Data Warehouses vs. Data Lakes: How to Improve Your Data Management?

When organizations reach a certain level of analytical maturity, they often face the same challenge: How can we get more value from the data available?

Answering this question is a complex task, even more so if we consider that when organizations grow and expand, they generally include new business units and multiple sources of information that make the use of data even more complex. 

At this point, it is essential to establish policies and guidelines that allow the information collected to maintain a certain level of coherence. 

However, there comes a point when the volume of information is such that it is necessary to use cloud-based services and tools to make the most of the resources available.

Below, we will discuss the possibilities that data management brings through the cloud and focus on the advantages of unifying all data sources in a data lake or enhancing the analytical capacity of the organization through a data warehouse. But first, let’s look at the key advantages of using the cloud for data management.  

Why is the cloud essential for data management?

When we talk about using the cloud for data management, analyzing and storing information without worrying about the limits of an on-premises server is just the beginning. 

The most attractive factors of this technology lie in the possibility of automating processes and enhancing the data strategy through technologies such as artificial intelligence and machine learning. 

Among the benefits of data management in the cloud, we can highlight the following:

• Getting rid of  information silos:

  The cloud allows data from various sources to be unified in one place. All this, accompanied by good ingestion and transformation policies, guarantees that all information is usable to achieve business objectives. 

• Creating a unified source of truth:

  When all the organization’s data is unified and processed under the same parameters, all business units can base their decisions on the data with absolute confidence.

• Having access to a unique view of the user and the business:

  The entire organization can access data in real-time, enabling accurate visualizations based on all available data.

• Analyses taking minutes, not hours

  Having access to information is essential to making decisions and taking advantage of opportunities.

• Getting access to deep analytics models

  The computing capacity of the cloud makes it possible to profile users with unprecedented accuracy, allowing the creation of personalized campaigns. 

The advantages of cloud data management are typically those of cloud services in general, including pay-as-you-go pricing, scalability, anywhere access, automated backups, and a single source of truth for organization-wide data.

What is a data lake, and why does your organization need it?

A data lake is a secure and flexible repository in the cloud that stores data from different information sources. 

As a result of this technology, it is possible to store, govern, share, and analyze the entire organization’s data for the benefit of each area. 

Organizations that decide to create a data lake manage to eliminate their data silos and optimize the use of information through visualization tools capable of displaying information in real-time through highly customizable filters that allow queries to be precisely aligned with the needs of each area. 

It is worth mentioning that cloud service providers, such as AWS, have created tools to facilitate the creation (Amazon Lake Formation) and governance of data lakes (S3).

Thanks to these tools, some of the most complicated processes can be simplified when implementing this type of solution, such as:

• Creating data ingestion pipelines. 
• Integrating tools for visualization or use of data.
• Managing different permission layers to access information. 
• Sharing the insights from the analysis with other areas or business units. 

A data lake is essential for an organization that manages significant volumes of data to take advantage of them efficiently. However, many organizations go one step further and create a data warehouse to take their analytics strategy to the next level. 

What is a data warehouse, and when should it be used?

The first thing to clarify here is that while a data warehouse can be the natural evolution of a data lake, there are cases in which data warehouses are implemented from on-premises databases or mixed solutions. 

Whatever the origin of the information you work with, the data warehouse always fulfills the same function: It organizes the data to take the analyses we can perform from it to the next level. 

If, in a data lake, we had a pile of information that came from various sources and had been processed through policies and guidelines to make it “speak the same language,”  the data warehouse would be as if we had taken all that information and organized it in a vast library that makes it easy to consult and use the latest technology tools to obtain actionable insights. 

A clear example is the ability of data warehouses to drive the organization through advanced analytics. In this case, learning algorithms are created to review the data and identify patterns representing risks or business opportunities. This is one of the most advanced phases of an organization’s analytical maturation process. Still, it is undoubtedly the horizon that must be aimed at to face future challenges. 

Are you looking for a partner to help you take advantage of the full potential of your data?
At Pragma, we have years of experience working with companies at diverse levels of analytical maturity, and our teams of data professionals are ready to get to work.