Apache Hadoop Essential Training

Explore how Hadoop MapReduce uses mappers and reducers to transform data into key-value pairs and aggregate results across a cluster. See a weather example that computes the yearly maximum temperature.

Demonstrates map reduce examples in Hadoop, including a word count program and a numeric reduction, and explains running jobs on multi-node clusters to speed processing and improve precision.

Build a basic map reduce example that compiles a dictionary program translating English words to other languages, writing a dictionary file and running it as a jar.

Develop a diabetes risk prediction app from a scenario, using medical history features to predict a measured outcome. Build and test the application with machine learning.

Demonstrate machine learning basics with linear regression for one and multiple variables, predicting house prices from size and other variables, supervised learning, and cost minimization to train autonomous applications.

Designing the application covers collecting diabetes data, preparing it, and estimating parameters, automating data import with cron jobs, ETL to transform, and visualizing results for users.

Build and run a Java-based application with Apache Hadoop, implementing a linear regression workflow, compiling to a jar, and executing the regression job to estimate costs.

Learn how Pig, a high-level data flow language for Hadoop, describes read-process-store workflows as a dag of operators with join, sort, and filter, compiled to MapReduce and run on hdfs.

Hive, a tool that lets developers write queries in a familiar MSPL dialect and translate them into distributed jobs. Emphasize batch processing for data warehousing, with latency and no transactions.

Explore NoSQL databases, their data models (key-value, big table, documents, graph), and how CAP forces tradeoffs between consistency, availability, and scalability in distributed systems.

Explore major commercial Hadoop distributions, including Cloudera and Hortonworks, and their open-source cores with components like yarn, zookeeper, hbase, impala, and oozie, plus real-time analytics and integration tools.

Learn how the Hortonworks data platform delivers a 100% open source enterprise with paid support, a Microsoft, HP, and Rackspace alliance, and open Apache Hadoop tooling for big data workloads.

Learn the next steps to deepen your Hadoop and big data understanding, set up environments, and practice building small programs to become proficient or a data scientist.

Recap the key concepts of big data and Hadoop, outline distributed system components, and highlight cluster setup, MapReduce basics, and open-source Hortonworks workflows.