I am working on a concept project that was built as a single tenant application. We have been tasked with refactoring this concept inot a multi-tenant application. During the onboarding process the company uses an [ELT Tool] to import customer data. As such the [ETL Tool], the defacto ETL utility used to import customer data into a new database instance of MadeUp Co., was initially designed to work with only ONE database technology and was meant to be run ONCE against said database. However, this utility now has the need to function against multiple database technologies and be run in a repeatable fashion. Some inroads have been made to make this utility work with additional databases, but the time has come to expand this prototype to a more scalable solution.


The goal of this document will be to define a meaningful path forward for the [ETL Tool] project so that we can reduce onboarding time for our customers and development time for engineering team with a more scalable solution that will support MANY database technologies and run MANY times. This “final phase” can then be used to work backward and define a phased roll out of the updates required.

Initial Assessment

Workflow Summary

current design

  • A defined exporter class is initialized with a block of Vendor X record IDs.
  • The exporter queries the DB for the records and any related entities related to them.
  • Export Phases
    • The exporter enqueues a delayed job with the individual entity hash.
    • The importer builds the entity and any cascading records
    • The exporter enqueues a delayed job with the individual record hash.
    • The importer builds the record and any cascading records

Technical Debt

  • This project has to be synchronized with Core DB and Model validations (duplication)
  • All transformation are run on a single server and single thread (single threaded operation)
  • Throughput is limited by the server running the task
  • Dividing data into manageable sections is a manual process
  • Each exporter is custom-tailored to the Vendor (does not ascribe to a contract)
  • Extraction and Transformation are handled by a single class (overloaded)
  • Low amount of encapsulation among classes (no unit testing does not ascribe to TDD)
  • Multiple DB connections/technologies to maintain/manage (manageable but not ideal)

Options For Improvement

To assess our ability and resources required to accomplish a refactor of this tool the following options have been provided. Regardless of the technology path chosen the solution must adhere to these basic requirements.


  • Must remove duplication created by keeping ETL project in sync with [Main Project project
  • Must be thread safe to process more transformations in less time
  • Must be testable (unit, integration and end-to-end)
  Option 1 Option 2
Description Kafka Backbone (see diagram) REST API (see diagram)
Pros removes duplication removes duplication
  leverages existing export code leverages existing export code
  creates thread-safe export creates thread-safe export
  asynchronous loaded asynchronous loader
  ERD is owned by [Main Project] ERD is owned by [Main Project]
  Built in redundancy (Kafka) Creates/Exposes a RESTful API
Cons Dev-Ops overhead Bottlenecking dependent on replication
Cost Large Medium

Kafka Diagram

kafka design proposal

REST Diagram

rest design proposal ]


In an effort to keep infrastructure low and reduce complexity we will move forward with the RESTful approach. This solution prooved to increase throughput and significantly reduced development time and code complexity. The clear interface and separation of responibility was key to the solution.