Data generation and storage as we know it has reached a period of unprecedented growth. With storage requirements growing at 40% per year, it’s no wonder that 79% of IT professionals consider managing this growth as one of their most prominent paint points. Even on a wider scale, almost 50% of all businesses in the USA describe data growth as one of their central three challenges they face.

These statistics reveal two core aspects of the current data landscape. The first revelation is that data growth has reached a point of no return; the exponential increase in the amount of data that a business needs to store is becoming more pressing every single day. Secondly, businesses feel unequipped to deal with data storage.

Considering the wide variety of different data storage options that are currently available, this is hardly surprising. From data warehouses to data lakes and even data marts, there are so many different possibilities for storage that many don’t know where to begin.

In this article, we’ll be navigating through these three main data storage options, explaining exactly what they are and how they’re most commonly used. Let’s get right into it.

What is a Data Warehouse?

Data warehouses are one of the leading formats for storing data, often holding large amounts of information at any given time. They are typically constructed right as a company begins to collect data, with the continual use of a singular warehouse then providing a great source of analysis for any historical or trend data that may need to be enacted.

Due to the expansive potential sources that a data warehouse can work with, they tend to become the ‘go-to’ location for general analysis. With SQL being the primary language of navigating through the data, analysis comes easy to data warehouses, providing valuable insights into trends, cost analysis, and any other metrics that a business may need.

As data warehouses need to be accessed by many different departments within a company, holding vast amounts of data, many businesses have turned to cloud data storage solutions to house all of their data. The scope of the data that is stored in the average warehouse has made the cloud an attractive and affordable solution instead of localized storage.

The cloud data industry has risen from this continual need for accessible data storage, with many companies now offering storage services. If you’re looking for more information on cloud data warehouses, then we recommend looking at a comparison of two leading companies, Clickhouse vs Snowflake, to see the general services that cloud businesses can offer.

The three main benefits of a data warehouse are as follows:

●  Stability – Once data is collected into a data warehouse, it is stabilized and will not change. This lack of volatility is incredibly useful for continual analysis.

●  Selective – While data warehouses are vast repositories, users can focus on a particular part of the data, pulling up information that is directly related to their area of work and cutting through any information they don’t need.

●  Structured – Data warehouses can pull from many different sources, stabilizing the data and creating a level of consistency that makes it easy to enact analysis upon.

What is a Data Lake?

Data lakes are centralized repositories where both unstructured and structured data are stored, providing a location where huge volumes of data can be sent without the need for initial processing. Due to the versatility of storage within a data lake, businesses can collect data from a variety of different sources with ease and store it all here.

While data warehouses were once the principal data storage architecture for businesses, as enterprises are now continually needing to pull analysis for several different sources at once, the flexibility that a data lake offers has become increasingly popular. Quite simply, there is a level of ease that comes with a data lake, with data scientists not having to structure data before it enters the pool.

Equally, considering that 95% of companies suggest that their inability to manage unstructured data is impairing their progress, data lakes provide a great way of circumventing this problem. Instead of actively having to manage and regulate data, as is needed within a data warehouse, a data lake can act as a catch-all for businesses.

The three main benefits of a data lake are as follows:

●  Democratized – Data lakes actively help to prevent data siloing in businesses as everyone has a centralized repository where they can place their data and access data produced by other departments or teams.

●  Native Storage – Data modeling and structuring takes both time and money. As data lakes can process unstructured, semi-structured, and fully structured data, they provide a way of storing all forms of data without incurring these initial costs.

●  Languages – Data warehouses are primarily accessed and navigated by using SQL. Data lakes can come in many forms, some of which allow you to conduct analysis and navigation through other languages.

What is a Data Mart?

A data mart is the most simple format of data storage, often being considerably smaller than both data lakes and warehouses. Their smaller size is due to the fact that they are typically used within one department, with a repository of useful data for that team being processed and stored within the data mart.

As a data mart provides a concentrated location for specific data, it becomes very useful when a certain team needs to get instant access to insights on data that impacts them. Instead of having to move through complicated and vast data lakes or data warehouses, a data mart provides them with easy access to exactly what they’re looking for.

Considering the vast amounts of data collected by modern businesses, data marts have become increasingly popular over recent years. Acting as a singular, centralized location where relevant data to a particular department or team can be stored, their convenience and speed is by far their largest asset.

The three main benefits of a data mart are as follows:

●  Quick – Smaller collections of data are easier to navigate and can, therefore, produce insights in a fraction of the time.

●  Departmental Consistency – If you provide a whole department with a data mart, they’ll all be working off the same data. Due to this, you’ll have consistent analysis and reporting from every team within the department.

●  Scalable – Necessary data can be rapidly added to a data mart, making this a great choice of repository for a project that will continuously be collecting new data and running analytics on it.

 

What are the central differences between these three forms of data storage?

Putting these three data architectures next to one another, core differences both in their use cases, as well as their size, scope, and complexity.

Let’s give a brief overview:

●  Data Warehouse – These are designed to support business analysis for entire organizations, often spanning over huge amounts of data – both past and present. Considering the wide use of the data, it is pooled from various sources and will store structured data that is ready for analysis.

●  Data Lake – While storing large amounts of data, just like a warehouse, a data lake focuses on both unstructured and structured data. Without a specific use in mind, lakes act as vast repositories of information that people can turn to for analytics. There is no predefined use, meaning data is often just left as it is, without necessarily being processed beforehand.

●  Data Mart – Finally, we turn to a data mart, which is a much smaller form of data storage. Instead of holding data for a whole organization, a data mart draws from fewer sources and stores much less data. Due to this, they are mainly used for a specific department, holding sales data, marketing data, or financial data. Small, specific, and incredibly focused are all descriptors of a data mart.

Within these three, if broad comparisons were to be made, you could suggest that data mart and warehouses are similar in intention, but not in size. On the other hand, data warehouses and data lakes are similar in size, but not in intent.

Final Thoughts

While there are a range of different methods for storing data for analysis within the world of business, each has a suggested use commonly associated with that method. Due to each having specific functions, no one form of data storage is more inherently useful than another.

While a data warehouse might have a vast quantity of data for analysis, if you want fast insights, then a specified data mart would be the better option. This scenario is the same when reversed, with different storage options being perfect for different intents. When constructing a data storage architecture, start with your intent and work backward, using different structures for the distinct use cases that you come across.

By using a blend of all three of these systems, you’ll have a comprehensive base to organize and manage the data that your business receives.