7 Critical Components of a Successful Data Pipeline

Technology has advanced exponentially in recent years- data is abundant, and utilizing this data to make imperative decisions has become key to navigate any successful operation. Technology continues to evolve, and new ways to coalesce new rich data sources emerge constantly, but the fundamental elements to get to a reliable flow of data insights are always the same.

Learn what a data pipeline is, and the seven critical components that make up a successful data pipeline.

What is a Data Pipeline?

A data pipeline refers to a system or process in which you move data from its source to a destination, optimized and ready for data insights.

Often a data pipeline consists of a collection of technologies- referred to as a data stack. But it is possible to have a single data pipeline - meaning the entire data pipeline process using a singular technology.

Regardless of the technology used, a data pipeline must contain all the following components to make data useful.

7 Critical Components of a Successful Data Pipeline

1. Ingesting or Extracting Data

Your data can’t live in a silo and be helpful. To make your data useful, the first step is to extract it reliably.

In a successful data pipeline, you must easily be able to pull the data from its original source for further processing or storage. It’s important to note that some data changes continuously, especially when extracted from live sources. So the way that you ingest or extract the data must contain critical information for historical value and comparison- such as when the data was last updated and who updated it.

Data sources can vary from documents, spreadsheets, software, SaaS applications, 3d Files, APIs, NoSQL sources, transactional systems, relation databases, on-premises databases, cloud databases, and more.

2. Warehousing Data

Once you extract data from its original source, you need dedicated storage to deposit the data.

A data warehouse stores incoming data so that it can be made available for use. This process is often referred to as warehousing data, but there are two distinct terms to keep in mind. Sometimes you need a data warehouse or a data lake. A data warehouse is a home for structured filtered data that has already been processed or transformed, and a data lake is a pool of raw data.

3. Transforming Data

It’s not enough to have the data available. It must also be clean and compatible between applications, systems, and types of data.

The data must be processed or transformed to ensure it is ready and easy to use in analysis. Transforming data can be a costly and resource-intensive process. Poorly formatted data will cause delays in data processing.

Data transformation includes data type conversion, filtering, summarizing data, anonymizing data, and more.

4. Enriching Data or Data Blending

When the term is used broadly, data enrichment can refer to data transformation as well. However, increasingly enriching data is referring to a distinct process.

In this context, when we refer to data enrichment and data blending, we mean merging of different data sources such as third-party data with an existing data source. Data enrichment makes the data more meaningful and substantial. You can increase its usability for decision-making.

For example, within the construction, a helpful use case would be to compare data from 3D models from Revit or Procore with excel data containing the unit cost of raw material to get the project’s entire cost for budgeting purposes.

5. Analyzing Data

Whether you are looking for particular insights or exploring data, certain functions must be available to perform descriptive analysis.

You must summarize or describe visible characteristics of a dataset (e.g., number of customers, total revenue.) These insights help you perform inferential data analysis to inform future decisions.

6. Visualizing Data

Visualizations are essential to a business or project's success. Visual data analysis makes data concepts stick and aids decision analysis.

Visualized data makes data easier to understand and detect patterns. By attributing visual elements to complicated data sets through charts, graphs, tables-you make the message clear.

7. Sharing Insights

When it’s time to share insights, you need more than your raw data or analysis. You share your data story.

You must have a platform that makes it easy to share the appropriate context with your audience. Too much detail or not enough context can render data analysis useless. Instead, you must synthesize and communicate the right amount of detail to stakeholders.

Your data story paints a picture of the value these insights provide. In this component of the data pipeline, it’s essential to have tools that provide meaningful context to the data, presented in a way that sticks to facilitate actions from stakeholders. The context that stakeholders derive value from must be obvious to be helpful.

How to Create a Data Pipeline

Conventional Data Pipelines

The technologies used to create data pipelines vary. Conventional data pipelines may even include multiple technologies for each critical component of the pipeline.