Compare data warehouse and lakehouse by data usage, team, governance and operation cost before hiring an analytics platform.
Index
The name of the architecture does not resolve the lack of definition of indicators or inconsistent sources. Data warehouse and lakehouse are options with different characteristics and ecosystems. The decision should start with the workloads and team that will maintain the solution, not with the promise of a universal platform.
Decision this guide helps you make: Choose a data architecture that the company can use and sustain.
List consumers and questions
Recurring reports, analytical exploration, and file processing may require different resources. Identify volume, frequency, history and need for updating. Avoid using extreme projections as an automatic justification for complexity. The first design needs to meet the known scenario and explain how it could evolve.
Assess operating capacity
Consider team skills, tools already hired and responsibility for incidents. A flexible architecture may require more organization and governance decisions. A more managed solution may have specific limitations or costs. Compare total work to keep data accessible, understandable, and controlled.
Run a test with the same set of data
Choose ingestion, transformation, and representative queries for the relevant alternatives. Measure execution time, maintenance effort and cost observed in testing. Do not extrapolate a small test as a guarantee for any scale. Record assumptions and points that require additional validation.
Preserve definitions and portability
Catalog, transformation rules and meaning of indicators should not just remain in the team's memory. Document proprietary service dependencies and export possibilities. The proposal should explain how a consumer discovers the correct source and how structure changes are communicated.
- Known workloads and consumers.
- Comparison of operation and costs.
- Documented data definitions.
A scenario to check out in the demo
Hypothetical example: the initial need is to consolidate daily sales from a few sources, but the proposal includes an extensive platform without defined consumers. A smaller first delivery can demonstrate intake, quality and consultation before scaling up services. The design must explain which limitations would justify future evolution, avoiding the permanent cost of components that no one uses.
Briefing to request a proposal
- Sources, consumers and frequency required for data.
- Skills and tools already available in the company.
- Criteria to compare operation, performance and possibility of expansion.
Hire architecture from a case
Quantum9 can organize a first analytical layer to prioritize decision and evaluate expansion. Bring sources, current reports and operational limitations. It is not necessary to adopt an entire platform before demonstrating that the data delivers utility.
Discover the scope of Data engineering and deepen the context in related guide.