Hadoop Developer In Real World

Explore Hadoop's history from Doug Cutting's nutch and Google file system to an open source project born in 2006. Yahoo funds Hadoop, it becomes a top-level Apache project in 2008.

Split files into fixed-size blocks, maintain a distributed cluster view, and replicate HDFS blocks across nodes to ensure data integrity and scalable big data storage with commodity hardware.

Explore how hdfs stores data as blocks and replicates them three times by default for reliability. Understand how size choices from 4 kb to 256 mb affect efficiency and performance.

Explore hdfs basics using dfs commands to compare dfs with the local file system, copy files, manage replication and permissions, and understand block storage.

Explore how HDFS handles read and write operations by detailing the roles of the name node and data node, block storage, and replication strategies across the cluster.

Explore MapReduce components in real world workflows, including input splits, mappers, reducers, shuffle and sort, data partitioning, and the role of combiners to optimize performance.

Learn how MapReduce counters provide a unified view of a job across mappers and reducers, including file system and framework counters, and how to use user defined counters.

Explore implementing a Facebook-like mutual friends feature with Hadoop MapReduce, parsing json records, mapping friend pairs, reducing to compute shared friends, and outputting results via sequence files.

Learn how Apache Pig translates simple Pig Latin instructions into MapReduce jobs to run on a Hadoop cluster, empowering non-programmers to process data with an optimized, flexible data flow language.

Explore loading a dataset, filtering by year, and computing top 10 stock symbols by highest average volume with Pig Latin on Hadoop, including scripts and local testing before cluster execution.

Explore how Pig Latin enables efficient joins in real-world Hadoop workflows, covering inner, left, right, full outer, cross, and multi-table joins, plus ways to project and optimize joined data.

Learn a simplified page ranking model based on backlinks, and prepare a pedia dataset to implement a custom input format and a map function.

Build a page ranking workflow by creating a custom input format and reader to extract page titles and links from a wikipedia-like dataset, then implement a page rank load function.

Explore how Apache Hive enables SQL-like queries on Hadoop data by creating Hive tables in HDFS. Understand schema on read, partitions and buckets, and MapReduce integration.

Discover how Hive tables are defined and stored: learn about database and table creation, table location and type (managed vs external), storage formats, SerDe and input/output formats, and metastore metadata.

Learn to stream real-time twitter data using java and a twitter stream library, filter tweets by food keywords, and save results for Hive analysis of nested tweet and user data.

Identify influential food conversations on Twitter by analyzing a hive dataset, ranking users by follower counts and hashtags, using hive data types like struct, array, and map.

Explore how Hive window and analytical functions enable ten-day moving averages by partitioning by symbol, ordering by date, and framing rows, contrasting with simple group by.

Explore deduplicating Kickstarter campaign data with Hive using analytic window functions like row_number, lag, and lead to surface top five pledged campaigns per year and their neighboring pledges.

Learn how window and analytical functions in hive group Kickstarter campaigns into bands by year, using a 10,000 pledge-difference threshold, and apply the same approach to user sessionization.

Explore hdfs architecture with name node and data nodes, how blocks and locations are tracked, and how replication factor ensures data health. Learn core-site and hdfs-site configurations and block management.

Learn how Hadoop achieves high availability through checkpoint and backup nodes, and a primary-standby name node setup with a shared edits log for seamless failover.

Learn to design and manually deploy a three-node Hadoop cluster on AWS, installing Cloudera CDH 5.4, configuring name node, resource manager, data nodes, and security.

Set up a three-node Hadoop cluster by installing JDK on all nodes, installing and configuring Hadoop components, and deploying configuration files before starting services.

Set up a Hadoop cluster by creating hdfs and yarn directories, formatting NameNode, and starting the name and data nodes. Validate via the UI using a stock data mapreduce job.