Our client is a leading retailer based out of the US. They offer leasing services for key items like furniture, appliances, and electronics through online/offline storefronts. They have around 2000+ offline storefronts across the country.
The client had a legacy middleware built on Java & on-premises Informatica jobs. These integration points served as a point-to-point interface that connect HRMS (ADP) & identity access management tools (Okta).
The employee datasets from HRMS are ingested in PostgreSQL database through Informatica jobs. Further, these employee datasets are synchronized with Okta through legacy middleware application (Java-based, Monolithic architecture).
They were looking for an integration partner who can collaborate and reengineer their integration points with cloud solutions.
Our integration experts embarked on and analyzed the ins & outs of the client’s existing integration points that synchronize the HRMS & IAM tool. Based on the business impact, organizational IT landscape & unique business needs, we proposed the modernization of legacy middleware & Informatica jobs with AWS Lambda solutions.
Eliminating the on-premises Informatica jobs and Java-based applications was paramount in this engagement. We reengineered their integration points as two segments.
Our experts kick-started the transformation by replacing their on-premises Informatica jobs with AWS Lambda service (Data ingestion Lambda). This integration service extracts the employee datasets from HRMS and ingests them in PostgreSQL transactional database at regular intervals. (For instance, at a cycle of 15 minutes).
Successively, to synchronize employee datasets in PostgreSQL with Okta, we replaced their legacy middleware(Java-based) with another AWS Lambda service(Data synchronization Lambda). We employed AWS Eventbridge triggers for periodical synchronization of datasets (15 minutes cycle).
For efficient performance, we migrated these datasets as batches to Okta. Also, in case of fallback, we built a separate process for incremental migration of leftover datasets.
Upon successfully establishing cloud-based integration solutions, we ran a unit testing framework using JEST. Based on this testing framework, we also recorded the preproduction & postproduction checklist for forthcoming validations.
On top of the above transformations, we adopted best-industry practices for multi-level logging, employee data masking, and alert notifications in this implementation.
While wrapping up, our team rendered immense postproduction support and fixed the minor real-time glitches on time to ensure continual operations.
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