Blog Tableau and MongoDB Analytics: Why It’s a Bad Marriage

Tableau and MongoDB Analytics: Why It’s a Bad Marriage

MongoDB Analytics with Tableau

The Challenges of MongoDB Analytics (or any other NoSQL analytics) with Tableau

First off, we are not here to bash Tableau. Tableau is an excellent analytics tool for structured relational data. Our point here is that data has moved beyond well-understood structured data and now includes semi-structured and unstructured data stored in newer NoSQL databases, like MongoDB. Trying to using analytics tools that are architecturally committed to relational structures for analytics on MongoDB (NoSQL) is the definition of putting a square peg in a round hole.

The Challenges of MongoDB Analytics (or any other NoSQL analytics) with Tableau

The Limitations of Tableau for MongoDB Analytics

To be fair, Tableau was developed before NoSQL before Big Data. It’s designed to understand SQL and nothing else, making analytics on newer data sources a significant challenge. To do it, customers typically perform data discovery somewhere else to model the data so they can build transformations and mappings to load it into a relational structure like a MySQL table. This action is accomplished either through ETL or using ODBC drivers that “map” unstructured data into a table-like structure, which is exactly what the MongoDB BI Connector does.

The MongoDB BI Connector Solution

In case you missed it, the BI Connector is moving data out of MongoDB into MySQL tables so Tableau will work. MongoDB is a powerful database solution for modern data. Moving data out of MongoDB into MySQL for analytics seems to somewhat defeat the purpose of the investment.

The Need for Future-Ready Analytics Tools

Questioning if Tableau is the right analytics tools to meet your future needs should be top of mind as Big Data becomes deeply integrated into your operational analytics.  As you begin to explore how to leverage advanced analytics and machine learning to develop new products and services, these systems are already complicated, so it’s hard to imagine how these legacy BI tools stay relevant when they add so much unnecessary complexity, overhead, and cost while limiting data availability and potentially impact data fidelity.

The Shift Towards a New Wave of Innovation

As the future gets closer, we are seeing the early signs of a shift.  A new wave of innovation in the analytics space that is driven by business expectations for instant insights and the fundamental change is data brought by cloud, Big Data and most recently, Internet of Things.

The Demand for Instant Data Discovery and Self-Service Analytics

Data engineers and business teams want the same capabilities of instant data discovery and self-service analytics with MongoDB data. They don’t understand the complexities behind why Tableau and other SQL-based analytics tools struggle to work in the same way with MongoDB as with a MySQL database. Because they don’t understand the complexities, they have little patience for waiting weeks or months for MongoDB data to be made available for analytics.

The Changing Landscape of Data and Analytics Integration

This lack of patience and business leaders expectations that analytics will drive decision making at all levels of their organizations means a fundamental shift in how data and analytics teams integrate modern unstructured and semi-structured data into their analytics architecture is underway. 

There is growing intolerance for building heavy ETL processes to move, transform, prep and load data into a staging area. In addition to slow projects down, the cost of changes is high making experimentation less likely to happen. The trend is towards simplifying data architectures with native integration to these modern data stores, like MongoDB, Cassandra, Couchbase, etc. 

Today, in many cases, to go native means building custom code and processes which limited the number of teams that could access the data. Again, this is pushing analytics tools to step up and manage data from new data sources differently and not require it to be moved and transformed back into relational structures. 

As I mentioned, we are at the early stages of the next wave of innovation in analytics where you will see changes in how analytics platforms interact with newer data sources and learn how to handle structured, semi-structured and unstructured data in the same way. Only then will business teams be able to leverage their data fully and experiment with new insights, machine learning and create data-driven actionable intelligence.

Knowi as an option for MongoDB Analytics

Our mission at Knowi is to simplify and shorten the distance between data and insights for all data: unstructured, structured and multi-structured. To accomplish we believe you need to: a) leave data where it is and b) enable data engineers to explore all data without any restrictions that result from mapping it to a relational structure.

We are a certified MongoDB partner and the only analytics partner to natively integrate. No ETL. No ODBC drivers. No proprietary query language. We also natively integrate with most other leading NoSQL, SQL, RDMS data sources, and REST APIs, enabling data engineers to create blended datasets and visualizations in minutes.

You can play around with a NYC Restuarant dataset in our MongoDB sandbox to see for yourself how nice it is not to move your data out of MongoDB to analyze it. 

We also natively integrate to most other leading NoSQL, SQL, RDMS data sources, and REST API’s enabling data engineers to create blended datasets and visualizations in minutes.

Download our solution guide: Why Native Matters or try our free 21 day trial

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