How to Decide if Databricks Is the Right Tool for You

Databricks is an all-in-one data platform, offering capabilities for a wide range of data use cases, from data engineering to machine learning. It combines functionalities traditionally spread across multiple tools — such as data warehouse/lake, governance, orchestration, automation, machine learning, and monitoring — into a single platform.

This makes Databricks a potential central hub for managing data within an organisation or for running specific parts of a data solution. While I’m a big fan of the platform, I don’t believe it’s the right solution for every scenario. Some tools do better at specific tasks, but we can debate whether other platforms integrate all these functionalities more effectively.

If you’re considering Databricks, you need to evaluate your specific environment and requirements. The decision is not easy, especially with so many alternatives available on the market. In this article, I’ll outline key considerations to help you determine whether Databricks is the right fit for your needs.

Do You Need a Data Platform or Just an Execution Engine?

Not every organisation needs a large-scale data platform. These platforms can be expensive to build and maintain, and not all companies have the data maturity required to realise the return on investment (ROI) from such a comprehensive solution. However, most organisations can benefit from data and are likely to become more data-driven in the near future.

A platform like Databricks helps standardise and bring together many different use cases and people along the entire data value chain, enabling value delivery at scale. However, individual use cases may not require all the capabilities of a platform like Databricks. If you plan to implement multiple use cases and lack an existing analytics environment, Databricks can be a solid starting point.

Do You Need a Data Warehouse, Data Lake, or Lakehouse?

While I won’t dive into the differences among data warehouses, data lakes, and lakehouses, it’s important to evaluate your storage and processing needs carefully. Databricks is well-suited for implementing a lakehouse architecture, which combines the best of both data lakes and warehouses. However, if your requirements lean heavily toward traditional data warehousing, other specialised solutions might be better suited to your needs.

Where Does Databricks Fit into Your Data Landscape?

One of the first questions you need to ask is how Databricks will integrate with your existing data landscape. Do you have structured or unstructured data sources? Databricks is well-equipped to handle both, but the answer can determine how you approach implementing it within your business.

If you’re dealing primarily with structured data, such as databases or data warehouses, Databricks can be a valuable tool for handling complex transformations and integrating multiple data sources. At the same time, Databricks is also well-suited for managing and processing unstructured data, such as logs, text, or multimedia files. Whether your organisation is working with structured or unstructured data, Databricks can support diverse use cases, making it a flexible platform for businesses at different stages of their data journey.

You should also consider how Databricks will fit into your current data architecture. Do you already have a data warehouse or lake in place? If so, Databricks can complement these systems by providing advanced analytics, machine learning capabilities, and data engineering workflows. If not, Databricks could become your central hub for both structured and unstructured data storage and processing through its lakehouse architecture.

Additionally, think about your data pipeline requirements. Do you need real-time data processing, or are batch processes sufficient for your needs? Databricks can support both but is particularly strong in scenarios that involve complex transformations, large datasets, or distributed computing needs.

Do You Need an Analytics Platform or a Backend for Your Applications?

Although Databricks can technically be used as an OLTP system, it’s designed primarily for building OLAP systems. One of the main concerns about Databricks has historically been latency, which makes it difficult to integrate into applications that need quick responses. Even with optimised serverless SQL warehouses, response times can exceed several seconds, which might not be acceptable for certain applications.

For batch processing and analytics, however, Databricks excels with its scalable and robust architecture. It’s often acceptable if reports take a few seconds to generate or if there’s a delay in processing large datasets, especially when the goal is to handle complex analytical queries or transformations. You need to assess your performance requirements and determine whether Databricks meets the needs of your specific applications.

Do You Need a Platform That Handles End-to-End Data Use Cases?

Databricks offers everything you need to cover the entire data lifecycle — from data engineering to analytics and machine learning. However, if your goal is to handle only specific components of data solutions, more specialised tools might be a better fit.

