6 principles for creating a modern data platform

Created in:
Jun 10, 2021

A modern data platform or Modern Data Stack enables prosperity in the business world. Know the principles and learn how to invest in it.

In this post, you have the opportunity to understand what a modern data platform is , why you should guide your company along this path and how to do so by following six basic steps.

If you want to revolutionize your business , it starts with the way you manage your data.

So, continue reading and check out the basics to get you started on this journey in the right direction.

What is a modern data platform?

The modern data platform or Modern Data Stack (MDS) is the structural foundation that a company needs to grow its current data operations in a highly scalable way .

This type of platform is made up of people , processes and tools that, together, guarantee the flexibility , adaptability and accessibility necessary for a company to maintain itself amidst constant changes in the market and technologies.

When compared to the traditional data approach , the transition from the ETL (extract, transform, load) method to the ELT (extract, load, transform) method , for example, is one of the main differentiators of Modern Data Stack .

Understand this and other MDS differences better .

Traditional Data Approach vs. Modern Data Stack

The main difference between a traditional data approach and the Modern Data Stack is the adoption of new methodologies and new independent tools . Only they give companies autonomy and can be replaced at any time by methods and solutions that meet current demands, without losses.

Let's explain.

There was a time when, to simply have access to data, it was necessary to have excellent financial conditions. Still, the service was very centralized and required more time to request and communicate than to actually analyze.

This was the reality of traditional data approaches that, until recently, helped a lot! After all, data was not yet generated at the current speed and quantity. So much so that ETL , also a traditional data transformation process , was still an efficient and modern method, and one that got the job done.

But innovation doesn't stop, does it?

And now, commercial teams can no longer be so distant and dependent on IT teams .

And the limited methodologies and tools , which cannot cope with the demands of big data , do need to be replaced.

To be able to adapt to the new reality and prosper with the use of data, each and every type of company should follow the principles of the Modern Data Stack . Starting with bringing the IT area closer to the business area and adopting the ELT process instead of ETL .

With a modern data platform like this, all companies are able to be data driven . Including yours!

Why build a modern data platform?

Because only with MDS is it possible to keep up with digital transformation and continuous changes in the market . It is with this that you will be able to assimilate advances without having to reformulate your entire data infrastructure every time contingencies or innovations arise .

Furthermore, to maintain competitiveness and scale your operations , you need to have ownership and control over your data and where it is stored. To achieve this, modern resources are available at affordable costs for companies of all sizes and sectors. Did you already know this?

And there's more!

By replacing ETL with ELT, your company will have numerous benefits , such as:

  • more agility to analyze large volumes of data;
  • less maintenance costs;
  • less spending on resources;
  • more collaboration between business teams and technical professionals;
  • greater efficiency and productivity in data operations.

So, if you want to succeed and maintain a competitive advantage , you know: build a modern data platform .

But for this to work, apply the following six principles.

6 principles for a successful Modern Data Stack

To build your modern data platform and be successful with it , follow the right path, which involves these six basic principles:

1) cloud based

To ensure scalability and flexibility of the modern data platform , the storage of this information must be completely centralized in the cloud ( cloud based ), with data warehouses and data lakes .

These are highly scalable and flexible technologies, which allow the processing of a virtually infinite amount of data in a secure environment .

With cloud services, you reduce infrastructure, installation and maintenance costs.

Some of the most accessible and well-known cloud computing tools on the market are:

  • Google Cloud
  • Azure
  • AWS
  • Locaweb

2) modularity

Separate the stages of your project. This way, you can use specific tools for each one, which allows teams to work incrementally, and speeds up project implementation.

For example, in the ELT process , you can separate the business rules from the extraction and loading steps using 3rd Party tools for data integration, such as Fivetran and Stichdata . And you can use other tools for the transformation stage, such as dbt .

3) simplicity

Simplify people's work and leave the complicated work to the tools.

In other words, instead of writing code in complex languages, such as Java , Python and Scala , centralize the transformation in a single language. Preferably, use SQL , the universal language of all big data tools today.

With this, you reduce training and maintenance costs, facilitate organizational understanding and gain many other advantages, but then we would have a subject for another article.

4) governance

Leave all information centralized and easily accessible in one place. Additionally, maintain streamlined documentation and good data governance .

By following these best practices, it will be much easier to create permission logic and manage sensitive data in an integrated way.

5) versioning

Define rules for versioning your files and data. Collaborative work is extremely important in data projects, and it is necessary to minimize conflicts generated by the different versions created.

With the tools used in the Modern Data Stack , this problem becomes less recurrent.

6) DataOps

Adopt the DataOps culture in your profession or company.

What does that mean?

Create distinct environments for separating raw data, transforming data and final data. This will facilitate access to different development environments, in addition to speeding up collaborative work and reducing production errors.

And maintain good testing practices in your data projects, like development teams do in modern software projects , right? Thus, the consistency and reliability of the results are guaranteed.

How to implement a modern data platform?

Implementing a modern data platform is simpler than ever. Follow these six steps:

  1. choose the correct architecture ;
  2. choose the right people and roles ;
  3. implement a data driven culture ;
  4. have a clear objective ;
  5. do not allow lock-in of tools;
  6. and focus on your core business.
Access our YouTube channel and see even more details about MDS .

And also watch our co-founder and head of data science, Daniel Avancini , teaching in practice how to create a modern data platform :

Don't know where to start implementing an MDS in your company?

We have a highly qualified team to help you. Get in touch today here .

Until later!

Modern Data Stack
Data platform

Bianca Santos


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