Complete ElasticSearch with LogStash, Hive, Pig, MR & Kibana

Set up a cross-platform development environment for elasticsearch, following a step-by-step single-node installation on macOS or Windows to prepare for later exercises.

Verify the Java version on your machine with java -version, ensure it is Java 8, and install it using brew update and brew install java if necessary.

Download and install Hadoop using wget and sudo, organizing files in /usr/local. Explore Hadoop, an open-source Java framework for distributed processing and storage with the Hadoop distributed file system.

Configure apache hadoop part 3 by defining resource manager and job tracker properties, format hdfs, start the daemons, and monitor progress via a graphical user interface.

Practice a hands-on installation of Elasticsearch, using wget to download the binaries, rename the files for simplicity, and configure Elasticsearch to run in your environment.

Configure Elasticsearch by setting ES home and related environment variables, edit the config for a unique node name and data and log paths, and prepare plugin directories for future installations.

Start the Elasticsearch daemon, load the necessary processes, and verify plugins. Open the browser to inspect the cluster, view nodes and indices, and note the green indicators and yellow status.

Explore how the inverted index powers Elasticsearch by transforming documents into terms, building a dictionary, and linking terms to documents to enable fast search results.

Learn how indexing relies on shards as the low level storage units in elasticsearch, with primary shards and replica shards to counter failures.

Learn how Elasticsearch stores data as shards across master and data nodes, forms a cluster, and processes search requests by querying across indexes.

Learn to monitor Elasticsearch cluster health using red, yellow, and green states; red marks inactive primary shards, yellow signals partial backups, green confirms all primaries have backups.

Explore horizontal scaling for an Elasticsearch cluster by adding small, inexpensive machines and using federalism to grow exponentially with application-level parallelism.

Explore rest APIs in Elasticsearch, covering create, update, and delete operations, and how external systems can send requests via a call utility; more on these APIs in upcoming lessons.

Explore elastic search fundamentals, including indexes, documents, shards, and clusters, and how to scale and use core operations. Preview the API and its classes with hands-on activities in upcoming sessions.

Explore how elastic uses document IDs to assign a unique id to each shard, ensuring every document maps to the same shard for fast, efficient retrieval across the cluster.

Master read operations in Elasticsearch by creating an index, inserting a document with id, class, subject, and about fields, and retrieving it with a get command.

Learn how Elasticsearch mappings control field data types and indexing, using underscore mapping to set types at index creation or when adding new fields, and avoid altering field mappings.

Learn to add a new field to an existing Elasticsearch document by defining a date mapping with a specific format, then update and verify the mapping using the put command.

Learn how to ensure data consistency by using index templates to automatically apply mappings with specific data types to new indexes that match a pattern, avoiding manual updates.

Learn CRUD operations on documents, including read, update, delete, and adding new fields, with hashing algorithms and automated mappings and templates in Elasticsearch.

Explore how Elasticsearch indexes large data sets and enables meaningful searching with an intelligent analytical engine on indexed data. See these capabilities demonstrated in action in the next lesson.

Discover types of search queries in Elasticsearch by using the domain-specific language to combine multiple conditions and criteria, enabling powerful searches across various features in development systems.

Execute a get query to pull all documents from an Elasticsearch index, inspect hits, scores, and metadata, and understand relevancy and performance implications.

Apply a querystring to filter documents by the skill field, retrieving only those with Java and observing score-based ranking across the matching documents.

Extend a BSL example with DSL queries to filter courses by skills, price under $250, and 2017 start dates using and/or logic.

Set objectives by outlining a data pipeline that uses Hive within the Hadoop ecosystem and an Elasticsearch-like interface to organize and store data.

Create a Hive table and load video game data to build a four-stage data pipeline toward an Elasticsearch index. Learn Hive table creation and data loading steps for indexing.

Create a Hive table linked to an Elasticsearch index, map fields with matching names, and run a Hive query to fetch data from Elasticsearch through the classic search Hadoop pipeline.

Master the steps to build pipelines from big to elastic and install the tool on your machine.

Perform a hands-on installation of Apache Pig by downloading the package from the specified location, extracting it to a local directory, and renaming the resulting directory for simplicity.

Pull the worldwide videogame sales dataset from the hive table bg_sales, connect to read the data, load it into memory, and index it into classic search for the exercise.

Build a data pipeline using the same dataset from a previous exercise, load the 2012 games index, apply a large elastic sort, and store the resulting dataset.