Overview
Product Information on Elastic Search
What is Elastic Search?
Elastic Search Pricing
Overall experience with Elastic Search
“Elastic Search Delivers Strong Performance but Increases Operational Complexity Challenges”
“Unstructured Data Handling Benefits, with a UI Learning Curve”
About Company
Company Description
Elastic enables organizations to securely harness search-powered AI so anyone can find the answers they need in real-time using all their data, at scale. By integrating AI with search technology, it facilitates the discovery of actionable insights from large volumes of both structured and unstructured data, addressing the need for real-time, scalable data processing. Our Elasticsearch Platform delivers search-powered AI for observability, security and search. Companies can now solve real-time business problems and achieve better business outcomes by taking advantage of massive amounts of structured and unstructured data, securing and protecting private information more effectively, and optimizing infrastructure and talent resources more efficiently. Elastic’s complete, easy-to-use cloud-based platform offers solutions in search, security, and observability, aimed at aiding businesses in leveraging AI technology securely and effectively.
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Key Insights
A Snapshot of What Matters - Based on Validated User Reviews
User Sentiment About Elastic Search
Reviewer Insights for: Elastic Search
Deciding Factors: Elastic Search Vs. Market Average
Performance of Elastic Search Across Market Features
Elastic Search Likes & Dislikes
1) Performance and scale: Sub-second queries across large, constantly changing indexes, even during peak ingestion windows 2) Search quality: Fuzzy matching, relevance scoring, and geospatial queries give us precise control to build Find exactly what I meant experiences 3) Flexibility and ecosystem: The ELK stack plus ingest pipelines, analyzers, and ML features make it easy to onboard new data sources and iterate quickly
We have a lot of unstructured data types flowing into the pipeline that we then run through our ML models and with ES, we can easily see if the expected data is coming into the top of the pipeline funnel. This helps with troubleshooting. The UI takes a while to learn and could be simplified but once you know how to use it, it's pretty straightforward.
Its main pros are scalability and speed. The ability to perform real-time searches and analytics on large datasets is a game-changer for many applications. Furthermore, it offers huge flexibility in terms of querying and indexing. Last but not least, the rich ecosystem of tools around it makes it a versatile solution.
1) Operational Complexity: Capacity planning, shard/replica tuning, and rolling upgrades require experienced hands or a move to a managed cloud 2) Cost governance: It's easy to turn on premium features and ingest more data than needed; without guardrails, costs can creep 3) Dashboarding constraints: Kibana is strong for ops, but highly bespoke business dashboards may require complementary BI tools
There is a learning curve with the product so it's not something we can enable larger groups on. We tend to only have specialized roles use the search options which can create some internal bottlenecks. Not a big deal but a friction point.
Its complexity could be a con, particularly for new users. Setting up and tuning ES for optimal performance can be challenging. A solid understanding of its architecture is a must have to properly setup it. Another point is that the need for monitoring and maintaining the infrastructure can be time-consuming, particularly when scaling to large environments.
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Peer Discussions
Elastic Search Reviews and Ratings
- SENIOR PRINCIPAL ENGINEER - FULL STACK1B-10B USDBankingReview Source
Elastic Search Delivers Strong Performance but Increases Operational Complexity Challenges
Elastic Search has become the backbone of our search and observability use cases. We use it to power merchant/transaction lookups with fuzzy matching and scoring, and to centralize logging for fast, reliable investigations. Performance has been consistently strong at scale, and the platform's flexibility lets us evolve the schema and relevance models without vendor lock-in. The trade-off is operational complexity--tuning shards, upgrades, and capacity planning require discipline--but the gains in speed to value and developer productivity have far outweighed the overhead. - VP, CUSTOMER SUCCESS50M-1B USDSoftwareReview Source
Unstructured Data Handling Benefits, with a UI Learning Curve
We use ES as the main indexing backbone for our data pipeline that feeds our Alerts system. It has scaled with our customer base and increased volume over time (6+ years as a customer from angel through series B). It's been a good fit for our use cases so far. - AI FACTORY MANAGER50M-1B USDSoftwareReview Source
Scalability and Speed Stand Out in Data Analytics and Log Aggregation Tasks
The experience has been very positive. It's an incredibly powerful and flexible search engine, particularly useful for handling large volumes of data. I've been using it for log aggregation and real-time analytics as well as search engine in several applications. The speed and scalability of Elasticsearch are impressive, especially when dealing with complex queries and large datasets. The ease of integration with other tools in the Elastic Stack makes it a very comprehensive solution for monitoring and data analysis - GLOBAL CISO50M-1B USDIT ServicesReview Source
Excellent and highly scalable search delivered fast with adequate infrastructure and expertise
We've deployed Elastic Search over our search and analytics infrastructure, we've been utilizing it for some time now, and general experience is quite positive. Search is fast, integrates well with our data pipelines, a number of features (a double-edged sword at times, though), and good community support. - DIRECTOR OF IT<50M USDIT ServicesReview Source
Elastic Search Enhances Data Integration Through Scalable and Flexible Indexing Features
Elastic Search is well known in the industry as the go-to solution when you need a scalable, fast and reliable solution. For years now I've been using Elastic Search and recently, in the past 1.5 years, I've grown to like their vector-based search for my Generative AI applications. In the past, Elastic Search was used in my startup with Elastic Search's Kibana and helped us "keep the head above the water" because it was so simple to use, but also effective. We could find bugs or search for specific patterns very easily, and we were no IT experts back then! Elastic Search's near real time indexing and its flexibility is a life saver when you want to define flexible schema. When your startup grows, you can scale easily across nodes.