Why Startups should consider Databricks as a top choice for their data platform for analytics, AI and data management.
In this blog, we will dive into my personal experience at various startups, as well as insights working with clients across various industries and experts who have provided valuable recommendations over time.
“ If I were to start a new tech-enabled services business or a startup, Databricks would undoubtedly be my top choice. “
“ I don’t see any alternative on the market that can match Databricks platform's value proposition. “
Why is Databricks the #1 option for me?
It’s true that my current specialization centers around the Databricks ecosystem, and without a doubt, I have a preference for this platform. However, it’s important to emphasize that my entire professional career has been in consulting, and throughout this time, I’ve always maintained a technology and vendor agnostic approach.
The reason I would choose Databricks over the alternatives comes from the conclusions I’ve drawn after promoting, selling, and developing with a variety of other data technologies across different business verticals in various countries, facing countless problems that could have been avoided—not all, but at least the vast majority, if Databricks had been the tool of choice.
3 key factors for evaluating a technology
1. Business Model -
Data platforms like Cloudera or SAS laid the groundwork for large-scale implementation of modern data stacks. However, their adoption has remained prohibitive due to licensing, consumption plans, infrastructure costs, specializations required etc. The same applies to Palantir, which, despite its well-executed ontology concept, has an economic barrier to entry that’s prohibitive for 99% of companies.
In general, discussing Big Data and AI was cost-prohibitive some time ago, but this has changed with the expansion of major cloud providers and Databricks, which democratized access to the Apache ecosystem, displacing platforms like Cloudera or Hortonworks. In practice, the options for startups are Databricks, Fabric and Snowflake, which we’ve already compared before.
The Databricks platform allows startups to start small (less than $100/month as an example) for a specific use case, prove the value, and scale as their customer base and use cases grow – whether it’s analytics, data science, GenAI, data exchange, or other scenarios.
While Databricks offers advanced capabilities for those with deep technical expertise, it is also designed to be accessible for startups at any stage. Startups can start with simpler use cases and gradually unlock the full potential of the platform as they grow and their technical needs evolve. This makes it an appealing choice not only for tech-savvy startups but also for those looking to scale efficiently without being overwhelmed by complexity from the start.
2. Access to Professionals -
When starting any type of project, we must ensure that we can attract the right professionals in the market to implement, manage, and scale a technology. This variable, after price, is probably the most important and is why many technologies don’t scale adequately internally.
A technology can be mature or well-established and have many developers globally, but not in the country where the project will be developed. For example, AWS has strong penetration in the Latin American market. It makes a lot of sense to work with AWS, but less so with GCP, where finding engineers can be difficult.
I’ve seen GCP projects stall in Latin American countries, as well as Snowflake projects that couldn’t get past the PoC phase due to the absence of specialized resources. Localization matters—a lot—when choosing a technology.
3. Technology Considerations -
After this analysis, the range of possibilities narrows considerably. From my point of view, it’s important to choose a technology that allows our data platform to scale and meet the majority of current and future needs, to avoid complex migration processes.
For companies looking for an initial solution that can cover data integration and reporting, Snowflake could be an option. However, as their data strategy evolves and they begin to require more advanced use cases, such as data science or AI, they might outgrow its capabilities.
In contrast, a platform like Databricks offers end-to-end flexibility right from the start—supporting ETL/data engineering, scaling into a robust data warehouse (EDW), and eventually handling more sophisticated data science and AI needs without the need for migration.
Why is Databricks the Choice? Databricks simplifies everything
Firstly, because it’s a solution you can start using without making an initial investment or committing to a certain level of consumption. They’ve made significant advances in implementing “serverless” services (pay-per-use or execution time), and there are no license fees.
In other words, it offers all the economic benefits of adopting cloud services to build a data platform but unifies them into a single product or data platform (here, the only real competition would be Fabric, which adopts a similar approach).
Secondly, Databricks is rapidly gaining popularity worldwide, thanks to its strong community and its foundation in open-source technologies that have become standards in the data and AI world (e.g., Apache Spark, Delta Lake, MLflow, Unity Catalog, etc.).
As a result, it is becoming easier to find qualified professionals specializing in Databricks. We also have a strong roster of skilled experts ready to support companies in harnessing the full potential of the platform.
Thirdly, it embraces all the libraries and tools that the community loves (TensorFlow, PyTorch, Keras, scikit-learn, XGBoost, Terraform, etc.)
Fourth, Databricks is cloud-agnostic, meaning it operates seamlessly with AWS, GCP, and Azure, so regardless of the cloud you work with and which has more presence in your country commercially and talent-wise, you can find skilled professionals.
Finally, as a technological product, it is a complete solution that can cover everything natively. Databricks can be your platform for data processing, machine learning and data science, traditional and generative artificial intelligence, big data and real-time analytics, data governance, reporting and data visualization, and also your collaborative platform where scientists, engineers, and analysts coexist in one place.
Having all this from the start was simply unthinkable a decade ago.
Conclusion
If you’re starting a new venture, Databricks is a strong option that can support your growth and help you adapt to the changing needs of the market from the very beginning.
I encourage you to explore Databricks early on; you’ll likely find that it simplifies processes and enhances your results as your business evolves.