Kibana and Elasticsearch: Data Analysis and Visualization

Explore retail analytics with Kibana by building dashboards and visualizations to analyze supermarket branches, product line sales, and customer segmentation, uncovering profits, demand, and customer experience insights.

Upload a csv to Kibana using the machine learning module's data visualizer, then configure mappings and index name before exploring results in the data visualizer and index management.

Create Kibana visualizations by dragging fields into Lens, selecting types like bar graphs and tree maps, and using controls such as city multi-select and range slider to filter and save.

Demonstrates creating and configuring diverse Kibana visualizations—goal, heat map, line graph, metric, pie chart, and vertical bar charts—split by city, product line, customer type, and branch.

Create and customize dashboards by adding visualizations and saving a supermarket dashboard. Use saved searches, filters, and drill-downs to analyze product lines, gross income, and customer ratings.

Upload a CSV file to the data visualizer, adjust mappings, and explore visualizations and dashboards to reveal customer segmentation, profit trends, and product line analysis.

Learn to fetch metrics from Elasticsearch with Python, build Kibana dashboards and visualizations, and generate filtered reports in Excel from your metrics.

Set up a Kibana and Elasticsearch project on a Ubuntu VM, configure the Elasticsearch and Kibana YAMLs, verify cluster health, and plan data retrieval with Python to generate reports.

Explore how metricbeat ships system metrics to Elasticsearch, enabling Kibana dashboards and an index pattern metricbeat-* to visualize CPU, memory, disk, and network across hosts.

Explore how to create custom visualizations and dashboards in Kibana with Metricbeat data, using terms aggregations, buckets, and data tables, then save and manage the dashboards.

Explore how to fetch real time data from Kibana visualizations using a Python-driven API workflow, instead of downloading static CSV, and build automated reports with time range based requests.

Explore how to use Python's requests module to fetch Elasticsearch and Kibana data, convert responses to JSON, and tailor a get request in dev tools for targeted metrics.

Learn to fetch JSON data with Python requests, set headers, and parse nested dictionaries. Break data into keys and aggregations for export to csv or excel.

Learn to fetch and filter fields in Elasticsearch with Python, interpret buckets and aggregations, and extract dot count and timestamp data from aggregations.

Traverse the third level of aggregation to compute sums from k.count and doc count, then generate a csv report with second and third level aggregates for Kibana and Elasticsearch visualizations.

Explore how the Elastic Stack analyzes and visualizes data, compare NoSQL and SQL databases, and understand document oriented JSON structures in Elasticsearch.

Explore NoSQL concepts with a schema-less, json-based blog post example. Identify key-value, document, graph, and column-store models, and see how Elasticsearch and Kibana support data analysis.

Install Elasticsearch and Kibana on Ubuntu using Debian packages and verify services on ports 9200 and 5601. Understand analysis, tokenizers, inverted index, and cluster concepts like nodes and master node.

Explore how documents identified by IDs use fields, scalar or nested, within an index that maps to a table, with primary and replica shards distributing data for reliability and performance.

Learn how shards, including primary and replica shards, distribute Lucene indexes; understand index as a namespace with source field, terms, and types, and how searches are scoped by type.

Discover how Elasticsearch maps JSON fields to data types and grows a dynamic schema, using on-the-fly and predefined mappings.

Create an index with a mappings schema, define a first type with name as text and age as integer, then verify mappings and see how enforced types prevent mismatches.

Explore Elasticsearch data types from arrays to binary, boolean, geo point, and range, and see how mappings handle arrays versus singular values in index design.

Explore how Elasticsearch maps booleans, strings, IP addresses, and dates, and model nested documents like comments with keyword vs text, providing practical indexing examples.

Explore dev tools in Kibana and Elasticsearch for data analysis and visualization, covering dynamic mappings, nested documents, and keyword vs text fields with analyzers that shape the inverted index.

Explore Elasticsearch analyzers and the analysis pipeline, including character filters, tokenizers, and token filters. See how HTML strip, mapping, and pattern replace character filters transform strings before indexing.

Discover how an analyzer in Elasticsearch uses character filters, tokenizers, and token filters. Learn how mappings apply the analyzer to the name field.

