While both data warehouses and lakes are big data storage solutions, they are useful in distinctly different situations. Data warehouses store structured data that can be accessed and interpreted by anyone with permission to do so, whereas a data lake is an unstructured storage space for large quantities of raw data.
The core difference between data warehouses and lakes
Data lakes store big data in its raw form, with minimal structure and few controls over what data is included or excluded from the storage space. By contrast, data warehouses are structured storage solutions in which data is stored for a specific purpose. Data warehouses are designed to allow anyone at the business to retrieve the insights that they require, whereas data lakes are difficult to use without data science training.
Questions to ask when choosing a data lake or warehouse
Data lakes are not inferior solutions to data warehouses. Provided that your organisation has the right skill set, data lakes can provide faster access to data insights than warehouses, and can be easier to maintain. However, data warehouses are designed to be more accessible and more broadly useful.
Below is a short list of questions that you should ask of your organization when weighing up an investment in either a data warehouse or data lake.
Who needs to be able to interpret our data?
One of the most important strategic considerations for your big data storage is who needs to access it. If you need your operational and service delivery employees to be able to retrieve and analyze your data easily, a data warehouse will almost certainly be preferable to a data lake.
If, on the other hand, you are working with data that only data scientists need to be able to analyze in order to disseminate insights to the wider business, a data lake is likely to be a more efficient solution, as it is faster and easier to manipulate if you have the right skills.
Do we know the use cases for our data already?
Data warehouses provide users with structured data and should be set up to make it easy for the organization’s employees to find what they’re looking for. To keep them streamlined and easy to use, they should only contain data that is currently useful to the organization’s employees.
If you have a clear idea as to why you need to be able to analyze your organization’s data, a data warehouse will help you to ensure that your goals are met.
A data lake, on the other hand, is intended to be a vast store of as much raw data as you want to put into it. If your organization has access to a lot of data that you want to keep hold of and potentially analyze, even if you don’t have a specific use for all of it right now, a data lake could be a good solution.
How often will our data requirements change?
The structured nature of a data warehouse means that it is harder to modify if your requirements change dramatically in the future. Modifying data warehouses’ structures to accept new data sources and types is of course possible, and especially if the data model has been well-designed in the first place it has been set up well in the first place (expert data insights services providers like Calligo can help here), but the more drastic the changes, the more time it will take.
Data lakes, on the other hand, can adapt easily to different requirements. New schema and scripts can be applied to manipulate the data at will, and new data (including new types and formats) can be added without causing any issues as there simply isn’t a predetermined structure to adhere to. You will need data scientists capable of working with the lake if you want it to be useful, but if those skills are available, then a lake can be much easier to modify than a warehouse.
If your data needs will remain stable or, at least predictable and minor, a data warehouse will suit your organization. If more frequent or substantial changes and variations are likely in your data inputs, then a data lake will provide the flexibility you need.
How many data types do we need to support?
Data warehouses work best when the data contained within them is easy to organize. Numerical data works best, whereas text and other formats are more difficult to structure. Data needs to be transformed (i.e. converted to a supported format) before it can be added to a data warehouse.
Data lakes can store any and all data types, and support the addition of new types in the future. For example, a data lake would be more suitable than a warehouse as a repository for written customer feedback or physicians’ notes in a healthcare organization.
Use cases for data warehouses vs data lakes
A data warehouse is likely to be a better big data storage solution for general organizational use, especially if a high number of employees without data science training need access to the data to draw insights and create their own reports. They are most effective when your data has a clear purpose and can be structured and organized in a logical manner.
An industry like finance has many uses for data warehouses, as there are many situations where the majority of a firm’s employees need regular access to numerical data, and the nature of the data is not going to change much over time.
A data lake, on the other hand, can be an efficient storage solution if your organization has the expertise to work with it. They may also be required when the data that you need to store is inherently difficult to structure, or when you need to store large volumes of data without an immediate need to interrogate or process it.
Healthcare and legal are examples of industries where data lakes might be more valuable than a warehouse. Vast quantities of unstructured data, like a physician’s historic notes or case details, may be stored in a data lake if their future usefulness is rare and relying on a data scientist to retrieve the data on those occasions is practical.
Do you need a data warehouse if you have a data lake (and vice versa)?
It is possible that your organization could make use of both a data warehouse and a data lake, especially if you already have one or the other and can see your needs evolving. The different strengths of lakes and warehouses not only give them different use cases, but also mean they can complement each other.
For example, if your organization already has a data lake containing vast quantities of unstructured historic data and a small team of data scientists capable of working with it, there may not be any need to replace it with a data warehouse. However, if you recognize that other employees need to be able to access certain data more efficiently, a data warehouse solution alongside the data lake would solve that problem.
Or perhaps you have a data warehouse that works well for your employees, but can’t cope with new types of data that you want to process, or doesn’t give you room to store historical data. In this situation, the data warehouse still serves purpose, but a new data lake would give you space to store the entirety of your data and enable it to be accessed in the future if needed.
For organizations that are determined to use their data to be more creative and make better decisions, or to create a data-driven culture for decision-making, then a data warehouse is more likely to be useful. It creates a single structure for all your complementary data sources to merge seamlessly into, and a unified dataset that offers far broader context and finer detail than any single data source or series of integrations could provide.
Data Warehouse as a Service with Calligo
Calligo’s data warehouse as a service (DWaaS) offering lets you leave the hard work of its construction and ongoing maintenance to us, so that you can focus on extracting value, not managing data.
We also offer machine learning as a service to help extract those insights, and data visualization services that put your data in your teams’ hands in an attractive, accessible way that gives them autonomy to make data-based decisions.gle, safe and secure data foundation so every team and department can make better decisions in full context and innovate faster