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Related lectures (14)
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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.
Data Wrangling with Hadoop
Covers data wrangling techniques using Hadoop, focusing on row versus column-oriented databases, popular storage formats, and HBase-Hive integration.
Integrating Scalable Data Storage and Map Reduce Processing with Hadoop
Covers the integration of scalable data storage and map reduce processing using Hadoop, including HDFS, Hive, Parquet, ORC, Spark, and HBase.
Big Data Ecosystems: Technologies and Challenges
Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.
Data Wrangling with Hive: Managing Big Data Efficiently
Covers data wrangling techniques using Apache Hive for efficient big data management.
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.
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 with Hadoop: Storage Formats and Hive
Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.
Big Data Best Practices and Guidelines
Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.
Spark Storage Layer
Explores the Spark ecosystem, Resilient Distributed Datasets, and the storage layer abstraction in Spark.
