Plan Your IoT Real-Time Data Streaming Process

November 5, 2020

IoT is the new demand, or we can say the requirement of technology. The changes, disruptions, upgrading, business demands, customers’ demand, everything queues to urge for the adoption of IoT. However, the adoption of IoT isn’t profitable until you are not stirring the best out of it. Nowadays, the most discussed topic in IoT is IoT real-time data streaming process. IoT data streaming is essential as it provides real-time decision making that is significant to many operations. An organization should have tools to accumulate data from sensors and devices then process it and transfer it to the database for analysis and real-time results.

Data streaming enhances performance and saves from the upcoming mishap as it sends alerts that prompt interference. For example, in a refrigerated truck, if a sensor reads a decrease in the required temperature, then IoT real-time data streaming and AI models will provide alert that the needed temperature is disturbed and it might cause the spoilage of the product.

Why organizations need IoT data streaming?

Well, data streaming is required to attain smooth operations and to get prepared for future challenges. It allows organizations to take prompt decisions and actions.

In short, Organizations use IoT data streaming for:

  • Detecting illegal network access
  • Recognizing upcoming machine failure before it actually occurs
  • Monitor the changes happening in a patient’s health and notify the doctor for the same before any mishap happens

Not just this, real-time data streaming also improves the organization’s competitive position. Let’s check an example of a clothing store. Today, some of the clothing stores are installing smart mirrors in their shops to provide better customer experience. Through smart mirrors, potential customers can virtually try on different dresses or items or get a specific look without any hassle of physically trying them on.

As per the market researcher MarketsandMarkets, the streaming analytics market may grow from $12.5 billion in 2020 and extent to $38.6 billion by 2025 at a compound annual growth rate of 25.2%.

IoT applications and expansion of GPS and geographic information systems that are used to monitor, trace and map events in real-time is essential pillars of data streaming process.

Do you know how the data streaming process runs?

The data streaming process involves three components: software, an operational database and an analytics engine. The operational database runs in real-time, whereas analytics engine extracts the data to provide insights.

For first-time data stream deployment, organizations gather all the three crucial components, which need awareness with the process steps and understanding of the complexities of the tools which are used in each step of the operation.

The first step involves ingesting the IoT data using some message streaming software or message brokers like Amazon Amazon Kinesis Data Streams or Apache ActiveMQ.

Once the ingestion is completed, in the next step ETL tool, i.e., extract, transform and load tool prepares the data for import into an analytics database. This type of process is typically an operational database based on a SQL platform. Organizations must create real-time analytics and machine learning models and applications to obtain useful business insights from gathered data.

Read More: How AWS IoT Things Graph Simplifies Hardware Integration Process?

It is noticed that many IT departments adopt this methodology, but with the emergence of the latest technology, there are many automated methods and platforms. There are data streaming and analytics services or platforms which simplifies the architecture and mechanics like Splice Machine SQL database and Confluent Platform or machine learning models.

What are the four best data streaming practices?

Organizations that build their process from the start and are looking for an off-the-shelf offering should check the following four best data streaming practices.

  1. Opt narrow business cases: The first practice one should follow choosing business cases which are solely dedicated to IoT data streaming that provides efficiencies, customer satisfaction, cost-saving and increased revenues. For instance, use of IoT data to detect which machines require high priority maintenance and if not provided then will fail on any assembly line, monitor network endpoints to block unauthorized access/ attacks or to trace the positions of the shipment.
  2. Simplify the design: Organizations should simplify the data streaming architecture to accelerate the time from insight to streamed data and cut-off the excess manual-coding. There are tools like Apache Kinesis Data Streams that automates the data ingestion operation, automate the ETL transportation of data into databases and removes the dependency on IT to facilitate the above function through extra coding. Splice Machine database also simplifies the operations as it automatically stores test database sandboxes. Thus, a user has to issue a single command and does not need manually set up the test database by a data analyst.
  3. Clean the data: Everything is essential in the data streaming process. Clean data is equally critical as architecture. The data cleaning process occurs at the time of data ingestion and happens in the processing/working of an ETL tool. Well, if an organization desires to automate portions of these operations, then must contact and work with their vendors and vendor’s toolsets to promise that they are capable of meeting the cleaning needs.
  4. Acknowledge the near-real-time & batch process: It is not suggested to perform every analytical function in real-time. Some data processing are periodically performed, near-real-time intervals like after every half-an-hour. In batch processing that is delivered during the day or overnight is also significant. Before the implementation of the process, organizations should find out which process need real-time and set workflows accordingly.

Well, to churn profits from IoT, it is necessary to know how to efficiently use it. IoT, along with other technologies, allows you to get a clear vision through data. The useful data enables you to take prompt decisions, and actions to stop the looming loses and mishaps. The IoT data processing is the best way to enjoy the best of IoT and make business operations smooth and efficient. Connect reliable and proficient IoT service providers to know more about IoT, get idea and services for IoT data processing. Your business needs IoT touch; don’t waste your time anymore. Connect us.

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