6 Strategies for Building a Robust Data Pipeline to Drive Business Growth

Leveraging big data is a major roadblock for businesses amidst the overflowing data and skyrocketing competition. Fragmented data sources, inconsistent formats, and incomplete pipelines prevent businesses from truly capitalizing on valuable analytics and insights.

A robust data pipeline acts as the critical bridge, enabling comprehensive data collection and transforming it into actionable intelligence that drives growth and fosters innovation. The increasing recognition of this value is reflected in the data pipeline industry, which according to global market research, reached a valuation of USD 6.81 billion in 2022 and is projected to experience annual growth exceeding 22%.

Companies that invest in modern data pipelines can expect substantial returns, including a measurable improvement in decision-making, accelerated innovation, and a rise in overall business performance.

In this blog, we will discuss essential strategies for evaluating your data readiness and constructing a robust pipeline to maximize the potential of your information assets.

6 best practices for building robust data pipelines

For businesses relying on data-driven strategies, a well-designed data pipeline is the key to unlock growth. Here are six best practices to ensure your data pipeline design is efficient, reliable, and ready to support your business needs:

1. Focus on business value with a data product mindset

Data teams often prioritize technical infrastructure. However, they should also focus on addressing business objectives and delivering value. Data products are assets designed to improve decision-making, processes, and insights. A data product approach ensures data engineers understand how their work will be used before building solutions.

It involves determining what end users need from the data, how they will use it, and what answers or insights they expect. Developing data products also requires collaborating closely with business stakeholders who provide and analyze data.

The goal is to treat data like an actual product being created for customers. By starting with the end in mind regarding value for data users, solutions can be intentionally built around meeting business needs rather than just technical factors. This shift in perspective emphasizes the strategic importance of data.

Learn how transforming data into packaged "data products" can help businesses maximize the value of their data and drive better decision-making.

2. Turn raw data into actionable intelligence with an intentional processing approach

The processing plan outlines the steps and transformations that will be applied to the data as it moves through your data pipeline. Consider these questions to layout an effective processing plan:

• What is the goal of your data pipelining and what type of insights or results do you want to generate from the data? This will determine how much processing is needed.
• What state is the data entering the pipeline in? Raw and unstructured data may require more processing than clean structured data.
• What tools or techniques will you use at each step to transform the data - things like filtering, aggregation, normalization etc.
• Which specific fields or subsets of data are most important and valuable for your needs? You may not need to process all the data.
• Is any redundant or unnecessary data present that needs to be removed through things like deduplication? Removing such data can streamline your pipeline and improve efficiency.

   

3. Remove scalability bottlenecks to support business growth

Beyond simply adding hardware resources, true scalability requires intelligent workload management systems that can dynamically distribute extraction, transformation and loading tasks across available infrastructure.

This distributed computing approach allows pipelines to seamlessly handle surges in throughput without compromising SLAs.

Pipeline operators must also optimize each stage of the ETL pipeline process through careful analysis and experimentation.

Techniques like parallelization and data streaming can significantly accelerate extraction and data transformation stages.

Even marginal performance gains, such as trimming seconds off runtimes, provide a strategic advantage for data-driven organizations. Whether enabling real-time business decisions or powerful data analytics at scale, speed remains critical as the data volume and velocity grows exponentially.

4. Welcome change with automated maintenance

Reliable pipelines are key to powering business intelligence applications that drive data-driven decisions. Maintaining data pipelines involves embedding maintenance and troubleshooting as standard practices to support scalability and adaptability. The following strategies can help you achieve this:

• Treat maintenance and troubleshooting as standard practices, not exceptions.
• Automate common, repetitive, and complex coordination tasks for data pipeline maintenance.
• Leverage automation for processes like pause/resume, retry failures, incremental updates, and pipeline rollbacks.
• Empower human intervention to resolve issues that automation cannot handle.
• Adopt automation and standardization to ensure maintainable and scalable data pipelines.

5. Enhance agility and efficiency in data processing with component-based approach

By developing a library of pre-validated, modular data processing components, organizations can create a flexible and adaptable infrastructure for handling diverse data streams.

This approach allows data teams to quickly assemble and modify data pipelines without extensive redevelopment, leading to several business benefits:

• Reduced time-to-market: With ready-to-use components, new data initiatives can be launched faster, allowing businesses to capitalize on opportunities more quickly.
• Cost efficiency: By reusing existing components, organizations can minimize redundant development efforts, leading to lower overall costs.
• Empowered teams: Front-line data professionals can configure and adapt pipelines without deep technical expertise, promoting agility and innovation.
• Risk mitigation: Standardized components reduce the likelihood of errors and inconsistencies, minimizing data-related risks.
• Faster problem resolution: When issues arise, modular systems allow for easier identification and resolution of problems.

6. Choose the right data pipeline tool

Building effective data pipelines requires selecting tools that align with your needs, architecture, and data governance for compliant and accountable data flows.

Businesses seeking an efficient and seamless approach can consider a higher-level platform like Databricks Delta Live Tables. DLT simplifies common pipeline development and management challenges that a data engineer often faces.

With DLT, you can build pipelines declaratively rather than through manual coding. Automated testing and monitoring help ensure data quality too. It also features tools to support data governance, allowing you to catalog assets, audit data usage, and enforce access controls throughout the pipeline lifecycle.

Source: Databricks

By reducing complexity, DLT makes reliable pipelines more achievable for businesses without armies of data science experts.

What’s Next?

Building a robust data pipeline requires careful planning and iterative improvement to continuously drive business value. As needs evolve, so must approaches to data management.

For the next steps, consider partnering with experts at Altudo. As a certified Databricks consulting partner, our team of engineers are highly skilled in developing sophisticated pipelines and analytics using Databricks technologies. Our extensive experience across industries ensures solutions are tailored precisely for your unique environment and strategic objectives.

Whether you need help designing scalable data architectures, migrating to the cloud, or just seeking to accelerate insights, contact Altudo today. We will empower you to develop a future-proof data infrastructure.