Tips to Build Effective Data Pipelines to Support your DataOps Strategy

1. Tips to Build Effective Data Pipelines to Support your DataOps Strategy At the center of DataOps is a continuous flow of data for analytics — the data pipeline. It is the backbone for streamlining the lifecycle of data collection, preparation, management, and development for machine learning, AI, and analytics. A solid data pipeline strategy involves planning both for creating new pipelines as well as enhancing existing ones. When followed correctly, the six design principles discussed below will support data growth, achieve security compliance, reduce downtime , reduce complexity, and increas productivity. Principle #1: Modularity Follow a single responsibility approach in designing the data pipeline components so that each component may be developed, changed, implemented, and executed independently of one another. The pipeline can be deconstructed into smaller executable modules based on business logic, selected technology, platform integration choice, and logical architecture components. This design principle and decoupled approach helps businesses achieve faster time-to-market while minimizing downtime. Principle #2: Auditability Establish a reliable audit trail to guarantee the reproducibility of issues or erros that can occur as part of data transformations and loads. Document various logs, errors, current state, service level agreement (SLA) breaches, and other such events. This aids in the detection, identification, and resolution of problems, as well as in improving the quality of preventive actions. Auditability ultimately reduces operational costs while ensuring compliance with audit regulations. Principle #3: Reliability Set up data pipelines to handle errors without manual intervention, with configuration-driven execution as the basis of the overall design. As failures will inevitably occur, any segment of the pipeline should be able to support re-runs. If a re-execution is required, the pipeline design should consider how re-execution will affect the overall data in terms of missing data or data duplication. Principle #4: Adaptability The data pipeline should be designed for the varying data requirements and access patterns of different business units and users. A good pipeline design supports centralized and decentralized storage, different access frequencies, and dataOps partitioning strategies. It also quickly adapts to changes in consumption requirements and data models over time. The data pipeline is always well integrated with business needs, and there are no superfluous complexities. Principle #5: Agility The Data pipeline should be able to quickly manage changes (like software version changes or infrastructure upgrades) without affecting other applications, components, and services. Open-source tools, a low-code/no-code technique, and a metadata-driven approach all foster agility and enable a future-ready design that is ready to support business growth. Principle #6: Security Focus on securing all endpoints, ensuring connections are only allowed over secured ports, and encrypting data while in transit. There should be clear access control policies and clarity in what privileges are available to each role. An effective data pipeline is designed based on a solid understanding of an organization’s requirements, data, and IT landscape. With a reliable, secure, and adaptable data pipeline strategy, businesses can improve their intelligence gathering and analysis. To learn more, download the InfoCepts guide on six fail-safe strategies for creating data pipelines that put your data first.