Your data architecture determines what questions you can answer, how fast you can answer them, and how your AI features perform. We design data systems that serve operational, analytical, and AI workloads efficiently.
Data architecture is not just an engineering concern. It is a product decision that determines what features you can build, what insights you can surface, and how quickly you can respond to new requirements. Poor data architecture creates invisible bottlenecks: queries that take minutes instead of milliseconds, analytics that are always a day behind, and AI models that train on stale or inconsistent data.
At Arthiq, we have designed data architectures for products that span social media analytics, invoicing systems, and AI agent platforms. Each domain has unique data characteristics, from high-velocity event streams in social media to strict consistency requirements in financial transactions. This breadth of experience means we can bring proven patterns to your specific challenges.
Our data architecture consulting addresses the full spectrum: transactional databases for operational workloads, analytical systems for business intelligence, streaming platforms for real-time processing, and vector stores for AI retrieval. We design unified architectures that serve all these needs without requiring separate systems for each.
The choice of database technology is one of the most consequential decisions in software architecture. We evaluate relational databases, document stores, key-value stores, graph databases, and time-series databases against your data model, query patterns, consistency requirements, and scale projections.
For most applications, a well-designed relational database like PostgreSQL is the right starting point. It offers strong consistency, flexible querying, and mature tooling. We add specialized databases only when clear requirements justify them: Redis for caching and session storage, Elasticsearch for full-text search, ClickHouse for analytical queries, or a vector database for AI retrieval.
Schema design is equally important. We design normalized schemas for transactional workloads and denormalized structures for read-heavy workloads. We plan indexing strategies that optimize query performance without creating excessive write overhead. We also design migration strategies that allow your schema to evolve as your product grows without downtime or data loss.
Modern products increasingly require real-time data processing. User activity feeds, live dashboards, fraud detection, and AI-powered recommendations all depend on processing data as it arrives rather than in batch. We design streaming data architectures using technologies such as Kafka, Kinesis, and Flink that process events in real time while maintaining reliability and ordering guarantees.
For products with analytical requirements, we design data pipelines that transform raw operational data into analysis-ready formats. These pipelines handle deduplication, schema evolution, data quality validation, and incremental processing. We favor lightweight approaches like dbt for transformation and managed services for orchestration to minimize operational overhead.
We also design the integration between operational and analytical systems. Many products need to feed analytical insights back into the operational system, for example using purchase history to personalize recommendations. We design these feedback loops with appropriate latency, consistency, and failover characteristics.
AI features place unique demands on data architecture. Training data must be curated, versioned, and quality-controlled. Feature stores must serve consistent features across training and inference. Vector databases must support efficient similarity search for retrieval-augmented generation. AI-ready data architecture designs these capabilities into the system from the start.
We help you design data pipelines that produce high-quality training datasets from your operational data. This includes handling personally identifiable information, managing data lineage, and implementing data versioning so you can reproduce model training runs. We also design feature stores that serve precomputed features to models with low latency and high consistency.
For products using large language models with retrieval-augmented generation, we design the document ingestion, chunking, embedding, and indexing pipelines that power semantic search. We evaluate vector databases such as Pinecone, Weaviate, Qdrant, and pgvector based on your scale, latency, and cost requirements.
As data regulations tighten globally, data governance is no longer optional. We help you implement data governance practices that cover data classification, access control, retention policies, audit logging, and compliance with GDPR, CCPA, and other applicable regulations.
We design architectures that support data subject rights such as access requests, deletion requests, and data portability. These capabilities must be built into the data architecture rather than handled as manual processes, because regulatory response times are strict and manual approaches do not scale.
We also address data quality governance: defining data ownership, establishing quality metrics, implementing validation rules, and creating monitoring that alerts on quality degradation. High-quality data is the foundation of reliable analytics and AI, and governance practices ensure quality is maintained over time.
Your data architecture determines what you can build and how fast. We design systems that serve operational, analytical, and AI workloads efficiently.