Introduction to Data Warehousing and Azure Synapse Analytics

Explore how data warehousing stores and analyzes data from HR, finance, CRM via ETL to centralize insights for reporting and business intelligence.

Explore the history of data warehousing from 1970s relational databases and mainframes to 1990s data consolidation for decision making. Witness cloud elasticity and Azure Synapse Analytics integrating OLAP and OLTP.

Explore the eight limitations of traditional data warehouses, including data silos, data duplication with complex ETM processes, and vendor dependency, and why modern cloud architectures enable scalability.

Explore how modern data warehouse architecture, cloud adoption, and technologies like MPP, data sharding, columnar storage, and HTAP enable scalable, cost-efficient data warehousing with Azure Synapse.

Gain a practical primer on essential data warehousing terminologies, including OLTP and OLAP, ETL processes, star schema, and introductory Azure cloud concepts in preparation for Azure Synapse Analytics.

Explore how OLTP and OLAP differ in data warehousing, with OLTP handling online transactional processing and OLAP powering analytics for business intelligence and data science.

Extract data from relational sources, transform with business rules, and load into a data warehouse, while exploring ETL vs ELT and staging for big data.

Learn how micro batch and change data capture enable near real-time data ingestion from source databases to a data warehouse, detailing CDC workflows, change tables, and capture and cleanup jobs.

Examine star and snowflake schemas, detailing fact and dimension tables, measures, and normalization, and decide when to use each design to optimize data warehousing with Azure Synapse Analytics.

Explore Azure Synapse Analytics, its core features and benefits for data warehousing and big data analytics, with pipelines, Spark, Delta Lake, and machine learning in a unified studio.

Demonstrates configuring an Azure Synapse Analytics workspace, creating SQL and Spark pools, loading data into a data lake, and analyzing with SQL native and Spark in Python and Scala.

Create an Azure Synapse Analytics workspace, configure storage and data lake assets, and import a sample dataset to demonstrate pipelines, SQL, and Spark capabilities.

Explore Azure Synapse Analytics Studio, a web-based environment for data scientists, analysts, and engineers to manage code, monitor artifacts, and review hubs: overview, data develop, orchestrate, and monitor and manage.

Create an Azure Synapse Analytics workspace, configure a resource group, storage account, security and networking settings, then deploy and access the workspace via the URL.

Explore the overview hub, the landing page for Azure Synapse Analytics Studio, linking tasks, artifacts, and documentation to ingest, explore, analyze, and visualize data.

Ingest a public dataset from an S3 bucket into Azure Data Lake Storage, using a linked service and a pipeline.

Explore the data hub architecture with workspace and linked workspace, understand data sources from education apps and storage accounts, and learn to browse, link, and manage datasets for pipelines.

This data hub demo configures spark pools and sequel pools, links Power BI, and demonstrates on-demand versus provisioned environments with parquet and csv ingestion.

Explore how Azure Synapse integrates notebooks and SQL scripts in one platform, enabling multi-language data analysis, visualization, machine learning, and data flows with data lineage and Power BI integration.

Explore a step-by-step demo of data wrangling, Spark provisioning, and linear regression in Azure Synapse Analytics, culminating in a Parquet dataset and Power BI report.

Explore how Azure Synapse SQL architecture uses a scale-out, decoupled compute and storage model to distribute processing across nodes, and compare SQL pool with SQL on-demand serverless architectures.

Explore azure synapse sql architecture, data warehouse units, and the massively parallel processing engine, with emphasis on storage and compute separation and its impact on cost and performance.

Explore the core components of Azure Synapse SQL-Pool architecture, including control node, AMP Engine, compute nodes, storage, data movement, and distribution types such as hash, round-robin, and replicated tables.