How to Build a Modern Data Stack in 2025

Hello everyone,

I hope you are having a good week!

In today’s edition of AI and Data Tech Trends, we will be looking at how to build a modern data stack that’s as up-to-date as possible for 2025.

Modern data stacks help generate faster insights by providing real-time access to data from cloud data warehouses. This enhanced data governance and improved data quality helps organisations of all sizes to make more informed decisions and grow.?

It’s an important topic to understand whether you work directly in data or in a more business-facing role, so I hope you’ll find this newsletter helpful!

What is a Modern Data Stack?

A data stack refers to the combination of software tools, processes and strategies to collect, process and store data on a well-integrated, cloud-based data platform. An effective data stack improves the robustness, efficiency and scalability of handling data.?

A modern data stack uses various tools to manage big data for organisations.?

These include:

• Extract, load, Transform (ELT) tools
• Data ingestion/integration services
• Data lakehouse
• Data migration tools
• Data orchestration tools
• Business Intelligence platforms

Taken together, these tools broadly make up the components of a data stack, which include:

• Data sources?
• Data ingestion
• Data management
• Data catalog and information

So what makes a data stack ‘modern’?

Well, the evolution of cloud computing and cloud data warehousing means technology is now at a point like never before, which has accelerated the need for an effective data stack in most organisations. A modern data stack utilises cloud-based migrations, services and integrations.?

The modern data stack as we know it today first originated around the early 2010s, when companies realised that traditional, on-premise data stacks were an unnecessary expense that could be transformed into a more efficient option. By storing data in the cloud rather than on servers, modern data stacks are more scalable, flexible and efficient than legacy data stacks. This period of time also saw the arrival of popular cloud data warehouses such as BigQuery, Redshift and Snowflake that all make a modern data stack viable.

Building a Modern Data Stack

Building a modern data stack requires the following four steps:

1. Determine which services and tools you need and how they’ll work together.
2. Find the best data platform to support your data stack.
3. Consider how you will migrate data from your existing data system to the new data stack.
4. Decide how best to train your team on how to use these new tools and services - or will implementation and management be fully outsourced?

While it may seem like a big task, it’s easier than you think!?

Below is a step-by-step guide you can follow to get started.?

A Step-by-Step Implementation Guide

Here is a step-by-step guide you can follow:

1. Deploy a data lakehouse

A data warehouse, or data lake as they’re also known, is essentially the central storage unit for your company’s data. Popular data lakehouses are Microsoft Fabric, BigQuery, Snowflake and Databricks, amongst others. A good data lakehouse will be both robust and reliable. To help ease data integration, choose a data lakehouse that offers secured ways to encrypt your data when in transit. This is especially important when moving data from on-premise sources to the cloud.

2. Use a data ingestion tool to connect your data sources

Once you have a data warehouse, you need to use a data ingestion tool to move your data into the warehouse. Depending on your data sources, you can use an API or write code to connect data sources to your data ingestion tool. Database replication is another option to move on-premise data to the cloud.

3. Clean and prepare your data

You should now have your data in the data warehouse - great! The next step is to clean and prepare the data for analysis, using a data transformation tool such as dbt, Dataform or Dataiku.

4. Data visualisation

Now we’re moving onto my favourite part. Once your data is clean and prepared, you can begin visualising it using BI tools such as Power BI or Tableau to transform that data into interactive visualisations for stakeholders. It’s also worth uploading your dashboards to a good, cloud-based platform for stakeholders to easily access your visualisations.

5. Use reverse ETL tools to send data to third-party apps

At some point, you may want to send data from your modern data stack to third-party apps, such as HubSpot or Zendesk. The best way to do this is through reverse ETL tools, which will help you map your data back so you won’t lose track of it in any third-party SaaS apps.?

6. Train your team

If you plan on outsourcing your data stack to a specialist data company, you don’t need to worry too much about this step. But if you’re keeping management in-house, then it’s worth investing in training sessions or workshops for your team to get up to speed with how best to work with the new data stack. This is a service we offer at Onyx Data, so if it’s something you’re interested in, let me know and I can share more information.?

So, that’s how you build a modern data stack. It’s worth investing the time into updating and modernising your data stack as it will help you make better data-driven insights and generate more revenue for your company.?

I hope you found this helpful!

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