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Best practices for integrating Big Data Platform with cloud storage for scalable analytics

Hi everyone,

I’ve recently started using the Syncfusion Big Data Platform in our project, and I’m trying to design a robust architecture that integrates cloud storage to store raw and intermediate data. My goal is to support scalable analytics pipelines, while keeping costs manageable and ensuring data integrity.

Here are a few questions I have—any advice or examples from your experiences would be much appreciated:

  1. What are recommended patterns for ingestion? For example, should I stage data first in cloud storage and then move into Big Data Platform’s processing engines, or ingest directly?

  2. How do I manage schema evolution when data is stored in cloud storage? Are there tools or metadata services that work well with Syncfusion’s platform for tracking versions?

  3. How to handle security: best practices for access controls, encryption (both at rest and in transit), especially for sensitive data in intermediate storage.

  4. Performance concerns: how do I minimize latency when reading from cloud storage in batch or near-real-time jobs? Any caching or partitioning strategies you recommend?

  5. Monitoring and cost monitoring: how to track (or estimate) costs of cloud storage + compute + data transfer so there are no surprise bills?

Would like to hear from someone who has built something similar or has benchmarked Syncfusion Big Data with cloud storage backends. Thanks in advance!


1 Reply
SU Suriya Syncfusion Team October 9, 2025 02:15 PM UTC

Hi Amelia,


Thank you for your continued interest in the Syncfusion Big Data Platform.
We’d like to inform you that Syncfusion has officially discontinued the Big Data Platform and it is no longer maintained or supported.

As part of our product strategy, we are focusing our efforts on modern data visualization, analytics, and integration platforms such as Bold BI and Bold Data Hub, which provide powerful data connectivity, transformation, and analysis capabilities across cloud and on-premises environments.

For users looking to continue building scalable big data and analytics pipelines, we recommend exploring the open-source alternatives that align with modern data lake and processing architectures.

If your goal is to analyze, visualize, or embed data-driven dashboards, we highly encourage transitioning to Bold BI or Bold Data Hub, which provide an integrated, enterprise-ready platform without the operational overhead of maintaining a separate big data cluster.

Thanks,
Suriya


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