What Is Columnar Storage and Why Does It Make Analytics Faster?
Discover how CrateDB's hybrid storage architecture combines row and columnar formats to optimize analytics, ensuring fast ingestion and real-time query performance.
Real-Time ML Inference on Live Sensor Data: Architecture with CrateDB and FastAPI
Discover how to achieve real-time machine learning inference on live sensor data using CrateDB and FastAPI, enhancing predictive maintenance in industrial IoT.
Time Series Forecasting with SQL: DATE_BIN, Window Functions, and Rolling Aggregations
Transform time series data processing by leveraging SQL for feature engineering, eliminating latency and enhancing real-time analysis with effective aggregation techniques.
Anomaly Detection on Industrial Sensor Data: Beyond Threshold Alerts
Discover how Isolation Forest anomaly detection can enhance industrial sensor data analysis, moving beyond static thresholds to provide context and insights for predictive maintenance.
How to Use SQL for ML Feature Engineering on Time-Series Sensor Data
Discover how to leverage SQL for efficient feature engineering on time-series sensor data, enhancing machine learning model accuracy without memory bottlenecks.
Why Industrial Teams Are Moving from Flux and InfluxQL to Standard SQL
Explore the challenges of migrating between InfluxDB query languages and discover the advantages of standard SQL for industrial IoT analytics.
How to Add a New Sensor Type to Your Industrial Database Without Pipeline Downtime
Discover how CrateDB's dynamic columns enable seamless integration of new sensor types in industrial databases without downtime, enhancing operational efficiency.
Data Sovereignty for Manufacturing Analytics: Why Cloud-Only Doesn't Work in DACH
Explore why DACH manufacturers need on-premises analytics databases to comply with strict data sovereignty laws while achieving real-time insights from production data.
Comparing MongoDB, TimescaleDB, InfluxDB, and CrateDB for an IIoT Use-case
In this blog post, we compare how MongoDB, TimescaleDB, InfluxDB, and CrateDB perform for an industrial IoT use-case.
Predictive Maintenance Database Architecture: From Sensor Data to Maintenance Trigger in SQL
Learn the three SQL patterns that power predictive maintenance: threshold triggers, trend detection, and cross-asset correlation. See how CrateDB serves as the data layer under ML models for industrial IoT at scale.