Elasticsearch: Efficient Search and Data Analysis (ELSSR)

Databases, NoSQL and Big Data

Elasticsearch is a powerful engine for full-text search and data analysis. This practical two-day course covers deployment patterns, core architecture and index design. You will learn how indexes, shards and documents work to scale search across large datasets efficiently.

During hands-on sessions you'll practice queries, aggregations and mapping strategies to tune relevance and performance. The course shows Ingest node usage, preprocessing pipelines and options for cloud and on-premise deployment and monitoring.

THIS TRAINING COURSE WILL HELP YOU:

  • Understand Elasticsearch architecture, indexes, shards and documents
  • Compose effective Full Text, Term and Query String queries
  • Use filters and aggregations to optimize search and analysis
  • Operate Elasticsearch on cloud and on-premise environments
  • Use Ingest node to preprocess and enrich data before indexing

WHO SHOULD ATTEND?

  • Developers implementing full-text search and analytics
  • Data architects designing search and analytics systems
  • IT professionals deploying and managing Elasticsearch
  • Data analysts performing advanced queries and analysis

COURSE LOCATION AND AVAILABLE DATES



This course is not scheduled as a public course.
It is delivered exclusively as customised training. The content, duration, date, and delivery format are tailored to the customer's requirements and can also be provided in English.

Request customised training

Course content:

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  • Introduction to Elasticsearch
    1. What Elasticsearch is and how it works
    2. Common usage patterns for search and analytics
    3. API access and client drivers for popular languages
  • Running Elasticsearch
    1. Deploying Elasticsearch in cloud vs on-premise
    2. Differences between Elasticsearch and OpenSearch
  • Elasticsearch architecture
    1. Overview of the architecture
    2. Key components: nodes, shards and replicas and their roles
  • Core concepts
    1. Index: what an index is and how it works
    2. Shard: sharding principles and data distribution
    3. Data types: structured, unstructured and complex types
    4. Document: how documents are stored, inserted and found
  • Mapping
    1. Basics of data mapping in Elasticsearch
    2. Data types and how to use them effectively
  • Querying in Elasticsearch
    1. Overview of query options
    2. Full-text vs term queries: when to use each
    3. Query String and Simple Query String approaches
    4. Match queries for effective document search
  • Data consistency
    1. Ensuring data consistency in a distributed system
    2. Principles of availability and consistency in Elasticsearch
  • Filters and Aggregations
    1. Using filters to optimize search results
    2. Aggregations: metrics, buckets and pipeline aggregations
  • Data analysis
    1. Using Elasticsearch for advanced data analysis
    2. Combining queries and aggregations for insights
  • Ingest node
    1. Overview of the Ingest node and preprocessing
    2. Practical use of Ingest node for transforming and enriching data before indexing
Prerequisites:
Basic knowledge of relational databases or backend application development.
Schedule:
2 days (9:00-17:00)
Language:
Česky

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