Clickstream Multi-Cloud Ingestion
Streaming data pipeline ingesting clickstream events, performing tumbling window aggregations with Apache Beam Dataflow, and loading into cloud analytics warehouses.
Who Can Join This Program
- Data analysts, database developers, and software engineers seeking to transition into multi-cloud data platforms
- Graduates and tech professionals with basic SQL/Python wanting enterprise Big Data engineering experience
- Cloud practitioners looking to gain dual expertise in Google Cloud Platform and Microsoft Azure data ecosystems
Laptop & System Requirements
- Fundamental understanding of relational database concepts and SQL queries
- Basic familiarity with Python programming
- Broadband internet connection for cloud console access
Target Career Opportunities & Preparation
Target Job Roles
Interview Preparation Structure
- Comparative cloud architecture: GCP BigQuery vs Azure Synapse Analytics storage and compute
- Spark internals: DAG execution, shuffles, partitions, memory management, and broadcast joins
- Lakehouse design patterns: ACID transactions on object storage, schema evolution, and time travel
- Data pipeline orchestration: SLA handling, retries, backfilling, and monitoring in Airflow/ADF
- Mock interviews with senior enterprise data architects and scenario-based query tuning
Questions About This Program
Why learn both GCP and Microsoft Azure data engineering together?
Modern enterprises increasingly adopt multi-cloud strategies to prevent vendor lock-in, optimize costs, and comply with regulatory requirements. Mastery of both GCP and Azure makes you exceptionally competitive in high-paying enterprise data roles.
Do I need cloud certifications before joining this course?
No previous cloud certification is required. The program starts from foundational Linux, SQL, and Python concepts before systematically walking you through GCP and Azure architectures.
Ready to Begin the Multicloud Data Engineering?
Register your profile for skill baseline evaluation or speak directly with an academic advisor via WhatsApp to confirm the next batch schedule.