Data lake vs database: everything you need to know

Have you ever been afraid to think what would happen if all the information you have stored on your computers were to be lost suddenly? Well, that can happen, but to protect you from that, Data Lake was created.

This is a centralized library designed to protect and store large amounts of structured data, regardless of its size.

The best part is that you can not only store the data but also send it to any location at the fastest possible speed. So, it's time to forget about losing vital information that is so important to your business. 

Would you like to take a look?

What is a data lake?

First, let's define what is it. It’s a storage facility that contains a large amount of raw data that is kept there until the right time comes. 

Its architecture is completely flat, which makes it easier to operate than a data warehouse. 

Each of the elements that make up the data warehouse is given a unique identifier and is tagged with a set of metadata. Then, when we need to solve a business problem, we ask the Data Lake for information related to that issue. 

Differences between a data lake and a data warehouse

There are many differences between a data lake and a data warehouse. Let's take a look at some of them below: 

1. The data lake stores all data

First of all, a data lake keeps all data, regardless of what is happening around it. It stores both the data it’s currently using and the data it might use in the near future. 

So, if you want to do an analysis of what’s happening, you can look at the information that’s available. 

2. Supports all types of information

The Data Lake approach encompasses all types of information, including non-traditional. 

We like the fact that it stores all information regardless of its structure and source. 

It’s always in its raw form and we transform it when we are ready to use it. 

3. Supports all users

Like the previous section, the Data Lake approach supports all data as well as all users. 

This way, data scientists can access the Data Lake and query any data set they need. 

4. Faster insight 

One of the key differences between the Data Lake and the Warehouse is that the Data Lake allows users to get information faster. 

This is because the Data Lake contains all the data and data types, and customers can query it before it has been transformed. 

5. Adapts to change

The Data Lake is characterized by the fact that it stores all raw data so that it can be accessed by anyone who needs it at any time.

8 Practices to Get the Most out of your data lake

There’s no doubt that building a data lake is one of the best actions we can implement in our business to keep all our data safe. 

Let's now take a look at some practices that you can carry out to get the most out of it.

1. Priority list

And we could not start in any other way than with the list of priorities. When starting a project of this type it’s essential to have a strong alignment with all lines of business, since the data lake provides the value that it does not receive from the data warehouse. 

For that, you can create new net revenue streams offered by the different types of business teams. 

2. Architectural monitoring

Secondly, we have architectural supervision, which is basically answering the following question: What components will we need and what features will the platform have? 

It’s normal when the answers do not come immediately, because at the end of the day, it’s a long-term investment, so you have to think about where the technology is moving to. 

However, a fundamental aspect is a need to develop an optimal data management strategy that includes metadata and data governance at all times. 

3. Security Strategy

Thirdly, we have security strategies, which must be robust enough to protect our information. 

We must pay close attention if our platform is going to be shared with multiple lines of business or by internal or external members of the company. In this sense, confidentiality and privacy are fundamental to maintain the stability of the project. 

Here you can implement rules that must be followed by everyone to maintain order in the system. Some users will have access to certain data and others will not, so this must be taken into account. 

4. Workforce skill set assessment

To achieve success with any data lake project it is essential to have the right workforce. 

Therefore, it’s important to check what the skills of your workforce are. Ideally, you should have people around you who have skills related to the creation of data platforms and who know how to manage large amounts of information so that everything flows properly. 

You also require data scientists who will be the first consumers of the platform and who will evaluate all aspects of the project to take it to the next level. 

5. I/O memory model

Continuing with the list, we have to think about what will be the scaling capabilities of the data lake. It’s essential to understand in depth each of the requirements according to the data ingestion to determine what the performance for storage and network will be. 

6. Operations plan

It’s very important that you have an operational team competent enough to solve each of the problems that will arise along the way. 

7. Disaster recovery project

How often do unforeseen events happen in business? More than we would like, and to some extent events happen that are not up to us, so you need to have a disaster recovery plan to know what to do when something like that happens. 

8. Communications plan

Okay, you have your data lake, so now it's time to communicate it to the world. Answer this question and increase your chances of success. 

4 Characteristics of the intelligent data lake

The data lake has a number of characteristics that we must understand before we start working. 

Let's look at some of them below: 

1. Data Search

One of the reasons we love the intelligent data lake is that it allows us to discover existing customer data through a 100% automated process based on machine learning. 

Through this process, we transform data assets into various intelligent recommendations of new data that are likely to be of interest to the practitioner. 

2. You discover the relationships that matter

Another feature of the intelligent data lake is that it analyzes data silos and comprehensively tracks their usage to maintain data lineage. 

In this way, business analysts benefit from all those insights derived from data assets that were previously shared. 

3. Share the data you need

Process velocity is one of the most important assets an organization has to satisfy user needs. 

In this sense, an intelligent data lake gives you the ability to share and prepare those data that are indispensable to create competitive analytics. 

4. Data preparation in reusable workflows

Last but not least, with an intelligent data lake we can store data preparation steps and then reproduce it in several 100% automated processes. 

Why use a data lake?

Data is the new treasure of the 21st century. Managing it properly is essential to overcome today's wide-ranging competition. 

There are already studies that show that companies that implemented a data lake outperformed their competitors by 9%, which is more than a respectable figure. 

Did you find this topic as exciting as we did? If your answer is a resounding YES, let us tell you that the comment box is waiting for you: