What is the Modern Data Stack? Beginners Guide

I’ve been working in the data world for a few years now, and one of the most common questions I hear is: “What is a modern data stack?”

It’s a great question, and the answer can be complex.

In this guide, we’ll go through the basics of modern data stacks and what they can do for you!

What is the Modern Data Stack?

The modern data stack is a combination of data storage, data processing, and analytics components that work together to produce modern insights.

It’s a way for companies to store, process, analyze, and use their data in the most efficient way possible.

The modern data stack can be defined as “a modern system for managing big data that is focused on scalability, speed, and security.”

The modern data stack is comprised of the following: 

• Databases
• Processing engines
• Analytics frameworks
• Visualization tools

You can quickly get insights from your data and build powerful applications using modern data stacks.

Importance of a Data Stack

The modern data stack is important because of its ability to quickly and securely process, store, and analyze large amounts of data.

Having a modern data stack in place makes it easier to do the following:

• Ingest and query your data
• Build powerful analytics applications
• Gain better insights into your data

It can also help speed up the development process by providing a modern platform with built-in scalability, speed, and security.

Efficient Data = Money

The modern data stack is also important because it can help you save money.

By having a modern system in place, you can ensure that your data is stored and processed efficiently. This means fewer resources are used, and fewer costs are incurred.

Using modern data stacks, you can also reduce the time spent on manual tasks such as ETL (extract, transform, and load).

Functions of a Data Stack

Now that we’ve gone over why modern data stacks are important let’s go over the main functions of modern data stacks.

The functions of modern data stacks include: 

• Data warehouse
• Data transformation
• Data tools suite
• Data pipeline

Data Warehouse

A modern data stack typically includes a data warehouse, a storage system that can store large amounts of structured or semi-structured data.

The data warehouse stores all the raw information and provides quick access for analysis.

Data Transformation

Data transformation is the process of transforming raw data into more usable formats.

It involves the following:

• Cleaning up data
• Converting it to a standard format
• Enriching it with additional information

Data Tools Suite

Data tools suites are collections of software and services that help you analyze your data.

They usually include the following:

• Analytics libraries
• Visualization tools
• Machine learning frameworks

Data Pipeline

The data pipeline is the process of moving data from one system to another.

It involves transferring, transforming, and manipulating data through various systems.

Modern Vs. Legacy Data Stack

We’ll go over the specific benefits of modern data stacks in more detail later, but it’s important to understand the difference between modern and legacy data stacks.

A modern data stack is built on modern technologies such as:

• Cloud computing
• Big data
• Machine learning
• Analytics

It emphasizes scalability, speed, and security.

A legacy system is an older system built on outdated technologies and lacks modern features such as scalability, speed, and security.

Legacy Data Stack

These older systems are usually more expensive to maintain and can be inefficient.

Technically “Heavy”

Legacy data stacks can be technically “heavy” because they require a lot of manual processes and technical expertise.

Requires Infrastructure

Legacy data stacks require a lot of infrastructure to maintain.

This includes the following:

• Hardware
• Physical storage
• Software solutions

This infrastructure can be expensive and time-consuming to maintain.

Time Intensive

Legacy data stacks are also time intensive.

This means it takes a lot of time and effort to maintain the system and extract insights from the data.

This is limiting and can prevent businesses from staying ahead of their competition.

Modern Data Stack

The modern data stack is a solution that helps you securely process, store, and analyze large amounts of data.

Here are some features of the modern data stack:

• In the cloud
• Easy to use
• Saves time

In the Cloud

Modern data stacks are built to run in the cloud.

This means they can scale quickly and provide better speed, security, and reliability than legacy solutions.

Having your data in the cloud makes accessing and sharing with stakeholders easier.

Easy to Use

Modern data stacks are designed to be easy for users of all levels.

These solutions have intuitive interfaces and require minimal technical expertise, allowing even the most non-technical people to use them successfully.

This is especially helpful for companies that want to modernize their data stack quickly and with minimal disruption.

Saves Time

Modern data stacks save time because they automate much of the process.

They also make accessing, analyzing, and sharing your data easier.

This means you can get more accurate, timely insights that help you make better decisions for your business.

Modern Data Stack Advantages

Modern data stacks provide many advantages for your business needs.

These include the following: 

• Saving time
• Saving money
• Increased speed
• Modular and Scalable
• Improved data security

Saving Time

One of the most powerful advantages of modern data stacks is time savings. By leveraging cloud-based solutions and utilizing automated processes, businesses can quickly access and process data in a fraction of the time it would take with traditional methods. 

