Real‑time Data Pipelines and Sub‑second Analytics
Streaming ingestion, scalable storage, and vector‑powered search to fuel AI applications.
Streaming & CDC
Apache Kafka, Flink, Debezium
- • Real‑time data ingestion and processing
- • Exactly‑once semantics and recovery
Storage & Analytics
ScyllaDB, Cassandra, Pinot, Druid
- • High‑throughput NoSQL for hot paths
- • OLAP systems for sub‑second queries
Vector Search
Weaviate, Milvus, pgvector
- • RAG‑ready embeddings storage
- • Hybrid keyword + vector search
Governance & Ops
Data quality, lineage, cost control
- • Observability, schema management
- • Security and compliance controls
Scale your data for AI
We architect end‑to‑end data platforms for analytics and intelligent applications.
Frequently Asked Questions
Common questions about modern data platforms, streaming, and analytics for AI.
Do we need real-time streaming for AI and analytics use cases?
Not always. Some workloads are well‑served by batch pipelines, while others benefit from near real‑time data (e.g., monitoring, personalization, fraud). We help you decide where streaming adds real value versus where simpler patterns are sufficient.
How do you choose between warehouses, lakes, and OLAP systems?
We look at your query patterns, latency needs, data volume, and cost targets. Often the right answer is a layered architecture—object storage plus a warehouse or lakehouse, with specialized OLAP or NoSQL systems for hot, low‑latency paths.
Can you work within our existing cloud provider and tooling?
Yes. We design architectures on top of your preferred cloud (AWS, Azure, GCP) and data stack, reusing what works and filling only the necessary gaps instead of forcing a full re‑platform.
How do you ensure data quality and governance at scale?
We introduce schema management, contracts, validation, lineage, and observability so issues are caught early. Governance is baked into pipelines and storage design, not treated as an afterthought.