Elasticsearch 9 and the Elastic Stack: In Depth and Hands On

Map and index data in elastic search by importing a dataset of movies and ratings. Practice inserting, updating, and deleting documents, and examine analysis, tokenization, and relational data modeling.

Explore the free MovieLens data set, a collection of user ratings with movie IDs, titles, and genres, and prepare for indexing in Elasticsearch by inspecting ratings.csv and movies.csv.

Learn how optimistic concurrency control uses sequence numbers and primary terms in Elasticsearch to prevent conflicting updates, with automatic retries on conflicts.

Explore data modeling in elastic search, comparing normalized and denormalized designs, two-hop versus one-hop queries, and parent-child relationships for scalable updates.

Explore how the flattened data type prevents mapping explosions by storing inner fields in a single flattened field. The trade-off is detailed inner-field analysis for keyword-only search.

Explore how elastic search helps customers find data quickly through various search interfaces, sorting, paginating, and filtering, with fuzzy queries, partial matches, and search-as-you-type features, in a hands-on section.

Explore phrase matching in Elasticsearch 9, using match_phrase, slop values, and proximity queries to control term order, distance, and relevance in title searches like star wars.

Learn how to implement pagination in Elasticsearch 9 using from and size parameters, counting from zero, with uri or json body approaches, plus caveats about deep pagination and upper bounds.

Explore edge n-grams for autocomplete in Elasticsearch 9, part 2, by building an autocomplete analyzer, mapping the title field, and reindexing movies data, with _analyze validation.

Install and configure logstash with a file input and grok for apache logs, set the date format, and output to localhost:9200 and the console using ruby debug.

Learn how to import data from an S3 bucket into Elasticsearch with Logstash, configure the S3 input and credentials, and use an Apache log grok filter for indexing.

Collect and centralize syslog data with Logstash by forwarding logs over TCP to Elasticsearch, or have Logstash monitor local log files and index them into Elasticsearch for rapid search.

Import movie ratings from the ML latest small data set into Elasticsearch via Spark, creating a new ratings index; adapt the CSV script to map lines to a rating class.

Explore how Elasticsearch acts as a vector database by storing and querying dense_vector embeddings for semantic search and retrieval-augmented generation, using OpenAI embeddings and a Python workflow.

Explore Elasticsearch aggregations, including metrics, histograms, buckets, nested aggregations, and pipelines, enabling moving averages, cumulative sums, and matrix-based analysis.

Explore how Elasticsearch handles time series data with date histogram aggregations by hour on the at_timestamp field, using log data to analyze website traffic and Googlebot hits.

Master data frame transforms in Elasticsearch to aggregate data by entities and create destination indices. Use pivot definitions and preview and start transform APIs for efficient, advanced analytics.

Explore Cabana to analyze a half-hour of 404 errors from 9:30 to 10 a.m. zulu time, identifying San Jose crawlers and their origins.

Wraps up by tying the elastic stack together, including Elasticsearch, Logstash, Kibana, and Beats, and shows how this simpler system prepares you to manage an elastic search cluster.

Learn how Elasticsearch index lifecycle management automates aging indices through hot, warm, cold, frozen, and delete stages using size- or age-based policies and a rollover alias.

Troubleshoot common Elasticsearch issues across node setup, cluster formation, and cluster state. Learn to use bootstrap checks, discovery, log analysis, and backups for resilient operation.

Watch failover in action as Elasticsearch adds a node, loses the master, and automatically rebalances shards across three nodes on a single vm, then recovers when the original node returns.

Automate backups for Elasticsearch clusters with Snapshot Lifecycle Management, configuring repositories and policies to schedule snapshots, include all indices, and set retention and deletion rules.

Learn to implement uptime monitoring with heartbeat in the Elastic Stack by using ICMP, TCP, and HTTP monitors, configuring YAML monitors, and visualizing results with a heartbeat dashboard.

Explore cloud options for renting Elasticsearch clusters, including AWS Elasticsearch Service on EC2 and Elastic Cloud, to save time and money.

Spin up an OpenSearch domain on AWS with Elasticsearch compatibility and IAM security. Learn cost aware settings, data nodes, and AWS integrations like Kinesis and Firehose.

Explore Elastic Cloud, a hosted Elasticsearch service with automatic cloud provisioning (Google by default, Azure or AWS options) and paid add-ons; set up a free trial and deploy.

Wraps up by recapping installation, mapping, indexing, searching, and aggregating data with the elastic stack, plus importing data from files and streaming sources, and visualizing with Kibana.