General Introduction to Big Data

Related lectures (32)

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

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

Explores the significance of reproducibility in data science and introduces Renku, a platform for managing data-driven projects.

Introduces collaborative data science tools like Jupyter notebooks, Docker, and Git, emphasizing data versioning and containerization.

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Covers collaborative data science tools, big data concepts, Spark, and data stream processing, with tips for the final project.

Covers Gaussian Mixture Models, Denoising, Data Classification, and Spike Sorting using Principal Component Analysis.

Explores Apache Hive for data warehousing, data formats, and partitioning, with practical exercises in querying and connecting to Hive.

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

Explores data representation, databases, cloud computing, and challenges in the cloud environment.