Data Wrangling with Hadoop

Related lectures (32)

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

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

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

Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

Covers the modeling of hydrological systems, focusing on the retention of floods and the example of the Routing System.

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Covers the basics of database systems, including data modeling, DBMS, data independence, and the course overview.

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

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.