This is a real success story and that’s how you migrate from money guzzlers to open-source money saver stack..g at the results they want to achieve. They made incremental changes in the code to arrive at the same result as the “tools” were producing. Once you compare the results coming in parallel for a month, during which DataQ can keep comparing and notify the differences. Then you could turn off the existing jobs.is continuously providing business value, though it’s a nightmare to maintain.
To make matters worse, it comes with expensive license terms, costs you seven hundred grands a year.
You ask your team how many jobs are there to migrate? They say 439 jobs to be precise.You get a really expert coder in, and she is able to write a code which does the 80-90% of code conversion from the “Tools” to Apache Spark and that’s bit of a saviour. You need the team to validate what converted code is doing is really ok and add the missing code so that you are able to prove that you have a code with which you can really turn-off the current job runs.
DataQ came in handy exactly at that point. Developers were able to make changes to the spark code, by looking at the results they want to achieve. They made incremental changes in the code to arrive at same result as the “tools” were producing. Once you compare the results coming in parallel for a month, during which DataQ can keep comparing and notify the differences. Then you could turn off the existing jobs.
This is a real success story and thats how you migrate from money guzzlers to open source money saver stack.
