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.

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The course:

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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
Assumed knowledge:
Basic knowledge of relational databases or backend application development.
Schedule:
2 days (9:00 AM - 5:00 PM )
Language: