OpenSearch: Challenges, Use Cases & Analytics with Knowi

TL;DR

• OpenSearch is a powerful open-source search and analytics engine.
  
• But it’s not designed as a full-fledged BI or analytics platform.
  
• Knowi brings native OpenSearch integration, joins across sources, real-time dashboards, and AI-powered insights, without any ETL requirement.

What Is OpenSearch?

OpenSearch is a community-driven, open-source search and analytics suite licensed under Apache 2.0, designed to make it easy to ingest, search, visualize, and analyze data. It is a fork of the open-source versions of Elasticsearch and Kibana, created after Elastic NV changed the licensing of its software away from the permissive Apache License. OpenSearch is now managed by the OpenSearch Software Foundation, a project of the Linux Foundation, and has seen widespread adoption with millions of downloads and contributions from many organizations

Read our comparison blog on OpenSearch vs Elasticsearch to learn which might be a good fit for your usecase. 

Why OpenSearch Is Great but Not Built for Analytics

OpenSearch is widely regarded as a robust, open-source search and analytics suite, offering powerful full-text search, distributed architecture, and flexible visualization capabilities. However, while it is frequently used for analytics, it is not specifically designed as a purpose-built analytics engine, which leads to several notable limitations and trade-offs.

Strengths of OpenSearch

Scalable Search: 

OpenSearch excels at indexing and retrieving large volumes of data, making it ideal for use cases like log analytics, application search, and enterprise search.

Open Source and Extensible:

 As an open-source project, OpenSearch benefits from community contributions and a wide range of plugins for additional functionality.

Security and Flexibility: 

It includes robust security features out of the box, and its architecture supports customization for various use cases.

Limitations for Analytics

Performance at Scale: 

OpenSearch can experience slow indexing and query performance when handling very large datasets or complex analytical workloads. Tuning is often required to maintain acceptable speeds, such as optimizing shard distribution, using bulk requests, or reducing query complexity.

Sub-optimal Sharding: 

Poorly configured sharding strategies can degrade performance, especially as data volumes grow. This requires careful planning and ongoing management.

Analytics Features: 

While OpenSearch supports analytics, it lacks some of the advanced features and optimizations found in specialized analytics engines. For example, it may not efficiently handle complex aggregations, joins, or multi-dimensional analytics compared to dedicated analytics platforms.

Operational Complexity: 

Setting up, tuning, and maintaining OpenSearch for analytics can be complex, especially for organizations without deep expertise in search and distributed systems.

Documentation and Support: 

Some advanced features, such as security analytics, may lack clear documentation or community support, making implementation challenging.

When OpenSearch Is Great and When It’s Not

Great For: 

Log analytics, real-time search, monitoring, and use cases where relevance-based search is critical.

Not Ideal For: 

Heavy-duty, multi-dimensional analytics, complex data modeling, or scenarios requiring high-performance, low-latency analytical queries at massive scale.

What Organizations Need from OpenSearch Analytics

Below are the main needs organizations have when leveraging OpenSearch for analytics, based on current documentation and industry practices:

Scalability and Performance

Handling Large Datasets: 

Organizations need OpenSearch to efficiently index, store, and query large volumes of data, supporting both real-time and historical analytics.

Fast Query Response: 

Rapid retrieval and analysis of data are critical for timely decision-making, especially in operational monitoring and security use cases.

Comprehensive Security and Compliance

Access Control: 

Granular permissions for users and roles, ensuring only authorized personnel can access sensitive data.

Data Encryption:

Protection for data at rest and in transit, meeting regulatory and internal security standards.

Audit Logging: 

Detailed logs for tracking who accessed or modified data, supporting compliance and forensic investigations.

Integration and Flexibility

Seamless Integration: 

Easy connectivity with other data sources and services (e.g., AWS S3, CloudWatch, Kinesis, Lambda) to streamline data ingestion and processing pipelines.

Customization: 

Ability to extend functionality through plugins and custom packages, tailoring the platform to specific business needs.

Reliability and Uptime

High Availability: 

Multi-AZ deployments and automated failover to ensure continuous operation.

Automated Backups:

 Regular snapshots and recovery options to prevent data loss.

Advanced Analytics and Visualization

Complex Query Support: 

Ability to run sophisticated queries, aggregations, and filtering for deeper insights.

Dashboards and Visualization: 

Tools like OpenSearch Dashboards for creating, sharing, and monitoring analytics dashboards.

Machine Learning and AI: 

Features for anomaly detection, root cause analysis, and predictive analytics to proactively address issues.

Operational Simplicity

Managed Services: 

Minimize operational overhead with managed offerings that handle scaling, updates, and maintenance.

Cost Management: 

Flexible storage tiers (hot, warm, cold) and cost-effective scaling options to control expenses as data grows.

The Knowi Advantage for OpenSearch

Knowi is an Unified Analytics Platform that can connect OpenSearch with SQl, NoSQl, databases, APIs, cloud, and even documents, making it part of a larger, queryable ecosystem.

Dataset-as-a-Service with Query Abstraction & Smart Caching

• Knowi’s Dataset-as-a-Service layer that lets you blend, join and transform data across your data sources including OpenSearch without writing complex queries. 
  
• These newly created datasets are reusable, queryable data models that hide the complexity of the underlying data source. Hence you don’t need to manually craft or optimize queries every time.

Native Multi-Source Joins 

Knowi natively integrates with OpenSearch and supports complex aggregations, multi-dimensional analysis, and joins across structured and unstructured data. You don’t need to extract or transform data first. It enables cross-source joins (e.g., OpenSearch + SQL/NoSQL sources), giving you a unified analytics layer that OpenSearch alone can’t offer.

Built-in AI-Powered Analytics

Knowi includes AI-driven insights, natural language queries, auto-generated dashboards, anomaly detection, and machine learning.

Advanced Dashboards

Create rich, interactive dashboards beyond what OpenSearch Dashboards offer including nested data visualizations, custom KPIs, and alerting.

Embedded Analytics

Embed dashboards into apps, allowing organizations to get analytics to their customers faster. You can also white-label.

Alerts and Triggers

Knowi Alerts allow you to monitor datasets, dashboards, and widgets in real time. You can receive automatic notifications when:

• Data updates fail
  
• Queries return errors
  
• Conditions in your data are met
  
• Anomalies are detected
  
• Thresholds are crossed

Enterprise-Grade Governance

Role-based access controls, row/column-level permissions and audit trails ensure analytics meet compliance and security standards

Summary: Why Use Knowi with OpenSearch?

Want to see Knowi + OpenSearch in action?

Request a demo and experience how we turn raw OpenSearch data into real, actionable intelligence

Conclusion

OpenSearch is powerful for operational search, but not a complete analytics solution. Knowi bridges the gap by turning OpenSearch into a business-friendly analytics engine, without any ETL complexity.