Product update - November 2021

This month again, we are excited to bring you new features that will make your life easier. First, we have developed a KPI Trend indicator for your device data visualization. Also, you now have the possibility to export or delete your device data. With the new output connectors to AWS Kinesis or Apache Kafka, you can develop your IoT project further in such cloud systems. With the latest addition of our notification service connectors to Microsoft Teams and Slack, you can be sure that your team is always up to date with what's going on in your IoT project.

Discover below what exciting updates we have prepared for you.

Assets

KPI trend indicator

Monitor your device data on the visual trend indicators on the device detail page. Device data of the past 24h are displayed as graphs so that you can get a quick overview of your device behavior.

Data export & delete

You can now export any device data that you saved in akenza as a .csv or .json file. Conveniently export all existing data or fine-tune a specific time frame.

Do you want to delete existing device data because you're removing a device or want to do some data housekeeping? You can do that easily now too.

Cloud connectors

Further use of IoT data in enterprise applications can be relevant to launching a successful IoT project. Therefore we have created several new Output Connectors to cloud systems and extended the connectors to different notification services.

AWS Kinesis

Use this output connector to forward data to Amazon Kinesis as an entry point to the Amazon Kinesis ecosystem.

Amazon Kinesis can be used to stream data to collect and process large streams of data records in real-time. It allows the creation of data-processing applications, known as Kinesis Data Streams applications. A typical Kinesis Data Streams application reads data from a data stream as data records. The Amazon Kinesis connector is available on both Data Flow and Rule Engine. Find out more about it here.

Apache Kafka

A key challenge in IoT is integrating devices and machines to process the data in real time and at scale. Apache Kafka and its surrounding ecosystem have become the technology of choice for integrating and processing these kinds of datasets.

Apache Kafka, as open-source software, is primarily used to build real-time streaming data pipelines. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

User benefits:

• Real-time handling: Handle real-time data pipelines at scale with zero downtime and zero data loss

• High-throughput: Handle high-velocity and high-volume data

• Low latency: Manage messages with very low latency in the range of milliseconds

• IoT use cases: Manage a variety of IoT use cases commonly required for a Data Lake for eg log aggregation, web activity tracking, etc.

The Apache Kafka connector is available on both Data Flow and Rule Engine. Find out more about it here.

Slack

We are glad to announce that Slack, one of the most popular business communication platforms globally, can now be used on akenza! Keep your teammates informed about your IoT project by sending automated text messages to your Slack channel.

The Slack connector is available on both Data Flow and Rule Engine.

Keen to try it out? Read the tutorial here about how to send notifications to your Slack channel.

Microsoft Teams

Use Microsoft Teams as your notification service on akenza to keep your team members up to date. Like all the other notification connectors, you can easily inform about the status of your IoT project by sending automated text messages to your MS Teams channel.

The Microsoft Teams connector is available on both Data Flow and Rule Engine.

Curious to send notifications to your own Teams channel? Read all about it here.

Towards large-scale IoT applications

As your project scales, our flexible pricing model will ensure you remain with a cost-effective solution. For a detailed comparison of our plans, make sure to visit our pricing page.

You can learn more about each feature mentioned in this article by heading to our product documentation page.

Do you miss a feature? Let us know and enter your feature request here.