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
This cosmetics generator allows you to generate the new Fortnite cosmetics, Search pak and search cosmetics!

COSMETICS GENERATOR This cosmetics generator allows you to generate the new Fortnite cosmetics, Search pak and search cosmetics! Remember to put the l

ᴅᴊʟᴏʀ3xᴢᴏ 11 Dec 13, 2022
Sample code for Harry's Airflow online trainng course

Sample code for Harry's Airflow online trainng course You can find the videos on youtube or bilibili. I am working on adding below things: the slide p

102 Dec 30, 2022
NFCDS Workshop Beginners Guide Bioinformatics Data Analysis

Genomics Workshop FIXME: overview of workshop Code of Conduct All participants s

Elizabeth Brooks 2 Jun 13, 2022
Probabilistic reasoning and statistical analysis in TensorFlow

TensorFlow Probability TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFl

3.8k Jan 05, 2023
WithPipe is a simple utility for functional piping in Python.

A utility for functional piping in Python that allows you to access any function in any scope as a partial.

Michael Milton 1 Oct 26, 2021
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
A data structure that extends pyspark.sql.DataFrame with metadata information.

MetaFrame A data structure that extends pyspark.sql.DataFrame with metadata info

Invent Analytics 8 Feb 15, 2022
Pipetools enables function composition similar to using Unix pipes.

Pipetools Complete documentation pipetools enables function composition similar to using Unix pipes. It allows forward-composition and piping of arbit

186 Dec 29, 2022
track your GitHub statistics

GitHub-Stalker track your github statistics 👀 features find new followers or unfollowers find who got a star on your project or remove stars find who

Bahadır Araz 34 Nov 18, 2022
BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems.

Vo Cong Thanh 1 Jan 06, 2022
Full automated data pipeline using docker images

Create postgres tables from CSV files This first section is only relate to creating tables from CSV files using postgres container alone. Just one of

1 Nov 21, 2021
A powerful data analysis package based on mathematical step functions. Strongly aligned with pandas.

The leading use-case for the staircase package is for the creation and analysis of step functions. Pretty exciting huh. But don't hit the close button

48 Dec 21, 2022
Reading streams of Twitter data, save them to Kafka, then process with Kafka Stream API and Spark Streaming

Using Streaming Twitter Data with Kafka and Spark Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream

Rustam Zokirov 1 Dec 06, 2021
Generates a simple report about the current Covid-19 cases and deaths in Malaysia

Generates a simple report about the current Covid-19 cases and deaths in Malaysia. Results are delay one day, data provided by the Ministry of Health Malaysia Covid-19 public data.

Yap Khai Chuen 7 Dec 15, 2022
Ejercicios Panda usando Pandas

Readme Below we add configuration details to locally test your application To co

1 Jan 22, 2022
TheMachineScraper 🐱‍👤 is an Information Grabber built for Machine Analysis

TheMachineScraper 🐱‍👤 is a tool made purely for analysing machine data for any reason.

doop 5 Dec 01, 2022
Evaluation of a Monocular Eye Tracking Set-Up

Evaluation of a Monocular Eye Tracking Set-Up As part of my master thesis, I implemented a new state-of-the-art model that is based on the work of Che

Pascal 19 Dec 17, 2022
VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

VHub - An API that permits uploading of vulnerability datasets and return of the serialized data

André Rodrigues 2 Feb 14, 2022
Analysis scripts for QG equations

qg-edgeofchaos Analysis scripts for QG equations FIle/Folder Structure eigensolvers.py - Spectral and finite-difference solvers for Rossby wave eigenf

Norman Cao 2 Sep 27, 2022
A Python module for clustering creators of social media content into networks

sm_content_clustering A Python module for clustering creators of social media content into networks. Currently supports identifying potential networks

72 Dec 30, 2022