Industrial data analytics – Working with Data and Analytics

We have seen the usage of data analytics in the past two sections and how it can be beneficial for our workloads. Now, let’s look at how it can benefit industry cases and how we can accordingly evaluate our deployments based on the best practices that are set out for us.

Evaluating performance

Use services such as CloudWatch metrics to monitor the performance of the IoT Analytics pipeline, such as the number of messages processed, the time it takes to process each message, and the number of errors that are encountered. This will be critical for use in further analysis and eventual optimization. The following are factors to consider in evaluating performance:

Analyze your data: We can use IoT Analytics SQL or other data analytics tools to identify any patterns or issues that we may need to address if they affect system performance.

Optimize your pipeline: From the analysis of the data, we can optimize the pipeline by adding data normalization, validation, and modeling to improve the performance of the data analytics workloads.

Use best practices: We need to adhere to best practices for data analysis, which includes techniques such as normalization, data validation, and data modeling. For the scope of this book, we will not be covering this, but we encourage you to look up more of these techniques in the Further reading section and read up on the topics listed there.

Usage of third-party monitoring tools: We can utilize third-party monitoring tools to collect and analyze performance metrics for our analytics workload and gain more insights into how our pipeline is performing.

Monitor and track usage of resources: We need to keep an eye on resources such as CPU, memory, and storage that are used by our data analytics workloads, especially if they are consuming more resources than expected. If necessary, we should perform actions such as scaling our workloads up or optimizing the pipelines further.

Having understood how to keep track of performance, we can now review some different use cases of data analysis within industry.

Use cases within industry

Industry has many different use cases for performing data analysis on a myriad of data. Here are just a few prominent examples:

Predictive maintenance: Within this use case, IoT devices are used to collect real-time sensor data that is processed and analyzed using AWS IoT Analytics to detect patterns and accordingly predict when maintenance would be required. This will help organizations schedule maintenance at the required times, reducing downtime and improving the efficiency of equipment.

Smart agriculture: IoT sensors can be used to collect data on soil moisture and temperature, which is then analyzed within AWS IoT Analytics to optimize crop yields, reduce consumption of water, and improve overall farm efficiency.

Smart cities: IoT devices can be used to collect data on various aspects of urban infrastructure such as traffic, air quality, and energy usage. The data can then be analyzed through AWS IoT Analytics where it can then be used to improve traffic flow, reduce pollution, and optimize energy usage to ensure that cities become more sustainable and livable for their residents.

With those use cases in mind, we can now take a look at a case study of a data analytics flow used within a production environment in an industrial setting.