How to drive trusted decisions without changing your current data infrastructure.
Learn more about DataOS® in our white paper.
In our previous post, The Pros and Cons of Leading Data Management and Storage Solutions, we untangled the differences among data lakes, data warehouses, data lakehouses, data hubs, and data operating systems. Remember to read part one if you need a quick refresher.
Companies need more than definitions. In a world where technology evolves, and data assets have exploded in volume, it helps to know the best use cases for each of these solutions and when to avoid them. Here’s a quick guide to get you started.
What factors are most important when building a data management ecosystem?
To choose the most suitable data management solution for your organization, consider the following factors:
By carefully evaluating these factors and understanding the features and limitations of each solution, you can select the most suitable data management approach for your organization’s needs.
Here is a quick guide for determining a solution for a specific use case and when to choose something different.
Choose a data lake if your organization:
Avoid data lakes if your organization:
Choose a data warehouse if your organization:
Avoid data warehouses if your organization:
Choose a data lakehouse if your organization:
Avoid data lakehouses if your organization:
Choose a data operating system if your organization:
Under most circumstances, there is never a reason to avoid data operating systems. Here’s how to choose a data operating system that helps your data strategy evolve.
DataOS is the only end-to-end data operating system, and it works with all other data management and storage solutions.
DataOS helps companies overcome integration challenges and operationalize their data. It connects all tools and data sources — from legacy systems to brand-new technology investments — within a company’s technology ecosystem and provides a flexible and composable way to operationalize data without disrupting business.
Additionally, it removes the need for heavy data expertise, empowering business users to access data insights quickly and easily. While IT can still build complex pipelines and data products using a command line interface, the self-serve capabilities within DataOS allow business users to simply drag and drop the data outcomes they need. DataOS puts organizations on the fastest path from data to insight.
No matter what you have in your toolkit — whether it’s a data lake, warehouse, lake house, or hub —DataOS is the operational layer you need to become a truly data-driven organization.
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