FAQs
- Which vector databases does Context Data support?
- Context Data integrates with all major vector databases and stores including but not limited to Pinecone, Weaviate, Qdrant, Milvus (Zilliz), LanceDB, Supabase, Singlestore and more coming soon.
- How long does it typically take to set up a data pipeline using Context Data?
- With Context Data, users can typically set up their first data pipeline in as little as 10 minutes. For more complicated pipelines which require additional discovery and transformations across multiple data sources, setup time can be a much as 2 working days.
- How does Context Data ensure data security?
- Context Data encrypts all stored and processed data and adheres to SOC2 data privacy and processing standards.
- Can Context Data handle real-time data processing?
- Context Data is designed to handle both batch and real-time data processing. The platform can ingest and process data in real-time, allowing enterprises to build AI models that respond quickly to new data inputs. Real-time processing is ideal for applications like fraud detection, risk assessment, and supply chain optimization.
- Context Data support data lineage tracking?
- Absolutely! For each pipeline created on the Context Data platform, a lineage graph is created. That way, users can see how their data is moving across sources.
- How does Context Data manage multi-tenancy in SaaS environments while ensuring data isolation?
- Context Data supports multi-tenancy by ensuring strict data isolation between different tenants in the platform. Each tenant operates within its own logically separated environment, and access controls are enforced to prevent cross-tenant data access. Data isolation is maintained at the database level, using role-based access control (RBAC) and encryption mechanisms. In cases where multi-tenancy requires shared infrastructure, Context Data applies virtualization and containerization technologies to maintain secure, isolated environments for each tenant’s data and processing workloads.
- What mechanisms does Context Data use to ensure fault tolerance and high availability?
- Context Data implements multiple layers of fault tolerance and redundancy to ensure high availability across its platform. This includes automatic failover for critical components, replication of data across geographically distributed nodes, and the use of load balancing to distribute workloads evenly. In the event of hardware failures or network disruptions, Context Data's built-in recovery mechanisms ensure minimal downtime, preserving data integrity and maintaining uninterrupted AI model training and inference.
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