Introduction to Spark Runtime Architecture

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

Data Wrangling with Hive: Managing Big Data Efficiently

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

Data Wrangling Techniques: HBase and Hive Integration

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

Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Digital Transformation: Solutions and Data

Explores digital transformation opportunities, big data, analytics, and technology innovations in business and research.

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

Big Data: Best Practices and Guidelines

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

Introduction to Applied Data Analysis

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.