Data pipeline: streamline your data analytics
Research shows that data science teams spend a lot of time preparing and transforming data before the actual data analysis. This is a required step to improve the quality of the data. But this step is often complex, labour intensive and time consuming. The good news: there are several ways to improve this.
Breaking down the steps in data analysis
Most data analysis starts with retrieving data from one or more data sources. Usually this data has to be formatted, cleaned or edited in other ways before it can be used in analysis or data science models.
In local or ad-hoc setups manual edits or using scripts provide a solution. When data analytics (including AI) has to be run in a production environment, manual solutions don’t work. Developing well tested Extract Transform Load (ETL) scripts takes time and is expensive. It also uses up a lot of capacity from data engineers.
Therefore many organizations run into challenges such as:
- How can I use the valuable time of my data engineers as efficiently as possible?
- How can I shorten the delivery times of data analysis and machine learning models?
- How can I deploy local analytical and AI models to a production environment in a repeatable and robust way?
A data pipeline as an accelerator
Luminis is often consulted to answer these kind of questions about data analytics. A solution that we often bring to the table is setting up a data pipeline. This is a solution that automates the overall data analytics process as much as possible. It enables organizations to automate the data engineering process, but also to deploy models from local or test environments to production environments.
The Luminis data pipeline solution consists of a cloud infrastructure with several cloud services. These provide the foundation for a flexible and scalable infrastructure. Using a standard configuration gives you a significantly shorter period to get your data analytics project off the ground. The total time to setup a production environment changes from 1 month to 1 day. Complex machine learning projects can be executed in 3 months instead of 9 months, from idea to working production environment.
More information
Sounds too good to be true? Are you curious whether a data pipeline can accelerate your data analysis? Please contact us and we will be happy to share our experience.