Data Warehouse, Data Lake & Data Lakehouse - Fundamentals

Explore databases as the backbone of digital apps, covering structured data, SQL querying, concurrency, security, and reliability, and contrast OLTP with OLAP workloads.

Explore OLTP, online transaction processing, designed for large numbers of short transactions with real-time updates, high concurrency, and maintained data integrity.

Explore olap, or online analytical processing, which enables multi-dimensional analysis of large historical data through data cubes with time, geography, and product dimensions, supporting reporting, decision making, and batch updates.

OLTP handles fast transactions on current data with normalized schemas for speed and reliability. OLAP analyzes large historical data with denormalized schemas for complex queries and insights.

Explore how databases organize structured data, scale for growing workloads, ensure high availability and security, automate tasks, and enable integration with APIs and data pipelines for data-driven decisions.

Explore database challenges in large-scale distributed environments, including design complexity, performance trade-offs between normalized and denormalized schemas, and data partitioning across services. Tackle costs, data duplication, governance, and observability.

Learn ETL and ELT data integration patterns, moving data from sources to a data warehouse, with pre-load data cleaning in ETL and transformations in ELT for cloud platforms and analytics.

Explore how a data warehouse supports OLAP analytics by collecting and transforming data from OLTP systems through an ODS, ETL processes, and data marts.

Consolidate data from CRM, ERP, and transactional systems into a single source of truth. Enable trend analysis, forecasting, OLAP, and machine learning with clean, integrated data and scalable performance.

Explore the challenges of data warehouses, including high initial cost and maintenance. Evaluate ETL complexity, scalability with semi-structured data, batch latency, data governance and quality, integration, and vendor lock risks.

Explore the technologies powering data warehouses and compare leading platforms like Amazon Redshift, Google BigQuery, Snowflake, and Azure Synapse Analytics to guide your data strategy.

Understand data lakes as scalable storage for structured, semi-structured, and unstructured data, guided by medallion architecture—bronze, silver, gold—with etl, spark, and access for bi, data science, and machine learning.

Explore the advantages of data lakes: scalable, cost-efficient storage for structured, semi-structured, and unstructured data with schema-on-read flexibility and support for real-time analytics and machine learning.

Explore the challenges of data lakes, including data swamp risk without governance, complex integration with legacy systems, and limited acid support affecting reliability, auditability, and performance in large-scale queries.

Explore data lake technologies across Amazon S3 with Lake Formation governance, ADLS, Google Cloud Storage and Big Lake, and Hadoop ecosystems with Hive and Spark, plus Iceberg and Hudi.

Explore how data lake house merges data lakes and warehouses into unified storage enabling performance analytics with governance and acid-compliant tables, using open formats like Delta Lake, Iceberg, and Hudi.

Explore how data lakehouse unifies lakes and warehouses to deliver cost-effective, scalable analytics, with real-time and batch processing, governance, security, interoperability, and support for Parquet, Avro, JSON.

Examine data lakehouse challenges, benefits, and the integration of Spark, Delta Lake, Iceberg, and Trino. Understand governance, metadata, performance, costs, and organizational readiness shaping implementation.

Explore how data lakehouses use storage formats and table formats with transaction layers and compute engines to enable time travel, metadata governance, and scalable analytics.

Compare data lakes, data warehouses, and data lakehouses, outlining schema on read versus schema on write, all data types support, real-time analytics, governance, and BI and machine learning use cases.

Compare databases, data warehouses, data lakes, and data lakehouses, outlining oltp vs olap, etl vs elt, governance, and architecture choices for scalable, future ready data platforms.