Hadoop Developer Course with MapReduce and Java

Explain how Hadoop, an open source Java-based distributed computing framework, provides reliable shared storage, fault-tolerant processing, and scalable data handling on commodity hardware.

Trace Hadoop history from early web crawlers and the distributed file system to Hive’s 2008 launch and 2009 summit, with references to elastic compute cloud concepts.

Compare HDFS with RDBMS to show how RDBMS handles online transaction processing of structured data, while HDFS scales large data for analytics.

Explore how a Hadoop cluster uses commodity hardware with a master node and worker nodes to run distributed processing, storing data as replicated blocks for fault tolerance.

Explore Hadoop features that deliver robust clustering, scalable performance, and data safety across distributed clusters worldwide for handling large data workloads.

Explore Hadoop cluster modes, including standalone and distributed configurations, and how HDFS acts as the input/output file system with master and data nodes.

explore Hadoop core components, including the distributed file system (HDFS) that stores large data sets and the distributed programming approach to analyze records at scale, using mapping and sorting.

Discover how hdfs stores data as 64 mb blocks, replicates them across a distributed cluster to improve throughput and availability, and offers a unix-like interface for efficient reads and writes.

Demonstrate block replication by creating replicas on different machines and verify that the correct replica is produced.

Discover how hdfs stores big data in blocks and tunes block size across a cluster. Learn how mapreduce applies functions to distributed data to map, process, and reduce results.

Explore how HDFS daemons coordinate with the name node and data nodes, manage blocks and replication, maintain the namespace, and perform heartbeats and snapshots for reliable storage.

MapReduce processes large data on a cluster of nodes, distributing data by keys and applying partitioning to generate scalable outputs from file systems or databases.

Explain why MapReduce matters in Hadoop by examining data replication and locality, locating data nodes, and expressing problems in any language through map and reduce.

Trace the history of MapReduce from its Java program origins to its open source roots at the Apache Software Foundation, noting Google's role and the 1999-2004 timeline.

Explore how MapReduce handles both structured and unstructured data to analyze customer behavior from web and retail data, unlocking opportunities to boost business.

MapReduce applications illuminate data analysis by extracting and transporting data from databases, enabling log analysis, validation for accuracy and consistency, and securing meaningful insights from complex data.

The lecture explains how a MapReduce program produces outputs as a key with a list of values, clarifying the program's goal and the result structure.

Explore how the map and reduce functions transform input into key-value pairs, group by keys like words, and produce aggregated outputs.

Build a MapReduce program in Java to compute the maximum temperature from text input in this hands-on session, using tokenization, input/output handling, and map and reduce logic.

Explore the dataflow in MapReduce, detailing how map outputs become input for reduce with key-value elements, and discuss reliability on commodity hardware.

Explore the end-to-end job submission flow in MapReduce, from input through map to shuffle, and finally to the reduce phase and output.

Explore a MapReduce style example that counts occurrences by splitting input into regions, mapping each element to a value, and reducing to a final output.

Explore how MapReduce daemons coordinate jobs across commodity hardware to process input data in parallel, manage job lifecycles, and produce reliable outputs while reducing costs.

Learn how to manage Hadoop MapReduce jobs with the job tracker, from preparing input files and submitting jobs to tracking results and updating job information.

Explore the task tracker concept, learning how to schedule and monitor a job, manage notifications, and ensure successful completion in a data processing workflow.

Follow how the job tracker handles task assignment by locating an available slot on a machine, preferring the same or nearest machine when possible and switching if unavailable.

Learn how to prepare, configure, and submit a MapReduce job, including setting input and output, packaging the code, and moving data.

Engage in a hands-on MapReduce workflow by extending map and reduce, processing input files, and emitting output via the Hadoop output collector.

Explore how the combiner reduces intermediate data and how the partitioner directs data into partitions before the shuffle in MapReduce.

Master dataflow with a single, multiple, and no reduce task in Hadoop, adjusting output and data production to optimize mapreduce pipelines.

Explore Hadoop 1.x architecture and its one-way data flow, detailing how a job splits into tasks, is scheduled by the job tracker, and runs on data blocks.

This lecture outlines Hadoop 1.x problems, including namespace and metadata limits, scalability with billions of items, and the absence of high availability necessitating manual recovery of the secondary.

Explore why the NameNode becomes a bottleneck and cannot scale horizontally beyond a limit, and why a different framework and architecture are required to achieve scalable Hadoop deployments.

Explains why there is no high availability in the NameNode, detailing fsimage and edit log roles, recovery implications, and the need to switch to a different framework.

Explore how the job tracker manages and schedules MapReduce jobs, monitors heartbeat signals from workers, and allocates job slots, highlighting the effects of an overburdened system on throughput.

Explore the key ideas of transaction processing, including online and financial transactions, and how system design impacts performance and profitability in complex workflows.

Explore Hadoop 2.x new features, including federation and high availability, as the architecture evolves to improve scalability, reliability, and API use.

