Introduction
Wtf even is a data warehouse?
Last updated
Wtf even is a data warehouse?
Last updated
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A data warehouse is fundamentally a specialized database designed to analyze and process large amounts of tabular data. But modern data warehouses, pioneered by companies like Snowflake and Databricks, have evolved into something much more powerful.
Think of a data warehouse as your organization’s analytical brain. Unlike traditional databases that handle day-to-day transactions, data warehouses are built specifically for analyzing and transforming large amounts of data. They serve as a central repository where companies can store, process, and analyze their data at scale.
Modern data warehouses excel at:
Analyzing massive datasets
Running complex queries
Transforming data at scale
Sharing insights across teams
The data warehouse industry experienced a fundamental shift in 2015 when Snowflake introduced a revolutionary architecture that separated storage from compute. This separation meant organizations could now scale their storage and processing power independently, leading to more flexible and efficient data operations.
This new architecture enables organizations to:
Store massive amounts of data efficiently without paying for unused compute
Scale processing power up or down based on actual needs
Allow multiple teams to work on the same data without interference
Process queries in parallel for better performance
Data Transformation
Modern data warehouses serve as powerful transformation engines. They take raw data from various sources and convert it into useful formats for analysis. This isn’t just about storage – it’s about making data actionable.
For example, a marketing team might join customer behavior data with transaction history to identify high-value customers. The data warehouse handles these complex transformations using SQL, making it accessible to business analysts and data scientists alike.
Data Marketplace
One of the most significant innovations in modern data warehouses is the concept of a data marketplace. Snowflake and Databricks have transformed their platforms into exchanges where organizations can share, discover, and monetize data assets. These marketplaces have become increasingly popular, with Snowflake reporting that 32% of their customers are now using their data sharing features.
Despite their capabilities, today’s data warehouses face significant challenges that affect both enterprises and users. The current model, while powerful, comes with substantial drawbacks.
The Cost Problem
The economics of current data warehouse solutions are increasingly problematic. Snowflake maintains a striking 77% gross margin on their product, which translates to significant costs for customers. In fact, users typically pay premiums of up to 87% compared to raw infrastructure costs.
The cost issue has become so significant that an entire ecosystem of startups (like Keebo.ai and Bluesky) exists solely to help companies optimize their warehouse spending.
Companies also face “double-spend” on storage, paying both for their original data location and warehouse storage. In addition to spending on the storage, they also pay to “move” their data resulting in significant “egress” spend.
Vendor Lock-in and Control
When organizations commit to a data warehouse provider, they often find themselves locked into a rigid ecosystem. This manifests in several ways:
Annual commitments required for better pricing
Credits that expire if unused
Loss of discounts when downsizing
Complex and costly migration processes
The Data Ownership Challenge
Perhaps most importantly, organizations lose effective control over their data once it’s uploaded to traditional data warehouses. They become dependent on the provider’s infrastructure for data access, face high costs for data egress, and have limited flexibility in how they can use and share their data.
End
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