![]() The data lake was invented in 2010 and rapidly gained mainstream adoption throughout the 2010s. However, this approach comes with several drawbacks, including the higher costs due to maintenance and vendor lock-in, necessitating the need for more cost-effective data management approaches. It prioritizes certain factors, such as the integrity of the provided data. This data warehouse process has its advantages. This data gets processed into a different database format that’s optimized for BI (business intelligence) use cases, where it’s more effective for complex queries. Usually, data warehouses pull data from databases, which have a specific structure known as schema. Defining data warehousesĪ data warehouse collects data from various data sources within an organization to extract information for analysis and reporting. To understand how a data lakehouse works, let’s first take a brief look at data warehouses and data lakes. What is a data lakehouse?Ī data lakehouse shores up the gaps left by data warehouses and data lakes - two commonly used data architectures. ![]() It lets them reach a middle ground where they can get the best of both worlds in terms of data storage and data management. ![]() This is where a data lakehouse has emerged as a major problem-solver in the last few years.Ī data lakehouse can help organizations move past the limitations of data warehouses and data lakes. That’s why data architects envision a single system to store and use data for varying workloads. Despite their pros, each also has its limitations. Both data warehouses and data lakes have been serving companies well for a long time. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |