Posted by Marbenz Antonio on September 21, 2022
IoT systems have access to a huge number of devices that produce a lot of streaming data. For some pieces of equipment, a single occurrence could be important to understanding and reacting to the machine’s health in real time, emphasizing the significance of accurate, trustworthy data. While previous data analysis and storage can offer chances to enhance procedures, judgment, and results, real-time data is still important.
Smart grids, which contain sensors and smart meters, generate a vast amount of telemetry data that can be utilized for a variety of applications, such as:
Real-time analytics can be achieved, for example, by combining a time-series database (such as InfluxDB or TimescaleDB) or a NoSQL database (such as MongoDB) with a data warehouse and a business intelligence tool:
Why would one use an operational database and still require a data warehouse, according to this architecture? To select a special-purpose database, such as a NoSQL database for document data, or a time-series database (key-value) for low costs and good performance, architects take such a separation into account.
This separation, however, also produces a data bottleneck because the analysis of data requires transporting it from operational data storage to the warehouse. Additionally, NoSQL databases struggle with analytics, particularly real-time analytics and complicated joins.
Exists a better approach? What if a high-performance, general-purpose SQL database enabled you to achieve all of the above? To support time-series data, streaming data intake, real-time analytics, and potentially even JSON documents, you would require this kind of database.
To provide real-time analytics, SingleStoreDB supports concurrent analytics for IoT data and quick ingestion with Pipelines (native first-class feature). You can utilize IBM® Cognos® Business Intelligence in addition to SingleStoreDB to assist you in making sense of all of this data. The earlier-described architecture then becomes:
SingleStoreDB’s pipelines let you rapidly and constantly load data. It is possible to simultaneously ingest millions of events per second from data sources including HDFS, cloud object storage, and Kafka. This means that both organized and unstructured data can be streamed in for real-time analyses.
But hold on, things improve…
Customers use SingleStoreDB for IoT applications in a variety of ways, with Armis and Infiswift being just two examples:
Here at CourseMonster, we know how hard it may be to find the right time and funds for training. We provide effective training programs that enable you to select the training option that best meets the demands of your company.
For more information, please get in touch with one of our course advisers today or contact us at email@example.com