Canonical’s Data and AI portfolio is growing with a new data and analytics tool. We proudly announce that Charmed OpenSearch version 2.14 is now available in Beta. OpenSearch® is a comprehensive search engine and analytics suite solution that thousands of organisations use for various use cases in search engines, security, and AI/ML.
From today, data engineers, scientists, analysts, machine learning engineers, and AI enthusiasts can take the Charmed OpenSearch beta for a drive and share the feedback directly with us and in our beta programme.
What is OpenSearch®?
OpenSearch is a thriving open source project and community with over 600 million project downloads, over 300 Github contributors and over 9100 Github stars. It is used in multiple cases, such as search, observability, event management, security analytics, visualisation, AI, machine learning and more. Let’s take a brief look at how companies are taking advantage of OpenSearch’s robust engine capabilities.
Search
Using OpenSearch as a search engine can be a powerful solution for various applications, ranging from e-commerce sites to large-scale enterprise data searches. One example would be customers needing to quickly find products from a large inventory with various data attributes like brand, price, and multiple categories. You can use OpenSearch for full-text search, faceted search with filter, autocomplete, suggestions, and personalisation.
Observability
Using OpenSearch as an observability tool is an excellent choice for monitoring, troubleshooting, and gaining insights into complex systems. You can use metrics, logs and traces for OpenSearch to understand a system’s health, performance and reliability.
Security Analytics
You can leverage OpenSearch to build a robust Security Information and Event Management (SIEM) system. This system enables real-time analysis of security alerts generated by hardware and software, network infrastructure, and applications. It can be used for data and log aggregation, alerting, threat detection, compliance reporting, etc.
Visualisation
OpenSearch has a Dashboard feature that can be used to create and customise visualisation tools to monitor and display data insights in real time. These tools can be used as charts, graphs, data filters, and customised panels.
Machine Learning
OpenSearch’s machine learning capabilities can be used for advanced data analysis, anomaly detection, predictive analytics, and improving search relevance. It can also be used as a vector database and model embeddings and is a good tool for Retrieval Augmented Generation (RAG) for large language model (LLM) projects.
What is Charmed OpenSearch?
OpenSearch is an all-in-one search engine, analytics and machine learning tool for multiple use cases. However, working with OpenSearch in production-grade use cases that process vast amounts of data and extensive data infrastructure can be challenging. Automating the deployment, provisioning, management, and orchestration of production OpenSearch data clusters can also be highly complex.
What if there was an easier way to get more out of OpenSearch through an operator – called Charmed OpenSearch? An operator is an application containing code that takes over automated application management tasks. Picture it as your technological virtuoso, orchestrating a grand performance that includes high availability, automated single multiple clusters deployment, implementing robust security measures like transport layer security (TLS), configuring initial user management, adding plug-ins and extension features, upgrades automation, observability of OpenSearch clusters, and even handling backup and restore operations. Charmed OpenSearch is an upgraded version of the OpenSearch upstream project that uses automation and can be deployed on any private, public or hybrid cloud.
With a primary mission of simplifying the OpenSearch experience, Charmed OpenSearch is your backstage pass to a world where OpenSearch isn’t just a search and analytics suite – it’s a seamlessly operated search engine and analytics powerhouse.
Try Charmed OpenSearch Beta today.
Are you a data engineer, analyst, scientist, or machine learning enthusiast interested in trying OpenSearch? Charmed OpenSearch can be:
- Deployed on your local machine
- Used for multiple use cases: observability, SIEM, visualisation and GenAI
- Improved with your feedback.
To get started, you must run Ubuntu OS, meet the minimum system requirements, and be familiar with OpenSearch concepts.
Simple deployment steps for Charmed OpenSearch in your Ubuntu VM:
juju deploy opensearch --channel 2/beta
Learn to use Charmed OpenSearch:
Share your feedback
Charmed OpenSearch is an open-source project that is growing because of the care, time and feedback that our community gives. This beta release is no exception, so if you have any feedback or questions, please feel free to contact us.
Further Reading
- What is OpenSearch?
- Large Language Models (LLMs) Retrieval Augmented Generation (RAG) using Charmed OpenSearch
- Future-proof AI applications with OpenSearch as a vector database
Trademark Notice
OpenSearch is a registered trademark of Amazon Web Services. Other trademarks are the property of their respective owners. Charmed OpenSearch is not sponsored, endorsed, or affiliated with Amazon Web Services.
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