Smart Data Pipelines: Tools,
Techniques, and Key Concepts
How data pipelines become smart and why savvy data engineers use smart data pipelines
What Is a Data Pipeline?
A data pipeline is the series of steps that allow data from one system to move to and become useful in another system, particularly analytics, data science, or AI and machine learning systems. At a high level, a data pipeline works by pulling data from the source, applying rules for transformation and processing, then pushing data to its destination.
What is the Purpose of a Data Pipeline?
There’s a lot of data out there. Each person creates 2.5 quintillion bytes of data per day according to current estimates, and there are 7.8 billion people in the world. Data pipelines transform raw data into data ready for analytics, applications, machine learning and AI systems. They keep data flowing to solve problems, inform decisions, and, let’s face it, make our lives more convenient.
Data pipelines are used to:
- Deliver sales data to sales and marketing for customer 360
- Link a global network of scientists and doctors to speed drug discovery
- Recommend financial services to help a small business owner thrive
- Track COVID-19 cases and inform community health decisions
- Combine diverse sensor data with AI for predictive maintenance
With so much work to do, data pipelines can get pretty complicated pretty fast.
The 2020 global pandemic made it abundantly clear that companies have to be able to respond to changing conditions quickly. The StreamSets data engineering platform is dedicated to building the smart data pipelines needed to power DataOps across hybrid and multi-cloud architectures. You can build your first data pipeline with StreamSets for free.