Data pipelines enable manufacturing engineers to simplify complex data management in support of their work.
As manufacturing engineers grapple with more and more data from diverse sources, they implement data pipelines to simplify their increasingly complex data management processes.
What are data pipelines?
Data pipelines are automated systems that manufacturing engineers use to read data from multiple data sources, transform the data and then write it to a destination database.
Examples of transforming data include the following:
- Changing key values such as customer code, part number or vendor number to a single set of values.
- Revising dates to a standard format.
- Aligning codes and related descriptions to a single set of values.
- Denormalizing data for improved application performance.
- Aggregating data to a uniform level of summarization.
Often, the destination is a data lake house or data warehouse. From there, the data is used for one or more of the following purposes:
- Operational applications such as manufacturing planning or control.
- Data analysis and visualization, including dashboards.
- AI applications for insights into manufacturing trouble shooting, optimization or forecasting.
Increasing importance of data pipelines
Data pipelines have taken on increasing importance for engineers as a result of the following application trends:
- Manufacturers are employing more advanced simulation and AI applications. These software trends require access to large volumes of high-quality data. Upgrading data quality, often by comparing values across datastores, is dependent on data pipelines.
- Manufacturers are integrating their various systems more tightly. This integration trend requires data pipelines to copy selected data from one system to another, improving data sharing.
- Manufacturing groups see value in data analytics and visualization. This analytics trend requires concurrent access to multiple data sources, which is often dependent on data pipelines.
Business benefits of data pipelines
The key benefit of data pipelines is to make data available in a timely and integrated manner for business processes across many parts of the organization, including engineering. That data availability can:
- Accelerate product development through more detailed simulation and faster iteration.
- Improve operations by improving decision quality.
- Enable AI and machine learning initiatives by ensuring trustworthy data sufficiency.
- Improve customer experience by reducing the time it takes to complete transactions.
- Reduce time to market for new products and services.
- Enhance risk management through more comprehensive risk identification.
More broadly, data pipelines enable the shift-left, or earlier in the engineering process, approach. Shift-left focuses on improving digital data availability to the initial stages of product or service planning, design, and development. The benefits of this approach include faster delivery, better quality and lower costs.
Types of data pipelines
Data pipelines operate differently depending on the characteristics of the application:
- Batch processing data pipelines – Load large batches of data from multiple data sources into a destination database at set time intervals. Organizations typically schedule batch pipelines during off-peak business hours. A good example is aggregating daily production quantities by product from multiple manufacturing facilities overnight for data analysis the following morning.
- Streaming data pipelines – Continuously process new or revised data generated in real-time by sensors or end-user interactions with an application into a destination database. Most streaming data pipelines operate continuously. A good example is streaming Industrial Internet of Things (IIoT) data from the manufacturing floor to monitoring applications used by engineers.
- Data integration pipelines – Merge data from multiple data sources into a single unified view in a destination database. Data integration pipelines can operate either as batch or streaming data pipelines. A good example is merging data from various Enterprise Resource Planning (ERP) modules with Customer Relationship Management (CRM) data and external data to build an integrated view of industry production trends.
Selecting data pipeline software
The capability of available data pipeline software varies widely. The following criteria will help engineers select software that fits the application requirements:
- Ease of use features that increase developer productivity and thereby reduce development cost.
- Features that minimize effort to respond to changes in data source schemas.
- Scalability to handle the estimated current and future data volumes.
- Ability to connect easily to the required diversity of data access technologies used by data sources.
- Security features for data encryption and authentication.
- Automation features that simplify operations.
- Operational monitoring features that quickly identify problems that require intervention.
- Vendor track record.
- Acquisition and operating costs are acceptable.
Frequent risks associated with data pipeline implementations
Manufacturing engineers should consider whether the following potential risks affect their data pipeline project and implement suitable mitigations:
- Data quality shortcomings that are expensive and time-consuming to address.
- Data integration complexities that require sophisticated and expensive software development.
- Data and accuracy loss caused by poorly designed data integration software.
- Ambitious goals or target states with large scopes that are beyond the capacity of the organization.
- Excessive processing latencies that create data anomalies for streaming data pipelines.
- Data pipeline performance issues that may occur when large data volumes are involved.
- Security vulnerabilities that may be introduced when a significant number of data sources are involved.
As manufacturing engineers grapple with increasing data volumes from diverse sources, they implement data pipelines to achieve faster delivery, better quality and lower costs.
