Desafio proposto pela IGTI em seu bootcamp de Cloud Data Engineer

Overview

Desafio Modulo 4 - Cloud Data Engineer Bootcamp - IGTI

Objetivos

  • Criar infraestrutura como código
  • Utuilizando um cluster Kubernetes na Azure
    • Ingestão dos dados do Enade 2017 com python para o datalake na Azure
    • Transformar os dados da camada bronze para camada silver usando delta format
    • Enrriquecer os dados da camada silver para camada gold usando delta format
  • Utilizar Azure Synapse Serveless SQL Poll para servir os dados

Arquitetura

arquitetura

Passos

Criar infra

source infra/00-variables

bash infra/01-create-rg.sh

bash infra/02-create-cluster-k8s.sh

bash infra/03-create-lake.sh

bash infra/04-create-synapse.sh

bash infra/05-access-assignments.sh

Preparar k8s

Baixar kubeconfig file

bash infra/02-get-kubeconfig.sh

Para facilitar os comandos usar um alias

alias k=kubectl

Criar namespace

k create namespace processing
k create namespace ingestion

Criar Service Account e Role Bing

k apply -f k8s/crb-spark.yaml

Criar secrets

k create secret generic azure-service-account --from-env-file=.env --namespace processing
k create secret generic azure-service-account --from-env-file=.env --namespace ingestion

Intalar Spark Operator

helm repo add spark-operator https://googlecloudplatform.github.io/spark-on-k8s-operator

helm repo update

helm install spark spark-operator/spark-operator --set image.tag=v1beta2-1.2.3-3.1.1 --namespace processing

Ingestion app

Ingestion Image

docker build ingestion -f ingestion/Dockerfile -t otaciliopsf/cde-bootcamp:desafio-mod4-ingestion --network=host

docker push otaciliopsf/cde-bootcamp:desafio-mod4-ingestion

Apply ingestion job

k8s/ingestion-job.yaml k apply -f k8s/ingestion-job.yaml ">
# primeiro mudar o nome unico do pod
cat k8s/ingestion-job.yaml |\
python3 -c "import sys,yaml,uuid;y=yaml.safe_load(sys.stdin);y['metadata']['name']=y['metadata']['name'][:-8]+str(uuid.uuid4())[:8];print(yaml.dump(y))"\
> k8s/ingestion-job.yaml

k apply -f k8s/ingestion-job.yaml

Logs

ING_POD_NAME=$(cat k8s/ingestion-job.yaml |\
python3 -c "import sys,yaml,uuid;y=yaml.safe_load(sys.stdin);print(y['metadata']['name'])")

k logs $ING_POD_NAME -n ingestion --follow

Spark

Criar Job Image

docker build spark -f spark/Dockerfile -t otaciliopsf/cde-bootcamp:desafio-mod4

docker push otaciliopsf/cde-bootcamp:desafio-mod4

Apply job

k8s/spark-job.yaml k apply -f k8s/spark-job.yaml ">
# primeiro muda o nome unico da Spark Application
cat k8s/spark-job.yaml |\
python3 -c "import sys,yaml,uuid;y=yaml.safe_load(sys.stdin);y['metadata']['name']=y['metadata']['name'][:-8]+str(uuid.uuid4())[:8];print(yaml.dump(y))"\
> k8s/spark-job.yaml

k apply -f k8s/spark-job.yaml

logs

SPARK_APP_NAME=$(cat k8s/spark-job.yaml |\
python3 -c "import sys,yaml,uuid;y=yaml.safe_load(sys.stdin);print(y['metadata']['name'])")'-driver'

k logs $SPARK_APP_NAME -n processing --follow

Azure Synapse Serveless SQL Poll

Acessar o Synapse workspace através do link gerado

bash infra/04-get-workspace-url.sh

Para começar a usar siga os passos

steps-synapse

Rodar o conteudo do script create-synapse-view.sql no Synapse workspace para criar a view da tabela no lake

Pronto, o Synapse esta pronto para receber as querys.

Limpando os recursos

bash infra/99-delete-service-principal.sh

bash infra/99-delete-rg.sh

Conclusão

Seguindo os passos citados é possivel realizar querys direto na camada gold do delta lake utilizando o Synapse

Owner
Otacilio Filho
Data Engineer Azure | Python | Spark | Databricks
Otacilio Filho
A collection of robust and fast processing tools for parsing and analyzing web archive data.

