When I first started my career in data, everyone was a data scientist. Over time, we began to differentiate—were you building data pipelines, or were you focused on building and training models? Were you the one on pager duty during an outage, or were you only on call when it was time to present to the executive board? All of us were asked to pick a side: are you a data scientist, or are you actually a data engineer?

A few years passed, and yet another division emerged. Were you a data engineer or an analytics engineer? Do you focus on maintaining and optimizing data pipelines, or is pipeline work simply a means to an end—an end that results in a business intelligence dashboard?

And now, it’s happening again. We’re being told that we must refine our roles even further. Are we focused on automation, performance, and data quality? If so, congratulations—you’re now a DataOps Engineer.

But hasn’t that always been the goal? Delivering business value through data has always been the essence of our work. Automation is not a new concept; it has always been the heart and soul of data engineering. Is DataOps Engineering the title that will finally unite us all? The resume decorator that will finally explain our contribution to business success? I hope so.

So, if we’re all DataOps Engineers now, the real question is—how do we become great at it?

What is DataOps?

DataOps seeks to treat data as the final and valuable product that it is. Data drives all innovation in business, from AI to automation, and DataOps finally seeks center data front and center where it properly belongs.

This is done by applying software engineering principles to the development, delivery, and management of data. For example, by leveraging automated performance testing and infrastructure as code (IaC), organizations can further optimize data operations to meet business demands with minimal latency.

One Possible Bottleneck: Storage that Lags Behind

Storage is the foundation upon which everything else is built. It's the fuel that feeds your engine, the raw material that your data pipelines process. If your storage solution can't keep up with the demands of your engine, you're going to experience performance bottlenecks. It's been said before, but it's worth repeating: slow queries kill AI initiatives

This bottleneck is a common challenge for DataOps teams. We invest heavily in sophisticated analytics engines and spend hours tweaking our code, but we sometimes neglect the storage layer when considering performance. We forget that even the most optimized engine can't perform miracles if it's constantly waiting for data to be retrieved from slow, traditional storage systems.

DataOps for Excellerating Your Data initiatives

DataOps can do more than jazz up your resume; it could also speed up your AI plans:

Faster Data Movement: Fast object storage, with its high bandwidth and low latency, significantly speeds up data ingestion from various sources (databases, streaming platforms, IoT devices). This rapid data movement is crucial for real-time or near real-time analytics, a cornerstone of many AI applications.  

Best Choice for Data Lakehouses: Object storage is the best option for building data lakehouses. Unlike traditional storage systems, object storage allows organizations to store vast amounts of structured and unstructured data without compromising performance. When object storage is paired with open table formats like Apache Iceberg, Delta Lake, and Hudi—alongside powerful compute engines—the lakehouse architecture delivers essential capabilities such as schema evolution, time travel, and ACID transactions. These features are critical for ensuring data integrity, scalability, and agility in an AI-driven world

Reduced Processing Time: By minimizing data transfer times, fast object storage enables faster data processing. This is critical for AI workloads that involve iterative training and model refinement, where every second saved translates to quicker results and faster model development cycles.

Enhanced Scalability: Scalable object storage solutions allow AI teams to seamlessly handle growing data volumes without compromising performance, ensuring that data pipelines remain efficient as data demands increase. The fact is that nobody has ever had less data than the year before and object storage is a future forward infrastructure choice.

Optimize for Speed and Performance

How can you ensure your storage infrastructure is optimized for speed and performance? Here are a few key strategies:

Choosing the Right Storage Solution: Not all storage solutions are created equal. Only high-performance object storage will be able to meet the demands placed on it by AI and other data-intensive workloads. While most object storage claims scalability and flexibility, only a few have the performance needed to keep your data pipelines flowing smoothly.

Leveraging Data Lifecycle Management: DataOps practices like data lifecycle management can help you identify and archive inactive data. This frees up valuable storage space for your hot data, the data that your analytics engine needs to access most frequently. As a next level of management, you can explore advanced functionality like tiering that can help optimize for both performance and cost savings.

Monitoring and Optimization: Continuously monitor your storage performance and identify any bottlenecks. By proactively addressing storage issues, you can ensure that your data pipelines run smoothly and your analytics engine fires on all cylinders.

Choose Smart

By selecting infrastructure for high performance, your data pipelines will hum and will deliver the insights you need when you need them. Remember, a well-executed DataOps strategy is all about removing friction and optimizing for speed. And that journey to success begins by choosing the right storage solution for the job. The second step on that journey is to train up. MinIO offers training and certification designed to help engineers become great at managing their data storage. You can request more information on these programs here.

Reach out to us with any questions at hello@min.io or on our Slack channel