Data Analytics Services

We help engineering teams turn slow, fragile databases into fast, predictable systems that scale.

Our work combines deep query profiling, index optimization, and architectural tuning, designed to eliminate guesswork and restore confidence in every execution path.

The engagement includes five core services:

• Query Behavior Profiling. Analyze execution plans, I/O patterns, and cache behavior across critical workloads. Detect N+1 issues, Cartesian joins, unbounded scans, and high-churn paths that erode performance at scale.
• Index Strategy Design. Evaluate existing indexes for redundancy, bloat, and selectivity. Recommend and implement composite, partial, or covering indexes tailored to access patterns — all with measurable performance lift.
• Schema Normalization & Partitioning. Redesign tables to minimize lock contention, improve referential integrity, and reduce row width. Apply time- or key-based partitioning strategies to large datasets for parallelism and archiving.
• Connection, Memory & Cache Tuning. Optimize engine parameters (e.g., work_mem, buffer_pool, max_connections) to match hardware capacity and query concurrency. Tune connection pooling and cache eviction for consistent responsiveness.
• Observability & Change Validation. Implement slow query logs, metrics pipelines (e.g., pg_stat_statements, Performance Schema), and CI-integrated benchmarks to validate improvements over time and detect regressions before they hit prod.

Output: a tuned, observable database stack engineered for low-latency, high-concurrency workloads — and the confidence to scale with less infrastructure.

We build analytics systems that ingest, process, and surface data in real time, so teams can respond to events while they’re still happening.

Our approach combines stream processing, event-driven architectures, and intelligent buffering to eliminate reporting lag and unlock instant insight.

The engagement includes five core services:

• Stream Pipeline Engineering. Design data pipelines using Apache Kafka, Flink, or Spark Streaming to handle high-velocity data sources. Implement schema registries, dead-letter queues, and delivery guarantees (at-least-once, exactly-once) from ingest to sink.
• Event Modeling & Transformation. Define a unified event model across systems. Implement stateless and stateful transformations — joins, windowed aggregations, deduplication — in the pipeline layer to prepare data for real-time querying and alerting.
• Low-Latency Storage Layers. Deploy fast-write, fast-read systems like Apache Druid, ClickHouse, or Rockset for sub-second slice-and-dice analytics. Optimize indexes and caching for dimensional filtering and time series analysis.
• Alerting & Streaming BI. Connect real-time flows to dashboards and alerting systems. Trigger notifications or business logic from data thresholds, pattern detection, or anomaly scoring — all within seconds of the source event.
• Resilience & Backpressure Management. Engineer for fault tolerance and surge protection. Implement backpressure protocols, auto-scaling consumers, and replayable queues to ensure durability under unexpected load.

Outcome: a responsive analytics backbone that keeps up with reality, streaming the right insight to the right stakeholder at the right moment.

We design modern data platforms that unify fragmented sources, standardize semantics, and deliver analysis-ready datasets reliably and at scale.

Our work blends ELT orchestration, warehouse modeling, and system-level observability to make data engineering robust, transparent, and maintainable.

The engagement includes five core services:

• Source Mapping & Ingestion. Connect to internal and external data sources — databases, APIs, flat files, logs — and define ingestion logic via batch or stream. Apply validation, deduplication, and versioning at the extraction layer.
• ELT Pipeline Development. Implement transformation logic in-warehouse using dbt, Spark, or SQL, supporting modular, testable models with dependency resolution, lineage tracking, and incremental loading.
• Data Vault & Star Schema Modeling. Design normalized (Data Vault) or dimensional (star/snowflake) schemas based on reporting and analytical requirements. Align structure to BI needs, business entities, and access patterns.
• Performance Tuning & Cost Optimization. Index, cluster, or materialize views for efficient querying. Monitor query plans and storage consumption. Optimize compute usage and warehouse size to balance performance and cost.
• Data Quality & Monitoring Frameworks. Automate testing for freshness, nulls, duplicates, and referential integrity. Integrate alerting and dashboards (e.g., Great Expectations, Monte Carlo, OpenMetadata) to catch issues before users do.

Outcome: a centralized, documented data warehouse with trusted, versioned data, powering BI, ML, and executive decision-making.

We make your data talk — across vendors, stacks, and silos — cleanly, securely, and in real time.

