Building Robust Data Pipelines: 9 Fundamentals and Best Practices to Follow

Data is a valuable resource, especially in the world of business. A McKinsey survey found that companies that use customer analytics intensively are 19 times higher to achieve above-average profitability. But with the sheer amount of data continually increasing, how can a business make sense of it?

The answer? Robust data pipelines.

What is a Data Pipeline?

A data pipeline is a series of processing steps that move data from its source to its destination. The various steps along the pipeline involve transforming, optimizing, cleaning, filtering, integrating, and aggregating the data. The end result is data that is ready to be analyzed.

Each step of a data pipeline ideally delivers an output that, in turn, becomes the input for the next step. This means each task depends on the successful completion of the previous one.

Data Pipeline Use Cases

Here are just a few examples of the goals you can achieve with a robust data pipeline:

Data Prep for Visualization

Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state. Many organizations use data visualization to identify patterns or consumer trends and communicate findings to stakeholders better.

Data Integration

A data pipeline can be used to gather data from various disparate sources in one data store. This makes it easier to compare and contrast information and provides organizations with a unified view of their data.

Machine Learning

Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible.

Data Quality

When using a data pipeline, data consistency, quality, and reliability are often greatly improved. This is due to the various cleaning processes data goes through as it moves through the pipeline. Checking the data quality before and after the cleansing steps is critical. Higher quality data helps organizations to draw more accurate conclusions and make better-informed business decisions.

McKinsey infographic chart on how the lack of data quality and availability can cause employees to spend a significant amount of time on non-value added tasks.

Components of a Data Pipeline

Data pipelines are constructed using multiple components. In order to build a robust pipeline, it’s important to understand each element and how they work together.

Data Sources

The first element of a data pipeline is where the data originates from. Any system that generates data for your business is classified as a data source.

Sources can include analytics data regarding user behavior, transactional data from ecommerce websites, and third-party data from other organizations. It’s worth noting that a data pipeline may have more than one data source.

Data Collection

Data collection, sometimes called data ingestion, is the process of moving data into the data pipeline. Ingestion tools are connected to various data sources. Then they collect data through a push mechanism, a replication engine, a webhook, an API call, or other methods.

A batch-processing model will collect data at set intervals, while a stream-processing model will ingest data almost instantaneously as it is created.

Data Processing

Data processing involves transforming data into a usable state. How much data processing that occurs will depend on the data’s state when ingested and how different the format is from the desired end state.

Most data processing tasks are completed using ETL (Extract, Transform, Load) or ELT (Extract, Load Transform) processes. Data classification, standardization, normalization, verification, validation, and deduplication are all examples of data processing tasks.

Data Storage

The data storage component of a pipeline provides secure, scalable storage for the data. Various data storage methods are available, including data warehouses for structured data or data lakes for unstructured, semi-structured, and structured data.

Data Consumption

The data consumption layer consists of tools that can deliver and integrate data from the data stores to be used for analytics.

Data Security and Governance

A data pipeline’s security and governance layer is responsible for safeguarding data throughout the pipeline and its systems, using methods such as auditing mechanisms, network security, access control, encryption, and usage monitoring.

The security layer will keep track of operations across the other pipeline layers, creating an audit trail that you can refer back to if necessary. All the data pipeline components should natively integrate with the security and governance layer to facilitate this.

9 Fundamentals and Best Practices for Building a Robust Data Pipeline

There are several key factors that you should consider during the construction of any data pipeline.

Set Goals

Determining what value you hope to extract from the data pipeline before developing it is important. Goals will help guide the pipeline’s construction and inform your decisions as you progress. You should also determine how you will measure its success in achieving them.

Choose the Right Data Sources

Once you’ve established what you want your data pipeline to achieve, you can start assessing which data sources will help you to meet your objectives.

You should consider whether you’ll use a single data source or draw data from multiple points of origin. It’s also important to consider factors such as what format the data will come in and how you’ll connect to the data sources.

Set a Data Ingestion Strategy

The next step is deciding how the data will be ingested into your pipeline. You can collect data in various ways. But your ingestion strategy usually consists of either a full refresh or some type of incremental update like Change Data Capture.

Have a Data Processing Plan

A data processing plan will dictate how your data is transformed as it moves through your data pipeline. Some data pipelines will feature more processing than others, depending on the goal of the pipeline, and the state the data enters the pipeline in.

Understanding how much transformation needs to be done to your data and what tools and methods you will use to carry it out are key factors of a data processing plan.

You should determine which data carries the most value to your organization. Will you use the entire dataset or just subsets of your data? If redundant data needs to be removed, consider how you can achieve this.

Set Up Data Storage

Once the data has been processed, it will need to be securely stored somewhere so that it can fulfill the needs of the business. Various data storage options are available, so you need to decide which option best suits your needs.Suppose a business is looking to build a robust data pipeline, they may also want to consider using dedicated servers to ensure the security and reliability of their data storage

On-premises and cloud storage are both viable options, with various benefits depending on the size and scope of your organization. Similarly, data lakes, data warehouses, and other types of data repositories have different pros and cons that you should consider.

Knowing what format your data will be stored in will help inform your choice of data storage solution.

Plan Your Data Workflow

Once you have determined the various components of your data pipeline, you’ll need to figure out the appropriate sequence of processes your data will go through.

You’ll need to pay special attention to which tasks depend on other tasks being completed first and sequence them accordingly. Tasks that can run in parallel may help to streamline the workflow.

Streamlining data workflows can help improve efficiency, just as a workflow management tool can help improve the overall productivity of an organization. Combining these tools can help create an optimal workflow across the board.

Implement a Strong Data Governance Framework

The data governance framework will help maintain the health of your data pipeline, monitoring aspects such as network congestion and latency to ensure data integrity and avoid system failures.

You’ll need to determine exactly what needs to be monitored and who will be in charge of monitoring it. They may or may not require a certification for data engineer, depending on the complexity of your pipeline!

Tools are available to help automate the process, so you should research these and see if any are applicable. A good data governance framework will often minimize manual processes to avoid latency.

It’s also important to consider how your organization will ensure data security and what measures will be put in place to mitigate data attacks.

Plan the Data Consumption Layer

When planning your data pipeline, the final consideration is how you will ultimately use the data.You should determine how the data will be processed, transformed, and delivered to applications or systems downstream.

Flexibility and Scalability Are Key

When planning your data pipeline, it’s important to plan for the long term as well as the short term. You should take steps to future-proof your data pipeline, ensuring it can scale to meet the needs of your business as they change, and the volume of data you’re dealing with changes accordingly.

Building Robust Data Pipelines

A robust data pipeline is essential to extract the maximum amount of value from your data. It will give you faster answers, allow team members to work independently, and means your AI models will be more proactive.

Properly planning your data pipeline will help you to select the right components for it and ensure that it works to meet the needs of your business.

Choose the right data sources, and determine how your pipeline will ingest data from them. Think carefully about how the data needs to be processed, where it will be stored, and how it’ll be consumed. You also want to make sure that there aren’t any issues with the data.

Finally, ensure that a strong data governance framework is in place to protect your data and your organization.

Follow these steps, and you’ll be able to build a robust data pipeline that will perfectly serve your organization, encourage collaboration amongst your team, and help you make the most of your data.