Top 8 Elasticsearch Use Cases & Best Practices in 2024

Elasticsearch is a distributed search and analytics engine built on Apache Lucene. Since its release in 2010, it has rapidly become one of the most popular search engines, widely used for log analytics, full-text search, security intelligence, business analytics, and operational intelligence use cases.

Today, efficiently searching and analyzing data is crucial for businesses. Effective data search and analytics enable organizations to make informed decisions, improve operational efficiency, and gain a competitive edge. With the increasing volume of data generated, tools like Elasticsearch have become indispensable for extracting valuable insights and driving business success.

This article explores the best of Elasticsearch use cases, illustrating how businesses can leverage this powerful search engine to enhance data retrieval and analysis. It also highlights practical applications across various industries, discusses common challenges, and provides insights into best practices, enabling users to maximize the benefits of Elasticsearch in their operations!

What is Elasticsearch?

Elasticsearch is a powerful, open-source, distributed search engine and analytics platform. It is built on Apache Lucene, a high-performance, full-featured text search engine library.

Elasticsearch’s core functionality lies in its ability to index large volumes of data from various sources, enabling users to search, analyze, and visualize it in real-time. It stores data in a JSON format, which is a widely used data structure, and supports full-text search, faceted search, aggregations, and other advanced features. One of the key components of Elasticsearch is the inverted index, which is used for text search. As data is ingested, Elasticsearch indexes it and stores it in a way that enables fast search and retrieval.

Elasticsearch is part of the Elastic Stack, formerly known as the ELK Stack, which includes Elasticsearch, Logstash (a data collection and log-parsing engine), and Kibana (a data visualization tool). This combination of tools is often used for log analytics, among other use cases.

Its distributed architecture allows Elasticsearch to scale horizontally to accommodate growing data volumes. It can distribute your indices across multiple nodes and rebalance these nodes to ensure high availability. It supports a wide range of queries, including fuzzy search for handling approximate matches, and it can handle time series data, making it suitable for log and event data analysis. It also provides APIs for interacting with the cluster and the data stored in Elasticsearch.

Why use Elasticsearch?

Here are some key benefits of using Elasticsearch:

• Scalability: Elasticsearch is designed to be highly scalable and fault-tolerant, which means it can handle large volumes of data and continue to function even if some nodes in the Elasticsearch cluster fail.
• Speed: Elasticsearch provides fast and relevant matches for full-text searches. Distributed search indices help retrieve data within a second. It is faster than a typical SQL database which may take several seconds.
• Real-time capabilities: Elasticsearch supports real-time indexing and can make new data searchable almost instantly. It allows for quick and efficient searching of large amounts of data.
• Advanced search features: Elasticsearch supports full-text search, faceted search, and various aggregations to help users quickly and easily find the information they need quickly and easily. It also supports fuzzy search.
• Logstash and Kibana: Elasticsearch is part of the Elastic Stack (ELK Stack), which includes Logstash and Kibana. Logstash is a data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a “stash” like Elasticsearch. Kibana lets users visualize data with charts and graphs in Elasticsearch.
• Lucene-based: Elasticsearch is built on top of Lucene and uses its search and indexing capabilities to deliver lightning-fast search results.
• JSON and RESTful API: Elasticsearch uses JSON for data representation and provides a RESTful API, making it easy to manage and search time-series data, such as logs and metrics.

Top 8 Elasticsearch use cases in 2024

Here are some of the top Elasticsearch use cases and how it utilized across industries:

