bert-etl 0.4.19

A microframework for simple ETL solutions

[](https://bert-etl.readthedocs.io/en/latest/?badge=latest)

# Bert A microframework for simple ETL solutions

## Begin with

Lets begin with an example of loading data from a file-server and than loading it into numpy arrays

` $ virtualenv -p $(which python3) env $ source env/bin/activate $ pip install bert-etl $ pip install librosa # for demo project $ docker run -p 6379:6379 -d redis # bert-etl runs on redis to share data across CPUs $ bert-runner.py -n demo $ PYTHONPATH='.' bert-runner.py -m demo -j sync_sounds -f `

## Release Notes

### 0.3.0

• Added Error Management. When an error occurs, bert-runner will log the error and re-run the job. If the same error happens often enough, the job will be aborted

### 0.2.1

• Added Release Notes

### 0.2.0

• Added Redis Service auto run. Using docker, redis will be pulled and started in the background

• Added Redis Service channels, sometimes you’ll want to run to etl-jobs on the same machine

## Fund Bounty Target Upgrades

Bert provides a boiler plate framework that’ll allow one to write concurrent ETL code using Pythons’ microprocessing module. One function starts the process, piping data into a Redis backend that’ll then be consumed by the next function. The queues are respectfully named for the scope of the function: Work(start) and Done(end) queue. Please consider contributing to Bert Bounty Targets to improve this documentation

https://www.patreon.com/jbcurtin

## Roadmap

• Create configuration file, bert-etl.yaml

• Support conda venv

• Support pyenv venv

• Support dynamodb flush

• Support multipule invocations per AWS account

• Support undeploy AWS Lambda

• Support Bottle functions in AWS Lambda

## Tutorial Roadmap

• Introduce Bert API

• Explain bert.binding

• Explain comm_binder

• Explain work_queue

• Explain done_queue

• Explain ologger

• Explain DEBUG and how turning it off allows for x-concurrent processes

• Show an example on how to load timeseries data, calcualte the mean, and display the final output of the mean

• Expand the example to show how to scale the application implicitly

• Show how to run locally using Redis

• Show how to run locally without Redis, using Dynamodb instead

• Show how to run remotly using AWSLambda and Dynamodb

• Talk about dynamodb and eventual consistency