There is an industry-defining unbundling of BI happening right now. The BI app is unbundling into two composable parts - the metrics layer and the consumption layer. A ๐Ÿงต๐Ÿ‘‡:
Firstly, WHY is this happening? A massive tailwind propelled by @SnowflakeDB and @getdbt. In particular, a well engineered, infinitely scaleable analytics warehouse and a transformation and modeling layer that extends into the metrics layer.
"Accessories" to the data warehouse help tremendously as well: @fivetran, @AirbyteHQ, @getcensus, and @HightouchData for forward and reverse ELT (piping data to and from operational tools - e.g. @salesforce - the data warehouse) are the must-haves.
.@LookerData had massive success in capitalizing on the shift of BI to the cloud. Its main value proposition is LookML, a modeling and metrics engine. But its reign is ending.
.@getdbt is the best positioned product to dethrone LookML as a metrics engine. And if they execute well, they will be the dominant metrics product in the next 5 years. Other smart players in the space include @supergraindata and @transformio. Time will tell.
There is, however, a gap in the market to enable the consumption and delivery of these metrics and processed data inside the data warehouse. Traditional dashboarding solutions such as @tableau just don't cut it due to the inflexible UX and lack of metadata integration.
UX: software users now expect real-time collaboration, flexible and web-native collaborative interfaces (think @NotionHQ and @coda_hq) and workflow capabilities (think @asana and @clickup) built into a consumer-grade tool.
UX: dashboards alone do not cover the vast consumption use-cases of BI. Read:
Metadata: a great consumption tool must talk to other parts of the data stack via metadata integration. This means native metadata integration between @SnowflakeDB, @getdbt, @AirbyteHQ, and more.
Armed with this strong thesis, we started @hyperquery to address this gap in the market. We built @hyperquery to be the single, standard consumption interface for the modern analytics stack. We've invented a new category of software: Collaborative Analytics.
.@hyperquery combines the best of notebooks, apps, and dashboards and supercharges them with a built-in knowledge-base and a data catalog.
It's the single workspace for data discovery, exploratory analytics (ad-hoc work), dynamic analytics (building app-based explorations), and productionized analytics (building recurring-use dashboards and apps), and a place to organize those assets in a Notion-styled wiki.
With @SnowflakeDB, @getdbt, and @hyperquery, everyone can finally set up the right infrastructure to tackle data-driven decision-making at any scale. We can this the "DSH" stack (pronounced "dash").
.@jthandy (CEO @getdbt), Christian Kleinerman (SVP Product @SnowflakeDB), and I (CEO @hyperquery) are working hard to build this new future. Come join us for a once-in-a-lifetime opportunity to refactor the modern analytics stack!

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More from @josephmoon_ai

16 Nov 21
The tale of three data interfaces: a ๐Ÿงต ๐Ÿ‘‡
The data ecosystem is rapidly evolving, and that means better interfaces are being built to consume data and analytics for everyone in an organization.
Traditionally, the dashboard has been used as the cure-all for all data consumption in an organization. Tools like @tableau, @LookerData and @Domotalk are the de-facto ways for business users and technical users to consume analytics.
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