Structuring multi-hop architectures for sensor data normalization in energy platforms - Databricks

[Danial_Gohar]

Energy platforms increasingly rely on high-frequency sensor telemetry to monitor assets, optimize performance, and drive predictive analytics. However, telemetry from field devices, substations, and distributed energy resources often arrives in inconsistent formats and structures. Normalizing this data is critical to ensure downstream accuracy, and multi-hop ingestion architectures offer a scalable modular solution. Leveraging Databricks significantly enhances these architectures, enabling scalable data transformation and analytics. 

Why normalization matters 

Sensor data originates from varied sources: legacy SCADA systems and smart grid assets, each with its own format, units, and schemas. Without normalization, analytics systems face integration issues, data errors, and unreliable outputs. Standardization supports consistency and regulatory compliance. Databricks helps ensure this process is efficient by providing a unified platform for transforming, validating, and routing sensor data with minimal latency. 

Architecting for modularity with Databricks 

Multi-hop architectures divide the ingestion process into stages, each focused on a specific transformation. This structure ensures scalability, ease of maintenance, and flexibility. Databricks is ideal for architecting modular systems. Its distributed processing engine (Spark) and cloud-based integration make it a perfect fit for high-performance, scalable pipelines. Traxccel recently deployed a multi-hop ingestion pipeline with Databricks' Delta Lake and Apache Spark, normalizing over 1 billion data points daily. This approach reduced data latency by 40%, improved anomaly detection accuracy, and laid the groundwork for predictive maintenance, all without disrupting legacy systems. 

Key transformation layers using Databricks 

A streamlined multi-hop design typically includes: 

Raw ingestion: Collects unaltered data from device APIs, gateways, or brokers. Databricks integrates with Kafka and Delta Lake to handle high-volume streaming data efficiently. 

Normalization: Aligns data through unit conversions, schema mapping, and field standardization. Databricks’ Spark engine allows for efficient data wrangling at scale, ensuring consistency across sources. 

Enrichment: Adds metadata like asset IDs, geolocation, and system hierarchies for context. Databricks can also apply machine learning models for advanced data enrichment. 

Validation and output: Performs quality checks and routes normalized data to storage or analytics endpoints. Delta Lake ensures data consistency, and Databricks simplifies routing data to cloud storage or analytics solutions. 

A foundation for energy innovation 

Multi-hop ingestion architectures empower energy platforms to process telemetry with precision and speed. By modularizing transformations, they reduce technical debt and streamline integration. Databricks supports scalability and simplifies architecture, ensuring energy providers can evolve alongside emerging technologies. Its ability to handle both batch and streaming data is crucial for energy platform innovation, without compromising performance. 

Learn more: www.traxccel.com/platform

[WiliamRosa]

Hi @Danial_Gohar,

Thanks for sharing. One tip for you, next time if you have something you'd like to share with community we have dedicated place for that: Community Articles.

[szymon_dybczak]

Thanks for sharing