ElasticSearch, LogStash, Kibana ELK #1 - Learn ElasticSearch

Explore elasticsearch, a distributed open-source search engine built for fast indexing and near real-time queries. Learn how documents are indexed and retrieved via a RESTful API.

Explore how Elasticsearch scales horizontally as a distributed framework, with master, data, and client nodes forming a cluster; learn how documents partition into shards, are replicated, and routed within indices.

Elasticsearch enables multi-tenancy by hosting multiple indices on the same cluster, with configurable primary and replica shards and types, and supports plugins like Head and big desk for monitoring.

Register user queries to percolate incoming documents and trigger alerts for matching logs in Elasticsearch, enabling real-time monitoring of severity and Cisco router events.

Explore essential Elasticsearch terminology, including clusters and nodes (master, data, coordinating, client), indices and documents, mappings and types, shards and replicas, and time-series data patterns.

Master Elasticsearch terminology, including replicas and primary shards for high availability and parallel searches, and learn mappings, mapping types, analyzers, and snapshot and backup strategies for reliable recovery.

Learn to install and configure Elasticsearch in a clustered environment with multiple nodes, and bridge Elasticsearch concepts to relational terms, including indexing, mapping, get/post/insert analogies, and aggregations.

Explore why Elasticsearch excels for real-time indexing and searching, featuring bulk and delta indexing, schema-free mappings, nested documents, inverted index, and advanced search capabilities.

Install and configure Elasticsearch by downloading packages from the Elasticsearch website and meeting prerequisites such as Java 1.7 or above, then create instances and build a cluster.

Launch and configure AWS EC2 instances to run an elasticsearch cluster in the cloud, including Ubuntu AMI, instance types, storage, tags, and security settings.

Connect to and manage two AWS EC2 instances, rename for clarity, verify initialization, and establish SSH access while exploring ElasticSearch binaries and package options.

Verify java installation, update Ubuntu packages, download the elasticsearch Debian package with wget, install with dpkg -i, and manage elasticsearch as a service using start, stop, restart, and status commands.

Install and run Elasticsearch as a service on Linux, start and verify it via curl to localhost:9200, and explore default cluster name Elasticsearch and version 2.3.4.

Edit elasticsearch.yml to set a unique node name, shard count, and replicas; configure unicast discovery, disable multicast, and bind network.host to a reachable IP using explicit hosts, then restart nodes.

Explore the Elasticsearch api editor using the Sense Chrome extension to send indexing requests, view responses, and learn http verbs, the default port 9200, and root status.

Install the head plugin from the elasticsearch bin folder to view nodes and indices in a browser UI; access it via the public IP on port 9200.

Configure the sense plugin to connect to your Elasticsearch cluster by its address, and use the two-panel interface and sense editor to write queries and view responses.

Learn how to add a new document to an Elasticsearch cluster by indexing blog entries via API requests, including fields like title, body, user, and date posted.

Use a PUT API request to store and index a document in Elasticsearch by specifying index, type, and id, such as a post in the blogs index with id one.

Master adding documents to Elasticsearch by issuing put and post requests, indexing blogs, assigning or generating unique ids, and diagnosing storage errors with shard and replica configurations.

Learn to retrieve documents from Elasticsearch using get requests by id or by index and type, and perform search queries across indices to return hits with the _source document.

Explore how Elasticsearch builds an inverted index to store tokens, term frequencies, and locations, and how analyzers, tokenizers, and token filters enable fast, accurate searches across documents.

Learn to search Elasticsearch with a JSON-based DSL, using match, multi match, term, range, and fuzzy queries; understand full-text vs term-level queries and similarity-based search.

Execute Elasticsearch search queries to filter blog post documents by user using match and multi match queries across user, title, and body, with case-insensitive results.

Explore Elasticsearch advanced features, including custom analyzers and plugins, grammatical equivalents, synonyms, autocomplete and suggestions, aggregations, multilingual support, and relevance scoring.

Explore Elasticsearch cat APIs to query cluster metadata via the rest api, list indices with readable, formatted output, and inspect health, shards, replicas, docs, and master node details.

Learn how Elasticsearch achieves high availability with multi-node clusters, replica and shard management, and transparent recovery after node failures to keep search results accessible.

Learn how to scale elasticsearch horizontally by adding a new node to an existing cluster, configuring cluster name and unicast discovery, and ensuring shards reallocate automatically for high reliability.

Review an IMDb actress dataset, convert it to JSON with a Python script, and bulk import 80,000 records into an Elasticsearch index called actresses to facilitate queries.

Import a large actresses dataset, parse with a Python script, convert to JSON, and load into an ElasticSearch index via bulk for practical data exploration.

Learn how to load large datasets into Elasticsearch using the bulk API, verify indexing across three data nodes, and search efficiently with operators and fuzzy matching.

Explore how Elasticsearch type mappings are created and updated, compare explicit and implicit mappings, and verify fields like name and year in actor and actress indices.

Explore how the elk stack delivers real-time analytics and search across unstructured logs and structured data. See NASA's curiosity telemetry and Uber location data illustrate elk search in action.

Define big data as large, complex data beyond traditional tools, and examine how volume, velocity, and variety enable real-time insights from structured and unstructured data.

Explore how the Elk stack enables large-scale data processing with Elasticsearch, Logstash, and Kibana. See how NoSQL databases and etl tools deliver actionable insights across domains.

Explore the ELK stack's real-world data analysis power across marketing effectiveness, geography- and seasonality-based segmentation, and fast, scalable search for high-volume events.

Celebrate finishing the course by exploring additional resources, a special discount, updates, and the newsletter on the Tatro tutorials website, and provide feedback to improve future courses.