Implementing a new data scaling system isn’t something that happens overnight. However, the differences between an efficient deployment and an inefficient deployment can be profound and can have a significant impact on the company’s bottom line.
While the exact timeline will vary depending on the client, the process can usually be divided into a few distinct steps, including the discovery sessions, software deployment and setup, re-designing and redeveloping the ETL pipeline, testing, and — finally — hypercare.
The “Discovery Session” is the first step of the process where the ETL service provider and data engineers learn as much as they possibly can about their prospective partner and prepare for the future of the project. This typically involves a variety of different tasks, including defining the migration plan, identifying limitations, and analyzing various feasibilities. In most cases, the discovery session can be managed completely remotely and will take about two days.
- Software Deployment and Setting Up
Once the discovery session has been completed, the next step in the process will be to set up the software and actually deploy it. The dynamics involved during this step of the process will depend on what (if any) data scaling and management tools have already been implemented. The ETL will also be configured and customized, as needed.
The professional service team at Visual Flow can provide assistance with installing Kubernetes, Apache Spark, and Argo workflows (workflow orchestration engine for Kubernetes) either on-premises or on cloud platforms such as Azure or AWS.
- Re-Design and Develop ETL Pipelines
As soon as the basic infrastructure has been established, the company will then have the opportunity to develop (and subsequently redevelop) pipelines that can help address their specific data scaling needs.
It will be important to define several different components of the project, including staging requirements, integration approaches, transformations, ETL jobs, and general pipeline structures. Typically, this part of the process takes about two weeks.
Next, the Visual flow team will carefully test every component of the data infrastructure, including all pipelines. If the tests reveal any sort of errors or inefficiencies, the team will then carefully adjust and test the pipelines again.
Eventually, the end goal is to have ETL jobs and pipelines that do not have any interruptions and can process data in just minutes.
Finally, the “hypercare” component of the process is the stage where the ETL data structure will continue to be carefully managed. This stage is crucial for successful data scaling for any e-learning company
This step will usually require making minor changes, providing ongoing support, and carefully documenting all ETL best practices.