This blog post is Human-Centered Content: Written by humans for humans.

Recently, here at InterWorks, we had a professional development day where we talked through popular partner systems. Databricks was one that really stood out to me. Rather than focusing on one specific feature, I decided to talk about 10 things that I believe makes them standout in the data warehousing and cloud architecture space. I hope you enjoy: 

1. Pay-as-you-go pricing model. 
   The emptying of pockets upfront in order to achieve data cloud platform bliss is no longer required. Centralizing data has been known to be a tall task in other environments, with surmounting pressure to get quick ROI if thousands of dollars are shelled out on software costs upon acquisition. Instead, you can stand up the platform quickly, so your users can store, process and analyze data from one location, as well as pay for kinetic value versus potential work. Cost transparency is refreshing. 
2. Coding language of choice. 
   Choose the language that works best for your developers. I know that Python, R and SQL are really popular options, but there are even options in the Databricks workspace for others like Scala and Java. Databricks offers a number of articles to help with overall data management, including “Getting Started with the workspace” and “Running your first ETL workload.” As you work through these in your preferred language, you’ll get scripts that help with data ingestion and transformation. 
3. Embraces open source. 
   Databricks touts a strong commitment to the open-source community. As a data intelligence platform in the cloud, that adds to their strengths with an extended interoperability to connect with data from multiple sources. It’s also helpful in terms of the data storage or data lake component, through the Delta Lake open-source storage framework that Databricks offers. 
4. LLMs and AI harmoniously at your disposal. 
   The power of utilizing large language models and generative AI in one platform makes it easier to automate, predict and audit. This tandem really speaks volumes to the system’s innovation. It’s easy to integrate OpenAI models or solutions from other partners like John Snow Labs into workflows. Customizing the LLMs relevant to a specific task can be done to create fine-tuned models quickly. The AI functions across your workflows offer consistent, high-quality datasets at scale. 
5. Data collaboration meets no bounds. 
   People within large organizations enjoy the Databricks environment because it simplifies data access to a broader range of users. The data scientists and the data engineers are modeling and cleaning, while the analysts are looking to see the data in aggregate and visualized form. As unification of data happens in the workspace, each user can carve their own experience. This can be in the form of a low-code dashboard or a bounded dataset that gets documented for stronger data governance and security. 
6. Versatility. 
   I am personally using Databricks for its ETL capabilities to help me reduce easily repeatable workflows. Some refer to a “ready-to-visualize” data table as a materialized view in Databricks. We’re all saying the same thing here. I want to have my data supported within a framework that provides reliable and efficient data pipelines. In Databricks, the underlying tooling focuses your data into Delta Live Tables or DLT for instant intelligence. Then again, I’m seeing trends where large companies are leveraging Databricks for its end-to-end data lakehouse architecture for scalable storage and more efficient processing. 
7. Great Partners. 
   As with most of our partnerships, we choose partners who offer mutually beneficial arrangements. You might be thinking about dollars and cents as a metric we care most about, but you would be 100% incorrect — we put greater value in finding partners that help our clients solve challenges and set clients up for long-term success. We have a motto at InterWorks, “We make technology work for people.” We put a human touch to your data and technology, and cherry pick partners we feel best accomplish this. With Databricks as a partner, mission accomplished! 
8. Free Training. 
   I love getting things for free. We know that Google and Apple are chipping away at your wallet for personal storage. I have seven streaming apps, also helping to empty out my pocketbook. But, getting access to free training videos and modules in Databricks is no myth. I was able to get through a full fundamentals training course with an associated certification within a single day. I really enjoy companies that make content free and easy to follow along with, expediting independent or corporate learning.  
9. Well documented support lifecycles. 
   As mentioned, Databricks is synonymous with innovation. As newer releases get rolled out, there are documented changes that are easy to follow along with. There is also a deep commitment to the UI with most of the periodical changes. The goal for Databricks is to use end-user feedback to help guide the direction of the system and its overall future roadmap and development. I think that’s smart because serving the end user will provide long-term stickiness in a clouded marketplace.  
10. What’s in a name? 
    I just love the name Databricks. It’s got me thinking of level 8 of Super Mario World on the original NES. You have to navigate strategically placed bricks that help guide Mario’s path to the princess. And you will need a token to play (in Databricks), so you can get in the game. Also, bricklayers are artists to me. They understand how to construct and repair buildings. Databricks is a perfect metaphor to unlocking access to data from behind secure foundations and walls, and with easy point-and-click layers for user menus and data assets. 

That’s a wrap! I hope you enjoyed hearing about the Databricks platform. If you need help getting started or have questions about its features and capabilities, drop us a line. We’re happy to offer advice or help with getting started in the tool from the ground up.