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A microframework for simple ETL solutions

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# Bert A microframework for simple ETL solutions.

## Architecture

At its core, bert-etl uses Dynamodb Streams to communicate between lambda functions. bert-etl.yaml provides control on how the initial lambda function is called, either by periodic events, sns topics, or s3 bucket (planned)events. Passing an event to bert-etl is straight forward from zappa or a generic AWS lambda function you’ve hooked up to API Gateway.

At this moment in time, there are no plans to attach API Gateway to bert-etl.yaml because there is already great software(like zappa) that does this.

## Warning: aws-lambda deploy target still considered beta

bert-etl ships with a deploy target to aws-lambda. This feature isn’t very well documented yet, and has quite a bit of work to de done so it may function more consistently. Be aware that aws-lambda is a product ran and controlled by AWS. If you incure charges using bert-etl while utilizing aws-lambda, you may not consider us responsible. bert-etl is offered under MIT license which includes a Use at your own risk clause.

## 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 $ -n demo $ PYTHONPATH='.' -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

## 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

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