Unified Data Architecture: Balancing Freshness, Cost, and Performance with Incremental Computing

The necessity of unified data architecture lies in the chaos and inefficiency caused by duplicated data construction, but the challenge is that different scenarios have different performance balance points.

For a data platform, freshness, low cost, and high performance cannot be achieved simultaneously; in various data computing scenarios such as typical batch processing, stream processing, and interactive analysis, the performance balance points differ—batch focuses on cost optimization, stream on data freshness, and interactive analysis on query performance—making it difficult for a single underlying architecture to maintain optimal performance across scenario switches. Therefore, the traditional approach is to adopt multiple different architectures combined, i.e., the Lambda architecture, to achieve optimal performance.

To break the technical barriers between architectures, CloudEngine adopts an incremental computation model that unifies batch, stream, and interactive modes, using the key variable "incremental time interval" to flexibly switch between computation modes, significantly reducing construction cost and simplifying architecture while maintaining stable optimal performance.

You are invited to join the DataFun and CloudEngine joint technical practice and principle sharing session held on December 19, 2023, to promote the process of data architecture integration!

Meeting Details

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