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from fireworks.client import Fireworks | |
import os | |
from dotenv import load_dotenv | |
import random | |
import json | |
from pydantic import BaseModel, Field | |
from enum import Enum | |
class DateTimeCategory(str, Enum): | |
BASIC_OPERATIONS = "Basic Operations" |
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import instructor | |
import base64 | |
import logging | |
import os | |
from openai import AsyncOpenAI, OpenAI | |
import asyncio | |
from pydantic import Field, BaseModel | |
from typing import List | |
from dotenv import find_dotenv, load_dotenv |
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#! /usr/bin/env sh | |
DIR=$(dirname "$0") | |
cd "$DIR" | |
echo $DIR | |
[ -d temp-dco ] && rm -rf temp-dco | |
git clone https://github.com/nehiljain/data-cost-optimizer.git temp-dco | |
cd "$DIR/temp-dco" |
https://pretalx.com/apache-airflow-summit-bay-area-2020/talk/review/Q3WNKPGR7LYYNMZTGLBSJTSC9NXAKBJ7
* Custom code for data source. 1:1 mappings for source to destinations
* ETLs bottleneck, not fast as biz landscape changes are
* Depletion o f trust. Wrong decisions made, bad data in production
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'''This is the extension on the alembic API to facilitate the custom autogenerate script''' | |
import json | |
import logging | |
import os | |
import boto3 | |
from alembic.autogenerate import comparators, renderers | |
from alembic.operations import MigrateOperation, Operations | |
from alembic.operations.ops import CreateTableOp, ModifyTableOps |
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files: | |
"/etc/td-agent/td-agent.conf": | |
owner: root | |
group: root | |
content: | | |
<source> | |
@type tail | |
path /var/log/eb-docker/containers/log.file | |
exclude_path ["/var/log/eb-docker/containers/exception.file"] | |
pos_file /var/log/td-agent/fluentd.log.pos |
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provider "aws" { | |
region = "us-east-1" | |
} | |
resource "aws_s3_bucket" "pipeline_bucket" { | |
bucket = "pipeline-logs" | |
acl = "private" | |
force_destroy = false | |
} |
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from datetime import datetime, timedelta | |
from airflow.models import DAG | |
from airflow.operators.s3_key_sensor import S3KeySensor | |
from airflow.operators.python_operator import PythonOperator | |
from airflow.utils.dates import days_ago | |
schedule = timedelta(minutes=5) | |
args = { | |
'owner': 'airflow', | |
'start_date': days_ago(1), | |
'depends_on_past': False, |
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