Data warehouses, data lakes, and data marts are all types of data storage solutions that are designed to help organizations store and manage large volumes of data. While these solutions share some similarities and often overlap in features, there are some fundamental differences that organizations should be aware of when deciding which type of solution is best suited for their needs, or how to implement the variations.
Data warehouses are a type of database that are generally used to store structured data - think clean, categorized and organized, though they can store semi-structured and unstructured data. They are optimized for query and analysis, allowing organizations to quickly and easily access and analyze data to gain insights into their business operations and the market landscape. Data warehouses are typically used for business intelligence and reporting, and are designed to support complex queries and reporting tools.
Data warehouses are typically built using a structured, relational database model - organized into tables, with each table representing a different type of data, and often employ a star or snowflake schema to organize data. Data warehouses are often optimized for read performance, allowing organizations to quickly and easily access and analyze data.
Data lakes are a type of data storage solution that are designed to store large volumes of unstructured and semi-structured data. They are designed to be highly scalable, allowing organizations to store large amounts of data at a relatively low cost. Unlike data warehouses, which are optimized for query and analysis, data lakes are designed to support a wide variety of use cases, including machine learning, analysis, data mining, and data exploration.
Data lakes are typically built using a non-relational, schema-less model, allowing organizations to store data in its raw, unprocessed form. Data is typically stored in a flat file format, such as JSON or CSV, allowing organizations to store large amounts of data in a highly flexible and scalable manner. Data lakes are often optimized for write performance, allowing organizations to easily ingest large volumes of data from a variety of sources.
One of our clients had monthly ingested data sets that regularly saw changes to the format and schema. And on top of that, their use cases grew and changed equally as often. Because both the data and the insights evolved over time, a data lake was a perfect solution. For each insight or type of analysis they needed, a model was built specifically for that process. The previous solution involved a growing number of scripts that were very similar with only slight variations to the code - enough not to violate DRY but close enough to bother any engineer with trace levels of OCD.
Data marts are a type of database that are designed to store a subset of data from a larger data warehouse or data lake. Data marts are typically designed to support a specific business function, such as sales or marketing, and are optimized for query and analysis, ‘optimized’ being the operative word. Data marts are often used to provide a subset of data to a specific group of users or applications, allowing organizations to quickly and easily access and analyze data without having to search through a larger data warehouse or data lake.
So, when should organizations use a data warehouse, data lake, or data mart? The answer depends on a number of factors, including the type of data that needs to be stored, the volume of data, the type of analysis that needs to be performed, cost, security, and regulatory compliance.
Data warehouses are best suited for structured data that is already organized into tables, and are optimized for query and analysis. They are typically used for business intelligence and reporting, and are designed to support complex queries and reporting tools.
Data lakes, on the other hand, are best suited for unstructured and semi-structured data, and are optimized for scalability and flexibility, like our example mentioned above. Data lakes are often used for machine learning, data mining, and data exploration, and are designed to support a wide variety of use cases.
Data marts are best suited for organizations that need to provide a subset of data to a specific group of users or applications. Data marts are optimized for query and analysis, and are often used to support specific business functions, such as sales or marketing.
Apothic Research Group specializes in cloud-native and on-premises storage solutions and we can work with you to find the best option.
