Designing big data processing pipelines
One of the critical mistakes many big data architectures make is handling multiple data pipeline stages with one tool. A fleet of servers managing the end-to-end data pipeline, from data storage and transformation to visualization, may be the most straightforward architecture, but it is also the most vulnerable to breakdowns in the pipeline. Such tightly coupled big data architecture typically does not provide the best possible balance of throughput and cost for your needs. When you are designing a data architecture, use FLAIR data principles as explained in the following:
- F – Findability: This refers to the capability to easily locate available data assets and access their metadata, which includes information like ownership and data classification, along with other crucial attributes necessary for data governance and compliance.
- L – Lineage: The ability to trace the origin of data, track its movement and history...
