☁AWS — Azure — GCP — Huawei Cloud Big Data Pipeline Comparison
Introduction
Hello all, I’m going to tell you the comparison to Big Data Pipeline Architecture comparison for 4 big hyperscalers on Cloud.
Only about 25% of the labor is involved in implementing big data analytics, data science, and machine learning (ML) applications in the actual world after analytics tuning and model training. The preparation of data for analytics and machine learning accounts for about half of the work. The remaining 25% of the work is devoted to ensuring that insights and model conclusions are widely used. Everything is combined by the big data pipeline. It is the line that ML’s magnificent and big wagons run on. Securing a long-term advantage requires optimizing the data flow.
Why do we create a pipeline?
Compared to manual deployments, consistently using deployment pipelines can have the following benefits:
- Increased efficiency, because no manual work is required.
- Increased reliability, because the process is fully automated and repeatable.
- Increased traceability, because you can trace all deployments to changes in code or to input artifacts.
Big Data Pipeline Comparisons
Common Considerations for Big Data Pipelines on the Cloud:
Scalability: Leverage the scalable infrastructure of cloud providers to handle varying data loads.
Managed Services: Explore managed services for simplified administration and maintenance.
Security: Implement security best practices and utilize built-in security features.
Cost Optimization: Optimize costs by using auto-scaling, reserved instances, and monitoring tools.
Integration: Ensure seamless integration with other cloud services and on-premises systems.
Monitoring and Logging: Use cloud-native monitoring and logging services for pipeline visibility and issue resolution.
When implementing big data pipelines on any cloud provider, it’s essential to align the architecture with specific business requirements and leverage the strengths of the chosen cloud platform.
AWS (Amazon Web Services):
— Services:
— Use Amazon S3 for scalable object storage.
— AWS Glue for ETL (Extract, Transform, Load) jobs and data cataloging.
— Amazon EMR for distributed data processing using frameworks like Apache Spark and Hadoop.
— Amazon Redshift for data warehousing and analytics.
— AWS Lambda for serverless computing in data processing workflows and much more…
Azure (Microsoft Azure):
— Services:
— Azure Blob Storage for scalable object storage.
— Azure Data Factory for ETL and data integration.
— Azure Databricks for collaborative Apache Spark-based analytics.
— Azure Synapse Analytics (formerly SQL Data Warehouse) for data warehousing.
— Azure Stream Analytics for real-time stream processing and much more..
GCP (Google Cloud Platform):
— Services:
— Google Cloud Storage for scalable and durable object storage.
— BigQuery for serverless, highly scalable analytics.
— Cloud Dataprep for data preparation and cleaning.
— Cloud Dataflow for both batch and stream processing.
— Cloud Composer for workflow orchestration using Apache Airflow and much more
Huawei Cloud:
— Services:
— Elastic Cloud Server (ECS) for scalable virtual machines.
— Object Storage Service (OBS) for object storage needs.
— Data Lake Service for big data storage and analytics.
— Cloud Stream Service for real-time data streaming.
— ModelArts for AI and machine learning capabilities and much more
Conclusion
This article shows the architectural comparison between 4 different hyperscalers on big data.
If you have any thoughts or suggestions please feel free to comment or if you want, you can reach me at guvezhakan@gmail.com, I will try to get back to you as soon as I can.
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**Note: GCP — AWS — Azure pipeline images are taken by ml4devs.com address.
