Startup data teams need to deliver analytics value without adding headcount or building infrastructure. Enter the modern data stack, which eliminates the need to build or maintain data pipelines and delivers normalized, analysis-ready data into a cloud data warehouse or data lake.
A modern data stack will scale with you as you grow, so you won’t need to continually engineer your analytics stack.
Our recent ebook, Scalable data infrastructure in under an hour, explains what the modern data stack is and how it can extend the capabilities of growing data teams — and in an accompanying video, we walk you through how to set up a modern data stack and use it to load and transform Salesforce data.
Setting up Snowflake, Fivetran and Sigma
We chose this particular stack because of its popularity with fast-growing Fivetran customers, but it’s by no means the only popular choice for smaller businesses. There are many great data warehouses and data lakes out there, as well as business intelligence and data visualization tools. We partner closely with most of them, including Google BigQuery, Databricks, Amazon Redshift, Tableau, Looker, Mode Analytics and many more.
Here are the components of the stack we chose for this demo:
The sub-hour elapsed time includes loading Salesforce data into Snowflake and running an in-warehouse transformation — a simple JOIN of Opportunity and Accounts to see Closed Won deals sorted by dollar amount. We also had time to make a couple of real-time calculations and visualizations using our BI tool, Sigma.
In a matter of minutes, then, we were able to start a new data warehouse, ingest Salesforce data, orchestrate a transformation on the warehouse, and make real-time calculations with our landed data.
Adding data sources to better understand causality
But that’s about as minimalistic as a modern data stack gets. What could we do next? If we wanted to understand how marketing spend impacted sales, we could effortlessly add connectors for Google Analytics, Marketo and one or two other marketing sources, and create that metric. Or we could load our production application’s statistics into Snowflake and infer how interactions with our customer success team are accelerating customer application usage.
Our modern data stack can accommodate all manner of analytics projects, and because it’s cloud-based and largely automated, it will scale effortlessly as you add sources and your data volumes grow.
Take a look at our new ebook and learn how a modern data stack can make life easier for growing data teams.