Building an open data pipeline in 2024

The article argues that modern data architectures should decouple storage from compute, using open formats like Apache Iceberg as the foundational layer. This separation allows organizations to select the most appropriate processing engine for specific needs, such as speed for BI reporting, cost-efficiency for batch jobs, or high availability for external customers. By keeping the data layer open and standard, companies avoid vendor lock-in and gain the flexibility to swap out processing tools as technology evolves or requirements change. Building a scalable data stack requires balancing multiple dimensions, including data volume, processing latency, and security governance. Smaller datasets may run on single-node tools, while massive volumes often necessitate distributed systems or GPUs. Furthermore, the architecture must account for data sensitivity, ensuring that raw, personally identifiable information is handled with stricter controls than aggregated metrics. Cost remains a critical constraint, often forcing trade-offs between performance and security, which should be managed by prioritizing requirements based on business value rather than technical preference. This approach is vital for open data because it ensures data remains accessible, portable, and interoperable across different environments. Storing data in open formats empowers users to query information directly using diverse tools, fostering innovation and reducing dependency on proprietary ecosystems. Ultimately, an open storage layer supports transparency and adaptability, allowing the broader community to leverage, analyze, and extend data without being restricted by the limitations of a single vendor’s infrastructure.

Source: blog.twingdata.com
Published on 2024-04-27

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