Devox engineers integration layers that don’t just sync data, they enforce standards, validate structure, and trace every byte in flight.

Here’s what we deliver:

• API Infrastructure, Built Right. Connect any system — Salesforce, SAP, Stripe, HubSpot, Snowflake. We build resilient interfaces with contract-first design, OAuth2, RBAC, schema validation, and SLA-level retries baked in.
• Event-Driven Sync at Scale. Change Data Capture with Debezium. Kafka-native streams. Dual-write resilience. We sync sources of truth without lag, replay drift, or broken joins. Real-time dashboards won’t stall. Warehouses stay fresh.
• Schema Mapping with Guardrails. We normalize messy inputs, align entities, and enforce referential integrity. Every transformation is tested, versioned, and auditable. We don’t patch — we standardize.
• Governed by Contract. No implicit trust. Every field has an owner. Every API has an agreement. PII stays masked. Changes get flagged, not missed. You stay compliant: HIPAA, GDPR, SOC2 by design.
• Observable by Default. From job status to payload shape — you see it all. We track failures, log lineage, and expose metrics on every sync. If something breaks, you’ll know where, when, and why.

Outcome: real-time, contract-governed, production-grade data flows that don’t break, even when everything else does.

We optimize transactional and analytical databases for sustained throughput, consistent query latency, and structural resilience across scale, concurrency, and load volatility.

Our approach integrates runtime telemetry, architectural redesign, and workload-aligned tuning to create high-performing data layers built for continuous analytics.

• Execution Path Reconstruction. We parse actual query traces to extract operator-level behavior. Focus areas: scan type distribution, join order decisions, index utilization, memory spill frequency, and I/O skew. Execution paths are benchmarked against row volume, cardinality, and workload variability.
• Index & Access Strategy Engineering. Access patterns inform all design. We classify predicates by frequency, sort stability, and filter cardinality. Indexes such as covering, partial, and multi-column are deployed based on actual scan paths. Index overhead, bloat, and fragmentation are tracked per engine and reconciled with storage allocation.
• Schema Normalization & Partition Design. Schemas are assessed for write amplification, lock contention zones, and query parallelization opportunities. For wide, high-churn tables: vertical splitting, archival partitioning, and late-binding materialized views. Schema variants are tracked as discrete model versions with lineage to upstream systems.
• Engine Parameterization by Workload Profile. Engine settings are calibrated via empirical replay. We adjust memory allocators (work_mem, sort_buffer_size), concurrency controls, background workers, and autovacuum cadence in line with actual QPS and connection concurrency. Tuning is benchmarked with transactional integrity, not synthetic metrics.
• Observability & Regression Surfacing. Every optimization ships with full-stack visibility: query latency histograms, lock wait analytics, heatmaps of index access frequency, and replication lag telemetry. Drift detection and rollback plans are embedded into CI/CD pipelines.

Outcome: a production-grade data plane with predictable performance under real-world workloads, validated, versioned, and ready to support scale-aware analytics.

We build event-driven data systems that support sub-second insight delivery, durable stream processing, and synchronized state between systems, designed for observability and high-frequency decision-making.

Our work integrates distributed ingestion, low-latency computation, and resilient orchestration across real-time and batch-aligned data planes.

• Streaming Ingestion Architecture. We implement Kafka- or Pulsar-based pipelines with partitioning logic aligned to key cardinality and throughput variance. Event serialization (Avro, Protobuf) is governed by a registry with enforced schema evolution rules. Each source is versioned, replayable, and bounded by delivery SLAs.
• Stateful Stream Processing. We deploy Flink or Spark Structured Streaming to execute stateful transformations: windowed joins, aggregations, watermark handling, and late event correction. Operators are containerized and orchestrated under autoscaling conditions with recovery guarantees from savepoints or changelogs.
• Analytical Storage Layer Design. OLAP engines, such as Druid, ClickHouse, or Pinot, are selected based on query concurrency, aggregation depth, and data freshness windows. Data is pre-aggregated or materialized selectively, based on access telemetry. Column pruning and segment replication are tuned per workload.
• Event-Triggered Activation. We connect streaming outputs to alerting, scoring, and operational workflows, pushing payloads into APIs, Lambda executions, or real-time dashboards. Decision boundaries are defined through rule engines or ML scoring models deployed as microservices.
• Observability & Flow Control. Every stage emits telemetry: ingestion lag, watermark staleness, operator latency, checkpoint duration, consumer group rebalancing, and record drop rates. Monitoring systems provide granular diagnostics for flow pressure, backfill delay, and anomaly patterns.