One example of how modern data stacks save time is by allowing teams to simultaneously access and work with datasets from multiple locations.

Utilizing the cloud with your business allows team members to collaborate in real-time without waiting for files to be sent via email or shared hard drives.

This eliminates valuable time spent dealing with file transfers and waiting for updates from other team members.

It also enables users to visualize trends much faster using interactive dashboards, which allow them to gain insights more quickly than ever before. 

Saving Money

Modern data stacks also save money by eliminating the need for expensive hardware and software solutions.

Instead, businesses can use modern cloud-based solutions that are much less expensive than traditional methods. 

The modern data stack also enables businesses to grow their operations quickly and efficiently without investing in additional infrastructure or personnel.

Increased Speed

Modern data stacks can quickly process large amounts of data, allowing businesses to make real-time decisions.

This is especially beneficial for companies that rely on current market information or customer feedback to stay competitive.

By leveraging modern data stacks, these companies can access their datasets faster than ever before and use them to make decisions quickly and accurately. 

Modular and Scalable

Modern data stacks are also modular and scalable, meaning that businesses can easily add or remove components to their stack as needed without any major disruption.

This enables companies to tailor their modern data stack specifically to their needs, which helps them get the most out of their datasets.

Improved Data Security

Modern data stacks are also more secure than traditional solutions.

They use encryption techniques, access control, and other security protocols to protect user data from unauthorized access or manipulation.

This ensures businesses can trust their modern data stack to keep their sensitive information safe and secure. 

How to Setup a Modern Data Stack

Setting up a modern data stack can be relatively simple if you have the right resources and know-how.

Here are the following steps to setting up a modern data stack:

• Data Pipeline
• Data Destination
• Data Transformation
• Data Visualization

Data Pipeline

The first step is to design a data pipeline, essentially the backbone of your modern data stack.

A data pipeline connects different sources and destinations, such as databases or web services, to create an automated flow of information.

A data pipeline consists of three core components: 

• Ingestion
• Processing
• Storage

Ingestion is pulling data from various sources like databases, websites, or other applications, then formatting it according to the target destination.

Processing occurs after ingestion, including filtering, combining data sets, or cleaning up messy datasets.

The final step involves transferring the processed data into a specific storage format so that different stakeholders can access it in the future. 

Data Destination

Once the data pipeline has been established, the next step is determining where to store the data.

Setting up a modern data stack involves understanding the components that are essential to creating an effective data destination. This includes the hardware, software, and services needed to store, process, and analyze data effectively. 

The first step is to determine the hardware needs of the organization.

This will include understanding the size and scale of their data operations and how much storage and processing power they will require.

For example, a small business may only need storage solutions such as hard drives and solid-state drives (SSDs). At the same time, larger organizations may require more sophisticated solutions, such as distributed file systems or cloud-based storage infrastructures. It is important to select hardware that is futureproofed, allowing for easy upgrades in the future should requirements change or expand.

The second step is identifying a software solution suitable for managing data operations.

The chosen software should be able to handle all of the data operations required by the organization while also offering scalability options, if necessary, to accommodate any changes in workloads or requirements over time. 

Thirdly, it is necessary to consider additional services that could help improve the performance and security of the data stack.

For example, setting up a reliable backup system can help protect against data loss due to equipment failures or cyberattacks; deploying analytic tools such as machine learning algorithms can provide valuable insights into customer behavior and trends, which can then be used for marketing or decision-making purposes. 

Data Transformation

Before the data can be used for analysis, it must first be transformed into a format that is easier to work with.

This involves cleaning up and structuring the data so modern analytics tools can more readily understand it.

Data transformation also involves combining different datasets and performing calculations or other operations on the raw data to create new insights or metrics.

Finally, data transformation can merge different datasets to create richer insights unavailable from any single source. 

Data Visualization

Once the data has been transformed and structured correctly, modern analytics tools can then be used to visualize it in meaningful ways.

Data visualization is the process of taking complex data sets and turning them into graphical representations such as charts, graphs, or maps. These can then identify patterns, trends, or relationships that may not have been obvious.

This can provide a much better understanding of the underlying data, which can, in turn, help inform decision-making processes.

Modern Data Stack Conclusion

The modern data stack involves many complex components and processes, but it is essential for any organization that needs to effectively make use of its data.

By understanding the components and processes involved, your business can design and build a modern data stack that will provide them with the insights and intelligence needed to stay competitive.

Do you have any questions? Let us know in the comments below!

Modern Data Stack FAQ