Knowi For Easy Elasticsearch Analytics: Business User Guide

TL;DR:

• Elasticsearch is powerful but can be complex for non-technical users.
• Writing queries in Elasticsearch’s DSL is difficult and limits accessibility.
• Kibana is popular but lacks integration with external data sources and user management features.
• Knowi solves these issues with:
  • Natural language querying.
  • Multi-index and multi-source joins without data restructuring.
  • No-code dashboards for business users and full control for technical teams.
  • Support for data sources across SQL, NoSQL, REST APIs, files and documents
  • AI-powered analytics for faster insights.
  • Flexible deployment options (cloud, hybrid, on-premise).
  • Role-based access control and user management for enterprise readiness.
• Knowi is more versatile than Kibana and Grafana while being easier to use and better suited for analytics.

In this article, we’ll explore the challenges business users face when working with Elasticsearch and how Knowi helps overcome them. We’ll also compare Knowi with other visualization and analytics tools to underscore its unique value. 

Table of Contents

What is Elasticsearch

Elasticsearch is a distributed, open-source search and analytics engine built on top of Apache Lucene. It’s designed to handle massive data volumes for real-time search and analytics applications, allowing businesses to store, search, and analyze large datasets almost immediately after ingestion.

Elasticsearch is schema-less, which means indexing can be done without a predefined schema. It naturally infers data types and adapts to different data structures, which makes data ingestion and indexing workflows a lot more flexible.

Moreover, Elasticsearch, as a core component of the ELK Stack, works alongside Logstash and Kibana to ingest, visualize, and analyze large-scale data. It also ensures high availability and fault tolerance through data replication so that data remains safe and accessible even if a server fails. 

While Elasticsearch has powerful features, it also contains some complexities that present considerable challenges for non-technical stakeholders and users. 

The Challenge of Analytics in Elasticsearch

Elasticsearch presents challenges such as complex querying and a lack of support for real-time multi-source joins or full relational-style data blending. These capabilities are essential for fast analytics across indices and external sources, as well as broad accessibility for business users.. Let’s take a closer look at the main challenges.

Query Complexity

Elasticsearch processes JSON requests and returns JSON data, so it requires writing queries in its domain-specific language (DSL), a JSON-style query language. This makes up a steep learning curve because it is often complex and error-prone for businesses unfamiliar with its syntax. 

Limitations in Joining Data

Elasticsearch also has several key limitations when it comes to joining data:

1. Across indices: Elasticsearch does not perform joins across indices, a limitation also reflected in Kibana, its native UI within the ELK stack. Some workarounds, like parent-child relationships, exist but are complex and don’t suit many use cases.
2. Across external sources: The inability to integrate data sources across external systems is a major drawback for Elasticsearch. Organizational data rarely resides in a single database. 
3. Across clusters: Lastly, Elasticsearch doesn’t allow joins across different clusters. This poses challenges for enterprises managing distributed or siloed environments and makes real-time unified analytics even harder.

Native Elasticsearch Integration with Knowi

Knowi overcomes Elasticsearch’s key limitations by making querying and joining data easier for organizations. Knowi removes the roadblocks that come with using Elasticsearch for analytics, and here’s how it does that:

Multi-Index Joins

Knowi enables cross-index queries in Elasticsearch, even across indexes with different data types, without requiring data restructuring. For instance, teams can join application logs and user events to analyze login errors before drop-offs, then visualize everything on a single dashboard.

Multi-Source Joins

Knowi’s biggest strength is joining Elasticsearch with other data sources. It builds a data virtualization layer that connects SQL, NoSQL, APIs, and cloud sources without ETL or schema changes.

Here’s how some combinations work:

• Elasticsearch with MongoDB: Both sources can be connected and matched using Knowi’s Join Builder, allowing indexes and collections to be seamlessly aligned. The combined data can then be visualized with native MongoDB Charts.
• Elasticsearch with MySQL: MySQL data can be queried and joined with Elasticsearch logs to uncover combined insights. The resulting data can be viewed in real time or stored for future analysis.
• Elasticsearch with Redshift: Native Redshift queries can be combined with Elasticsearch data to create a unified view. The results can be visualized, exported, or embedded into dashboards with ease.
• Elasticsearch with REST API: Live API data can be merged with Elasticsearch events using loop joins and pagination. This works well for enriching campaign insights or integrating third-party data.
• Elasticsearch with Couchbase: Couchbase buckets or N1QL queries can be combined with Elasticsearch data to enhance search capabilities. Knowi supports native analytics, making it easy to embed the results into our applications.

Additionally, Knowi includes an AI-powered feature that can automatically generate dashboards. Users can prompt the system for insights. Knowi then analyzes the connected data sources and delivers visual insights such as charts or key metrics instantly, making data exploration faster and more intuitive.

Natural Language Query

Knowi adds a natural language query layer on top of Elasticsearch, allowing data to be queried in plain English without dealing with DSL complexity. This makes analytics accessible to all users, not just analysts or engineers.

