MapReduce Fundamentals: Learn Big Data Processing

Explore secondary sort in Hadoop by sorting output by values rather than keys, and apply partitioning and grouping in the map phase and reducers.

Create a word count composite key class for MapReduce, combining word and count, implement readFields and write for serialization, and compareTo to sort output by values.

Develop a word count workflow using a composite key in MapReduce, and implement map, sort by value, and custom group comparator and partitioner to drive reducers.

Design a custom composite key for word count in MapReduce, implement input key grouping, and apply hash-based partitioning to route values to the correct reducer.

Explore partition logic in MapReduce by building a word count pipeline with a composite key, partitioner, and reducer to produce sorted output.

Run a mapreduce job on cloud x using word count output, and view hadoop dfs files with hadoop fs commands, culminating in secondary sorting by value using composite keys.

learn how to perform joins in Hadoop with map-side and reduce-side strategies, and the prerequisites of partitioning, sorting, and in-memory datasets for efficient processing.

Create a configuration object and leverage Hadoop's distributed cache to perform a map-side join by loading a small dataset into memory and joining customer data by customer ID.

Learn how the mapreduce setup method initializes the job, loads configuration and distributed cache files, builds a hash map from cached datasets, and reads input lines for mapping.

Use a map side join mapper to read lines, split by comma, build a customer id–name map from a cached table, and emit joined order and customer data.

Demonstrates map-side join in Hadoop using distributed cache to combine orders and customers on customer id, showing output with customer id, order id, and customer name.

Explore how the combiner in Hadoop reduces map output and cuts reducer input to boost performance, with a code example and guidance on when not to use it for averages.

Learn to implement a word count MapReduce with a combiner in Hadoop, including mapper, reducer, and combiner classes, configuring the job and running it end-to-end.

Upload the combiner jar and run a Hadoop job with a combiner to merge map outputs into a reduced, combined output. The example shows hello twice before final reduce.

Explore a real world example of joining movies and ratings data in Hadoop using MapReduce, tagging records from movie and rating files, and counting ratings per movie with reducers.

Develop a ratings mapper in a new MapReduce class, parse ratings data using a delimiter, emit movie-specific rating counts, and prepare data for the reducer.

This lecture shows a mapreduce workflow to join movie data with ratings using movie id as key, producing movie name and its rating count.

Learn how to run a mapreduce job on hadoop to count movie ratings by joining movie and ratings data. Handle comma-delimited input, configure mappers and reducers, and interpret the output.

Calculate the number of ratings per user by joining the users and ratings files in a Hadoop MapReduce job, using two mappers and a tagged join.

Explore building a user rating reducer for a MapReduce workflow, integrating a ratings mapper, user data, and movie counts to produce structured output.

Join user data with ratings in a MapReduce workflow to compute per-user movie ratings, using user ID as the join key and running on Hadoop to generate per-user results.

Discover how Yarn transforms Hadoop 2 architecture into flexible cluster resource management, using resource manager, node managers, containers, and application masters to run MapReduce and other processing models.

Learn how yarn coordinates resources via node managers, heartbeats, containers, and application master to run MapReduce and Hive jobs with retries and high availability via ZooKeeper.