1. Full-text website search: Elasticsearch uses an inverted index for fast full-text searches and supports complex search queries. This makes it ideal for implementing advanced search features on websites. For example, e-commerce platforms like eBay use Elasticsearch to provide accurate and fast search results to their users.
2. Instant searches with autocompletion: Elasticsearch’s fuzzy search feature allow for instant searches with autocompletion. This is particularly useful in search bars on websites, where users start typing and relevant suggestions appear instantly. For instance, Wikipedia leverages this feature to provide real-time search suggestions as users type their queries.
3. Real-time log analysis & monitoring: With the help of Logstash in the Elastic Stack, Elasticsearch can ingest and analyze logs in real-time. This is crucial for monitoring applications and infrastructure, enabling quick identification and resolution of issues. Companies like Netflix use Elasticsearch for real-time log analysis and monitoring to ensure optimal performance and user experience.
4. Application monitoring: Elasticsearch, in combination with Kibana for visualization, is used for application performance monitoring. It can store, search, and analyze large volumes of time series data, providing insights into application performance and helping identify bottlenecks. Uber, for instance, uses Elasticsearch for application monitoring to maintain high service availability and performance.
5. Real-time security threat detection: Elasticsearch’s real-time analytics engine can be used to detect security threats as they happen. By analyzing logs and other data, it can identify patterns and anomalies that may indicate a security breach. Cybersecurity firms like Symantec use Elasticsearch for real-time threat detection and response
6. Enterprise-wide search: Elasticsearch can be used to create a search engine capable of searching across an entire enterprise. It can handle structured and unstructured data, making it possible to search across different types of data stores. Companies like Adobe use Elasticsearch to provide a unified, enterprise-wide search experience across their various products and services.
7. Scalable and high-availability solutions: Elasticsearch’s distributed nature and scalability make it suitable for building high-availability solutions. It can handle large amounts of data and still provide fast search and retrieval capabilities. Large-scale web services like LinkedIn use Elasticsearch to ensure high availability and fast search experiences for their users.
8. Data integration: Elasticsearch can integrate with various data sources, including relational databases and NoSQL stores. This makes it a versatile tool for data integration tasks, allowing businesses to bring together data from various sources and gain a unified view of their data. Companies like Cisco use Elasticsearch for data integration to consolidate and analyze data from various sources for better decision-making.

Elasticsearch best practices

Elasticsearch can be optimized for better performance, scalability, and security. Here are some commonly followed best practices:

• Data indexing: Use the right number of shards for your Elasticsearch indexes. Too many shards can lead to overhead and performance issues. Also, consider using time-based indices for time series data, which can improve search performance.
• Querying: Use filters instead of queries, when possible, as filters are cached and can improve performance. Also, avoid using wildcard queries, which can be slow. Instead, consider using the fuzzy search feature for similar results.
• Cluster management: Regularly monitor your Elasticsearch cluster’s health and performance using tools like Kibana. Also, ensure that your cluster has enough resources (CPU, memory, disk space) to handle the load.
• Performance optimization: Use the inverted index structure of Elasticsearch for fast text search. Also, consider using Logstash for log analytics, which can help in identifying performance bottlenecks.
• Scalability: Design your Elasticsearch cluster for scalability. Use horizontal scaling (adding more nodes) rather than vertical scaling (adding more resources to a single node) for better performance and high availability.
• Security: Secure your Elasticsearch cluster using features like role-based access control and encryption. Also, regularly update Elasticsearch to get the latest security patches.
• Data structure: Structure your data correctly for Elasticsearch. Use the right data types and mappings for your Elasticsearch schema. Also, consider using JSON format for storing data, as Elasticsearch works well with JSON.
• API usage: Make effective use of the Elasticsearch API for tasks like indexing, searching, and retrieving data. Also, consider using the RESTful API for interacting with Elasticsearch.

Easily access live Elasticsearch data with CData Drivers and Connectors

CData Drivers and connectors empower businesses by enabling live data access between their data sources and applications. By adhering to established standards for data access, these connectors plug directly into your tools, providing seamless integration with over 300 SaaS, NoSQL, or Big Data sources.

The CData Elasticsearch Drivers and Connectors allow users to connect to live Elasticsearch data from any BI, analytics, and reporting tools. They offer unmatched query performance, comprehensive access to Elasticsearch data and metadata, and integrate seamlessly with your favorite analytics tools. By leveraging these drivers and connectors, businesses can unlock the strategic value of their data, simplifying connectivity and enhancing data-driven decision-making processes.

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