Query Elasticsearch Templates

Knowi natively supports querying Elasticsearch templates. These are predefined queries with Mustache-scripted placeholders that act like stored procedures. This enables:  

• Secure parameterized queries without exposing query logic.  
• Simplified execution of complex searches through placeholders.  
• Consistent troubleshooting using application-layer templates.  

Users simply pass parameters into templates such as {“query_string”: “Marketing”}, while Knowi handles template execution. This prevents query-building logic from being exposed and maintains query integrity by preserving the core structure of the search.

Built for Business and Technical Users

Knowi bridges the gap between business and technical users by offering tools that suit both ends of the spectrum:

1. Knowi makes data exploration simple for business users with a drag-and-drop interface while giving technical teams full control to build complex and customizable dashboards.
2. Knowi offers three deployment options. It can run fully in the cloud with no installation required. There’s a hybrid approach using the on-premise Cloud9Agent to connect to private data sources while the cloud handles everything else. There’s also a fully on-premise option where all components run entirely within the enterprise.
3. Knowi supports native Elasticsearch DSL queries and a SQL-like interface, making it easy to query indices directly or use the visual query builder. Queries can run in real time or on schedules, with results stored for dashboards and alerts.
4. Knowi highlights matched terms in search results, making it easy to spot relevant information within large text fields. 
5. Knowi supports querying Elasticsearch templates, which act like stored procedures with parameters. Templates use placeholders for variables, letting users run complex queries by simply passing parameters without exposing the underlying query logic. 

Knowi also natively integrates with OpenSearch, the open-source fork of Elasticsearch backed by Amazon Web Services (AWS).

Knowi vs Kibana & Grafana: A Quick Comparison

In many real-world use cases, teams juggle SQL and NoSQL databases, pulling in data from files, APIs, and various cloud apps. As dashboard users often include non-technical stakeholders, there’s a growing emphasis on delivering quick, intelligent insights with minimal configuration.

In this comparison, we’ll look at Knowi vs. Grafana and Kibana to help you choose the right fit for your business.

	Grafana	Kibana	Knowi
Focus	Operational monitoring and observability of metrics, logs, and traces.	Operational log analysis and visualization within the Elasticsearch ecosystem.	Business-focused analytics with real-time multi-source integration and machine learning.
Query Language Support	Supports Grafana’s query language and DSL.	Uses Kibana Query Language (KQL) and Lucene for Elasticsearch queries.	Supports natural language, DSL, and SQL-like queries.
Machine Learning	Does not support machine learning.	Relies on Elastic’s ML features (anomaly detection, outlier detection).	Built-in ML algorithms (regression, classification, time-series anomaly detection).
Visualization	Offers customizable graphs, dashboards, and panel plugins (time series, heatmaps).	Provides charts, tables, maps, and dashboards (limited to Elasticsearch data).	30+ visualization types (pivot charts, bubble charts, etc.), customizable with CSS/JS.
Learning Curve	Requires understanding of query editors and plugins.	Requires learning KQL/Lucene for Elasticsearch.	Easy for non-technical users.
Integration with Data Sources	Supports 14+ data sources via plugins (Elasticsearch, InfluxDB, Prometheus, etc.).	Only integrates with Elasticsearch (ELK stack).	36+ native integrations (NoSQL, SQL, REST APIs, files).
Data Join Capabilities	No multi-source joins (limited to transformations within a single source).	Limited to Elasticsearch join types (nested/has_child queries).	Supports multi-source joins (inner, outer, and loop joins across databases).
Log Search and Analysis	Limited log analysis (depends on data source).	Specialized for Elasticsearch log analysis (KQL, Lucene).	Supports log querying with NLP and structured queries.
Search Performance	Fast for time-series metrics; log search depends on the data source.	Fast (searches Elasticsearch indices directly).	Optimized for NoSQL/SQL hybrid queries.
Online Community	Active open-source community (forums, Slack, plugins).	Strong ELK stack community.	Limited community; relies on customer support (Zendesk, knowledge base).
Pricing	Free (open-source) + Paid plans.	Paid only (bundled with ELK stack).	Free trial available with paid plans afterward.
Proactive Alerts	Supports alerts (email, Slack, Grafana UI).	Alerting via Elastic Stack (webhooks, PagerDuty, etc.).	Real-time alerts (email, Slack, webhooks).
Transformations & Calculations	Supports data transformations (combine, rename, summarize).	Limited (KQL cannot aggregate/transform).	Advanced calculations and data preparation (ETL-like features).
User Authorization	Provides user management (Cloud/Enterprise).	No built-in user management (relies on Elasticsearch security).	Role-based access control (RBAC) and user management.
Purpose	General-purpose monitoring and visualization (metrics, logs, traces).	Focused on Elasticsearch log analysis and visualization.	End-to-end analytics platform (supports multi-source data, ML, NLP ).

Final Thoughts

Knowi addresses these challenges by enabling natural language queries, joins across multiple indexes and data sources, and intuitive dashboards. This makes Elasticsearch analytics easy and accessible for broader teams.

See how you can use Knowi to simplify Elasticsearch Analytics. Request a demo today! 

Frequently Asked Questions