A deep dive into data pipeline

This article gives you a basic overview of data pipelining, including what it means, how it’s put together, data pipeline tools, and why we need them.

Our digital world churns out gigs of data daily, information that’s essential for governments to function and business to thrive. But managing today's complex data systems needs a systematic approach to handle the entire data lifecycle, whether it is customer interactions, financial market trends or operational metrics. The data pipeline architecture provides this structured framework that breaks down the silos of data sources and destinations.

What is data pipeline

It?is?a?set of?processes that extract data from various sources, transform data into a?usable format, and load it?into a?designated storage location. Data pipelines enable the efficient movement of?data between systems and ensure that the data is?accurate and consistent.

Example uses of Data Pipeline

Organizations employs a data streaming pipeline that transfers change data from on-premise databases to Google Cloud. This enables them to deliver a seamless shopping experience for their customers, whether they shop online or in-store.

Similarly, other Organizations utilizes a streaming data pipeline that moves real time or near real time data related to Google BigQuery. This data is analysed by data scientists who continuously refine the machine learning model that powers the monitoring solution.

In healthcare, this processes can be used to collect and analyse patient data to improve medical research and treatment outcomes.

Financial industry, uses to analyse market data and make more informed investment decisions. This set of processes can also be used in manufacturing to monitor equipment performance and identify potential issues before they become major problems.

Components of Data Pipeline

Data pipeline design patterns

Data pipeline tools

1. Apache Airflow is an open-source tool to programmatically author, schedule, and monitor workflows. It is one of the most robust platforms used by Data Engineers for orchestrating workflows or pipelines.
2. Azure Data Factory is Azure's cloud ETL service for scale-out serverless data integration and data transformation.
3. Google Cloud Dataflow is a fully-managed, scalable data processing service for executing batch, stream, and ETL processing patterns.
4. The StreamSets platform is a cloud-native platform for building, running, and monitoring data pipelines.
5. Stitch is a powerful Extract, Transform, Load (ETL) service, which integrates your various data sources into a central data warehouse.
6. AWS Glue is a serverless data integration service that makes data preparation simpler, faster, and cheaper. User can connect to over 70 diverse data sources, manage data in a centralized data catalogue, and visually create, run, and monitor ETL pipelines to load data into data lakes.

Data pipeline architecture

A data pipeline architecture refers to the design of tools and processes that help transport data between locations for easy access and application to various use cases. These use cases may be for business intelligence, machine learning purposes or for producing application visualizations and dashboards. Data pipeline architecture aims to make the data pipeline process seamless and efficient by designing an architecture that helps improve the functionality and flow of data between various sources and along the pipeline.

Types of data pipeline architecture

Life cycle of Data pipeline

Final Words

According to a McKinsey Global Institute study, data-driven businesses are 23 times more likely to acquire new customers, nine times more likely to keep customers, and 19 times more likely to be profitable. However, data volumes (and sources) are on the rise. As a result, organizations must seek the most efficient way to collect, transform, and extract value from data to stay competitive.

When it comes to data pipeline architecture, think strategically. Take the time to plan and design it carefully – it is worth the effort. It is an upfront investment that pays off big in terms of operational efficiency, scalability, reliability, and decision-making speed.