What is a Data Pipeline?

A data pipeline is a means of moving data from one or more sources to a destination (such as a data warehouse or an excel spreadsheet) while simultaneously optimising and transforming the data.

Typically, this process includes loading raw data into an interim staging area, where is it cleaned and transformed, before uploading it to the destination tables.

The pipeline constitutes the series of steps involved in aggregating, organising, cleaning and moving data. This can of course be done manually but modern data pipelines aim to automate many of the manual steps involved in transforming data loads.

Common Steps involved in a Data Pipeline

1. Data Ingestion: The process of gathering data from various sources (such as databases, business applications, APIs, IoT devices etc)
2. Data Integration: The process of bringing together data from multiple sources to provide a complete and accurate dataset for business intelligence (BI), data analysis, use by other applications and business processes. Data is transformed and processed the to achieve the desired structure
3. Data quality: Clean and apply data quality rules
4. Data Storage: Save the transformed data into your preferred target storage solution

Benefits of Setting up an Automated Data Pipeline

• Get the Complete Picture. Source systems often have different methods of processing and storing data than target systems. So data pipelines are used to automate the process of extracting data from many disparate source systems, transforming, combining and validating that data, and loading it into your target repository. In this way, building data pipelines breaks down data silos and creates a single, complete picture.
• Increased Efficiency. While manual transformation of data may be manageable for small datasets, as data needs scale it can become an expensive overhead and is increasingly prone to introducing errors. Because Data pipelines automate data workflows, they can reduce manual effort and increase overall efficiency in data processing. This ensures a seamless and error-free conversion of raw data into trusted actionable insights, as the data arrives in a state that can be more easily analysed.
• Promoting data governance. Data pipelines ensure that data is handled in a way that complies with internal policies and external regulations. For example, in insurance, data pipelines are used to manage sensitive policyholder data during claim processing. They are set up to ensure compliance with regulations like the European Union’s General Data Protection Regulation (GDPR), safeguarding data and building trust with policyholders.
• Data quality improvements. Embedded data cleansing and transformation processes enhance data quality and ensure accuracy for analysis and decision-making. By maintaining high data quality standards, organisations can rely on trustworthy insights to drive decision making.
• Cost-Effectiveness. Data Pipelines can help to optimise resource utilisation by minimising costs associated with manual data handling and processing. Through reducing the time and effort required for data operations, organisations can allocate valuable resources more efficiently.

Data Pipeline Disadvantages

That said, there are also a number of potential disadvantages to consider