​Data Pipelines.

Accelerate your analytics - free up your time.

Our data pipelines enable your business to move data seamlessly and automatically between different systems within your organization.
Only robust end-to-end data pipelines can properly equip you to source, collect, manage, analyze, and effectively use data so you can generate new market opportunities and deliver cost-saving business operations. 

ELT (Extract, Load and Transform) systems are a kind of data pipeline in that they move data from a source, transform the data, and then load the data into a destination. But ELT is typically just a sub-process. Depending on the nature of the pipeline, ELT may be automated or may not be included at all. On the other hand, a data pipeline is broader in that it is the entire process involved in transporting data from one location to another.

What is a data pipeline?

​A data pipeline is a series of data processing steps that moves data from different sources to a destination. What happens to the data in that process depends upon the business use case and the destination itself. A data pipeline may be a simple process of data extraction and loading, or, it may be designed to handle data in a more advanced manner, such as applying an algorithm to a dataset.

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Sources
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Extract data from different source systems which includes databases, applications or files. The data is loaded in near-real time or in scheduled batches. Data ingestion includes Change Data Capture.

Destination

A destination is typically a data storage - such as a data warehouse, a database, or a data lake, or it may be any kind of application.

Transformation

A Data Pipeline may include transformation steps. Transformations involve cleaning and standardising data as well as manipulations such as filtering, joining aggregating, and grouping.

Orchestration

Orchestration involves sequencing and dependency management of processes. Workflow dependencies can be of technical nature or business-oriented.

Monitoring

Data pipelines need to be monitored to ensure data integrity by sending notifications or data alerts.  On the one hand data quality needs to be tested, on the other hand server health requires regular checks.