A practical guide for building analytical data products that power business-critical automation or customer-facing applications.
When analytics datasets power operations or customer facing systems there is no stakeholder to flag the errors. When data goes wrong, the business goes wrong. It puts a new demands on data teams to ensure data products work 24/7.
Neither anomaly monitoring, data contracts, data diffing or data testing is a silver bullet to achieve data reliability. Instead, we need a strategy that considers how these techniques fit together.
Chapter 1
Outlining the clear relationship between data reliability and business impact.
Chapter 2
A framework to define data products to manage tiers of data assets and SLAs for maintenance.
Chapter 3
An approach to robust platform architecture that creates the right interfaces.
Chapter 4
We build a testing and monitoring framework that that maximises errors caught and minimises alerts.
Chapter 5
Developing scalable ownership and efficient incident management processes to quickly resolve issues.
Chapter 6
Establishing feedback loops and learning processes to continuously enhance data reliability practices.
