PowerBI vs Knowi for NoSQL Analytics: Side‑by‑Side Comparison

TL;DR

• Power BI excels in traditional SQL scenarios with rich visualizations and a large community.
  
• Power BI lacks native support for NoSQL data, requiring ODBC drivers or custom connectors for MongoDB.
  
• Knowi connects natively to over 30+ NoSQL databases, including MongoDB and Elasticsearch, allowing querying without ETL or data modeling.
  
• Knowi offers simpler connectivity and faster time-to-insight for organizations using both SQL and NoSQL datasets.
  
• Knowi pricing is quote-based.
  

Table of contents

1. Introduction
   
2. Quick verdict
   
3. Deep comparison
   
   1. NoSQL connectivity
      
   2. Query building
      
   3. Data preparation
      
   4. Pricing breakdown
      
   5. Performance benchmarks
      
4. When to choose which
   
5. Migration guide
   
6. Frequently Asked Questions
   

Introduction

PowerBI has become synonymous with business intelligence, but its roots lie firmly in the relational world of SQL. As enterprises adopt NoSQL databases for flexible schemas and horizontal scalability, analysts find themselves stuck: how do you build a dashboard when the data lives in MongoDB? This isn’t an edge case, ISG research shows that 22 % of survey respondents already use a NoSQL database in production and another 34 % plan to adopt or evaluate one within two years. With multi‑source architectures becoming the norm, we compare PowerBI and Knowi side by side to see which tool better serves NoSQL analytics.

Quick verdict

Deep comparison 

NoSQL connectivity 

PowerBI: PowerBI does not provide a first‑class connector for MongoDB (see our MongoDB analytics guide) or other NoSQL engines. To query MongoDB Atlas you must install the MongoDB Atlas SQL ODBC Driver and configure a federated SQL interface This adds maintenance overhead and may limit access to advanced query capabilities. Connecting to Elasticsearch often requires third‑party connectors or exporting data into a relational warehouse.

Knowi: Knowi treats SQL and NoSQL sources as equal citizens. It offers native connectors for more than 30 NoSQL databases,including MongoDB, Elasticsearch, Cassandra and Couchbase,and REST APIs. You configure a connector by supplying host, database and authentication details, and Knowi generates a schema on the fly. Because the data remains in the source, there is no need to centralise data into a warehouse or transform it before analysis.

Example:

PowerBI connecting to MongoDB via ODBC (simplified):

driver: MongoDB Atlas SQL ODBC Driver
server: example-atlas-data-000.mongodb.net
database: mydb
username: <your‑username>
password: <your‑password>

Knowi connecting to MongoDB natively:

{
  "type": "MongoDB",
  "host": "cluster0.mongodb.net",
  "database": "mydb",
  "username": "user",
  "password": "password"
}

Query building 

PowerBI: Query creation in PowerBI relies on DAX formulas and SQL‑friendly syntax. When working with NoSQL sources through ODBC, you write SQL queries against a virtual schema. This abstraction can hide the expressiveness of MongoDB’s aggregation framework, and complex nested documents often need flattening to fit relational patterns. Example query:

SELECT customerId, SUM(orderTotal) AS revenue
FROM sales
WHERE orderDate >= DATEADD(month, -6, GETDATE())
GROUP BY customerId;

Knowi: Knowi exposes the native query language of the underlying source. For MongoDB you write an aggregation pipeline (learn more about MongoDB aggregations), and for Elasticsearch you work with JSON queries. It also supports its own DSL for cross‑source joins.

Example pipeline:

[
  { "$match": { "orderDate": { "$gte": ISODate("2025-02-01") } } },
  { "$group": { "_id": "$customerId", "revenue": { "$sum": "$orderTotal" } } }
]

Performance Note: This aggregation on 1M documents completes in ~2.3 seconds on indexed fields, with total dashboard render time of 2.8 seconds

Because the query runs on the database, Knowi can leverage indexes and complex aggregations without flattening nested documents.

Data preparation 

PowerBI: Data modelling is central to PowerBI. You typically define star schemas, create relationships between tables and perform ETL to reshape semi‑structured data into a relational model. While this structure supports fast DAX calculations, it introduces latency and requires engineering effort.

Knowi: Knowi eliminates much of the modelling work by querying data in place. It supports direct querying on nested documents and cross‑source joins (see how to join MongoDB with MySQL and others) without centralising data. Analysts can start exploring NoSQL data immediately and iterate quickly. When joining large datasets, Knowi’s engine partitions and processes data in memory and on disk to handle millions of records efficiently.

Pricing breakdown 

PowerBI: Licensing is straightforward: the PowerBI Pro plan costs $14 per user per month and the Premium per‑user plan costs $24 per user per month. For ten users, expect about $140 per month on the Pro plan. Hidden costs include: (1) data warehouses or ETL tools to move NoSQL data into a relational store, (2) possible fees for third‑party connectors, and (3) training to manage DAX and the semantic model.

Knowi: Pricing is not publicly disclosed; prospective customers must contact the vendor. Knowi generally offers subscription tiers based on data volume, number of users and features such as embedded analytics. Because it eliminates the need for ETL and a separate data warehouse, total cost of ownership may be lower for NoSQL‑heavy workloads.

Performance benchmarks

Direct, apples‑to‑apples benchmark data is scarce, but architecture provides insights. PowerBI performs best when data is imported into its in‑memory engine; however, ODBC access to NoSQL sources can add latency. Joining across multiple collections requires staging or flattening data. In contrast, Knowi queries data at source and is designed to process millions of records across disparate sources using multi‑threading and partitioning. Dashboard refresh times depend on the performance of the underlying database; for example, running a MongoDB aggregation on one million documents may complete in seconds if the collection is indexed. Knowi’s no‑ETL approach reduces time‑to‑insight but may rely on source database resources for concurrency.

When to choose which 

Choose PowerBI if:

1. Your datasets live primarily in relational databases and you need tight integration with Microsoft products.
2. You require advanced DAX calculations and a vast library of visualisations.
3. Cost is a priority and you want predictable per‑user pricing.

Choose Knowi if:

1. You rely on NoSQL databases like MongoDB, Elasticsearch or Cassandra and need native connectivity.
2. Time‑to‑insight is critical and you want to avoid building data pipelines or star schemas.
3. You plan to embed analytics in web apps or need cross‑source joins without replicating data.

Not sure if you need to migrate? Read our guide on choosing the right NoSQL reporting tool first.

Migration guide 

Moving from PowerBI to Knowi doesn’t require rewriting everything at once. Follow these steps:

1. Inventory your data sources. Identify which datasets are stored in SQL vs NoSQL and document existing dashboards.
   
2. Set up connectors. Configure Knowi connections for each data source. For MongoDB or Elasticsearch, supply host, database, authentication and optional SSL.
   
3. Recreate queries. Convert existing DAX or SQL queries into native MongoDB aggregation pipelines or Knowi’s DSL. Start with key metrics and replicate logic gradually.
   
4. Rebuild visualisations. Use Knowi’s drag‑and‑drop builder and 40+ visualisation types to recreate charts. Take advantage of AI‑powered insights and suggestions.
   
5. Embed or share dashboards. Publish dashboards to internal stakeholders or embed them into applications. Iterate and optimize query performance as usage grows.
   

Frequently Asked Questions

Related Resources

– MongoDB vs SQL: Complete Comparison

– Elasticsearch Analytics Without Kibana

– Why Traditional BI Fails with NoSQL

– Embedded Analytics Overview

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