Cloud Analytics Solution | Module 5.8 | Surfalytics
Cloud analytics solutions are the core of what you’ll build and use every day in data engineering and analytics. In this lesson we go through the big picture: what pieces every solution has, how we got from data warehouses to lakehouses, and what AWS, Azure, and GCP actually offer—with real project examples and a clear path to hands-on practice. 📚 What you'll learn Theory • Key elements of analytics solutions: storage, integration, AI/ML, and access • Simple 3-layer architecture that fits most real systems • Evolution: data warehouse → data lake → lakehouse (ACID, Delta, Iceberg) • Data platforms: Snowflake and Databricks as all-in-one solutions • Roles: data engineer, BI, analyst, ML—and the “expert” goal • AWS: Glue, Kinesis, Redshift, S3, Athena, and typical patterns • Azure: Synapse, Fabric, Data Factory, Event Hub, Databricks • GCP: BigQuery, Dataflow, Looker • How to learn: certifications and pet projects per cloud Hands-on • AWS workshop and options for Azure and Google tutorials ⏱️ Timestamps 0:00 – Intro: why cloud analytics is the core of data work 0:33 – Quote: data → information → insight 1:33 – Key elements of analytics solutions 2:25 – 3-layer architecture (sources, ETL, warehouse, consumers) 3:41 – System design framework 6:28 – Evolution to lakehouse (ACID, Delta, Iceberg) 10:35 – Data platforms (Snowflake, Databricks) 11:52 – Key roles and the “expert” goal 13:58 – AWS data analytics stack 19:11 – Real AWS migration example 22:00 – Azure analytics stack 26:10 – GCP analytics stack 29:49 – Learning path: certs + pet projects 30:25 – Hands-on: AWS workshop and Azure/GCP options 📌 Resources mentioned • Surfalytics blog: system design framework (blog.surfalytics.com) • AWS data analytics workshops and tutorials • Azure and Google Cloud data/analytics tutorials • Surfalytics Discord for questions and hands-on guidance ✅ Key takeaways • One simple architecture fits most analytics solutions • Lakehouse = data lake + ACID and table semantics • Snowflake and Databricks are full platforms, not just warehouses • AWS #1 adoption; Azure strong in enterprise; GCP strong with BigQuery • Learn via certifications (general + data) and hands-on workshops --- Surfalytics – FREE course for beginners. Module 5: Cloud Computing. Lesson 8. #Surfalytics #DataEngineering #CloudAnalytics #AWS #Azure #GCP #DataWarehouse #Lakehouse #DataLake #Snowflake #Databricks #BigQuery #Redshift #Synapse #FreeCourse #DataAnalytics #BI #Beginners