Choosing the Right Data Storage: A Comparative Guide
Time-Series Databases, Relational Databases and Data Lakes, the key difference? It's all about time and trust. Relational databases, time-series databases, and data lakes each tackle data uniquely. But when it comes to managing time-stamped process data and keeping it rock-solid, the game changes.
The Manufacturing Database Showdown
In the clash of relational databases, time-series databases, and data lakes, the stakes are high. It’s all about handling time-stamped process data with precision and keeping it trustworthy.
Why does it matter? Because managing manufacturing data isn’t a walk in the park. The job demands:
It’s not just a database—it’s the backbone of modern manufacturing.
Time-Series Databases: The Backbone of Sensor Data
Digital transformation is driven by sensor-based data, powering advanced analytics, AI, machine learning, and even AR/VR models. But here’s the catch: traditional relational databases aren’t cut out for the job. Enter time-series databases—rising stars in data management, with market growth outpacing their older relational counterparts, as noted by ARC Advisory Group.
Why the buzz? Unlike relational databases, which rely on rows and columns, time-series databases prioritize time. They align sensor data with time as the main index, making it easier to collect, contextualize, and share.
Two main types dominate the scene:
To unlock the full potential of sensor data, timing is everything—literally. Each data point must be tied to its proper timestamp, whether it marks the moment of measurement or the moment of storage. Getting it right isn’t optional; it’s essential for accurate insights.
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Relational Databases: Great for Relationships, Not Real-Time
Relational databases are the masters of mapping relationships—think metadata like alarm limits, control thresholds, customer spending habits, bounce rates, or geographic trends. But when it comes to real-time data? That’s not their wheelhouse.
Unlike time-series technologies—whether slick open-source options or tried-and-true historians—relational databases aren’t built for real-time sensor data. Sure, they can handle it, but not without a hefty dose of data prep, cleaning, and elbow grease.
The catch? Scaling up with relational databases can turn messy. Ensuring data quality, governance, and context becomes an uphill battle, making them less ideal for time-sensitive operations.
Data Lakes: Big on Storage, Low on Usability
Data lakes shine when it comes to scalability and cost-effectiveness per GB. But ease of access? Not their strong suit. While they hold massive volumes of data, they often have fewer users—unsurprising given their challenges with usability. Like time-series technologies, their adoption and application will ultimately be decided by market demand.
The Future is Digital
Sensor-driven data is at the heart of today’s technological evolution, fueling advanced analytics, AI, machine learning, and immersive AR/VR models. Industrie 4.0 and major disruptions like the pandemic are accelerating this shift, driving sustainability and operational resilience to the forefront. The result? A rapid transformation of industrial operations and manufacturing like never before.
Want to Learn More?
Download our Digital Transformation Roadmap and learn what steps you can take to achieve data-driven success in manufacturing.