![]() ![]() Data Scientists needing to be able to process large amounts of raw data of dubious quality.So, along came the Data Lake to help ease these common pain points: Architecture of a simple data platform using just a data warehouse The name is also confusingly used to identify a type of Database, such as AWS Redshift, Azure Synapse, and Snowflake, which specialise in storing and querying large amounts of data.ĭata Warehouses have their issues they can be more expensive than a Data Lake when processing large amounts of data, and work best only when data is of reasonable quality and in a tabular structure. ![]() ![]() The three most common Data Warehouse architectures are the Kimball Star Schema, Data Vault, and One Big Table. It is where you store your tabular data in a way that can be easily used by business intelligence applications, such as Tableau or Power BI, web applications, and even other data warehouses. So, can you have the best of both worlds with the Data Lakehouse? And what is the best Lakehouse to use?īefore we answer those questions, we must ask, “what is a Data Warehouse, Data Lake, and a Data Lakehouse? What is a Data Warehouse, Data Lake, and a Data Lakehouseĭata Warehouse is a data architecture that has been around since the 90s and is still relevant today. Running both in tandem on a data platform can have serious costs and maintenance associated. Add on your data science builds and storing your raw data cheaply, plus adding a Data Lake just for good measure, and the costs soon start adding up. When using your Data Platform to improve your Business Intelligence with useful dashboards, and reports, you’ll more than likely want to use a Data Warehouse. ![]()
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