Learn how to Analyse Hadoop Data using Apache Pig

Explore big data through the volumes of structured, semi-structured, and unstructured data. See how data arrives from multiple sources at velocity and variety, challenging traditional processing with petabytes.

Explore Apache Hadoop, an open source framework for distributed processing of big data across clusters using MapReduce and HDFS, emphasizing fault tolerance, scalability, and high throughput on commodity hardware.

Explore how HDFS in Hadoop stores data reliably by distributing data into blocks across data nodes, coordinated by the name node, with replication for high availability and write-once, read-many semantics.

Explain how Hadoop MapReduce processes large datasets on clusters of commodity hardware. Use a map and reduce model with key-value pairs and a shuffle phase for scalable, fault-tolerant data processing.

Discover how Apache Pig provides a platform for ad hoc mapreduce on Hadoop and uses Pig Latin to abstract programming, with user defined functions in Java, JavaScript, or Ruby.

Compare pig vs mapreduce; pig is a high level declarative language for analyzing large datasets on a Hadoop cluster, offering shorter code and faster development than MapReduce.

Explore where to use Apache Pig for processing web logs on a Hadoop cluster. Identify when not to use it for unstructured data such as video, audio, and raw text.

Explore Pig Latin as a data flow language for Hadoop, learning to load, store, filter, group, join, and sort data with script-based operations.

Learn how to run Pig in local and MapReduce modes, using local execution for testing on a single machine and MapReduce on a Hadoop cluster.

Explore the Pig architecture in Hadoop, where the Pig engine translates statements into executable code, optimizes queries, and runs produced jobs on a Hadoop cluster, delivering results to clients.

Explore Pig’s data model by distinguishing scalar and complex types such as bag and map, and practice loading, describing schemas, and dumping data in a Hadoop environment using Apache Pig.

Learn how to use cast and comparison operators in Apache Pig to convert data types, with examples of loading, grouping, and producing typed outputs.

Analyze data with cast and comparison operators in Pig, including equals, not equals, less than, greater than, and regex matches, filtering data and producing output.

Delve into Apache Pig's relational operators, mastering cross, distinct, and filter, plus foreach, to transform data with joining, projecting, sorting, and grouping.

Learn relational operators in Apache Pig, focusing on group by, inner joins, and left, right, and full outer joins, with practical examples and schema construction.

Learn how to use relational operators in Apache Pig to transform and query data. Explore order by, rank, and split for sorting data and creating multiple relations on Hadoop data.

Learn pig streaming in apache pig to process hadoop data by piping input to external scripts, define aliases like square, load data into a relation, and dump computed results.

Explore evaluation functions in Apache Pig, including built-in and user-defined functions, with examples of avg and concat, group by, and foreach on a sample GPA dataset.

Explore eval functions in Apache Pig by counting elements in bags, grouping by fields, and handling null values with count that ignores nulls and count on nulls.

Explore eval functions in Apache Pig, focusing on the diff operator to compare two bags, wrap non-bag fields in tuples, and return differences as a bag.

Learn how to use Apache Pig eval functions to compute sums, handle nulls, group by owner, and tokenize strings, with load, dump, and for each generate.

Explore loading and storing data with Apache Pig's load and store functions, including bin storage and big storage, use of schemas, and applying user defined functions to manage binary data.

Learn how to load and store data in Apache Pig using PigStorage as the default storage function, handling UTF-8 text, delimiters, and structured text formats.

Explore tuple and bag functions in Apache Pig, converting expressions to double or bag types, and applying for each to produce outputs from loaded data.

Learn to create and run Pig scripts to analyze Hadoop data by loading a student dataset, grouping by name, and computing maximum GPA in local or MapReduce mode.

Learn how to write Java user-defined functions (UDFs) for Apache Pig, register jars, and use the Piggy Bank repository to share and reuse UDFs.

Learn to write and deploy a Java udf for Apache Pig by building a Java project, exporting a jar, and using it to filter 0, 1, 4, 5, 9.

Learn how to embed Pig in Java and run Pig scripts from a Java program by creating a Pig server, registering queries, loading data with load statements, and storing results.

Explore how to define and import macros in Pig, pass arguments, and reuse logic with macro definitions to load, filter, and sort data in Hadoop.

Define and use Pig macros with a relation variable and field name to filter data, import macro files into scripts, and run a big script that dumps filtered results.

Learn to use fs shell commands to create directories, list directories, and copy data from local to the target environment, and invoke sh shell commands within scripts.

Explore utility commands in the cruncher shell to manage data with scripts: load into relations, apply limits, dump results, and use help, history, run, and kill to interact.

Learn to run Apache Pig scripts in local mode, observe outputs, and use set commands to debug, name jobs, and store results in big storage.

Explore how Apache Pig handles compressed files in Hadoop, loading compressed input, applying filters such as 1993 movie data, and writing compressed output in formats like zip, gz, and snappy.

Enable compression of intermediate results between map and reduce tasks in Apache Pig to reduce storage space and network I/O, potentially boosting execution speed of Hadoop data analysis.

Explore diagnostic operators in Apache Pig, including describe for schema, dump for on-screen results, explain for logical, physical, and map execution plans, and illustrate for step-by-step data transformations.

Learn how to test Apache Pig scripts with big unit and G-Unit, using a sample student scores dataset to group by student and compute the maximum score with big storage.

Learn to perform big unit testing of Apache Pig scripts using the G-Unit framework, creating fixed input data and expected output, and running tests in Eclipse.