Hadoop: Execution Models

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Related lectures (32)

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Hadoop Ecosystem: Architectural Choices & MapReduce Programming

Explores the Hadoop ecosystem's architecture and MapReduce programming model, emphasizing strengths and limitations.

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 Hive: Managing Big Data Efficiently

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

Big Data Ecosystems: Technologies and Challenges

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

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.

Optimizing Recursive Queries

Explores optimizing recursive queries in database systems using Datalog and semirings, discussing the challenges and solutions in data analytics.

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.

Data Wrangling with Hadoop

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

Scheduling Decisions: Data Locality and Multitenancy

Explores data locality in scheduling decisions for multi-tenant platforms and discusses Hadoop's architecture, execution engine optimizations, and fault tolerance strategies.