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vitorgithub/README.md
  • 🌎 Engenharia de Software & Engenharia de Dados;
  • 🔎 Autodidata, analítico, multidisciplinar e detalhista;
  • 🧐 Crio e desenvolvo documentos autorais teóricos e práticos de estudo;
  • 📖 Minhas referências: (Documentações oficiais, livros e cursos qualificados);

Guia de tecnologias que utilizo para pipelines de dados ETL:

DATA SOURCES Data Ingestion Data Processing Data Storage Workflow scheduling Monitoring data pipelines
SGBD SQL
- MySQL
- Postgres
- MS SQL SERVER
- AWS Aurora

SGBD NoSQL (document)
- MongoDB
- Azure Cosmos DB
- ElasticSearch (data retrieval)

SGBD NoSQL (key-value)
- AWS DynamoDB
- Redis

SGBD NoSQL (wide-column)
- GCP Bigtable
- Apache HBase
- Apache Cassandra

SGBD NoSQL (graph)
- AWS Neptune
- Neo4j

TELEMETRIA
- Application logs
- Device sensors

FORNECEDORES DE DADOS EXTERNOS
- APIs (ex: RESTful, SOAP)
- Web scraping (ex: BeautifulSoup, Scrapy)
Event Streaming
- Apache Kafka (c/ RabbitMQ)
- AWS Kinesis (c/ AWS SNS & SQS)
- Azure Event Hubs

Data Movement
- Apache NiFi
- StreamSets
- Talend
- AWS Glue

Messaging
- RabbitMQ
- AWS SNS & SQS
- Azure Service Bus
Batch Processing:
- Apache Spark
- Apache Flink
- Apache Hive
- Apache Pig


Stream Processing:
- Apache Kafka
- Apache Storm
- AWS Kinesis
"batch" Distributed File Systems
- Hadoop HDFS
- Azure Data Lake Storage

"hybrid" Data Lake (Storage for Pre-processing)
- AWS S3
- Azure Blob Storage Gen2
- GCP Cloud Storage

"hybrid" Data Lake (Storage for Post-processing)
- Delta Lake
- Apache Hudi
- Apache Iceberg

Data Warehouses
- Azure Synapse Analytics
- AWS Redshift
- Snowflake

Data Marts
- Apache Druid
- Apache Pinot
Workflow scheduling
- Apache Airflow
- Azure Data Factory
Monitoring data pipelines
- Datadog
- Prometheus
- Sentry
- Grafana

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