In an old world of on-premise warehouses like Teradata and Oracle, the cost of running queries and experiments on your warehouse was expensive. We believe the distinction between “legacy” and “modern” is key. Unlike the legacy tools, they don’t run an extract of your data set and query data on a scheduled or ad-hoc basis in your warehouse. Tools in this category query the data “where it lives”, i.e. they schedule and run an extract of the full data set from your cloud warehouse on a regular basis. The key point though is that they ingest data into their platform before analysis happens, i.e. Tools in this category offer an installable version of their product or a SaaS subscription.

There’s a commercial entity behind the open source project, and the business model consists of providing support and enterprise add-ons. Open source – “Modern”: The source code is available for download, with a vibrant developer community.Let’s start by dividing BI tools into three simple categories: Fundamental Separation of BI toolsīI tools facilitate exploration and visualization of data, and for the purpose of this post, we’re only including tools that support the major data warehouse ( Amazon Redshift, Snowflake, Google BigQuery, Azure SQL Data Warehouse.) So in this post, we’re helping you find the answers, by looking at the current BI tool landscape and their differentiators. There’s a bit of confusion on how these tools work and their use cases. data acquisition and ingestion, ETL pipelines and data discovery layers. But much of the hard work to build a data platform happens underneath the visualization layer, e.g. What seems to matter to buyers of analytics is the “eye-candy” of the dashboards. We believe that the visualizations and charts are a commodity. They have brought down the cost and complexity to build a data platform, in a shift away from Hadoop, with BI tools as the catalyst to make data exploration and visualization available to a much wider audience. That’s a lot of movement in a short time for a segment of enterprise software that’s been somewhat static in the past 25 years.Ī major driver in the uptick of M&A and venture activity are cloud warehouses like Amazon Redshift, Google BigQuery and Snowflake. The team behind Superset is rumored to have raised $18M in Q1 2019.Mode announced a $23M Series C on 2/19/19.Metabase announced a $8M Series A on 4/23/19.Salesforce acquired Tableau on 6/10/19 for $15.7B.Google acquired Looker on 6/6/19 for $2.6B.Qlik acquired Attunity on 2/21/19 for $560M.SiSense acquired Periscope Data on 5/14/19 for an undisclosed amount.In recent months, there’s been a wave of acquisitions and fundings in the business intelligence (“BI tools”) and analytics space: “Modern” Commercial BI Tools (Looker, Chartio, Mode Analytics, Periscope Data).“Legacy” Commercial BI Tools (Tableau, Dono, Qlik, PowerBI).Open-source BI tools (Metabase, Apache Superset, Redash).

Opensource bi tools