Data Lake Fundamentals

Explore data lake architecture across storage, processing, and management layers and learn schema on read. Compare data lakes with traditional data warehouses to highlight flexibility and scalability.

Examine the principles of data lakes, contrast schema on read with schema on right, and highlight metadata's role in effective data discovery, governance, and lineage.

Compare data lakes and data warehouses to clarify differences, use cases, and performance, guiding your data strategy with schema on read versus schema on write.

Avoid data swamp in your data lake by enforcing governance, metadata tagging, and data quality across ingestion, storage, and lifecycle, while empowering users with data literacy and automated management.

Explore how data lakes serve as scalable repositories across healthcare, finance, retail, manufacturing, and media, enabling predictive analytics and personalized experiences; evaluate data volume, variety, and governance to determine fit.

Data lake technologies underpin operations with Hadoop, AWS, Azure, and Google Cloud, while open source tools like Apache Hadoop and Apache Spark enable scalable data processing.

Plan and implement a scalable, secure data lake by defining objectives, selecting a technology stack, and managing ingestion, storage, metadata, cataloging, formats, and governance for analytics in cloud storage.

Explore data lake ingestion strategies—batch processing, real-time streaming, micro batching, and event-driven ingestion—and learn how volume, velocity, variety, and timeliness guide selecting the right approach.

Choose csv, json, parquet, and avro formats for data lake storage, weighing analytical performance, scalability, schema support, and compatibility to optimize data access and analysis.

Develop a metadata management strategy for a data lake by cataloging data, building a metadata repository, and leveraging metadata for lineage, quality, and compliance.

Champion data lakes across dev teams by articulating their value, enabling data accessibility and democratization, and aligning with business and technical objectives. Address objections on complexity, governance, and cost while highlighting advanced analytics, data science, agile development, and real-time BI.

Secure the data lake through access control, encryption, and monitoring, aligning with GDPR and CCPA, and implement role-based and attribute-based controls, data mapping, and data subject rights.

Explore data lakes' scalability, flexibility, and cost effectiveness while addressing governance, security, and complexity, and learn best practices for implementation, metadata, and collaboration.

Explore how data lakes evolve with AI and ML, real-time analytics, and governance practices, while lakehouse architectures merge flexibility with scalable big data strategies.

Discover data lake architecture, schema-on-read, and flexible analytics, plus ingestion strategies, formats, metadata management, and security governance that enable AI and real-time analytics.

Dustin Vannoy explains data lakes and data lake houses, their integration with data warehouses, Delta Lake, Parquet, and Photon for fast analytics, plus governance and data intelligence platform.