TL;DR
- NoSQL databases offer scalable, flexible, high-performance storage but are hard to integrate with traditional BI tools.
- Key challenges include lack of direct BI integration, complex ETL processes, and poor unstructured data support.
- Industries using NoSQL include e-commerce, healthcare, finance, telecom, logistics, and media.
- Popular NoSQL databases: MongoDB, Couchbase, DocumentDB, Redis, DynamoDB, Elasticsearch, Cassandra, OpenSearch.
- Traditional BI tools fall short due to lack of native NoSQL support.
- You need modern BI platforms that connect directly to NoSQL data.
Table of Contents
- Introduction
- Key Challenges in Working with NoSQL for Analytics
- Use Cases of NoSQL Databases Across Industries
- Overview of Key NoSQL Databases
- Analytics on NoSQL Data: What to Look For
- Why Traditional BI Falls Short
- Conclusion
Introduction
NoSQL databases are non-relational systems that store data in flexible, non-tabular formats. Designed for modern apps with big data needs, they offer unmatched scalability and performance. But they break traditional BI workflows designed for rigid SQL schemas.
Key Challenges in Working with NoSQL for Analytics
- Traditional BI tools are optimized for relational data.
- NoSQL data often requires ETL to be made BI-friendly.
- Most tools rely on JDBC/ODBC drivers or APIs to connect.
- ETL processes are slow, complex, and hard to maintain.
- Lack of native support for unstructured/semi-structured data.
Use Cases of NoSQL Databases Across Industries
- E-commerce: Product catalogs, inventory, recommendation engines (Redis, Cassandra).
- Finance: Real-time fraud detection, mobile payments (MongoDB, Elasticsearch).
- Healthcare: Streaming medical device data, patient records (Couchbase, OpenSearch).
- Telecom: Network topology, churn prediction (HBase, TigerGraph).
- Logistics: Fleet tracking, delivery monitoring (DynamoDB, REST APIs).
- Media: Content personalization and engagement scoring (CouchDB, Redis).
Overview of Key NoSQL Databases
MongoDB
- Flexible, JSON-like documents.
- Great for content-heavy apps and rapid development.
- High memory usage, lacks joins.
Read our blog on how you can simplify your MongoDB Analytics.
Couchbase
- Multi-model access: document, key-value, SQL-like queries.
- High performance and concurrency.
- Complex queries can be slow.
Looking to easily analyze and visualize Couchbase data? Read this blog to get more insights.
Amazon DocumentDB
- Managed MongoDB-compatible service by AWS.
- Tight AWS ecosystem integration.
- Costly, limited analytics capabilities.
If you are exploring Analytics tools for DocumentDB, don’t forget to check out the top analytics tools comparison for DocumentDB by the Knowi team.
Redis
- In-memory key-value store.
- Ideal for caching and real-time use cases.
- Memory-bound, potential for data loss.
DynamoDB
- Fully managed, serverless NoSQL database.
- Built for sub-millisecond latency.
- Limited join/query support, expensive at scale.
Elasticsearch
- Near real-time search engine for structured/unstructured data.
- Great for logs and full-text search.
- High learning curve, CPU intensive.
Exploring tools to analyze your Elasticsearch data? Read this ultimate comparison blog comparing Knowi, Grafana and Kibana
Apache Cassandra
- Distributed, highly available NoSQL database.
- Ideal for write-heavy and time-series data.
- Complex setup, limited query capabilities.
OpenSearch
- Fork of Elasticsearch + Kibana.
- Real-time dashboards for observability and monitoring.
- Less suitable for structured/transactional workloads.
Read about OpenSearch in more detail here. If you are exploring analytics tools for OpenSearch, don’t forget to check out our detailed blog on Best Tools for OpenSearch analytics 2025.
Analytics on Data: What to Look For
- Native support for NoSQL data sources (no ETL).
- Performance: low-latency querying and caching.
- Ability to join across SQL + NoSQL + APIs.
- AI-powered querying or natural language interfaces.
- Robust visualizations and embedded analytics.
Why Traditional BI Falls Short
- No native support for NoSQL (ETL is a must).
- Poor handling of schema-less, unstructured data.
- Dependency on tech-heavy solutions (JDBC/ODBC).
- Real-time streaming and AI insights are missing.
Conclusion
NoSQL is here to stay, but most BI tools aren’t built for it. If your organization works with MongoDB, Cassandra, Elasticsearch, or Redis, you need tools that natively understand these systems.
Knowi natively connects into NoSQL databases, avoiding the costly ETL processes and let’s you blend, join, analyze and visualize your data in one place. Knowi also offers AI analytics capabilities that allow you to autogenerate dashboards, talk to your data in plain english, use conversational interface to analyze your data, detect anomalies and build predictive models. Knowi’s AI capabilities are enterprise-ready and private and secure, ensuring you get most out of your data without the worry of your data being sent outside to third-party LLMs.
Interested in seeing how Knowi can help you with your NoSQl data? Talk to our team for a personalized demo on your data.
Frequently Asked Questions: NoSQL Analytics in 2025
What is NoSQL analytics?
NoSQL analytics refers to the process of extracting insights from data stored in non-relational databases like MongoDB, Cassandra, or Elasticsearch. Unlike traditional BI tools that rely on structured, tabular data, NoSQL analytics handles unstructured or semi-structured data using purpose-built platforms that often bypass complex ETL.
Why are traditional BI tools not ideal for NoSQL databases?
Traditional BI tools:
- Expect relational, structured data with predefined schemas.
- Require complex ETL to flatten NoSQL data into tabular form.
- Use JDBC/ODBC connections that don’t work well with all NoSQL engines.
- Often lack support for unstructured, real-time, or document-based data.
What industries use NoSQL databases for analytics?
Industries leveraging NoSQL for analytics include:
- E-commerce: Product catalogs, recommendation engines (e.g., Redis, Cassandra)
- Finance: Real-time fraud detection (e.g., MongoDB, Elasticsearch)
- Healthcare: Medical device streams, patient data (e.g., Couchbase, OpenSearch)
- Telecom: Network monitoring, churn prediction (e.g., HBase, TigerGraph)
- Logistics: Fleet and delivery tracking (e.g., DynamoDB, APIs)
- Media: Engagement scoring, personalization (e.g., Redis, CouchDB)
Which NoSQL databases are most popular in 2025?
Some of the top NoSQL databases include:
- MongoDB – JSON-like documents, ideal for dynamic content.
- Couchbase – Multi-model access with high concurrency.
- Amazon DocumentDB – Managed MongoDB-compatible database.
- Redis – In-memory, ideal for real-time use cases.
- DynamoDB – Serverless, high-speed database.
- Elasticsearch – Near real-time search and analytics.
- Apache Cassandra – Highly scalable, write-intensive workloads.
- OpenSearch – Open-source fork of Elasticsearch + Kibana.
What are the biggest challenges in analyzing NoSQL data?
The key challenges include:
- Lack of native support in most BI tools.
- Complex, time-consuming ETL pipelines.
- Difficulty in joining NoSQL with SQL or API data.
- Poor support for unstructured or semi-structured data formats.
- Limited real-time or AI-assisted analytics.
Do I need ETL to analyze NoSQL data?
In many traditional setups, yes, ETL is required to flatten and normalize NoSQL data. However, modern BI tools like Knowi offer native NoSQL integration, eliminating the need for ETL entirely.
Can I perform real-time analytics on NoSQL data?
Yes, but it depends on your tooling. Databases like Redis, Elasticsearch, and DynamoDB support real-time operations. BI platforms with real-time streaming support and in-memory querying are critical to enable live dashboards and alerts.
