Data Warehouses and Data Lakes

Related lectures (32)

Page 1 of 4

Introduces data warehousing fundamentals, challenges, and the innovative concept of a 'lakehouse'.

Covers data wrangling techniques using Apache Hive for efficient big data management.

Explores data warehouses, decision support systems, OLAP, data lakes, multidimensional data models, and query optimizations.

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Explores combining data at rest with data in motion, emphasizing the Lambda architecture complexities and quality assessment of streams and batches.

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Covers the principles of data stream processing and its applications in real-time data analysis.

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.