Do you know what a data lakehouse is? If not, don't worry – you're not alone. With new architectures like data lakehouse, data mesh, and data fabric all competing to reshape the market, staying on top of the innovation is anything but easy.
A data lakehouse is a new term that's been gaining traction in the world of big data and analytics. But what does it mean? And more importantly, what are the common use cases and benefits of using a data lakehouse?
What is a data lakehouse?
A data lakehouse is a data management architecture that combines the features of both a data lake and a data warehouse to provide a centralized repository for all your organization's data, making it easy to find and use. When built as part of a modern data stack with a self service analytics front end, it also allows anyone in your organization to gain insights that can help improve your business. The combination of these two features makes a data lakehouse a powerful tool for any organization that wants to make the most of its data.
Does a data lakehouse replace a data warehouse?
A data lakehouse does not replace a data warehouse. It combines the best features of data warehouses and data lakes. Data warehouses are good at storing and querying structured data, while data lakes are good at storing and processing large amounts of unstructured data. A data lakehouse combines these two capabilities, allowing organizations to store and query both structured and unstructured data in one platform.
Common use cases for a data lakehouse
Machine learning
Organizations can train machine learning models with large amounts of data by using a data lakehouse, which can then be used to make predictions or recommendations.
Advanced analytics
Data lakehouses can be used for advanced analytics, such as predictive or prescriptive analytics. They are well-suited for advanced analytics because they can handle large amounts of data from multiple sources.
Storing and analyzing large amounts of data
Data lakehouses can store and analyze large amounts of structured and unstructured data. This can be helpful for organizations that need to maintain a large database and want to analyze both types of data.
Benefits of using a data lakehouse
By combining the best features of data warehouses and data lakes there are many benefits that data lakehouses can offer. Some of these benefits include:
Robust data governance
A data lakehouse offers more robust data governance than a traditional data warehouse. This is because it enforces strict controls on who can access and modify data. This helps to ensure that only authorized users can access sensitive information.
Reduced costs for data storage
A data lakehouse reduces costs by storing data in its native format. This means that you don't need to convert the data into a format that can be read by a traditional database.
Simplified schema
A data lakehouse offers a simplified schema, because it uses a schema-on-read approach. This means that you don't need to define a schema upfront. Instead, you can define the schema when you're ready to query the data.
Easier administration
A data lakehouse is easier to administer than a traditional data warehouse. This is because it doesn't require you to manage separate databases for different types of data.
Immediate access to data analysis tools
Partnerships between companies like Databricks and ThoughtSpot enable you to immediately connect a data analysis platform to a data lakehouse. This is because it doesn't require you to extract data from a database before you can analyze it.
Gain insights from your data lakehouse in minutes
Data lakehouses are becoming popular for organizations that want to gain insights from their data more easily and quickly. By combining the best features of data warehouses and data lakes, it’s easier to get started with analytics. If you're thinking about implementing a data lakehouse from organizations like Databricks or Starburst in your organization, ThoughtSpot connects directly with these platforms. Start a ThoughtSpot free trial for yourself and see how you can unleash the value of your data.
