When you're reading a case study, you're usually reading about what things are like after all the dust has settled. Everybody is living happily ever after. But what about all of the decisions that come before that?
Technology leaders very rarely have the benefit of hindsight to help them make decisions. Part of our job at CloudGeometry is helping our customers anticipate the impact of smart technology decisions, and that means putting ourselves in their shoes and seeing things from their perspective.
Recently we were asked by a company we work with in the a green energy sector to help with the choice of platform for data handling and integration: DIY Spark (open source) or a commercial system like Databricks.
After meeting with the client—let's call her "Roberta"—we had the following observations.
As the Chief Infrastructure Architect of a green energy company, Roberta is tasked with evaluating technology to drive innovation, optimize operations, and help the company achieve its sustainability goals. In the rapidly evolving energy sector, data is a critical asset, and managing it effectively can be the difference between success and missed opportunities. Recently, the company has been considering whether to standardize on Databricks as its data platform. The more Roberta evaluates it, the more she sees Databricks as a smart starting point—one that offers both immediate benefits and future flexibility. But is it right for the company?
Why Start with Databricks?
For a company like Roberta's, the decision to adopt a data platform isn't just about raw power; it's about ease of use, speed to deployment, and the ability to scale without getting bogged down in technical complexity. Key advantages include:
Data Variety and Unified Access
Modern organizations like Roberta's generate and consume a wide range of data types—structured (relational data), semi-structured (JSON, logs), and unstructured (images, videos). Traditional platforms, which often focus on a specific type of data or workload, fall short when it comes to supporting these systems and data diversity at scale. An entirely new way of managing data was in order.
Databricks' unified platform handles all these different types of data in one environment, so Roberta can leverage everything from real-time streaming data to complex machine learning models without requiring a variety of tools.
Rapid Implementation with Minimal Overhead
Databricks helps get up and running with a streamlined, all in one platform. Unlike a DIY approach, which involves setting up and maintaining complex infrastructure, Roberta could see that Databricks provides a managed environment that takes care of the heavy lifting.
One problem with the DIY approach—which would involve embedding data logic directly into Roberta's IaaS (Infrastructure as a Service)—is that it often leads to a tangled mess of services and custom configurations. Again, this can quickly become difficult to manage and scale. Cloud environments become cluttered with scripts, manual processes, and ad-hoc infrastructure setups. It easily affects upstream development — not to mention build, deployment and monitoring processes
With Databricks, the company can quickly start processing data from its renewable energy sources—solar, wind, and storage systems—without the need to invest heavily to build and manage their own big data infrastructure and without this spaghetti of manual tasks. This would enable them, Roberta thought, to focus on extracting insights and driving innovation, rather than getting bogged down in technical setup.
Integrated Tooling for Immediate Impact
Databricks comes with built-in tools that support everything from data ingestion and processing to advanced analytics and machine learning. As a green energy company, this means Roberta's company can start analyzing energy production data, optimizing grid management, and predicting energy output pretty much right out of the box.
This integration is particularly valuable as it allows their teams—data engineers, data scientists, and analysts—to collaborate seamlessly in a single platform. They don’t have to worry about stitching together disparate tools and workflows, which can be time-consuming and error-prone.
Also, keeping data centralized on Databricks makes governance across different types of data and objects—such as structured energy production data, unstructured logs, and machine learning models—significantly easier. This is especially critical for a green energy company like Roberta's, where sensitive operational data has to be managed carefully for regulatory purposes and to protect intellectual property.
Scalability Without the Hassle
As their data needs grow, particularly with the expansion of renewable energy projects, Databricks offers scalability, another of Roberta's concerns. Its cloud-based architecture allows them to scale up or down based on demand, ensuring they only pay for what they use.
This scalability is crucial during peak periods, like extreme weather events when energy production and consumption data spike. Databricks can handle these fluctuations without any extra effort from Roberta's team, allowing the company to maintain operational stability without overprovisioning resources.
In addition, as their renewable energy projects expand, keeping data centralized within Databricks offers another major advantage—avoiding the complexity of managing data across multiple cloud environments, which can lead to operational inefficiencies. Disconnected systems and proprietary data formats make integration difficult, while siloed stacks increase architecture complexity.
The Safety Net: Flexibility to Transition to Open Source
But one thing Roberta knows is that when she takes this recommendation to her bosses, they're also going to want to consider a long-term strategy. What if the company's needs evolve, or if they want to take more control over their data infrastructure? The good news is that starting with Databricks doesn't lock them in—they can transition to open-source solutions if and when it makes sense.
Gradual Migration to Open Source
If they decide to move off Databricks in the future, they can do so gradually. By initially leveraging Databricks to get its data strategy off the ground, the company buys themselves time to build internal expertise and prepare for a potential transition to open-source tools like Apache Spark standalone, Hadoop, or other frameworks.
This approach allows the company to maintain momentum in the short term while preserving the flexibility to shift gears if its strategy or budgetary constraints change. When the time comes, it can export its data and workflows from Databricks, gradually integrating them into an open-source environment without disrupting its operations.
Cost Management and Long-Term Planning
Starting with Databricks also allows them to manage costs more effectively in the short term. The pay-as-you-go model ensures that they only pay for what they use, which is particularly important as they scale their operations and refine their data needs.
As the company grows and its data processing needs become more predictable, Roberta figures it can evaluate whether transitioning to an open-source solution would provide cost savings. At that point, it will have a clear understanding of its workloads and be able to make an informed decision about whether a DIY approach would be more cost-effective.
Leveraging Databricks’ Expertise
By starting with Databricks, they also gain access to a wealth of expertise and best practices that can guide their data strategy. Roberta likes that this reduces the risk of missteps early on and ensures that they're building a solid foundation for future growth.
She also likes the fact that if and when they transition to open-source tools, they'll be doing so from a position of strength, with a clear understanding of what works and what doesn’t. This makes the potential migration smoother and less risky.
Databricks as a Strategic Starting Point
For Roberta's team, the decision to standardize on Databricks is not just about choosing a powerful data platform—it’s about making strategic choices that balance immediate needs with future flexibility. Databricks offers a smoother on-ramp to data functionality, allowing them to quickly harness their data's unused potential to drive innovation and operational efficiency.
But equally important is the understanding that starting with Databricks doesn’t mean they're locked in forever. Should their needs change, they have the flexibility to transition to open-source solutions, along with the knowledge and experience they've gained along the way.
In the fast-paced world of green energy, where the ability to adapt is crucial, Databricks provides them with the tools and flexibility they need to stay ahead of the curves in the supply and demand landscape.
