How companies manage their data is one of the biggest challenges facing every business at the moment. Organizations need to cope with not only ever-growing quantities of data, but also an increasing variety of data types from multiple sources, thanks to innovations such as Internet of Things (IoT) sensors.
At the same time, there are growing demands that this raw data be turned into useful insights in or near real-time. All of this means more pressure than ever on the systems you need for gathering, storing and processing data.
When it comes to managing this wealth of data, the two main options are a data warehousing solution or a data lake. The former is a concept that has been around for many years and so should be familiar to any IT manager, while a data lake is a relatively new term that has arrived with the era of big data.
Some people may believe that the data lake is just an evolution of the older data warehouse and you may see the two terms used interchangeably to refer to any central repository for a firm's data. But there are actually a few key differences between the two. Understanding what these are and what the implications will be for data handling will therefore be vital when deciding which of these technologies is right for your business.
Raw vs processed data
One of the key differences between a data warehouse and a data lake is the type of information they store. Essentially, a data lake is a system into which a business can drop any and all types of data, including raw, unstructured data from every source the organization has access to. A data warehouse, on the other hand, handles preprocessed, structured data that has been cleaned and transformed into a format ready for analysis.
So does this mean the data warehouse is the better option for analytics? Not necessarily, as it depends on the type of data your company generates, and what you intend to do with it. For example, financial services firms will find warehousing highly useful, as much of the details they store will already be highly structured and easy to manage.
Some sectors, on the other hand, deal with much more unstructured data. Healthcare, for example, will have lots of clinical details, patient notes and medical imaging that will need to be analyzed. However, given the wide variety of types and sources involved and the lack of clear structures, warehouses are not the most efficient way of handling data for these organizations.
Learn more: Your Data Lake Has Become a Data Swamp - Here's How to Fix It
Do you have clear plans for your data?
Because of the greater structure that data warehouses provide, these solutions are much more suited for businesses that have a very clear idea of exactly what they want to do with their data and the types of outcome they are expecting.
Typically, data stored in a data warehouse will be easier to study and derive insight from than the less structured data lake. What's more, as it will have already been processed before being loaded into the warehouse, this means it is likely to have been used for a specific purpose and so will be more relevant than raw data.
While data lakes lack this structure, they can offer much greater agility than a warehouse. Being able to work directly with raw data means users can try new techniques, reconfigure their models and queries to answer a wider range of questions, and be more innovative in how they handle data. So if you don't already have a clear understanding of what purpose you'll use your data for, data lakes are an ideal place to experiment.
Who's interacting with the data?
Over the last few years, there has been great interest in the idea that data should be for everyone, and not only restricted to IT experts. But if you're encouraging business units and operational professionals to engage directly with your data, having a warehousing solution is likely to be the better approach.
Again, this is because the more structured format of these assets means it will be simpler for individuals without extensive knowledge and experience of programming to interact with the system and derive insight. Data lakes, on the other hand, can be harder to navigate and require more specialist skills to make the most of them. They may be able to provide answers to a greater number of questions than a data warehouse, but they will require expert data scientists to do this.
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