Become a Big Data Hadoop Developer from scratch

Demonstrates how hdfs, a distributed file system, enables high performance data access across clusters by storing files as 64 mb blocks replicated three times for fault tolerance and high availability.

Discover how HDFS uses master name node and data nodes to manage a block-structured file system, with metadata in fsimage and edit logs and a secondary name node performing checkpoints.

Explain how the NameNode stores metadata, maps filenames to blocks, and tracks blocks with DataNodes via heartbeats and block reports; the Secondary NameNode is not a hot standby.

Learn how to perform HDFS file read and write operations, including opening a file, locating blocks, writing via data output streams with authentication tokens, and reading with DFS input streams.

Learn to manage data on the hdfs with commands like ls, ls -R, cp, mv, rm, cat, and du, including copying between local and hdfs and creating directories.

Explore the MapReduce programming model for processing data sets in parallel on distributed hardware. Learn how map, shuffle, and reduce stages transform input data in Hadoop workflows.

Process input data with the map phase, emitting key-value pairs from lines, then aggregate using the reduce phase to produce final results stored in the distributed file system.

Submit a MapReduce job to the cluster, distribute data to nodes, map inputs to (word, 1), shuffle by keys, and reduce to final word counts stored on the nodes.

Coordinate map and reduce tasks across nodes, optimize data locality, and manage communication, failure recovery, and debugging within the MapReduce architecture.

Discover how combiners summarize map outputs per key to cut network traffic between map and reduce, and how partitioners route intermediate data to reducers with a hash-based scheme.

Explain how multiple map tasks process input, shuffle and sort intermediate key-value pairs, partition data with a hash function, and merge results in a final reduce task.

Build a max temperature MapReduce program in Java for Hadoop, creating an Eclipse project and implementing a mapper and reducer to determine the maximum temperature from input text.

Configure a maximum temperature mapreduce program by defining the job name, map and reduce classes, and text input and output formats to identify the highest temperature for a given year.

Explore the mapper, reducer, and driver classes in Hadoop MapReduce; implement the mapper interface, define input/output types, configure and submit the job to the cluster via the driver’s main method.

Explore the differences between the new API and the old API, including map-based mappers, interfaces, and job configuration practices, with practical contrasts.

Learn how GenericOptionsParser interprets common options and sets them on a configuration object, and how Tool and ToolRunner enable running with dynamic configurations and property mappings.

Learn how writables in Hadoop enable Java-based serialization for transferring data between clusters, using the implements pattern to read and write data across network and local storage.

Explore how serialization converts objects into a byte stream for network transmission and persistent storage, and how deserialization reconstructs those bytes into structured objects for Hadoop workflows.

Learn how schedulers allocate resources to jobs in a distributed environment, describing three scheduler types and capacity-based allocation, and explore how a distributed cache supports job configuration across machines.

Explore the sequence file structure, with a header and one or more records, using a magic number, version, and user-defined key-value clauses, plus a sync boundary to minimize storage overhead.

Explore mrunit testing to unit test map operations in isolation and verify outputs against expectations, following testing standards for Hadoop development.

Discover Apache Pig, a platform for building large-scale data analytics with Pig Latin and an execution framework that translates scripts into MapReduce tasks, supporting ETL and UDF extensions.

Apache Pig provides an open-source data processing tool for structured and unstructured data, with a simple data-manipulation language, used by Yahoo, Google, and Microsoft for ad hoc processing.

Compare Apache pig and mapreduce, highlighting pig’s high-level declarative language and simpler data transformations that reduce lines of code and development time on Hadoop clusters.

Explore where Pig is best suited within Hadoop workflows, covering job scheduling, log processing, DFS storage, ad hoc queries with access controls, and rapid prototyping for petabyte-scale data using MongoDB.

Identify scenarios where using Pig for big data processing is not ideal, such as nested data formats, unstructured data, audio data, and text, highlighting Pig's slower performance versus alternatives.

Learn pig latin, a data flow language for Hadoop, detailing load and store operators to load, transform, filter, group, join, and split data in interactive or batch modes.

learn to run Pig in local and distributed modes on Hadoop, using local file systems on a single machine, and debug and manage jobs with job tracker.

Discover how HBase, a distributed NoSQL data store, supports random real-time data access and scalable storage on commodity hardware, while lacking traditional RDBMS features.

