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Learn different data lake vs. data warehouse uses
Data lakes and data warehouses both store big data. When choosing a lake or warehouse, consider factors such as cost and what insights or analytics you need to gain from the data.
Data lakes and data warehouses both store data, however, there are several key differences between them. These differences result in varied use cases that may or may not meet the needs of a data center as it grows and scales.
Many organizations look to data lakes and data warehouses to help them gain insights from their data. However, they are not interchangeable, and organizations must consider their needs when they allocate resources for a data lake or warehouse. In general, data lakes are better for organizations that need flexibility, and warehouses are better for predetermined needs.
What is a data lake?
A data lake is a storage repository that can hold raw structured and unstructured data. Data lakes typically store data using a flat architecture, which gives users more flexibility for data management. They commonly store sets of big data and can support various schemas that enable them to handle different types of data in different formats.
Data scientists can use them as a platform to fuel big data analytics and data science applications and dig into the data to prepare and analyze it. Data lakes are flexible, so they are better for storing data from a variety of sources. They can break down data silos by combining data sets from different systems in one place.
A good way to think of a data lake is to envision its namesake: a lake. Like a lake can hold a significant amount of water, a data lake can hold a vast amount of raw data. Organizations can pour any type of data -- from unstructured to semistructured and beyond -- into the lake, and it all pools together in one place. This can be handy for storing data in a centralized location, but pulling specific data out of the lake can be difficult when it's pooled together with no rigid schema.
What is a data warehouse?
A data warehouse is a storage repository that can hold data generated by and extracted from internal data systems and external data sources. Rather than a flat architecture, data warehouse architecture is often split into layers or tiers, including a data integration layer that extracts data from operational systems, a data staging layer that cleans and organizes the data, and a presentation layer that makes the data available for more users than just data scientists.
The key factor here is the organization of the data. Whereas a data lake can accept raw data, data warehouses are generally designed to store data from multiple sources. Warehouses also use predefined schemas to organize that data, which makes it easier for users to access and query relevant data. They are a much better fit for structured data. While pooling any raw data into a data lake has its advantages, data warehouses can provide better consistency and data quality. This can directly impact the speed and accuracy of analytics applications.
However, data warehouses may limit the number and types of analytics tools or business analytics software organizations can use since they have to clearly define the schemas for each. There's less flexibility, but organizations with well-defined, specific needs can use data warehouses to accelerate analysis.
Data center use cases for different storage models
There are various factors to consider when examining data lakes vs. date warehouses and how to use them. The deciding factor isn't necessarily which technology is best, but rather the business needs.
Organizations that need as much access as possible to feed real-time data analytics benefit from a data lake because they enable the movement of raw data into an analytics environment. Conversely, organizations that need to keep highly organized data to meet regulatory demands benefit from a data warehouse because it provides the structure needed and the ability to easily visualize that data.
| Data lake | Data warehouse | |
| Data structures |
Data lakes are better suited for processing of data stored in its native format. They are also better for when the purpose of the data is not yet determined. |
Data warehouses are better suited for structured data extracted from transactional systems and predefined schemas. |
| Cost |
Data lakes cost less than data warehouses. They usually require less management and use lower-cost storage, resulting in lower costs. |
Data warehouses cost more than data lakes and require more management since they need more computational resources for queries. |
| How data is processed |
Data follows extract, load and transform, or ELT, so data is structured after extraction from storage. |
Data follows extract, transform and Load, or ETL, so data is structured prior to extraction. |
| Schemas |
The schema is defined after data is stored. |
The schema is defined before data is stored. |
| Who uses them |
Data lakes are better suited for data scientists or engineers who benefit from seeing data in raw formats to gain business insights. |
Data warehouses are better suited for managers and regular operational users only interested in KPIs. |
Data lakes are ideal for performing real-time analytics, predictive analytics, custom analytics or big data analytics, as well as implementing machine learning projects. They also enable organizations to run root cause analysis to trace problems to their roots.
Data warehouses are more suited for ad hoc analysis, transactional reporting and visibility into the hierarchical dimensions of data. They are also a better fit to present data to business users and for data mining to discover patterns in data.
Organizations can also implement data lakes and data warehouses at the same time to meet different business needs. Data lakes are typically easier and cheaper to build, so organizations can always start there and add data warehouse capabilities.
In addition, organizations can build out a data lakehouse with a hybrid architecture to address the challenges of data lakes and warehouses on their own.
