In the last three years, we have been fortunate enough to work with some of the best companies out of the Netherlands and the United Kingdom. Looking back, we couldn’t have imagined that some of these companies would give us the opportunity to help them create their Data Strategy or allow us to help engineer their Analytics stack. A lot of our success, we believe is because we were able to partner with some great companies like Looker, Fivetran, Matillion, but most importantly Snowflake.
If you aren’t familiar with Snowflake, despite their massive successful IPO, they are a Cloud-native Data Warehouse company, which has been able to help thousands of enterprise companies, change the way they store and process structured and semi-structured data. Having to compete against the likes of Amazon, Microsoft and Google, a lot of VC’s gave them a hard time, but with their ongoing success, their victory is therefore so much sweeter.
Having worked in large enterprise companies myself, one of the challenges I continuously ran into was the decreasing performance of databases like PostgreSQL, Microsoft SQL Server, and others. For anyone who has been a data analyst or data scientist in an enterprise or scale-up, they could tell you how their need for speed is continuously challenged by developers, who believe you need to become better at writing queries, instead of pointing out the slowness of their infrastructure.
It wasn’t until I was working with a growth company, who was willing to solve their issues with providing data to their clients, that I really started looking into this problem. I had of course worked with Amazon’s Redshift, and played around with Google BigQuery on other projects, but as the nature of the financial services industry was changing, from highly structured to also using semi-structured or sometimes even unstructured data, I had to find something different.
In my quest for a solution, my now co-founder Wesley and I, had a conversation about this. He was working for a large BI consultancy firm at the time, and was tasked with helping companies visualise their data. Together, we spend hours looking over all sorts of solutions, from Open-Source to PaaS (Platform as a Service). After stumbling on Snowflake, we had some great conversations with both the technical and sales team at Snowflake.
Snowflake allowed us to run critical data workloads on one platform, including data sharing, data lake, data warehouse, and custom development capabilities, in effect serving a data PaaS. But the best part about it for us, was the fact that was able to be deployed on other IaaS data service providers including Amazon Web Services, Microsoft Azure, and Google Cloud Platform, meaning that we didn’t have to go into lengthy arguments with CTO’s about changing their Cloud platform.
So when Wesley and I decided to start DataBright, we didn’t have to think long about how we wanted to help companies build their Analytical stacks. For the last 3 years, we have been a proud partner of Snowflake, and have worked on many projects, not just advising on how to build a Modern Analytical stack, but especially on implementing a great Analytics stack with the help of our Analytics Engineering team.