- Continuous and disruptive database design updates while trying to have one design pattern to fit all use cases.
- Sub-par performance while loading, retrieving, and querying data.
- You want to shorten time-to-market of the projects aimed at data delivery and consumption.
- Unnecessarily complicated database design limits usability of the data and requires knowledge of specific data structures for their effective use.
Our Advice
Critical Insight
- Evolve your data architecture. Data pipeline is an evolutionary break away from the enterprise data warehouse methodology.
- Avoid endless data projects. Building centralized all-in-one enterprise data warehouses takes forever to deliver a positive ROI.
- Facilitate data self-service. Use-case optimized data delivery repositories facilitate data self-service.
Impact and Result
- Understand your high-level business capabilities and interactions across them – your data repositories and flows should be just a digital reflection thereof.
- Divide your data world in logical verticals overlaid with various speed data progression lanes, i.e. build your data pipeline – and conquer it one segment at a time.
- Use the most appropriate database design pattern for a given phase/component in your data pipeline progression.