A fast, lean, and expandable data stack can transform data teams into strategic assets rather than cost centers. By combining Weld (ingestion & modeling), BigQuery (cost-efficient warehousing), and Steep (self-service analytics), companies can optimize performance while maintaining flexibility. Advanced teams can swap dbt for better control and testing or Snowflake, Databricks, or other tools instead of BigQuery for different workloads. This setup can be deployed in less than two hours and also sets the stage for AI-driven automation and business intelligence.
The modern data landscape demands efficiency, flexibility, and scalability. Traditional data teams often face scrutiny over cost and complexity, leading some organizations to see them as a liability rather than an asset. However, with the right data stack, businesses can unlock the true potential of their data teams, turning them into invaluable drivers of growth and automation.
This post introduces a fast, lean, and expandable data stack that balances performance, affordability, and adaptability. It consists of:
For organizations that require more customization and governance, the stack remains flexible. Teams can replace Weld’s modeling capabilities with dbt for better control and data testing, or swap BigQuery for Snowflake to suit specific workloads. This foundation also sets the stage for automation and AI-driven applications.
Data ingestion and modeling should not be complex. Weld simplifies these processes by providing an all-in-one platform for data ingestion and transformation. It eliminates the need for custom pipelines, reducing maintenance overhead while ensuring data is always fresh and ready for analysis. (Check out our comprehensive review of Weld for a deeper dive)
Figure: Weld’s intuitive data ingestion platform simplifies data pipeline management
Managing a data warehouse should not break the bank. BigQuery delivers industry-leading performance at a fraction of the cost of legacy systems. It is serverless, highly scalable, and optimized for analytical workloads, ensuring fast query execution without expensive infrastructure investments.
Alternative: If your team prefers Snowflake, it offers similar benefits, with a different pricing model and unique performance optimizations that may better fit your needs.
Traditional BI tools often require deep expertise to build reports, slowing down decision-making. Steep modernizes analytics by focusing on metrics, making it easier for teams to define and track key business indicators. It provides self-service analytics without the complexity of traditional BI solutions. (Read our in-depth review of Steep for more insights)
Alternative: If your team has different preferences, other visualization tools like Looker, Tableau, or Power BI can be used instead of Steep to meet specific needs.
Figure: Steep’s intuitive metrics dashboard enables self-service analytics and faster decision-making
For organizations needing additional control and governance, this stack is flexible:
This modular approach ensures businesses can tailor their stack as they scale, without needing to overhaul their entire infrastructure.
Once this data stack is in place, businesses have a solid foundation for AI-driven automation. Clean, structured, and well-modeled data is essential for:
By shifting the perception of data teams from cost centers to growth enablers, organizations can maximize their return on data investments.
A fast, lean, and expandable data stack—composed of Weld, BigQuery, and Steep—empowers businesses to make data-driven decisions with speed and efficiency. With the flexibility to incorporate dbt, Snowflake, or Databricks, it scales as business needs evolve. Most importantly, it positions data teams as a strategic asset rather than an operational burden, unlocking new possibilities for AI-driven automation and business intelligence.
If you’re ready to transform your data operations, start with a stack that is built for growth, efficiency, and impact.
