Reduce time to insights with effective data pipelines

Data is everywhere, enabling unprecedented levels of insights within all businesses and industries for decision-making. Data pipelines serve as the backbone to enable organizations to refine, verify, and make reliable data available for analytics and insights. They take care of data consolidation from various sources, data transformation, and data movement across multiple platforms to serve organizational analytics needs. If not designed and managed well, data pipelines could quickly become a maintenance nightmare having a significant impact on business outcomes.

Top Two Reasons for a Poorly Designed Data Pipeline:

Designing a data pipeline from scratch is complex and poorly designed data can impact data scalability, business decisions, and transformation initiatives across the organization. Below are the top two reasons amongst many which lead to a poorly designed data pipeline.

1. Monolithic pipeline – Monolithic pipelines lack scalability, modularity, and automation feasibility. Minor changes in the data landscape needs huge integration and engineering efforts.
2. Incorrect tool choices – Data pipelines in an organization grow from one tool to multiple quickly. The correct tool to be deployed depends on what use case it is supporting, and a single tool cannot be used for all business scenarios.

Creating an Effective Data Pipeline

Looking at the criticality of data pipelines, it is particularly important for organizations to spend a good amount of time in understanding the business requirements, the data and IT landscape, and then designing the pipeline. The below steps should be part of any data pipeline strategy planned by organizations –

Modularity – A single responsibility approach should be followed while designing the data pipeline components so that it can be broken into small modules. By this approach, each pipeline module can be developed, changed, implemented, and executed independent of each other.

Reliability – Data pipelines should be set up to support all downstream (Service Level Agreements) SLA requirements of consuming applications. Any pipeline should support re-runs in case of failures and executions should be automated with the help of triggers and events.

There are many other factors and principles that impact data pipelines and should be part of its design strategy. Infocepts Foundational Data Platform Solution enables you to adopt the right-fit data pipeline strategy early and avoids any future complexities, migration needs, or additional investments. A well-thought-through data pipeline strategy helps improve business intelligence and comprehensive analysis by delivering only the required data to end users and applications.

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