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Related lectures (31)

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Data Wrangling with Hadoop: Advanced Techniques

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

Data Stream Processing: Apache Kafka and Spark

Covers data stream processing with Apache Kafka and Spark, including event time vs processing time, stream processing operations, and stream-stream joins.

Data formats and data wrangling with Hadoop

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

Data Wrangling Techniques: HBase and Hive Integration

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

Data Wrangling: Structuring and Cleaning Data

Explores data wrangling techniques, error detection, functional dependencies, denial constraints, and data temporality.

Data Wrangling with Hadoop

Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.

Collaborative Data Science: Tools and Techniques

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Data Wrangling with Hadoop: Storage Formats and Hive

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

Data Wrangling: ETL Process and Wrangling Issues

Explores the ETL process, data wrangling stages, and common issues.

Data Accuracy: Assessing Faithfulness and Error Detection

Explores data accuracy through faithfulness assessment, error detection, outlier handling, correlations, functional dependencies, violation detection, denial constraints, and data repairing techniques.