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README.md

Overview

This library is about helping you get your data easily and repeatably into Redshift, and it'll give you a little bit of sugar on top (e.g. convenience around managing files in s3, COPYing and LOADing directly to Redshift). aws_etl_tools won't help you manage the dependencies of complex workflows, and it won't help you schedule jobs on instances or in containers. It WILL help you reduce the code needed to write those jobs, and it'll give you some standardization and auditability. This is especially useful if you are using Redshift to enable querying across lots of different data schemas from lots of different places, and if you are running batch jobs using python3 to get it there.

Perspective

This library is relatively opinionated about how to send data to Redshift but not about where it's coming from. These opinions are what allow for the benefits of standardization. There are sources, and there are destinations.

Destinations: RedshiftTable

This is is an object wrapping the context of where the data is going. Currently, there is only one that makes sense for this library, and it's RedshiftTable. It's (1) the target Redshift database instance, (2) the name of the table, and (3) a uniqueness identifier. Let's go through each of these.

database

The Redshift instance is a database class that wraps the credentials necessary to execute commands. You can instantiate one like this:

from aws_etl_tools.redshift_database import RedshiftDatabase
creds = {'database_name': 'my_db'
         'username': 'admin'
         'password': 'PASSWORD'
         'host': 'amazon.redshift.db.com'
         'port': 5439}
my_db = RedshiftDatabase(creds)

If you're going to be loading the same database a lot, we recommend subclassing this object and adding your own logic. For example, if you have an application that can run against a staging and a production environment, it might be a good idea to add the concept of an environment to your database abstraction. We do this at Shopkeep.

target_table

The name of the table must explicitly include the schema: e.g. public.user_events

upsert_uniqueness_key

The uniqueness identifier is a tuple of the columns in your table that will be used for overwriting data that's already there. In many cases, this would be a composite primary key if Redshift allowed you to have one. For performance reasons, you might want to upsert on something other than your logical primary key based on distribution and sort keys that you have set up. The base upserting logic will use these values in your data to upsert (last-write-wins) to the target table.

Configuration

For a lot of the higher level functionality of this library (e.g. all the cool source -> destination stuff), you'll need to set an S3_BASE_PATH in the config, so aws_etl_tools can handle shuttling the data through S3 on its way to Redshift. The easiest way to do this is to set an environment variable on the instance / container or to do it yourself just before you import the library:

import os
os.environ['AWS_ETL_TOOLS_S3_BASE_PATH']='s3://ye-bucket/this/is/where/we/work'
import aws_etl_tools

You could also set it directly on the config object like this:

from aws_etl_tools import config
config.S3_BASE_PATH = 's3://ye-bucket/this/is/where/we/work'

If you don't do this, you'll get a helpful error message.

Sources

There are several of these which can be found in aws_etl_tools/redshift_ingest/sources.py. Let's dive into some. If you check the code, you'll notice that many of them call others.

from_in_memory

Let's imagine you have a list of tuples of data that you'd like to upsert to Redshift. Here's how you might do this:

source_data = [(5, 'skittles'), (6, 'eminems'), (7, 'snickers')]
destination = RedshiftTable(
    database=my_subclassed_database_object,
    target_table='foods.v1_candy_names',
    upsert_uniqueness_key=('id',)
)
from_in_memory(source_data, destination)

from_in_memory is going to do a bunch of things for you. It will take your list of tuples and write them to a local temp file, upload that file to a location nested under the s3_base_path that you've configured, and then run through the ingestion logic defined on your database object (or the default logic which can be found in ingestors.py)

from_manifest

documentation under construction but the functionality works great

from_s3_file

documentation under construction but the functionality works great

from_s3_path

documentation under construction but the functionality works great

from_local_file

documentation under construction but the functionality works great

from_dataframe

documentation under construction but the functionality works great

from_postgres_query

documentation under construction but the functionality works great

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