Big Data: Best Practices and Guidelines

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Wrangling with Hive: Managing Big Data Efficiently

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

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.

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 Techniques: HBase and Hive Integration

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

Data Wrangling with Hadoop

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

Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Data Wrangling with Hadoop: Storage Formats and Hive

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

Introduction to Data Stream Processing: Concepts and Applications

Covers the principles of data stream processing and its applications in real-time data analysis.

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.