Discover the diverse use cases of Apache Pig in big data environments, from real-time data storage and processing to advertising selection and user event analytics across large clusters.

Learn about NoSQL databases, a non-relational data model that stores and retrieves data beyond traditional tables. Explore how these databases use data manipulation techniques to solve big data problems.

Explore the four NoSQL database categories - key-value stores, column-family databases, document databases, and graph databases - and how each stores and accesses data, with notes on hash table methods.

Apache HBase offers horizontal scalability with strong read/write consistency, automatic region splits and rebalancing, and a Java API for programmatic access.

HDFS is a fault-tolerant, high-throughput distributed file system for low-cost hardware and large data sets; HBase is a non-relational distributed database on HDFS enabling random, low-latency lookups for large tables.

Explore where to use HBase for high-volume data and unstructured data, store data in column-oriented stores on existing hardware, and write with Apache HBase.

Learn when not to use HBase: for very large data volumes, it suits, but for small datasets a traditional database can be better, and HBase lacks robust optimizer and joins.

Explore the building blocks of Apache HBase: tables, rows, columns, cells, and column families, and learn how regions and region servers use automatic splitting for scalable storage and API operations.

Learn how a column family groups related columns by prefix in hbase, stores them together in the storage file, and uses qualifiers like family:column to access data.

Explore how data is physically stored in column families on disk, with columns such as employee name, address, date of birth, and gender organized into files.

Explore how timestamps act as versions within column families to retrieve latest or historical values by specifying row, column family, column name, and timestamp.

Explore general HBase commands to interact with the Hadoop file system, check cluster status, and manage daemons, masters, and region servers, with user authentication and basic identity checks.

Explore data definition language in HBase, defining schemas with create table and column families, and using describe, list, exists, drop, and disable to manage tables.

Explore data definition language in HBase, practicing commands to disable and enable tables, enable all, and check table status, with practical steps to list and manage tables.

Explore the Apache architecture in a master–slave model where the master coordinates requests and meta data, while region servers manage regions and enable load balancing and failover.

Learn why Zookeeper coordinates masters and region servers in a distributed big data architecture, and how it manages heartbeats, region assignments, and crash recovery with a persistent store.

Learn how a client interacts with the HBase cluster: locate the master via ZooKeeper, route requests through region servers, and manage data with in-memory storage and block-level file formats.

Explore bloom filters in HBase, a probabilistic data structure that tests membership, saves space, and can be enabled at the column family level to reduce misses and speed lookups.

Explore the HFile in HBase, focusing on edge files and their block-based, segmented structure with optional compression, indexes, and trailer metadata for efficient storage and search.

Create a table in Apache HBase using Java API by configuring, building a table descriptor, adding column families, and invoking the create table method.

Learn how to delete a table in Apache HBase using the Java API by disabling the table and invoking the delete table method via the Edwin class.

Learn to disable a table in Apache HBase using the Java API, verify with isDisabled, and apply disableTable via the admin interface while handling configuration.

Insert data into an Apache HBase table using the Java API by configuring, instantiating the table interface, applying put with column family and qualifier values, and verifying with a scan.

Learn how to integrate Apache HBase with Pig by loading data into HDFS, creating a table with a column family, and storing data into HBase using Pig.

Create and verify hdfs directories, copy local data to hdfs, and inspect files. Load data with pig and view results using dump with a defined schema.

Explore integrating HBase with Apache Sqoop by importing data from an RDBMS into Hadoop, including table creation, data insertion, and configuring connect, username, and password for transfers.

Explore how to integrate Apache HBase with Sqoop by connecting to a MySQL server, creating a database and table, inserting data, and importing it into HBase.

Master filtering operations in HBase by using get and scan with custom filters to return a subset of data, reducing network bandwidth and client data transfer through server-side filtering.

Explore the only filter in HBase, which returns only the key component of each value, demonstrated with a multi-column family table and scan.

Apply the first key only filter in HBase to return first value from each row, and use the prefix filter with a prefix to return rows starting with that prefix.

Learn to use the column count get filter to return the first columns of a table and the page filter to fetch a limited number of rows, with syntax examples.

Learn how the column prefix filter returns values in a column that start with a given prefix, and how multiple column prefix filters apply prefixes across columns using logical operators.

Explain value filter with compare operators to return the key value from a table, using iscan syntax, limit, and filter, and applying it to the source column.