Big Data Pipelines on AWS, Azure & Google Cloud

Referred Link - https://www.linkedin.com/feed/update/urn:li:activity:6960116635974168576/

The article explains the structure of general #Big #Data pipelines in a very clear and easy to understand way. Recommended to any #Data #Engineer - working in a cloud environment or not.

Navigating the complexities of data pipelines across these platforms unveils a spectrum of unique functionalities and innovations. Each platform excels in key phases: ingestion, data lakes, processing, data warehousing, and presentation.

Here’s a comprehensive guide to get you started:

𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻
🔹 Azure: Azure IoT Hub, Azure Function, Event Hub, Data Factory
🔹 AWS: AWS IoT, Lambda Function, Kinesis Streams/Firehose, Data Pipeline
🔹 GCP: Cloud IoT, Cloud Function, PubSub, Dataflow

𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲
🔹 Azure: Azure Data Lake Store
🔹 AWS: Glacier, S3 Lake Formation
🔹 GCP: Cloud Storage, BigQuery Omni, Preparation & Computation
🔹 Azure: Databricks, Data Explorer, Azure ML

𝗦𝘁𝗿𝗲𝗮𝗺 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀
🔹 AWS: EMR, Glue ETL, Sage Maker Kinesis Analytics
🔹 GCP: DataPrep, DataProc, DataFlow, AutoML, Dataprep by Trifacta

𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴
🔹 Azure: Cosmos DB, Azure SQL, Azure Redis Cache, Data Catalog, Event Hub Synapse Analytics
🔹 AWS: RedShift, RDS, Elastic Search, DynamoDB, Glue Catalog, Kinesis Streams
🔹 GCP: Cloud Datastore, Bigtable, Cloud SQL, BigQuery, Memory Store, Data Catalog, PubSub

𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
🔹 Azure: Azure ML Designer/Studio (EDA), Power BI, Azure Function
🔹 AWS: Athena (EDA), QuickSight, Lambda Function
🔹 GCP: Colab (EDA), Datalab Data Studio, Cloud Function

Each platform tailors its approach to accommodate the entire lifecycle of data, from initial collection to insightful visualizations that drive business strategies.

Whether it’s the comprehensive analytics solutions of Azure, the scalable and customizable nature of AWS, or the real-time, user-friendly interfaces of GCP, the choice depends on your specific needs, budget, and tech stack.