Analyze the Hadoop 2.x architecture, focusing on how data is handled and recovery processes within the system. Explore how context and resource visibility shape understanding of the architecture.

Demonstrate high availability in Hadoop 2.x architecture with HDFS, using standby and secondary mechanisms to keep data accessible even when components go down, including snapshots.

Explore how YARN extends Hadoop by splitting resource management from job scheduling, moving beyond MapReduce. See how it enables online transaction processing, streaming, and batch processing for applications and jobs.

Explore how Yarn architecture supports multiple processing applications under a single resource manager, with application masters, per-machine containers, and resource negotiation.

Explore how MRv2 (YARN) manages resources and schedules MapReduce jobs, using a resource manager to negotiate for job execution, monitor status, and ensure applications restart when needed.

Explore how a yarn mapreduce application submits to the resource manager, the scheduler allocates resources to the application, and core work runs inside containers.

Understand how the resource manager allocates cluster resources to multiple applications through scheduling, capacity sharing, and container management, while the application manager handles job submission and monitoring.

Explore MapReduce 2.x cluster architecture by examining how the resource manager and application master coordinate monitoring, resource management, and client jobs.

Explore how the resource manager, application master, and yarn coordinate distributed apps, with hands-on CDH configuration, delegation security, and map-based graph processing concepts.

Learn how input splits divide large files into blocks by the cluster size, how the record reader extracts key-value pairs, and how maps consume data via these mediators.

Explore the Hadoop MapReduce workflow with mapper, reducer, and driver class, showing how input is split into chunks and read as key-value pairs (offset, data) for processing to output results.

Explore the differences between the old Hadoop MapReduce API and the new API, including job configuration, drivers, mappers, reducers, and key-value contexts and outputs.

Explore the generic option parser in Hadoop, showing how it simplifies running MapReduce jobs by parsing standard command line arguments into a configuration object, the driver class, and arguments.

Configure Hadoop jobs with GenericOptionsParser and ToolRunner by specifying configuration files and hyphen options such as -D property=value and -conf. Copy and archive files across file systems to prepare resources.

Explore writables in Hadoop and how to serialize data as byte streams for mapreduce. Implement primitive Java types like long and use equals and toString for data handling.

Learn how an object's state becomes a sequence of bytes through serialization for network transmission or long-term storage, and how deserialization reconstructs the object from streams.

Learn to chain multiple MapReduce jobs in a Hadoop workflow by wiring each job in the driver class, using the previous job’s output as the next input.

Learn to list active Hadoop MapReduce jobs and terminate unwanted ones, observe start times, and manage runtime status within a Java-based Hadoop workflow.

Discover how the Hadoop distributed cache makes common data available to all map tasks, improving performance, scalability, and data consistency.

Explore how Hadoop MapReduce counters track statistics about the entire job, using grouped and dynamic counters in Java to tally records across tasks and retrieve aggregated results.

Explore unit testing for Hadoop MapReduce on terabytes of data, verifying input processing, map and reduce logic, and expected outputs.

Learn how Hadoop schedulers allocate cluster resources to MapReduce jobs, scale with resource requests, and support pluggable strategies for fair sharing and preemption to maximize utilization.

Implement the fair scheduler in CDH by configuring Hadoop with a pre-configured distribution and writing code to enable fair scheduling.

Learn how data compression in Hadoop reduces input and output sizes, improves performance for MapReduce jobs, and when to apply compression to map input and intermediate data.

Explore Hadoop compression techniques, including block-based and record-level approaches, their impact on speed and storage, and how compatibility and full support influence MapReduce workflows.

Experience a data compression workflow and compute the maximum temperature from an input file through a hands-on session, tracing the program flow from compression to final output.

Learn to handle multiple inputs in MapReduce using Java, including reading input files, joining data, and producing outputs with practical map and reduce strategies.

Improve MapReduce job performance by tuning, using combiners to reduce data, enabling intermediate compression when beneficial, and profiling distributed executions to identify bottlenecks.

Explore profiling map and reduce tasks using a Java profiler to optimize map and reduce phases, reduce shuffle overhead, and lower network utilization.

Learn how filtering and projection in the map phase reduce shuffled data by omitting unneeded fields and keeping only relevant records, enhancing map output efficiency and later shuffle performance.

Learn how the combiner reduces data transfer by applying a local reduction to mapper outputs before they reach the reducer, with input and output types matching.

Explore how to analyze XML data with the Map Reduce framework in Hadoop, configuring jobs, processing XML inputs, and producing text-indexed outputs.

Learn to implement a custom partitioner in MapReduce that routes records by month using IP address keys, with driver setup to produce month-based outputs.

Learn how joining in MapReduce merges datasets on a common key, such as department id, using employee and department records, with map, reduce, and driver configuration to produce joined output.

Explore different input and output formats in MapReduce, including text input format and sequence file formats (text and binary), with keys, values, headers, and compression.