Time series data and real-time data acquisition is growing at a 50% faster rate than static, latent, or historical data. In some ways, it has become more important than any other type of data, as it provides real-time decision making, enables autonomous decisions at the edge, and allows for more complex Machine Learning (ML) applications. Time series data and real-time data acquisition dominate industrial use cases, as it is ubiquitous with the manufacturing process. One would find it hard not to find it in most, if not all, manufacturing processes.
Manufacturing IIoT sensors generate extremely high data volumes, through multiple connection protocols and from multiple sources, complicating efforts to capture, align, and enable fruitful use of time series data. Time series data from continuous processes is a waste if that data can’t be collated and aligned to its time of origin. Timestamp alignment of events and the process to make it into manageable insights requires ad-hoc, line-of-thought access, and tools enabled through scalable data query engines.
However, technology to support its use has been limited for decades, now technology advances enabled by a 60% decrease in industrial sensor costs, high connectivity and bandwidth enabled by 5G, computing power-driven by the last decade’s big data trends and big data platforms, now enable unprecedented analytic insights. Today’s solutions offered by Cloudera are easily deployable and the benefits are well understood, proven, and measurable.
Cloudera’s open source-based solution has made great strides in simplifying the use of time-series data. Cloudera’s new innovations in standardized technologies eliminate the latency between sensor data input and business insight.
No other data platform can scale to the level that modern time series analytics demands, and at the same time provide the flexibility, ease of access, and ease of use expected today. Cloudera can, with a hybrid and multi-cloud platform that takes full advantage of the variety of data processing and cloud-native options deployed in real-world applications.
Cloudera Data Flow and Real-Time Data Mart, on the Cloudera Data Platform (CDP), bring together the best technology standards on the market into one simple and scalable cloud-agnostic experience. CDP allows one to not only capture the much higher sample frequency demanded, across multiple sources, but it also enables the simple combination of real-time data with contextual data, with the ability to query it in a few seconds. This integrated platform enables self-service real-time insights accessible to any level of your organization.
CDP can also help you easily leverage Machine Learning (ML) based modeling over the same data, so you may learn from the historic data and apply and combine it with real-time incoming streams, for even greater in-the-moment actionable insight. A modern approach is delivered, as CDP has all these capabilities built into the same platform, allowing large scale data ingestion, processing, and querying – unmatched performance at every stage of the data lifecycle.
Cloudera has developed significant expertise by helping many manufacturing organizations optimize their time-series analytics and increase the value and quality of the insights generated. With our proven time-series analytics solution, manufacturing companies can embark on a journey of digital transformation.
A solid plan to create a real-time data mart solution, at scale, should include these steps and best practices:
- Start With Process Monitoring – the creation of operational dashboards that allow monitoring and understanding of your manufacturing process. These dashboards monitor and measure data in real-time allowing operators to make conscious adjustments to the operating conditions within established criteria – with an instant effect.
- Move To Process Optimization – where optimums are well understood through analytics over more complete data, and which will lead to enabling rules-based decisions implemented on the edge, promoting automated and autonomous process control and optimization.
- Apply Machine Learning – reach the level where a time-based historical data lake is created, characterizing the process, and leveraged to create Machine Learning algorithms and train high-accuracy models for predictive maintenance, driving improved OEE through improved uptime, optimized equipment utilization, and improved quality.
- Exceed – quality metrics by leveraging computer vision, multi-sensor environments, and unstructured data inputs (vibration, humidity, color, smoke detection, etc) for quality inspection, optimal yield, and contamination control
The road to digital transformation maturity is a journey that needs to start with an understanding of how to leverage the data lifecycle. Cloudera can partner with you steering you on your way. Gain insight into your industrial processes that are affected by seasonal variations, day to day production variances, machine wear predictions ( predictive maintenance applications), and others along your production process.
Watch this webinar “Time Series Analytics – Making Manufacturing Use Cases Come to Life” to learn more or visit: https://www.cloudera.com/products/data-warehouse.html