Really great analysis. I've been thinking much the same for a while now and also chewing on how, as we get better at understanding what it takes to create that utopian, unified data platform, and the lengthy list of tools and capability it's gonna need. How do we encourage better behaviours and architectures when everything is so damn expensive? We have an industry with a rich history of gouging its customers and making every little thing ultra expensive. I can only hope that we see what happened in the software world over a decade ago, and see a surge of free open source tooling driven by engineers fed up with doing everything so badly. Given the influx of software engineers into the data field, I am crossing my fingers.. As Dave points out in his comment, there is value in decoupling a semantic layer for data from visualisations and dashboards... It also goes far beyond just that.
Hi Benn. I do believe that this announcement is an important one because it explicitly acknowledges that the semantic layer has value separate and distinct from the consumption layer. I started AtScale in 2013 with this core concept in mind and it's great to finally see the large ecosystem players come to the same realization. Have no doubt, though, that Google's intentions are to drive customers to BigQuery and consequently, GCP. Their investment in BI Engine is another example of trying to compete with Snowflake by positioning them as just a dumb data store.
Interesting analysis. Some pieces do however feel a bit off to me because Looker is marketed and sold as a premium, powerful and yet consumer friendly BI platform first. It is a very expensive product and they had the means to put such a heavy price tag because they closed the gap between a powerful data modelling layer and best in class visualisations and reporting tools. Shifting their strategy to focus on data modelling would put them right next to open source solutions that solve those problems very elegantly and essentially for free or as a dedicated online service (e.g. DBT Core & DBT Cloud).
As you suggest, it does seem to me as a way to "align with the horizontal grooves the industry is carving" in order to stay close and a relevant alternative when compared to the competition. But I don't see that as a shift of strategy.
Really great analysis. I've been thinking much the same for a while now and also chewing on how, as we get better at understanding what it takes to create that utopian, unified data platform, and the lengthy list of tools and capability it's gonna need. How do we encourage better behaviours and architectures when everything is so damn expensive? We have an industry with a rich history of gouging its customers and making every little thing ultra expensive. I can only hope that we see what happened in the software world over a decade ago, and see a surge of free open source tooling driven by engineers fed up with doing everything so badly. Given the influx of software engineers into the data field, I am crossing my fingers.. As Dave points out in his comment, there is value in decoupling a semantic layer for data from visualisations and dashboards... It also goes far beyond just that.
Hi Ben, I’d like to have you join us on Data Sharks. Let me know if you’re interested. https://youtube.com/playlist?list=PLIBwmCV8YuLPOKsqwovvBgMhBDwYYyB85
Hi Benn. I do believe that this announcement is an important one because it explicitly acknowledges that the semantic layer has value separate and distinct from the consumption layer. I started AtScale in 2013 with this core concept in mind and it's great to finally see the large ecosystem players come to the same realization. Have no doubt, though, that Google's intentions are to drive customers to BigQuery and consequently, GCP. Their investment in BI Engine is another example of trying to compete with Snowflake by positioning them as just a dumb data store.
Interesting analysis. Some pieces do however feel a bit off to me because Looker is marketed and sold as a premium, powerful and yet consumer friendly BI platform first. It is a very expensive product and they had the means to put such a heavy price tag because they closed the gap between a powerful data modelling layer and best in class visualisations and reporting tools. Shifting their strategy to focus on data modelling would put them right next to open source solutions that solve those problems very elegantly and essentially for free or as a dedicated online service (e.g. DBT Core & DBT Cloud).
As you suggest, it does seem to me as a way to "align with the horizontal grooves the industry is carving" in order to stay close and a relevant alternative when compared to the competition. But I don't see that as a shift of strategy.