Modern Data Stack: the guide to business success

The Modern Data Stack (MDS) is a concept that has arrived to revolutionize and modernize companies' data infrastructure.

This guide is for you to understand what Modern Data Stack is, what it is for, why and how to implement an MDS in your organization, and what are the principles that make up this entire business methodology that will make your company leverage, for sure.

And if you still have any questions about the Modern Data Stack, we are always available and very accessible to help. So, just contact us through one of our communication channels.

Now, enjoy this complete guide on the Modern Data Stack that we have prepared for you.

What is the Modern Data Stack?

The new combination of best practices with tools for creating data infrastructures is known as the Modern Data Stack.

One of its most striking features are the analytical tools and open-source technologies that meet the demands of a complex data infrastructure in a highly efficient way.

What does this mean in practice?

That with a Modern Data Stack it is possible to combine tools that perform different functions, such as integrating, storing or visualizing data, to create a modern, changeable and much more independent data structure.

Maybe you've already come across different names for the same thing:

• Modern Data Stack (MDS)
• modern analytics stack (MAS)
• analytics stack

However, even though there is no standardized nomenclature on the topic, its concept is the same.

And much more than a technology, this approach brings together all the elements necessary to solve the data science and analytics challenges of modern companies.

See an example of how the Modern Data Stack works

Consider a company that has drastically increased its customer base and needs to expand its data storage solution.

If it uses the Modern Data Stack, it will have two options:

1. simply adapt your current solution to new demands.
2. replace it with another tool that meets your needs.

In other words, with MDS, organizations have more flexibility to make specific adjustments and reinvent their structure without having to completely transform it. The result?

• Less costs.
• More scalability.
• More autonomy.

Today, thanks to new technologies and tools available, it has become much easier to adopt the Modern Data Stack.

And for you to understand the differences between a traditional analytics approach and an MDS, we will explain in detail the advantages of modernizing your company.

Traditional analytics vs Modern Data Stack

The main difference between traditional analytics and Modern Data Stack is the adoption of new methodologies and independent tools. Only they give companies autonomy and can be replaced at any time by methods and solutions that meet more current demands.

Let's explain better.

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

This is the reality of traditional data approaches, which work, but which tend to be progressively replaced. As well as ETL, also a traditional process of efficient data transformation, but which no longer responds as well as other methods, such as ELT.

So this is all changing. Business teams no longer need to be so distant and dependent on IT teams. And the methodologies and tools are limited and need to be renewed to cope with the demands of big data.

To be able to adapt to the new reality and prosper in the world of data, any type of business 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 Stack like this, all companies are able to be data driven. Including yours.

However, to be successful in implementing these practices, it is necessary to understand what exactly an MDS needs to have.

What is a Modern Data Stack?

The Modern Data Stack, or modern analytics stack (MAS), is the structural foundation that a company needs to keep up with the growth of its current data operations in a highly scalable way .

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

The transition from the ETL (extract, transform, load) method to the ELT (extract, load, transform), for example, is one of the main infrastructure differentiators in the Modern Data Stack.

Discover now some reasons to work with this new approach.

Why build a Modern Data Stack?

Because only with a Modern Data Stack is it possible to keep up with digital transformation and continuous market changes. It is with this that you will be able to assimilate advances without having to reformulate your entire 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.

And there's more!

By replacing ETL with ELT, as we superficially explained above, your company will already have numerous benefits, such as:

• more agility to analyze large volumes of data.
• less maintenance costs.
• less resource expenditure.
• more collaboration between business teams and technical professionals.
• greater efficiency and productivity in data operations, among others.

So, if you want to be successful and maintain a competitive advantage in the data era, you already know Indicium's tip: build a Modern Data Stack.

6 principles of the Modern Data Stack

New tools and cutting-edge data applications emerge every day. Therefore, before implementing a Modern Data Stack in your organization, you need to understand the principles that guide your infrastructure.

With this in mind, here are 6 principles that every modern approach to data needs to have.

Principle 1: cloud-based

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

It is a highly scalable and flexible technology, which allows the processing of a virtually infinite amount of data in an online and secure environment. With cloud services, you reduce infrastructure, installation and maintenance costs.

Want a tip about cloud computing tools?

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

• Google Cloud
• Azure
• AWS
• Locaweb

Principle 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 extract and load 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.

Principle 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 topic for another article.

Principle 4: governance

Make every effort to keep all information centralized and easily accessible in one place. Additionally, maintain streamlined documentation and good data governance .

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

Principle 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 generated.

With the tools used in the Modern Data Stack, this problem is becoming less and less common.

Principle 6: DataOps

Adopt the DataOps culture. Do you know what that means?

Create distinct environments for separating raw data, transforming data and final data. This will facilitate access to different development environments, as well as speed up collaborative work and reduce production errors.

And maintain good testing practices in your data projects, as development teams do in modern software projects. This way, the consistency and reliability of the results will be guaranteed.

6 Implementing a modern data infrastructure following these principles is simpler than you think!

1. Choose the right 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. Focus on your core business.

Watch our co-founder and head of data science, Daniel Avancini, teaching IN PRACTICE how to create a Modern Data Stack.

How to build a modern data stack?

To be successful in implementing the Modern Data Stack, it is necessary to understand how all its pieces fit together, from the steps of the data stack to the technologies and tools recommended for its execution.

Data stack

Firstly, what is a data stack?

