When choosing a data lakehouse platform, you should consider the following key factors:
Scalability & Performance
- Can it handle large-scale data ingestion, processing, and querying?
- Does it support distributed computing for fast analytics?
- How well does it perform under high concurrency?
Data Storage & Management
- Does it separate storage and compute for cost efficiency?
- Supports structured, semi-structured, and unstructured data?
- Offers ACID transactions for consistency and reliability?
Data Processing & Analytics
- Supports batch and real-time processing?
- Compatible with Apache Spark, SQL, and machine learning tools?
- Has built-in optimization techniques (e.g., indexing, caching)?
Governance & Security
- Role-based access control (RBAC) and data encryption?
- Auditing, compliance (GDPR, HIPAA, etc.), and data lineage tracking?
- Supports fine-grained access control across multiple teams?
Integration & Interoperability
- Connects with existing BI tools (Tableau, Power BI, Looker)?
- Compatible with cloud providers (AWS, Azure, GCP)?
- Open-source or vendor lock-in concerns?
Cost & Pricing Model
- Pay-as-you-go vs. subscription model?
- Storage and compute pricing transparency?
- Hidden costs for data movement, API calls, or queries?
Vendor Support & Community
- Active development and community support?
- Strong documentation and training resources?
- Reliable SLA (Service Level Agreements) and technical support?