The Internet of Things (IoT) is one of the driving forces for the increase in today’s data volume and diversity. The Iot Devices create lot of data as they communicate with each other. This data is used for immediate analysis/action as well as long term historical analytics. The IoT platform must allow user to retain consumable data over a period to analyse trends and make inferences to make decisions for actions in present or future. Volume of data, speed of processing, analytical power etc. are some of the critical aspects when considering storage of data.
A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. It can store large amount of structured, semi-structured, and unstructured data for e.g. raw copies of source system data, sensor data, social data etc., and transformed data. Cloud data lakes are cost-efficient and scale almost infinitely.
Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. It removes the complexities of ingesting and storing all your data while making it faster to get up and running with batch, streaming and interactive analytics. Read More
Cumulocity IoT is an independent device and application management IoT platform. Some of the key features are: -
- Connect devices, receive, and send data securely to platform.
- Manage your devices, manage device onboarding, manage lifecycle like sending software updates and firmware updates.
- Support real time streaming and predictive analytics and helps users to visualize results in very modern dashboards.
- It is also API enabled which allows you to integrate wide enterprise and enhance business process and business applications.
Software AG’s Cumulocity IoT DataHub
Make your IoT data your advantage with Software AG’s Cumulocity IoT DataHub. With DataHub, you can bridge the gap between streaming and historical analytics in a way that simplifies processes for IT administrators and enables the business to gain new insights about operations and performance. Some of the key features are
- Simplified management of long-term data storage
- Lower cost for IoT data storage
- Scalable SQL querying of long-term IoT data
- Standard interfaces to BI & data science tools
The greater advantage lies in using the Software AG on-premise and/or cloud integration platforms in conjunction with the IoT platform. This will help in processing the IoT data before it is loaded to the data lake. Also, it helps to integrate data from various SaaS applications which might give a holistic view of the entire company data in the data lake.
webMethods.io Integration is a powerful integration platform as a service (iPaaS) that provides a combination of capabilities offered by ESBs, data integration systems, API management tools, and B2B gateways. Read More
webMethods CloudStreams Connectors for on-premise Integrations
webMethods CloudStreams helps you create and govern connections between any combination of cloud and on-premises applications, databases and more. WebMethods CloudStreams Provider for Microsoft Azure Data Lake Store contains predefined CloudStreams connectors that you use to connect to on-premises versions of Microsoft Azure Data Lake Store. Using webMethods CloudStreams, you can configure the CloudStreams Microsoft Azure Data Lake Store connector to create directories, folders, and files in your Azure Data Lake Store instance that can store and retrieve data.
We can use Microsoft Power BI to analyze and visualize the IoT data that is stored in Azure Data Lake Storage Gen2. Read More
Some of the DataHub use cases are
- Analysis using BI tools on offloaded historical data identifies usage metrics for equipment, including how often specific equipment was used, how long it was used for and how intensively it was used.
- Historical analysis helps understand trends about how the medical devices are used in the workplace so that future investment can be best focused.
- Analysis using historical data helps systems understand how long pressure testing and initial filling takes for specific types of containers. Enables predictions for how much time must be allocated for testing new types of products etc.