Explore how character filters, analyzers, tokenizers, and token filters produce tokens that populate the inverted index in Elasticsearch, enabling blazing fast searches and precise document retrieval.

Explore how a synonym filter works with an analyzer in Elasticsearch, after the tokenizer, replacing terms with defined synonyms like galaxies and cosmos; inverted index stores both terms for search.

Configure Elasticsearch index mappings with keyword and multi-field variants, using edge ngram and synonym analyzers to enable exact and synonym search alongside autocomplete.

Explore how tokenizers and token filters shape the token sequence in Elasticsearch using edge n-gram and word delimiter filters. Learn to configure analyzers, synonyms, and multi fields in an index.

Configure Elasticsearch by defining cluster.name, path.data, path.logs, memory settings including bootstrap.memory_lock, and network.host bindings for stable multi-node operation.

Configure discovery for a multi-node cluster by listing initial hosts for discovery ping requests, and set minimum master nodes to ensure cluster formation and healthy shard routing.

Configure gateway shard recovery with constraints on expected, master, and data nodes and a recover-after time, and optimize http cors and the field data cache for sorting and aggregations.

Discover how field data caches and query caches influence disk IO and performance, and how to allocate memory and configure master, data, ingest, and tribe node roles for efficiency.

Prevent split brain in Elasticsearch by avoiding two masters after network failure, and configure discovery.zen.minimum_master_nodes to (n/2)+1 (rounded), e.g., with three nodes set to two.

Learn how to avoid split-brain in Elasticsearch by configuring discovery.zen.minimum_master_nodes for two-node and three-node clusters, manage master elections, and ensure high availability with replicas and primary shards.

Explore how Elasticsearch uses query context and filter context, and how scoring reveals match quality. Examine full-text match on a text field and basics of match all and match none.

Explore how filter context builds boolean or queries, switch operators to and, apply cut off frequency to refine scoring, and use match phrase and zero terms queries to maintain results.

Explore match phrase query behavior in Elasticsearch, compare it with match and match phrase prefix queries, and learn how multi match and best fields with tiebreakers affect scoring.

Explore how text and title queries score documents in Elasticsearch using best field, most fields, and cross field methods, with tiebreakers and explain options.

Explore term level queries in Elasticsearch, learning how term queries match exact indexed terms on keyword fields. Contrast with analyzed match queries and see how the inverted index shapes results.

Explore term level queries in Elasticsearch, contrasting exact matches with analyzed match queries, and learn to combine conditions using boolean must and should across multiple fields.

Explore how to build boolean queries in Elasticsearch using term and terms queries, with must and should clauses on the name and gender fields, including nesting and query boosts.

Discover how Elasticsearch range queries filter documents by age and date fields, using GT and LTE with boolean must and should, and you can omit one side for open-ended ranges.

Explore Elasticsearch date math in range queries by using now minus X to filter by relative dates, and round to day or week boundaries.

Explore prefix and exist queries in Elasticsearch, learn to build boolean queries with prefix and range filters, and understand how null values and field existence affect results.

Explore how to index and query geoshape data in Elasticsearch, using geohash or quad tree prefixes, precision, and orientation to represent circles, polygons, and multipart shapes.

Create polygon geo shapes by connecting coordinate points into a linear ring to form pentagons, rectangles, and other polygons, then index and search them in Elasticsearch.

Index geo point documents (Bangalore and Chennai) and sort by geo distance from Mumbai. Filter results by distance and switch between ascending or descending order in Elasticsearch.

Learn to filter documents by geo distance in Kibana and Elasticsearch using a geo distance filter, with a practical restaurant example and a geo point dataset.

Kibana and Elasticsearch teach how to perform a geo polygon query that returns documents with geo points falling inside a defined polygon, using a geo polygon filter.

Index documents with geo point locations and a geo polygon, then run a geo polygon query to identify points inside the polygon using array, string, or comma-separated formats.

Explore how keyword, text, and all fields behave in Elasticsearch by building an index with exact value and text value, and observing tokenization and search behavior.

Convert a complex SQL query to an equivalent Elasticsearch JSON query by modeling data, creating mappings and analyzers, and applying a prefix and like clause with an n-gram text search.

