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How do you manage and scale sensor data storage and processing in cloud-based or hybrid IoT architectures?

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Sensor data is the lifeblood of IoT applications, but it also poses significant challenges for storage and processing. How do you handle the volume, velocity, variety, and veracity of sensor data in cloud-based or hybrid IoT architectures? In this article, we'll explore some best practices and tools to manage and scale sensor data storage and processing in IoT scenarios.

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Ankur Verma, PhD

Founder CEO @ Lightscline | SME 30 under 30

1 Choose the right data storage

The first step is to choose the right data storage solution for your sensor data. Depending on your data type, size, frequency, and retention requirements, you may need different types of storage, such as relational databases, NoSQL databases, data lakes, or data warehouses. You should also consider the scalability, availability, security, and cost of your data storage options. For example, you may use a cloud-based service like Amazon S3 or Azure Blob Storage for storing large amounts of raw sensor data, and then use a data warehouse like Amazon Redshift or Azure Synapse Analytics for aggregating and analyzing the data.

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Ankur Verma, PhD

Founder CEO @ Lightscline | SME 30 under 30
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Sensors with high sampling frequencies can generate 100,000 rows of data each second, which create a lot of data to store and handle. In these cases, it's better to consider whether to store the raw data or features or some compressed representation of the raw data.

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2 Streamline the data ingestion

The next step is to streamline the data ingestion process, which involves collecting, filtering, transforming, and routing sensor data from the edge devices to the cloud or hybrid infrastructure. You should use a reliable and scalable data ingestion framework that can handle high-throughput and low-latency data streams, such as Apache Kafka, Amazon Kinesis, or Azure Event Hubs. You should also apply data quality checks, data compression, data encryption, and data partitioning techniques to optimize the data ingestion process and reduce the data storage and processing costs.

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Ankur Verma, PhD

Founder CEO @ Lightscline | SME 30 under 30
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Some questions to consider: 1. what amount of data is needed for making what decision? 2. Where to compute on that data, edge or cloud? 3. Do I need to store the data for historical use or discard the raw data and keep the features?

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3 Optimize the data processing

The third step is to optimize the data processing pipeline, which involves applying various analytics and machine learning techniques to extract insights and value from sensor data. You should use a flexible and efficient data processing framework that can handle batch and real-time processing, such as Apache Spark, Apache Flink, Amazon EMR, or Azure Databricks. You should also use a suitable data processing model, such as lambda architecture, kappa architecture, or micro-batch architecture, depending on your data latency and complexity requirements. You should also leverage parallel and distributed computing, caching, and indexing techniques to speed up the data processing and reduce the resource consumption.

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4 Visualize the data outcomes

The fourth step is to visualize the data outcomes, which involves creating interactive dashboards and reports that showcase the key metrics and trends derived from sensor data. You should use a powerful and user-friendly data visualization tool that can connect to various data sources and formats, such as Tableau, Power BI, or Grafana. You should also use a clear and consistent data visualization design that can convey the data story effectively and intuitively. You should also enable data exploration and drill-down capabilities to allow users to discover more insights and details from sensor data.

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Ankur Verma, PhD

Founder CEO @ Lightscline | SME 30 under 30
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Conventional techniques for 1-D sensor data like Fourier Transform, Spectrograms, are useful to understand the frequency information in the signal. This is indicative of faults in rotating machines like: 1. Unbalance 2. Misalignment 3. Broken gear tooth 4. Resonance 5. Loose foundation Machine learning models can be trained to detect these faults as well. Moreso, given the increasing amounts of data being collected.

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5 Monitor the data performance

The fifth step is to monitor the data performance, which involves tracking and measuring the quality, reliability, and efficiency of your sensor data storage and processing pipeline. You should use a comprehensive and proactive data monitoring tool that can collect and analyze various metrics and logs from your data infrastructure and applications, such as Prometheus, Datadog, or Azure Monitor. You should also use a robust and responsive data alerting system that can notify you of any data issues or anomalies, such as PagerDuty, Opsgenie, or Azure Alerts.

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Ankur Verma, PhD

Founder CEO @ Lightscline | SME 30 under 30
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Different techniques can be used: 1. Amplitude based thresholding 2. Frequency based thresholding 3. Consider the periodicity of data to decide the time interval for monitoring 4. Deploy trained ML models for automatic anomaly detection

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6 Improve the data governance

The final step is to improve the data governance, which involves establishing and enforcing the policies, standards, and best practices for managing and securing your sensor data throughout its lifecycle. You should use a centralized and collaborative data governance platform that can provide data cataloging, data lineage, data quality, data privacy, and data compliance features, such as Apache Atlas, Collibra, or Azure Purview. You should also use a consistent and transparent data governance framework that can align with your business goals and regulatory requirements.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you manage and scale sensor data storage and processing in cloud-based or hybrid IoT architectures?

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1 Choose the right data storage

2 Streamline the data ingestion

3 Optimize the data processing

4 Visualize the data outcomes

5 Monitor the data performance

6 Improve the data governance

7 Here’s what else to consider

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