Elasticsearch
Master Search and Analytics at Scale – Learn Elasticsearch for Full-Text Search, Logging, and Real-Time InsightsPreview Elasticsearch course
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A product search engine with filters, full-text matching, and relevance scoring
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A real-time log analytics pipeline with Kibana dashboards
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A geo-search service to track and query location-based data
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Set up Elasticsearch clusters locally and in the cloud
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Index, update, and delete documents using RESTful APIs
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Use powerful search queries, filters, and relevance tuning
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Perform aggregations for real-time analytics
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Visualize data with Kibana and create custom dashboards
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Secure and monitor Elasticsearch in production environments
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Backend developers building search features or REST APIs
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DevOps engineers setting up centralized logging with the ELK Stack
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Data analysts exploring real-time metrics
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Full-stack engineers integrating Elasticsearch with React, Node.js, or Python
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Anyone working on search, recommendation, or log intelligence systems
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Learn the Core First – Understand indexing, queries, and mappings
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Use Dev Tools in Kibana – Practice using Elasticsearch’s REST API
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Create Real Projects – Build use cases like search, filtering, and dashboards
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Experiment with Data Types – Work with text, numbers, dates, geo, and nested objects
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Apply Aggregations – Gain insight through metrics and buckets
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Secure Access – Practice authentication, roles, and TLS
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Monitor Performance – Use built-in tools and APIs for cluster health
Course/Topic 1 - Coming Soon
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The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
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Understand Elasticsearch architecture, nodes, and clusters
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Index and query documents using REST APIs
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Configure mappings, analyzers, and tokenizers for custom search logic
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Use Elasticsearch Query DSL for precise queries
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Perform complex aggregations to generate insights
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Integrate with Logstash, Beats, and Kibana
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Monitor and secure Elasticsearch in production
Course Syllabus
Module 1: Introduction to Elasticsearch
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What is Elasticsearch?
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Use Cases and Real-World Applications
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Installing and Running Elasticsearch Locally
Module 2: Elasticsearch Architecture
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Nodes, Clusters, and Shards
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Indexing and Replication
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Document-Oriented Storage
Module 3: Indexing and CRUD Operations
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Creating Indexes and Documents
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Bulk Inserts and Updates
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Deleting Documents and Indexes
Module 4: Mapping and Data Types
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Default and Custom Mappings
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Field Types: Text, Keyword, Date, Geo, Nested
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Dynamic vs Static Mappings
Module 5: Search Queries and Filters
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Full-Text vs Term-Level Queries
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Match, Term, Range, and Bool Queries
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Highlighting and Relevance Scoring
Module 6: Elasticsearch Query DSL
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Query Context vs Filter Context
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Composing Complex Queries
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Boosting and Scoring Mechanisms
Module 7: Aggregations and Analytics
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Buckets and Metrics
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Date Histograms, Term Buckets
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Nested and Pipeline Aggregations
Module 8: Integrating Elasticsearch
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Using Elasticsearch with Python, Node.js, or Java
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Elasticsearch and Logstash for Data Ingestion
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Beats for Lightweight Log Collection
Module 9: Kibana for Visualization
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Setting Up Kibana Dashboards
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Querying with Kibana Dev Tools
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Building Real-Time Visualizations and Alerts
Module 10: Scaling and Security
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Monitoring Cluster Health and Performance
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Securing with TLS and Role-Based Access
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Index Lifecycle Management
Module 11: Projects and Use Cases
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Product Search Engine
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Real-Time System Monitoring
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Geospatial Data Explorer
Module 12: Elasticsearch Interview Questions & Answers
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Core Concepts and Use Cases
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Query Logic and Best Practices
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Deployment and Monitoring Strategies
Upon successful completion of this course, learners will receive a Certificate of Completion from Uplatz that verifies their expertise in Elasticsearch-based search and analytics systems. This certification enhances your profile for roles involving full-text search, log intelligence, observability, and data engineering.
Elasticsearch is one of the top technologies for building real-time search and analytics applications. This course prepares you for roles such as:
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Elasticsearch Engineer
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Data Platform Developer
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Site Reliability Engineer
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Search Backend Developer
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Observability and Monitoring Analyst
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What is Elasticsearch and how does it work?
Answer: Elasticsearch is a distributed search engine based on Apache Lucene. It stores JSON documents and allows fast indexing and full-text search through RESTful APIs. It uses indices, shards, and nodes to scale horizontally. -
What is the difference between term and match queries?
Answer:-
Term queries match exact terms and are used for keyword fields.
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Match queries analyze text using an analyzer and are suitable for full-text search on text fields.
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How does Elasticsearch handle replication and fault tolerance?
Answer: Elasticsearch uses primary and replica shards. Each document is indexed into a primary shard, and replicas provide redundancy and allow parallel searching. If a node fails, a replica takes over automatically. -
What are mappings in Elasticsearch?
Answer: Mappings define the structure of documents and data types for fields within an index. It is similar to schema in relational databases but more flexible. -
How do aggregations work in Elasticsearch?
Answer: Aggregations group and summarize data. Bucket aggregations group documents (e.g., by date or term), while metric aggregations calculate stats like sum, average, and max. -
What is Elasticsearch Query DSL?
Answer: The Query DSL is Elasticsearch’s JSON-based language for defining complex queries. It supports filtering, full-text search, relevance tuning, and combining multiple conditions. -
How can Elasticsearch be secured?
Answer: Security features include TLS encryption, API key or basic authentication, role-based access control (RBAC), and audit logging. Elastic Stack's security features can be enabled for free with Elastic’s Basic license. -
What is the difference between shards and nodes?
Answer: A node is a single instance of Elasticsearch, while a shard is a subdivision of an index. Shards are distributed across nodes to enable scalability and parallel processing. -
How do you monitor Elasticsearch performance?
Answer: Use tools like Kibana Monitoring, APIs (_cluster/health
,_cat/indices
), Prometheus exporters, and Elastic’s Stack Monitoring to monitor resource usage, indexing rates, and search latencies. -
Can Elasticsearch be used for analytics?
Answer: Yes. Elasticsearch supports real-time analytics using aggregations, Kibana dashboards, and even integration with BI tools. It is widely used for monitoring, metrics analysis, and anomaly detection.