Build a Winning Data Pipeline Architecture on the Cloud for CPG

Data Challenges for CPG

  • First-party data which are internal to the company such as ERPs and CRMs
  • Second-party data from retailers and eCommerce companies
  • Third-party data from aggregators like Nielsen, DunnHumby, ScanTrack, and Media spend data
  • Open-source data such as weather data, environment data, COVID data, and more.

Data Pipelines

  1. Collecting and ingesting data
  1. Data transformation
  1. Data monitoring
  • Rate, or throughput, is the amount of data that a pipeline can process within a set amount of time. With a continuous stream of data that CPGs are dealing with, developing pipelines with high throughput is a given.
  • Fault-tolerant data pipelines are built to anticipate and mitigate the fundamental and most common faults such as downstream failures or network failure. Faults in the data pipeline can jeopardize critical CPG analytics initiatives. Keeping this in mind, CPG companies need to create distributed data pipeline architectures that offer immediate failover and alert data teams in case there is an application failure, node failure, or fault in some of the other services.
  • Latency refers to the time needed for a single unit of data to travel through the pipeline. For effective data pipelines, low latency can be a deterrent in terms of both price and processing resources.
  • Idempotency or re-runnability pertains to the re-application of a function, in this case, re-execution of a pipeline. Data pipeline may be needed to be retriggered in a variety of scenarios such as faulty source data, bugs in the transformation logic, or adding a new dimension to the data. Idempotence is important to maintain the operability of the pipeline.

Data Mesh for Modern CPG Data Architecture

Conclusion

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