Big Data Hadoop - The Complete Course

Explore big data by querying a public dataset, download samples, and perform data processing and exploration to derive insights.

Learn to select names, define a destination, and work with a tree while filtering by gender and handling an error message.

Create and configure a big table instance within a big data Hadoop environment. Examine table design, storage choices, and performance considerations.

Explore the pub-sub topic, define topics and subscriptions, and recap how subscriptions relate to topic definitions, as discussed around examples like museum's collection.

Explore how Hadoop enables open source distributed processing across a cluster of computers, combining storage and processing to handle large data sets.

Analyze how rack descriptions demonstrate data replication across locations to improve availability and resilience, examining three copies, placement strategies, and disaster scenarios in big data environments.

Explore how hdfs write operation works: a client contacts the NameNode, checks file existence and permissions, then writes data with three replicas and acknowledges back to the client.

Explore selection of data nodes and how node distance influences block placement and replication, including multiple copies and processing time considerations.

Learn how serialization converts structured objects to a byte stream for network transmission and how deserialization restores them for inter-process communication, enabling communication between programs written in different languages.

Explore HDFS caching of frequently accessed blocks in memory to boost performance, and explain fencing and failover using ZooKeeper-controlled standby name nodes to ensure a single active node.

This lecture explains HDFS federation as a feature to balance load by adding multiple name nodes that partition namespace and block pools, improving scalability and fault tolerance.

Explore high availability in HDFS by coordinating primary and secondary name nodes, standby name nodes, and fencing to prevent a single point of failure and data corruption.

Explore Hadoop 2.0 features, including federation with multiple namespaces and name nodes, high availability, and enhanced processing with binary compatibility for MapReduce and snapshots.

Set up a Hadoop cluster on Unix by installing Java, creating dedicated Unix users, configuring HDFS, and using start-stop scripts to manage cluster operations and optional management tools.

Explore using the Hue HDFS file browser to open existing files, upload and create new files, read contents, and download items while managing cache for efficient access.

Explore using the HDFS file browser to open directories, manage user group permissions, upload files in multiple formats, and observe file metadata while refreshing views for up-to-date listings.

Learn to create and manage a cloud SQL instance, reading cloud resources and applying a serious, practical approach to building cloud skills within the Big Data Hadoop course.

Learn to create a bucket in Google storage, ensure unique bucket names, and upload files and objects like photos and documents into the bucket.

Explore how to submit a job on the platform, discuss managing objects and segments, and understand why a real job may take time.

Explore the function of job design, viewing jobs as configurable maps with properties. Learn to create and manage jobs, use configuration types, and track statuses to reduce burden.

examine how a Hue metastore manager coordinates store data, including customers and employees, with real-time creation times, locations, and storage permissions to manage databases and data sets.

Explore how YARN manages resources across a Hadoop cluster using a single resource manager and multiple node managers, launching containers to run application processes within constrained resources.

Hive provides a processing layer over data infrastructure to process data and store schema; not a relational database, open source by the Apache Software Foundation, used by Facebook and Amazon.

Discover Hive basics for data warehousing on Hadoop, enabling analysis of large data volumes by storing data in tables and databases and querying via a command line interface.

Discover Hive architecture by examining the file system, processing, and execution engine, then learn how interfaces and a database store schemas and data types with mappings.

Master Hive queries for big data analysis in the Hadoop ecosystem, learning to write and run selects, joins, and sorts, and to view results via dashboards and editors.

Explore a comparative overview of Pig, Hive, and MapReduce, highlighting language types, levels of abstraction, performance, and suitability for structured and unstructured data.