It's All About The Data

Machine learning is only as good as the data that it is trained on. Without access to enough data, machine learning algorithms will struggle to make accurate predictions or classifications. This is why data availability is at the center of the AI revolution. The more data you have access to, the more success you can achieve with ML and AI algorithms.

However, collecting data is not always straightforward. Data can come from a plethora of sources, such as sensors, cloud storage, APIs, MQTT brokers, and more. Each of these sources may provide data in different formats, which can make it difficult to integrate and process the data effectively. This is where IoT connectors come in.

SORBA.ai has always used channels as its primary way to collect data, creating channels and mapping tags to those channels. However, SORBA.ai has now added a new way of collecting and moving data to and from external sources via IoT connectors. This expands data collection, processing, and delivery to a new level of versatility.

IoT connectors use many drivers for different databases and communication protocols, meaning that you can move data to and from any available option using the available formats. Another powerful feature of IoT connectors is how data is transformed from the source format to the SORBA Standard Format and then transformed again to the destination format. This is so that a pipeline of processing functions can be applied to the data while it is in the SORBA Standard Format.

SORBA.ai's IoT Unified Platform has many predefined Data Processors that you can choose and configure in a pipeline inside the IoT Connector itself. These predefined Data Processors can perform tasks such as data filtering, aggregation, and data normalization. However, for more advanced users, new custom data processors can be easily defined in the "Script Nodes".

By using IoT connectors, industrial engineers can collect data from a wide range of sources, transform it into a standardized format, and deliver it to the right destination at the right time. This means that they can spend less time on data integration and more time analyzing the data to make informed decisions. Additionally, by having access to more data, industrial engineers can train more accurate machine learning models, which can lead to improved productivity, reduced downtime, and better overall performance.

IoT (Internet of Things) has revolutionized the way we collect and analyze data. By enabling devices to communicate with each other, IoT has made it possible to collect data from a wide range of sources, including sensors, cloud storage, APIs, MQTT brokers, and more.

Who uses IoT Connectors?

IoT connectors are for anyone who needs to collect, process, and deliver data from IoT devices. This includes industrial engineers, data analysts, software developers, and more.

In a manufacturing setting, an industrial engineer could use an IoT Connector to collect and process data from various sources, such as sensors and machines on the factory floor, to monitor and optimize production processes.

For example, an IoT Connector could be used to collect data from sensors on a conveyor belt that transports products through the manufacturing process. The data collected could include information such as the speed of the conveyor, the weight of the products, and the temperature and humidity of the environment. This data could then be transformed into the SORBA Standard Format and processed using predefined or custom data processors within the IoT Connector. The processed data could be used to identify inefficiencies or anomalies in the production process and optimize the workflow to improve productivity and reduce costs.

When do you use it?

IoT connectors are used whenever you need to move data from one source to another. This can include collecting data from sensors, transforming it into a standardized format, and delivering it to a cloud storage platform for analysis. IoT connectors can also be used to move data between different databases, or to feed data into machine learning algorithms.

The Solution

IoT connectors solve the problem of data integration. With so many different data sources, it can be difficult to collect and process data in a standardized way. IoT connectors provide a standardized framework for collecting, processing, and delivering data, making it easier to integrate data from different sources.

IoT Connectors Supported by SORBA.ai

IoT connectors are well-suited for collecting data from a wide range of sources, including sensors, cloud storage platforms, APIs, and more. The best data sources for ETL transformation are those that provide a large amount of data in a structured format, such as CSV files, JSON data, or database tables.

• AMQP
• AWS S3
• COSMOS
• DYNAMO
• ELASTICSEARCH
• HDFS
• HTTP/ HTTPS
• IBA HD
• IGNITION
• INFLUX 1.X
• INFLUX 2.X
• KAFKA
• KCF
• KINESIS
• MONGO
• MQTT
• NEO4J
• PI WEB
• REDIS
• REDSHIFT
• SPARKPLUG
• SQL
• UDP

An Engineers Dream

Industrial engineers rely on data to optimize production processes and improve efficiency. By using IoT connectors to collect, process, and deliver data in a standardized way, industrial engineers can spend less time on data integration and more time analyzing data to make informed decisions. This can lead to improved productivity, reduced downtime, and better overall performance. IoT connectors can also help industrial engineers identify potential problems before they occur, allowing them to take proactive measures to prevent downtime and minimize disruptions.