Data was to be dumped into a S3 bucket (minio), this created an event and anouther team had built an orchestrator which would do a couple of things but eventually supply an endpoint a reference to a plain/txt file for analysis.
For the Java devs they had to [modify the example camel docs.](https://camel.apache.org/manual/rest-dsl.html) and use the built in jackson library to convert the incoming object to a class. This used the default AWS S3 api to create a stream handle which fed into the OpenNLP docs. .
The Python project first hit a wall in setting up Flask. They followed the instructions and it didn’t work from setup tools. The Java team had just created a new maven project from the Intelij but the same approach didn’t work for the Python team using pycharm. It lost them a couple days, I helped them overcome it.
Then they hit a wall with Boto3, the team expected to stream data but Boto3 only supports downloading, there was also a complexity issue the AWS SDK in Java waa about 20 lines to setup and a single line to call, it was about 50 lines in Python. On the positive side I got to explain what all the config meant in S3.
This caused the team anouther few days of delay because the team knew I used a 350MiB Samsung TV guide to test the robustness and had to go learn about Docker volume mounting and they thought they needed a stateful kubernetes service and I had to explain why that was wrong.
Basically Python threw up a lot of additional complexity and the docs weren’t as helpful as they could have been.
It was a mixture of factors.
Data was to be dumped into a S3 bucket (minio), this created an event and anouther team had built an orchestrator which would do a couple of things but eventually supply an endpoint a reference to a plain/txt file for analysis.
For the Java devs they had to
[modify the example camel docs.](https://camel.apache.org/manual/rest-dsl.html)and use the built in jackson library to convert the incoming object to a class. This used the default AWS S3 api to create a stream handle which fed into the OpenNLP docs. .The Python project first hit a wall in setting up Flask. They followed the instructions and it didn’t work from setup tools. The Java team had just created a new maven project from the Intelij but the same approach didn’t work for the Python team using pycharm. It lost them a couple days, I helped them overcome it.
Then they hit a wall with Boto3, the team expected to stream data but Boto3 only supports downloading, there was also a complexity issue the AWS SDK in Java waa about 20 lines to setup and a single line to call, it was about 50 lines in Python. On the positive side I got to explain what all the config meant in S3.
This caused the team anouther few days of delay because the team knew I used a 350MiB Samsung TV guide to test the robustness and had to go learn about Docker volume mounting and they thought they needed a stateful kubernetes service and I had to explain why that was wrong.
Basically Python threw up a lot of additional complexity and the docs weren’t as helpful as they could have been.