Data Orchestration At Developer Fingertips

Waylay
Data orchestration
Illustration: © IoT For All

Low-Code Data Orchestration Platforms

Low-code automation platforms are becoming dominant developer-friendly automation tools where we use visual interfaces with simple logic and drag-and-drop features instead of extensive coding. Then by default, these platforms should also take care of data orchestration across the increasing complexity of the data ecosystem and their associated frameworks, clouds, and storage systems. All with minimal setup effort and extremely rapid deployment. In this new developer world order, applications can be built in just days/weeks and not years.

Many have claimed to offer low-code automation solutions before. Still, few have been able to live up to the expectations to drastically shorten time to market and provide the orchestration environment where developers only
need to focus on what they love most: developing applications. This is also a boon for entrepreneurs who rely on software developers at huge expense and risk to build software from the bottom up. They can now
build their applications on top of an automation stack of high quality in a reliable pre-built software environment.

Data Orchestration

To address the complexity of creating a data automation platform, the basic building blocks of the low-code platform should be small snippets of code that, like Lego bricks, are reusable across different Applications. To implement logic to these code snippets, we need a mighty rules engine that can orchestrate these code snippets to build and support these complex Lego structures.

The data orchestration platform takes care of the following 4 major steps:

1. Data Ingestion

Data has to get into the system one way or another. Data ingestion can be a mess when the data sources have their origin across different input channels as SQL databases, via MQTT, or just plain .txt logs. The Data Orchestration platform should integrate all different data sources to process the required combined data set.

2. Data Processing

Collected data is rarely in the right format when entering the data automation platform. Data needs to be combined, unpacked, and decoded. At this step, data aggregation is also performed to use data at a higher abstraction level.

3. Decision Making

Once the data is ready, it is time to make decisions based on rules and automated workflows. The low-code automation platform should provide an easy way to create rules and even have the ability to utilize AI or ML models and let these call the shots. Using a graphical interface in the automation rule designer instead of programming thousands of lines of code, the developer can focus on the fun part of creating automation flows and experiment with data.

4. Taking Actions

In the last stage, it is time to take action. Based on processed data and previous decisions, the platform should take actions inside and outside of its ecosystem. Actions range from generating alarms to turn devices on/off, scheduling a field service engineer for a maintenance intervention, or sending quarterly usage figures to accounting for use-based invoicing.

Developer New Role

The best data orchestration platforms are flexible in each of the 4 steps described above. They allow integration with other platforms and provide a way to define new logic quickly.

Author
Waylay
Waylay
Waylay, leading enterprise automation software company at the forefront of digital transformation
Waylay, leading enterprise automation software company at the forefront of digital transformation