The dawn of data warehousing: How we got here

Every marketer today has heard of Snowflake or works at a company that uses something like it. Data warehouses are no longer just tools for engineers or analysts. They’ve become the foundation for modern marketing. They connect every touchpoint, power customer 360 views, and enable personalization at scale. But how did we get here? Why does a technology that started in the back offices of IT departments now sit at the center of marketing strategy? To answer that, we have to look at where data warehousing began and how it evolved into what we now call the data cloud.

For decades, organizations have been trying to answer a simple question: how can we make sense of all our data? The story of data warehousing begins long before cloud computing, long before Snowflake or BigQuery, and long before marketing dashboards became a daily routine. It started with a very basic need: turning raw data into business insight.

The world before data warehouses

In the 1970s and 1980s, most companies stored information inside transactional systems. These databases were designed to handle everyday operations like processing orders, recording payments, and tracking inventory. They were fast and reliable for transactions but terrible for analysis.

When teams tried to run large reports directly on those systems, performance slowed dramatically. In some cases, entire production environments went down. Companies needed a way to analyze data without interfering with day-to-day operations. That challenge sparked the concept of the data warehouse.

The birth of the data warehouse

In 1988, IBM researchers Barry Devlin and Paul Murphy published a paper describing an “information warehouse.” Their idea was to create a separate environment for reporting and analytics, fed by data from multiple operational systems. This was a revolutionary concept at the time.

By the early 1990s, companies like Teradata, Oracle, and IBM were offering commercial data warehousing products. These systems allowed organizations to store massive amounts of historical data and perform complex queries across it. Executives could finally view unified reports on sales, marketing, finance, and operations. The era of business intelligence had begun.

The rise of enterprise data warehousing

Through the late 1990s and early 2000s, data warehousing became a standard part of enterprise IT. Companies built massive on-premise systems that handled terabytes of data. Vendors like Teradata, Oracle, and Microsoft dominated this era, offering powerful but costly solutions.

The systems were rigid, expensive to scale, and difficult to maintain. Hardware upgrades could take months. Adding more storage or compute power required physical infrastructure. Meanwhile, as the internet grew, so did data volumes. Businesses were generating more information than their warehouses could handle efficiently.

This set the stage for a new wave of innovation.

The arrival of the cloud

In the early 2010s, cloud computing started to change everything. Amazon introduced Amazon Redshift in 2012, offering a cloud-native data warehouse that made large-scale analytics more affordable and accessible. For the first time, companies could spin up clusters in minutes instead of months.

Soon after, Google launched BigQuery, a fully serverless warehouse that removed infrastructure management entirely. Users could query massive datasets using SQL without worrying about provisioning or scaling servers. These cloud platforms made analytics faster, cheaper, and more elastic.

However, each solution came with trade-offs. Redshift required tuning and scaling efforts. BigQuery worked best inside Google’s ecosystem. Most importantly, both systems were tied to their respective cloud providers.

Enter Snowflake

In 2014, a startup called Snowflake emerged with a new vision for data warehousing. Its founders designed Snowflake from the ground up to solve the limitations of existing systems.

Snowflake separated storage and compute, allowing them to scale independently. This meant multiple workloads could run simultaneously without competing for resources. For example, one team could run complex data transformations while another generated dashboards, without performance issues.

Snowflake also built its platform to work across major cloud providers, including AWS, Azure, and Google Cloud. That flexibility gave customers freedom from vendor lock-in. Combined with its pay-for-what-you-use model and easy setup, Snowflake offered the simplicity of Software as a Service (SaaS) with the power of enterprise analytics.

Why this history matters

The evolution of data warehousing mirrors the evolution of business itself. Each generation solved a major pain point from the last:

• Early warehouses solved performance and reporting problems.

• Cloud warehouses solved infrastructure and cost challenges.

• Snowflake solved scalability, concurrency, and accessibility across clouds.

Today, data warehousing is about more than storage and analytics. It is about sharing, collaboration, and activation. Companies are using Snowflake not only to analyze data but also to power customer experiences, personalize marketing, and train machine learning models.They might do this through a variety of additional software, like Simon Data, Hightouch, Census and more. 

Understanding this history helps explain why Snowflake has become such a dominant player. It didn’t appear in a vacuum. It was the logical next step in a decades-long effort to make data easier to use.

The road ahead

We are now entering the era of the data cloud, where analytics, machine learning, and data sharing all converge. The lessons from the past still apply: data is only valuable when it is accessible, reliable, and usable.