In today’s technology landscape, organizations have many solutions to choose from for building and enhancing their IT stacks. With such an extensive range of options, managing and aligning data effectively becomes a significant challenge. Organizations must control their data and ensure it is structured in a way that truly adds value. This means finding the right software tools and systems that collect and store data and enable insightful analysis and integration across various platforms. The goal is to transform raw data into actionable insights that drive smart business decisions and innovation.
Elastic offers a platform that consolidates data, allowing organizations to use their information more effectively. In this article, I will discuss Elastic’s infrastructure, its various applications, and the challenges the company faces.
Background on Elastic
Elastic was founded in 2012 in Amsterdam. Its core product, the Elasticsearch platform, is designed to put data to work to help users search, analyze, and visualize their data more effectively. Over the years, the company’s offerings have expanded to include a variety of solutions for enterprise search, observability, and security, all capable of handling large volumes of data and complex queries.
The Elasticsearch platform has a variety of ways to turn raw data into insights. It securely stores large amounts of data to analyze using AI and ML. Elasticsearch has multiple search methods, including keyword and vector search. It has tools for visualizing data to identify trends. Plus the platform can automate workflows by implementing rules and taking actions based on alerts.
Elastic has become a significant player in data integration, offering enterprises effective methods to manage and analyze data across multiple environments. Companies with large volumes of unstructured, semi-structured, and structured data can use the Elastic platform to run their data operations as well as applications that have complex search requirements. In fact, any application of this type can benefit significantly by using Elastic as the underlying engine for advanced searches.
While the company’s infrastructure can appear complex, describing it is straightforward. At the heart of the platform is Elasticsearch, which combines a search and analytics engine with a data store for various types of data—structured and unstructured—including textual, numerical, and geospatial data. Next is Kibana, which includes interfaces for users, system management, and configuration. Elastic Agent offers integrated host protection and central management services. Logstash is a data processing pipeline for ingesting data into Elasticsearch or other storage systems. Finally, Beats is a single-purpose data shipper used for sending data from edge machines to Elasticsearch or Logstash. The whole platform can be deployed in the cloud or on-premises, and it supports AWS, Google Cloud, and Microsoft Azure.
How Organizations Use Elastic
Elastic helps customers manage and protect their data—and get the most out of it—across several different functions.
Enterprise Search — Organizations use Elastic for their internal search platforms. For example, Wikipedia has Elastic handle millions of searches per day across its extensive repository of articles, providing quick and relevant search results to users worldwide.
Observability and Monitoring — IT teams leverage Elastic to monitor the health and performance of physical, virtual, and cloud infrastructure, ensuring systems are running smoothly and efficiently. For instance, Uber uses Elastic to monitor its extensive infrastructure and applications, providing performance monitoring and quick troubleshooting of issues for its ridesharing and delivery services.
Security Information and Event Management (SIEM) — Security analysts use Elastic SIEM to aggregate and analyze security data from various sources, detect threats, and respond to security incidents. Goldman Sachs, for example, uses Elastic for SIEM to monitor, analyze, and mitigate security threats in real time within its financial services infrastructure.
Log and Event Data Management — Companies employ Elastic for log aggregation and management, consolidating logs from various systems and applications for analysis and archiving. For instance, Adobe integrates Elastic to manage logs and event data across its numerous cloud-based applications for operational visibility and efficiency.
Real-Time Analytics — Businesses use Elastic to analyze data in real time for immediate insights into customer behavior, operational efficiency, and market trends. One company that does this is eBay, which leverages Elastic for real-time analytics to understand user behavior and optimize its e-commerce platform for an improved user experience and performance.
Anomaly Detection — Elastic’s ML features allow companies to detect unusual patterns and anomalies in data, which can indicate potentially harmful issues or opportunities to improve equipment or configurations. For example, Sprint utilizes Elastic’s ML capabilities for anomaly detection in network traffic, helping to maintain service quality and security.
Challenges That Elastic Faces
While the Elastic platform offers many advantages for organizations, there are challenges, too. One common issue for customers is storage capacity, as many of them face large-scale data storage and management complexities. Another challenge is to integrate and optimize various Elastic features. ML-driven analytics and advanced search capabilities are key areas for customers, and it’s crucial for Elastic to continuously innovate and enhance these features.
Performance is a top priority for organizations using the platform. Elastic is addressing several challenges within the Elasticsearch ecosystem by optimizing storage drives and the network to enhance indexing performance. To overcome performance issues, Elastic suggests reviewing network configurations and replacing traditional disks with solid-state drives (SSDs), which can lower latency and increase throughput. Additionally, using local storage instead of remote storage can help avoid performance slowdowns.
Wrapping Up
Coming from a background in manufacturing, I appreciate solutions that address specific industry needs. Elastic does this across numerous markets. The Elasticsearch platform is making strides in the public sector by providing access to governmental data, while financial services companies benefit from real-time fraud detection and risk analysis. Healthcare organizations rely on Elastic to manage patient records and support medical research. Retail and e-commerce organizations leverage Elastic to analyze customer behavior based on sales, shopping interactions, and inventory data. In the manufacturing and automotive sectors, Elastic improves predictive maintenance, supply chain management, and quality control by analyzing data from machinery and production lines. Technology companies enhance software performance and innovation using Elastic’s search and data analysis capabilities, while telecom providers use Elastic to manage network traffic and operations more efficiently. These tailored solutions show the effectiveness of Elastic’s platform across various domains.
Elastic has built a platform to maximize data utilization, but what’s on the horizon? The company is actively investing in generative AI technology across its platform to enhance functionality and improve user experience. This should increase data personalization and efficiency. Elastic also integrates AI to improve observability and security.
The company’s investment in AI gives it the ability to simplify data processes. The platform uses predictive analytics to forecast trends and behaviors so businesses can make informed decisions more quickly and effectively. AI improves search functions by interpreting the context of queries, leading to more accurate results. AI also optimizes data indexing, increasing the efficiency of data storage and retrieval. As the data revolution evolves and AI continues to improve data management, Elastic looks to keep innovating to better manage organizations’ growing volumes of data.