Outcome: a fault-tolerant, low-latency analytics stack that supports continuous insight delivery, with deterministic behavior under spike, skew, and schema evolution.

We build analytical backplanes that consolidate data across fragmented systems, enforce semantic consistency, and expose clean, query-ready datasets through governed, scalable models.

Our solutions cover full lifecycle orchestration, from raw ingestion to consumption-layer optimization, driven by lineage, modularity, and runtime observability.

• Source System Ingestion & Replayability. We design ingestion logic per source category — RDBMS, APIs, logs — with batch and stream modalities supported. Ingestion pipelines are idempotent, resumable, and tracked via metadata tags. Load status, freshness, and record drift are exposed as first-class metrics.
• Model-Centric ELT Design. Transformations are built using dbt, Spark SQL, or native warehouse SQL under modular dependency graphs. Logic is version-controlled, test-covered, and production-certified. Every model outputs a contract shape, types, and volume expectation for downstream consumers.
• Warehouse Schema Architecture. Star, snowflake, or Data Vault schemas are selected based on lineage complexity, business domain coverage, and compliance needs. Dimensional hierarchies, surrogate keying, and slowly changing dimension logic are formalized. Documentation is embedded into the DAG.
• Performance Modeling & Cost Efficiency. We benchmark and tune materializations, clustering, partitioning, and caching based on access telemetry and cost–performance ratios. Workload-aware warehouse sizing (e.g., Snowflake virtual warehouses) is adjusted per SLA, concurrency, and update frequency.
• Data Quality Enforcement & Auditing. Testing layers validate null ratios, uniqueness, referential integrity, and statistical drift. Test failures halt promotion pipelines. Audit tables capture historical diffs and model version transitions. Quality metrics are exposed to dashboards or alerting tools.

Outcome: a governed, documented, and scalable warehouse backbone, architected for change, optimized for cost, and trusted for analytics and ML workloads.

We architect governance models that enforce data integrity, align with regulatory obligations, and operationalize ownership across data domains, without impeding delivery velocity.

Our approach unifies policy, stewardship, and system enforcement under a modular framework deployable across centralized, hybrid, or domain-oriented architectures.

• Governance Model Architecture. We define the governance operating model (enterprise, domain, or BU-aligned) based on organizational scale, data platform topology, and regulatory surface area. Each data domain is assigned a steward, contract, and quality budget.
• Metadata Strategy & Lineage Mapping. We integrate metadata layers — technical, operational, and business into a unified catalog (via tools like Collibra, Amundsen, or OpenMetadata). Lineage is extracted at the column level from ETL/ELT DAGs, API contracts, and data flows, with bidirectional traceability.
• Access Policy Frameworks. We implement attribute-based or role-based access models (ABAC/RBAC), linked to identity providers (e.g., Okta, Azure AD). Column-level controls, data masking, and usage logging are configured per sensitivity classification and jurisdictional requirements (GDPR, HIPAA, SOC2).
• Quality Monitoring & Policy Enforcement. We define domain-specific rulesets: null tolerances, schema conformance, reference integrity, and volume thresholds. Rules are enforced at ingestion, transformation, and publish layers, with alerts routed to stewards and violations stored with audit trails.
• Governance-as-Code Enablement. All governance logic, from access controls to validation checks, is codified and version-controlled. Policies are embedded in CI pipelines and promoted with infrastructure. Every change has a diff, owner, and rollback strategy.

Outcome: a traceable, enforceable governance layer embedded into your data stack, aligned to business priorities, measurable by domain, and extensible by design.

We design analytics platforms with embedded security primitives, from ingestion to access to ensure compliance, auditability, and operational trust at scale.

Our work combines encryption, identity governance, isolation layers, and runtime monitoring, aligned to the architecture and regulatory footprint of your organization.

