It’s safe to say that 2020 showed us that data warehouses have not only found a new home in the cloud but have cemented their position as the foundation of every organization’s data strategy moving forward. By 2022, Gartner predicts the overwhelming majority of all databases (75%) will be deployed or migrated to a cloud platform. And the shift makes sense—this new breed of cloud data warehouses are superior in so many ways—cost, agility, speed of deployment, etc…
But cloud data warehouses don’t act alone. That is to say, the full benefit of a cloud data warehouse are realized in conjunction with other elements of the modern data stack including data integration and preparation. Modern data preparation platforms allow a variety of users of differing technical abilities to not only access data stored in cloud data warehouses and lakes, but also clean, prepare and automate data pipelines to support the growing variety of analytics projects moving to the cloud. The result is a faster, more scalable analytics process that enables a broader set of users to clean and onboard data into their data warehouse of choice.
In this eBook, we take a closer look at three different but well-known companies that have benefited from the combination of a cloud data warehouse and data preparation platform. Each company is different—from their industry to the way they approach their data strategy, even the type of cloud data warehouse they have. The one commonality? They’ve all invested in Trifacta to prep data being onboarded into their data warehouse.
Read this ebook to:
- See how Trifacta integrates with the three leading cloud data warehouses: Snowflake, BigQuery, and AWS Redshift
- Understand the major benefits that the companies have experienced upon implementing Trifacta in conjunction with their cloud data warehouse
- Learn about the negative impacts of manual or outdated data onboarding processes
- See how three companies that come from different industries (and use very different data) are able to improve the speed, scale and quality of analytics with data preparation