Data Lakes: Structure and Optimization

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

Covers Spark Data Frames, distributed collections of data organized into named columns, and the benefits of using them over RDDs.

Introduces compiler theory, language processing, and the essential concepts behind building compilers.

Explores optimizing recursive queries in database systems using Datalog and semirings, discussing the challenges and solutions in data analytics.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Covers best practices and guidelines for big data, including data lakes, typical architecture, challenges, and technologies used to address them.

Covers OLAP queries, examples of comparisons, ranking, and optimizations for query performance.

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.

Covers data warehouses, data lakes, OLTP vs. OLAP, data quality, and the Data Lakehouse concept.

Explores the Hadoop ecosystem's architecture and MapReduce programming model, emphasizing strengths and limitations.