An ETL Pipeline is a set of processes that include the extraction of data from a source, its transformation, and then loading into the target data warehouse, data mart or database for data analysis or any other purpose. The main purpose is to centralize data across the company and present the ‘single version of the truth’.
An ETL pipeline typically works in batches, which means that the data is moved in one big chunk at a particular time to the destination system. ETL remains relevant and is useful in situations where:
On the other hand, Data Pipeline is a more generic term that describes any process that moves data from one platform to another, and includes ETL pipeline as a subset. The main purpose of a data pipeline is to ensure that all these steps occur consistently to all data. The data may or may not be transformed.
Data pipelines can be used to accurately fetch and analyse data insights. The technology is useful for individuals who store and rely on multiple, huge amounts of siloed data sources, require real-time data analysis, and have their data stored on the cloud. For example, a data pipeline platform can be used to deliver predictive analytics to identify future trends.
A data pipeline is run as a real-time process (such that every event is managed as it happens) instead of in batches. Data pipelines can stream both transformed and untransformed data, allowing for continuous updates instead of batching at specific intervals. Moreover, the data pipeline doesn’t have to conclude in loading data to a data warehouse, but can load data to any number of target systems, or activate another process or workflow, for instance.
It supports the needs and unique requirements of data consumers (analysts, engineers etc) while promoting collaboration, reusability and extensibility of data pipelines, knowledge sharing on data and data preparation.
ConnectIQ user experience offers simplicity and visual transformations for fast, easy data preparation by your teams without the need to write a single line of code. Through data-centric security, and governance features it enables reliable, automated, and secure data pipelines for consistent data flows.
With DataOps capabilities, it can process high volumes of data as well as concurrent data pipelines, support for both time-based and data-driven data pipeline execution with ability to re-run the failed ones with complete audit trail and version control. In terms of data preparation, the platform supports data cleansing, blending and enrichment, advanced data transformations as well sophisticated ways to group, aggregate, and slice-and-dice data.
- The data source and the warehouse are different and require different data types
- The data sets are small to moderate but involve computer-intensive transformations
- The data is structured
On the other hand, Data Pipeline is a more generic term that describes any process that moves data from one platform to another, and includes ETL pipeline as a subset. The main purpose of a data pipeline is to ensure that all these steps occur consistently to all data. The data may or may not be transformed.
Data pipelines can be used to accurately fetch and analyse data insights. The technology is useful for individuals who store and rely on multiple, huge amounts of siloed data sources, require real-time data analysis, and have their data stored on the cloud. For example, a data pipeline platform can be used to deliver predictive analytics to identify future trends.
A data pipeline is run as a real-time process (such that every event is managed as it happens) instead of in batches. Data pipelines can stream both transformed and untransformed data, allowing for continuous updates instead of batching at specific intervals. Moreover, the data pipeline doesn’t have to conclude in loading data to a data warehouse, but can load data to any number of target systems, or activate another process or workflow, for instance.
Build Enterprise Data Pipelines in Minutes
Validata ConnectIQ is a modern data pipeline platform, with a no-code, self-service environment and an easy-to-use graphical interface for creating and managing your data pipelines end-to-end.It supports the needs and unique requirements of data consumers (analysts, engineers etc) while promoting collaboration, reusability and extensibility of data pipelines, knowledge sharing on data and data preparation.
ConnectIQ user experience offers simplicity and visual transformations for fast, easy data preparation by your teams without the need to write a single line of code. Through data-centric security, and governance features it enables reliable, automated, and secure data pipelines for consistent data flows.
With DataOps capabilities, it can process high volumes of data as well as concurrent data pipelines, support for both time-based and data-driven data pipeline execution with ability to re-run the failed ones with complete audit trail and version control. In terms of data preparation, the platform supports data cleansing, blending and enrichment, advanced data transformations as well sophisticated ways to group, aggregate, and slice-and-dice data.
Benefits:
- Secure: Data governance and data-centric security features that meet the demands of highly regulated industries and ensure data privacy and compliance, in on-premise, cloud and hybrid landscapes.
- Increase ROI and Reduce costs: ConnectIQ accelerates the speed of data preparation and delivery, as well as reducing your data engineering costs.
- Speed and Performance: Enables teams to build and manage end-to-end data pipelines and workflows in just minutes, in a code-free way.
- Flexibility and Scalability: It can scale to your data processing needs, all in an automated way, including out-of-the-box functions ranging from simple transformation to more complex and sophisticated ones.