Learn to build Elasticsearch queries in Kibana with prefix and term clauses, and a boolean query, using an N-gram analyzer; index documents with mappings and sorting.

Learn to build a city-scoped product catalog autocomplete in Kibana and Elasticsearch, using an edge n-gram analyzer for product name and alias, with fuzziness and partial search.

Configure a product catalog in kibana and elasticsearch by defining edge n-gram analyzers and mappings, including product name, category, price, alias, and city, for autocomplete and precise search.

Explore modeling searchable fields such as product name, category, city, and alias with term filters and edge n-gram analyzers, contrasting match versus term queries and basic scoring.

Explore boosting in Elasticsearch to optimize autocomplete search, prioritizing product name matches over aliases and categories. Learn how to tune score with match queries, boosting values, and Kibana demos.

Learn to index a single field with multiple analyzers, including autocomplete, list search, and sortable, using multi fields in Elasticsearch for synonyms-enabled and partial searches.

Configure three custom analyzers in Elasticsearch, including an autocomplete analyzer with edge ngram, and a sortable analyzer, set up tokenizers and filters, verify the json, and index sample documents.

Explore indexing multiple fields with different analyzers and enabling field data for sorting. See autocomplete and list-search using edge N-gram tokenization and term or bool queries.

Explore dynamic templates in Elasticsearch to define custom mappings for fields as they are added. Apply match mapping type, match and path conditions, and concrete values.

Explore dynamic templates and mappings in Elasticsearch by indexing documents and observing how age maps to long, using pattern matching and unmatched and exclude conditions to control field types.

Explore dynamic templates in Elasticsearch, mapping fields with match, unmatched, and path match patterns to long or text, including nested address dot fields.

Learn how dynamic templates create multi-field mappings, with a keyword field and a tokenized text subfield, to index uncertain third-party json data in Elasticsearch.

Design a dynamic template driven Elasticsearch index for user data, mapping address email and phone, area codes to integers, and enabling user autocomplete with a dedicated analyzer.

Explore building and refining dynamic templates in Elasticsearch to map customer email and phone fields, using pattern capture tokenizers, regex match patterns, and dedicated analyzers.

Utilize dynamic templates to map address area attributes to integers, apply email and phone analyzers, and enable user autocomplete by mapping first and last names and date fields.

Learn to use the Elasticsearch health API and cluster state API to monitor cluster health and state, including level, wait_for_status, routing, and indices.

Explore how the Elasticsearch cluster APIs expose metadata, indices, templates, mappings, and routing with primary and replica shards, and review cluster stats, nodes, JVM, memory, and plugins.

Explore cluster reroute commands to move or cancel shard allocations and disable allocations. Understand dry-run capabilities, pending cluster tasks, and how cluster update settings distinguish persistent and transient configurations.

Explore Kibana and Elasticsearch data analysis and visualization by examining node and cluster stats, including indices stats, OS, JVM, thread pools, and transport metrics for cluster reroute insights.

Explore the indices APIs in Elasticsearch, including create index API with settings, mappings, and aliases, and delete and get index APIs for managing shards, replicas, and document properties.

Learn to create and inspect a slot index in Elasticsearch, including mappings and settings, retrieve index details with get index, verify existence with head, and manage open or closed states.

Explore open and close index APIs to manage index availability and recovery, use the analyze API to return token breakdowns from built-in analyzers, and shrink indices to fewer primary shards.

learn to create and update index mappings with the put mapping api, fetch them with get mapping, and explore using index aliases as views across multiple indexes.

Explore how to use Elasticsearch index APIs to create aliases for one or more indices, and apply query DSL filters to build filtered views for searches.

Discover how Elasticsearch document APIs manage indexing, dynamic mappings, versioning, and optimistic concurrency control, with auto index creation, id generation, routing control, and per-document retrieval.

Master the get API in Elasticsearch for real-time document retrieval, learn how to control returned fields with _source, stored fields, and mappings, and use delete by id.

Learn how delete by query deletes matching documents in batched search and bulk requests, and how the update API uses inline painless scripts to modify fields and merge partial documents.

Learn how the update API merges partial documents and how bulk enables index, delete, create, and update in a single newline-delimited request, plus reindexing across indexes with no mappings copied.