ThoughtSpot adds support for Databricks ‘lakehouse’ to analytics platform

ThoughtSpot has expanded the number of backend data sources that can be accessed via its cloud-based analytics platform to include the Databricks cloud service based on the Apache Spark framework.

A ThoughtSpot for Databricks offering now makes it possible to directly run queries through the ThoughtSpot search engine against a Databricks Lakehouse, a data architecture that combines the features of data lakes and data warehouses, according to Databricks.

For nearly a decade, ThoughtSpot has been making the case for an alternative approach to analytics that eliminates the need to rely on a data analyst or IT professional to construct a dashboard. Instead, it presents end users with a search interface through which they can employ natural language to query multiple backend data repositories.

That approach enables end users to interrogate data in a more interactive fashion that is not constrained by the limitations of how a dashboard was constructed, said Seann Gardiner, senior vice president of business development for ThoughtSpot. Users can launch those search queries in a single click, accessing backend data via SQL or levying Databricks’ machine learning algorithms for data science teams.

Organizations can also embed ThoughtSpot within third-party applications using low-code tools to create an alternative to dashboards, Gardiner said.

More flexibility means better decisions

Databricks is now one of several backend platforms that ThoughtSpot now supports. Rather than standardizing on a visualization tool optimized for a single platform, Gardiner said, ThoughtSpot lets end users launch queries against multiple backend data sources using an interface that provides a familiar consumer-grade experience.

For the future, ThoughtSpot is also evaluating how to use speech interfaces to make its platform even more accessible to end users, Gardiner said. “Certainly, speech is the next generation of this,” he said.

Visualization tools that IT teams build generally limit queries to a set of frequently asked questions. A search engine approach makes it easier for end users to explore data by asking questions in a more open-ended manner, where the next query can be informed by the answer to a previous query. That’s especially critical for business conditions that are highly volatile. End users don’t want to wait a week for the IT team to construct a new type of query every time they want to investigate a trend that is currently beyond the scope of the data visualization tool.

As analytics continues to evolve, IT teams are becoming less bogged down in daily tasks. They still play a critical role in provisioning a platform, but the days when business users waited for IT teams to email reports to them based on a set of canned queries derived from a set of pre-defined key performance indicators (KPIs) have come to an end. IT personnel can be reallocated to more pressing tasks that have yet to be automated.

Business leaders, in the meantime, will hopefully be able to make better fact-based decisions faster. One of the reasons many business leaders still tend to rely on gut instinct is they are not always sure what question to ask. A more iterative approach to launching queries makes it simpler for business leaders to explore relationships between data without having to perfectly frame their initial query.