While Databricks started as a best-in-class solution for managed Spark, it has since evolved into a broader platform that covers a range of capabilities. That said, this versatility doesn’t always translate to being the best at everything. For instance, other tools might outperform Databricks in real-time data streaming, specialised data warehousing, or dedicated machine learning workflows. Before committing to Databricks, evaluate whether other platforms may be better suited to your particular requirements.

Do You Have Specific High-Performance Requirements? Do You Benefit from Distributed Computing?

Databricks is essentially a managed Spark solution, but not every scenario requires the power of distributed computing. For some tasks, especially those that don’t involve massive datasets, single-node libraries like Pandas or Polars may provide sufficient performance at a lower cost. If your use cases involve large volumes of data that benefit from distributed computation, Databricks could be the right choice. Otherwise, a simpler setup might better meet your needs.

Do You Have the Expertise to Customise, Implement, and Maintain Solutions?

Databricks offers a range of features out of the box, including governance, orchestration, and data serving. Attempting to replicate this setup with your own on-premise or custom solution would require integrating multiple tools, which can increase both complexity and maintenance overhead. While a custom setup might offer savings in consumption over time, the upfront investment in infrastructure, development, and ongoing maintenance can be significant.

For most businesses, especially those that are not primarily tech companies, building such a setup from scratch is often not worth the effort or cost. Without the necessary technical expertise and resources, trying to manage this in-house could quickly become overwhelming.

Starting with managed services like Databricks is often a more practical approach. These services handle much of the complexity for you, allowing your team to focus on delivering value from your data. Once you hit the limits of managed services or find that the costs are outweighing the benefits, then it may be time to consider transitioning to an in-house or custom solution. But for most organisations, especially early on, the ease and efficiency of managed services are hard to beat.

Do You Have the Use Cases to Deliver ROI?

Databricks is not a cheap solution. Its ease of use, integration, and broad capabilities come at a cost. That’s why it’s crucial to assess whether the use cases you plan to run on Databricks will deliver a return on investment (ROI) that justifies the expense.

For some smaller businesses, especially those with limited data needs, the cost might seem high at first glance. However, if the benefits — such as time savings, operational efficiencies, or in a best case scenario even new capabilities— are significant, even a small organisation can see substantial value. In fact, the managed services approach can help avoid the complexities and costs associated with building and maintaining a custom infrastructure, making Databricks a practical option for companies of any size, provided the benefits align with their needs.

At the same time, not every large enterprise may have the data maturity or use cases to fully leverage Databricks’ capabilities. Without a clear vision for how the platform will drive results, the investment might not pay off as expected. It’s important to evaluate whether Databricks addresses specific pain points and creates enough value to justify the cost, regardless of your company’s size. In some cases, organisations may find that starting with Databricks makes sense, but as they scale and their needs evolve, transitioning to a more customised or in-house solution might be a more cost-effective option.

In short, the decision depends not only on the size of your business but on whether Databricks can deliver the value you need, based on your current and future data strategies.

Do You Prioritise Ease of Use Over Cost?

When discussing the trade-off between ease of use and cost, the comparison between Apple and Windows comes to mind. Apple products are more expensive but are often chosen for their seamless integration and user experience. Similarly, Databricks offers a streamlined, user-friendly interface that simplifies complex workflows, allowing teams to develop and deploy data solutions quickly. At the same time, Databricks allows you to dive deep into technical details for optimisation and customisation.

If ease of use, integration, and reduced time to market are priorities for your organization, Databricks may be worth the higher cost. However, if budget constraints are a concern, it might be worth exploring other options that offer similar functionality at a lower price point, albeit with more complexity and less intuitive interfaces.

Conclusion

Deciding whether Databricks is the right tool for your organisation requires a careful analysis of your specific needs, existing infrastructure, performance requirements, and budget constraints. While Databricks offers a powerful and versatile platform that can simplify data workflows and accelerate analytics and machine learning projects, it is not a one-size-fits-all solution.

Organisations must consider factors such as the scale of their data operations, the expertise available within their teams, and the expected return on investment. By thoroughly evaluating these aspects, you can make an informed decision that aligns with your strategic goals and maximises the value derived from your data initiatives.