ChatNoir Resiliparse A collection of robust and fast processing tools for parsing and analyzing web archive data. Resiliparse is part of the ChatNoir

ChatNoir 24 Nov 29, 2022
Hangar is version control for tensor data. Commit, branch, merge, revert, and collaborate in the data-defined software era.

Overview docs tests package Hangar is version control for tensor data. Commit, branch, merge, revert, and collaborate in the data-defined software era

Tensorwerk 193 Nov 29, 2022
Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python.

Fast Laplacian Eigenmaps in python Open-source Laplacian Eigenmaps for dimensionality reduction of large data in python. Comes with an wrapper for NMS

17 Jul 09, 2022
PipeChain is a utility library for creating functional pipelines.

PipeChain Motivation PipeChain is a utility library for creating functional pipelines. Let's start with a motivating example. We have a list of Austra

Michael Milton 2 Aug 07, 2022
Gathering data of likes on Tinder within the past 7 days

tinder_likes_data Gathering data of Likes Sent on Tinder within the past 7 days. Versions November 25th, 2021 - Functionality to get the name and age

Alex Carter 12 Jan 05, 2023
Very basic but functional Kakuro solver written in Python.

kakuro.py Very basic but functional Kakuro solver written in Python. It uses a reduction to exact set cover and Ali Assaf's elegant implementation of

Louis Abraham 4 Jan 15, 2022
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
Toolchest provides APIs for scientific and bioinformatic data analysis.

Toolchest Python Client Toolchest provides APIs for scientific and bioinformatic data analysis. It allows you to abstract away the costliness of runni

Toolchest 11 Jun 30, 2022
Scraping and analysis of leetcode-compensations page.

Leetcode compensations report Scraping and analysis of leetcode-compensations page.

utsav 96 Jan 01, 2023
Python ELT Studio, an application for building ELT (and ETL) data flows.

The Python Extract, Load, Transform Studio is an application for performing ELT (and ETL) tasks. Under the hood the application consists of a two parts.

Schlerp 55 Nov 18, 2022
MeSH2Matrix - A set of Python codes for the generation of biomedical ontologies from the MeSH keywords of the PubMed scholarly publications

A set of Python codes for the generation of biomedical ontologies from the MeSH keywords of the PubMed scholarly publications

SisonkeBiotik 6 Nov 30, 2022
BIGDATA SIMULATION ONE PIECE WORLD CENSUS

ONE PIECE is a Japanese manga of great international success. The story turns inhabited in a fictional world, tells the adventures of a young man whose body gained rubber properties after accidentall

Maycon Cypriano 3 Jun 30, 2022
Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit.

Tablexplore is an application for data analysis and plotting built in Python using the PySide2/Qt toolkit.

Damien Farrell 81 Dec 26, 2022
Describing statistical models in Python using symbolic formulas

Patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design mat

Python for Data 866 Dec 16, 2022
Performance analysis of predictive (alpha) stock factors

Alphalens Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open sour

Quantopian, Inc. 2.5k Jan 09, 2023
Projects that implement various aspects of Data Engineering.

DATAWAREHOUSE ON AWS The purpose of this project is to build a datawarehouse to accomodate data of active user activity for music streaming applicatio

2 Oct 14, 2021
Data Science Environment Setup in single line

datascienv is package that helps your to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

Ashish Patel 55 Dec 16, 2022
Tools for analyzing data collected with a custom unity-based VR for insects.

unityvr Tools for analyzing data collected with a custom unity-based VR for insects. Organization: The unityvr package contains the following submodul

Hannah Haberkern 1 Dec 14, 2022
Program that predicts the NBA mvp based on data from previous years.

NBA MVP Predictor A machine learning model using RandomForest Regression that predicts NBA MVP's using player data. Explore the docs » View Demo · Rep

Muhammad Rabee 1 Jan 21, 2022
PyTorch implementation for NCL (Neighborhood-enrighed Contrastive Learning)

NCL (Neighborhood-enrighed Contrastive Learning) This is the official PyTorch implementation for the paper: Zihan Lin*, Changxin Tian*, Yupeng Hou* Wa

RUCAIBox 73 Jan 03, 2023