The data stack, or data stack, is the collection of processes, tools, applications and technologies responsible for automating data management in the business at all stages of the data pipeline.

The first step in implementing the Modern Data Stack, therefore, is structuring and subsequently configuring the data stack.

This makes it possible to respond to current data operations demands in a highly efficient way throughout the data flow.

The 5 stages of the modern data stack

Now, see more details about each of the 5 stages of the Modern Data Stack.

Stage 1: Data collection, integration and cleansing

Companies have their own process for collecting relevant data. At this stage of the data stack, it is possible to collect and integrate data from multiple sources such as CRMs, Excel spreadsheets, social media, etc., centralizing them in a data warehouse efficiently.

It is at this moment that, with the help of the correct tools, the necessary adjustments are made so that the data is prepared for the next stage of the data stack.

Stage 2: Data storage and management

Now, data must be prepared and stored in data warehouses and data lakes, scalable and secure structures that enable large-scale analysis and information management . These tools are fundamental components of the data stack.

Stage 3: Data transformation

Considering the massive volume of data for processing, instead of traditional ETL (extract, transform, load), modern ELT flow is used, a faster and more flexible approach to data transformation.

In ELT, the transformation process occurs immediately after the collection and integration of information in a centralized data repository, and not before, as occurs in the cases of ETL .

With this, it is possible to transform raw data into modeled data within a data warehouse or data lake.

Stage 4: business intelligence and data analytics

Analytical intelligence is the ultimate priority of the Modern Data Stack. Thus, after configuring the previous steps, the information and insights that add value to business decision making finally become accessible to end users in real time. As a result, managers and business departments can visualize data, identify trends, optimize processes and act quickly with the help of business intelligence tools, interactive dashboards and intelligent reports, connected to a data warehouse.

Stage 5: advanced analytics

In the last and most advanced stage of MDS, it is possible to apply and develop advanced machine learning, artificial intelligence and highly complex predictive modeling techniques , such as recommendation models and prescriptive modeling, within the Modern Data Stack configured in the previous stages.

Modern Data SStack for everyone

In practice, any analytics stack built based on the 5 basic stages described above fulfills the necessary requirements to support the scalable growth of modern data operations.

Therefore, even though the architecture of a data pipeline varies according to companies, all of them must have these processes incorporated.

All of these concepts covered so far will be extremely useful as we move forward in the discussion about the recommended tools and technologies for implementing a Modern Data Stack in your business, below.

The main tools of the Modern Data Stack

In the Modern Data Stack, in addition to the data flow steps, the tools and technologies used in each step of this methodology are essential elements that determine the success of implementing the MDS.

We analyzed the main tools available on the market for building a scalable and flexible Modern Data Stack and we will share this analysis with you now.

Data collection, deployment and transformation tools

The Modern Data Stack 's data flow begins with collection, a stage in which the data is already integrated by specialized tools in the process. In addition to this, the deployment tools are already properly parameterized, so that everything occurs efficiently in the data storage and transformation stages.

Data collection and integration

Tools like Fivetran and Stitchdata are the leaders in cloud data integration.

They allow you to move data from hundreds of sources, such as ERPs, CRMs, databases, REST APIs, etc., directly to a data warehouse (in the cloud or on-premises). Furthermore, they can be combined.

Therefore, there is no need for large investments in software licenses or implementation hours.

Deployment

Tools such as Docker and Kubernetes are widely used to perform deployment in conjunction with orchestrators, such as Airflow and Prefect.

The difference between these technologies is that all the “Lego pieces” talk harmoniously to each other, ensuring that data flows in harmony throughout the data structure.

Data transformation

The three main Modern Data Stack tools used for data transformation are dbt (data build tool), Dataform and Spark. Together, these technologies allow the execution of the ELT process to transform raw data into modeled data within a data warehouse, a fundamental step in the Modern Data Stack.

Data storage and management tools

Today, there are two practical and efficient options for storing data both on-premises and in the cloud: data warehouses and data lakes. Both are viable alternatives, however, they must be evaluated on a case-by-case basis, as they present technical and conceptual differences in terms of architecture and purpose.

Data warehouses

The scalable architecture of cloud data warehouses, such as Amazon Redshift, Snowflake, Google Big Query and Azure Synapse, allows you to store and query huge volumes of data quickly. Therefore, these are essential tools in building a Modern Data Stack.

Data lakes

Data lakes store all types of data – structured, unstructured and hybrid – in one place. For this purpose, we recommend the following tools: Dremio, Databricks and Amazon S3.

Cloud computing

Currently, the main cloud computing providers are AWS, Google Cloud and Microsoft Azure. You can choose any of these options according to your company's needs for storing and managing your data.

Data analytics tools

Data analytics involves several activities regarding data analysis, which vary in terms of degree of complexity and tools.

To make it easier to understand, you can divide them into two categories: business intelligence and advanced analytics tools.

Business intelligence

With a Modern Data Stack in place, you can use different business intelligence tools to visualize, analyze and generate insights from data. To this end, there are several robust open-source alternatives, such as Metabase, as well as SaaS platforms, such as Microsoft PowerBI, Looker, Tableau, among others.

Advanced analytics

Machine learning, artificial intelligence and modeling are techniques applied in advanced analytics for more complex analyzes within the analytics stack. To this end, in addition to the various libraries in the R and Python languages, tools such as MlFlow and Kedro help in the execution ofand prescriptive models, and optimize the development process, reducing the time between modeling and data use.

Don't know how to implement MDS in your company?

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