Configure and Move Tailored Datasets with Pipelines

Pipelines are the center of data movement and payload orchestration in the Intelligence Hub. Use Pipelines to curate, apply logic, and optimize datasets for specific applications using a simple but flexible graphical user interface. Build out stages in a pipeline to model, filter, buffer, or transform data flows to optimize delivery for consuming applications and device nodes.

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Oversee data movement between systems with Pipelines.

Data Motion

Orchestrate the movement of data on time intervals or logic-based events. Delay the delivery of data or buffer data based on time or size. Maintain the state of data that has been moved.

Data Processing

Sequentially transform data with pre-built processing stages, custom expressions, or even third-party JavaScript libraries. Process complex event streams of data structures with modeling, validation, and end-to-end observability. Tailor modeled data to meet the needs of multiple consuming applications and services.

Data I/O

Compose data extraction routines from simple input reads to sophisticated parameter-based queries, multi-stage lookups, cross references, payload appending, and more. Publish data to destination systems and dynamically drive schema, topics, keys, and identifiers. Define success/failure criteria of an integration and enforce these criteria with custom error handling.

Simplify Data Pipelines

The transportation of contextualized data continues to be a key pain point for industrial organizations. Solve this pain point with Pipelines. Read data from an input or instance source and then publish that data to one or many target connections with Pipelines. Pipelines can be triggered by events or intervals, while running the execution of each stage in the pipeline. Replay pipeline runs to observe data transformations from stage to stage, observe and react to any errors, and monitor pipeline and stage statistics for delays.

Curate Payloads

Most target systems have limitations on how they can process inbound data. They may be incapable of parsing or filtering structured data. They may consume multiple records in batches or files. Use Pipelines to dynamically break up objects and arrays, discard unnecessary elements to facilitate easy consumption, and buffer data on time or record count. Publish as JSON payloads or as CSV and Parquet files—or compress those files prior to delivery. This enables target systems to efficiently consume industrial data regardless of how source systems produce and transmit it.

Engineer Sequential Stages and Conditional Actions

Some data sets must be sourced from multiple systems, where the data from one system is being used in the subsequent read. Based on the values sourced, the pipeline data may go down one or multiple conditional paths with unique structuring, transformation, filtering, and writing to target systems. Pipelines can employ metadata to facilitate dynamic reads as well as curate the presentation of data to the needs of the systems consuming it.

Model Data in Pipelines

Industrial data is not only produced as primitive tags or points, but also as complex structures. Instead of decomposing these structures and explicitly mapping them into model instances, Pipelines can validate and re-shape incoming data structures to adhere to model definitions. This is ideal for enforcing data quality or modeling many data sources that produce similar data structures with subtle differences.

Manipulate data with these Pipeline Stages.

Stage Type	Stage Name
Common	Breakup Filter Flatten Model Model Validation Size Buffer Timed Buffer Transform
Control	On Change Switch
File Format	CSV Gzip JSON Parquet Zip
I/O	Read Smart Query Write Write New
Trigger	Event Flow Polled

PIPELINES FEATURES AND FUNCTIONALITY

Use HighByte Intelligence Hub to complete the following tasks:

Trigger pipeline execution based on an event or polling rate

Read inputs and instances or query the Namespace to build the pipeline value payload

Model, filter, buffer, transform, format, and compress data for the unique data consumption needs of target systems

Use the on-change stage to enable event-based delivery and report-by-exception

Persist variables according to the length of the manufacturing event with state management capabilities

Track individual event executions at a granular level, including execution time and success/failure

Use the switch stage to apply switch statement logic within a Pipeline; in the event of a failed write, easily define how to handle and remediate the error with conditional logic

Employ metadata and logic in sequenced stages to dynamically shape the presentation and delivery of data

Compose custom transformation stages with JavaScript expressions to satisfy advanced use cases

Manage and monitor Pipeline data processing stages, status, performance, and individual event executions in real time

Observe state and errors at-a-glance for each Pipeline stage

Monitor performance with high-level metrics tabulating completions, queues, errors, and more

Configure and Move Tailored Datasets with Pipelines

Oversee data movement between systems with Pipelines.

Data Motion

Data Processing

Data I/O

Enable complex data processing with the Intelligence Hub for a variety of use cases.

Simplify Data Pipelines

Curate Payloads

Engineer Sequential Stages and Conditional Actions

Model Data in Pipelines

Manipulate data with these Pipeline Stages.

Stage Type

Stage Name

PIPELINES FEATURES AND FUNCTIONALITY

Use HighByte Intelligence Hub to complete the following tasks:

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