• Access Control & Privilege Models. We implement tiered access models such as RBAC, ABAC, or attribute-based entitlements — across data warehouses, dashboards, and APIs. Least-privilege defaults are enforced via IAM integration, scoped roles, and conditional policies (e.g., geo-aware or time-bound access).
• Encryption Policy Implementation. All data is encrypted in transit (TLS 1.2+/mTLS) and at rest (AES-256 or KMS-backed envelope encryption). Partition-specific or row-level encryption is applied for sensitive datasets. Key rotation and access audit are automated via HSM integration or cloud-native KMS.
• Network & Resource Isolation. We enforce logical isolation between workloads using VPC segmentation, subnet zoning, and dedicated compute layers for analytics pipelines. External data egress is routed through monitored, allow-listed endpoints. Data sharing is governed by private links or token-authenticated layers.
• Anonymization & Masking Controls. We apply deterministic or probabilistic masking to PII, PHI, and financial fields based on usage context. Tokenization pipelines are deployed for regulated exports. Aggregation thresholds and k-anonymity policies are encoded in data-sharing interfaces.
• Audit Trail & Compliance Telemetry. We activate field-level read/write logs, access attempts, query history, and permission changes, piped into SIEM or audit platforms. Every sensitive access is correlated to identity, timestamp, resource, and intent classification. All policies support SOC2, GDPR, HIPAA, and ISO 27001 standards.

Outcome: a hardened analytics backbone engineered for continuous compliance, breach resilience, and traceable insight delivery, without slowing down analytical velocity.

We run diagnostic audits that combine rule-based checks, runtime tracing, and AI-powered pattern recognition to expose systemic data issues before they surface in production.

Our audits go beyond snapshot profiling: we integrate temporal analysis, anomaly detection, and impact correlation across the full data lifecycle.

• Query Path Diagnostics. We analyze actual query logs (e.g., pg_stat_statements, slowlog, QueryInsights) to detect degradation trends, blocking events, plan volatility, and access skew. High-cost paths are mapped to schema structure, index state, and runtime conditions.
• Index Efficiency Evaluation. We compute index scan ratios, hit/miss rates, and write amplification by table and index. AI models flag underused, overlapping, or regressed indexes based on historic performance baselines. Each recommendation is tied to a net gain estimate in IOPS or CPU.
• Schema Drift & Integrity Audits. We trace schema evolution over time — column type shifts, nullability changes, key drops, and correlate changes to query failures, data quality regressions, or application-level exceptions. Drift rules are embedded into CI/CD gates.
• Storage & Bloat Profiling. We surface dead tuples (e.g., PostgreSQL), page fragmentation, table bloat, and index redundancy. Threshold-based alerts identify oversized relations, cold partitions, and ghost data accumulations degrading I/O or vacuum cycles.
• Data Quality Inference. AI models detect semantic drift, unexpected null surges, and distributional anomalies by comparing current snapshots to historical seasonality. Violations are prioritized by downstream dependency and surfaced as action items per steward or domain.

Outcome: a forensic-level audit trail that quantifies risk, localizes technical debt, and prioritizes remediation, structured to reduce entropy across your data estate.

We help enterprises transform internal data assets into external-grade data products with defined ownership, packaging standards, and monetization models.

Our work spans product design, value modeling, delivery architecture, and operationalization — all built on top of governed, contract-aligned data infrastructure.

• Data Asset Qualification & Scoping. We evaluate existing data sets by completeness, uniqueness, velocity, and addressable market potential. Candidate assets are scored by monetization viability direct sales, embedded insights, or value-added SaaS.
• Product Architecture & Delivery Modeling. Each data product is architected by delivery type: API, flat file, warehouse integration, or real-time feed and coupled with availability SLAs, schema contracts, and observability endpoints. Delivery modes support licensing, pay-per-use, or subscription tiers.
• Pricing & Packaging Strategy. We define monetization models based on the DIKW stack value (data, information, knowledge, wisdom). Products are priced by volume, enrichment level, or business outcome supported, informed by competitive benchmarking and buyer persona analysis.
• Data Governance & Compliance Alignment. All productized assets pass through privacy vetting (PII risk classification, masking/anonymization), contract control (usage rights, redistribution clauses), and audit readiness (row-level lineage, change logs, consent provenance).
• Go-to-Market & Product Enablement. We support GTM planning: messaging, product docs, usage dashboards, trial provisioning, and integration support. Sales and customer success teams receive enablement materials and performance telemetry to drive adoption and retention.

Outcome: a market-ready, fully governed data product portfolio with defined revenue models, delivery contracts, and operational scalability, built to unlock enterprise-grade data value.