Zing vs. ThoughtSpot: Zing is 3x faster at Uploading and Querying Excel File

Zing vs. ThoughtSpot

“Virtually all ABI platform vendors are now focused on adding NLQ capabilities through the integration of LLMs” - Gartner’s Magic Quadrant for Analytics and Business Intelligence Platforms

Zing Data currently implements NLQ capabilities, allowing users to perform queries using natural language, which simplifies user interaction and makes data analytics more accessible to non-technical users. ThoughtSpot, though robust in querying, currently requires more specific syntax and lacks the intuitive NLQ feature.

Comparing the process of uploading and querying a three table Excel file in both Zing Data and ThoughtSpot, we found that Zing completed the process in a third of the time.

The Process

Uploading the Superstore Data******

In both Zing Data and ThoughtSpot, we attempted to query the Excel Superstore Data file shown above, containing three pages: Orders, Returns, and People.

• Zing Data: Offering a more streamlined approach, we were given the option to drag and drop the Excel file into Zing, instantly uploading all three pages of the dataset.
• ThoughtSpot: In order to upload the dataset, we had to manually convert each page of the Excel dataset into a CSV file, and upload each file separately. We were then taken to review the estimated attributes for each variable, such as Postal Codes being stored as “additive,” which they are not.

Joining the Dataset

• Zing Data: The entire Superstore dataset was instantly uploaded.

  • Even if we were unable to upload the whole file together, Zing supports seamless integration of multiple datasets within the same data frame, automatically handing the joining process when necessary.

• ThoughtSpot: To join each CSV file in ThoughtSpot, we manually joined the ‘Orders’ file to the ‘Returns’ file by joining the two sets at a common variable, and then repeated the process for joining the ‘People’ file. We then created a new worksheet based on our newly joined and clicked on each column of information we wanted to include, which is all of them. Lastly, we had to delete the overlapping common variables we used to join the three files with.

Querying the Data

• Zing Data: Upon uploading the Excel file, we were instantly able use Natural Language Processing (NLP) for intuitive querying such as “What are the highest sales per region?” Zing would then process the question and output a graphical visualization and AI analysis.

• ThoughtSpot: After creating and modifying a worksheet with all of the Superstore Data, we were able to query that dataset. However, ThoughtSpot was unable to compute a graphical analysis for “What are the highest sales per region?” It required inputting solely the specific variable names from the dataset, such as “Sales by Region”

In Summary

Zing Data shines in scenarios where speed and ease of use are paramount, making it a strong choice for users who value efficiency and simplicity. Zing was able to upload and compute summary statistics in 2 minutes and 3 seconds with just a couple clicks from the user, allowing them to directly query their dataset using natural language processing. On the other hand, ThoughtSpot required 6 minutes and 12 seconds to upload, join, edit, and query the Superstore Dataset, constrained to the use of specific variables.