Pipelines fail. Pipelines silently corrupt data. Pipelines wake your engineers at 3am. NKKTech ships production-grade data pipelines on Airflow, Dagster, Prefect, Kafka, Kinesis, Pub/Sub — with SLA-backed orchestration, observability via Datadog or OpenTelemetry, and runbook-driven on-call. Fixed-scope from USD 30K. Singapore-law MSA. 5.0/5.0 Clutch verified across 9 client reviews.
Six pipeline engagement patterns we run most often. Bias toward production reliability — every pipeline ships with retry logic, alerting, SLA monitoring, and a runbook.
Single-pipeline build: source extraction (APIs, files, DBs), staging in cloud storage, transformation to warehouse-ready format, load with idempotent upserts. Includes retry logic, DLQ, alerting via Slack/PagerDuty, and a runbook. Orchestrator: Airflow OR Dagster OR Prefect (we recommend Dagster for new projects).
Real-time data pipeline: producers, schema registry (Avro/Protobuf), Kafka topics with proper partitioning, consumer groups, stream processing via Kafka Streams or Flink, sink to warehouse or vector DB. Includes exactly-once semantics where required, backpressure handling, replay support.
Change-data-capture from Postgres / MySQL / MongoDB / DynamoDB via Debezium → Kafka → downstream sinks (Snowflake / BigQuery / Iceberg). Schema evolution handling, transaction-boundary preservation, snapshot + incremental modes. Critical for: real-time analytics on transactional databases without OLTP impact.
Feature store integration: Feast, Tecton, or Vertex AI Feature Store. Online + offline parity, point-in-time correctness, freshness SLAs. Connects warehouse (training) to low-latency feature lookup (serving). Critical for: production ML systems with sub-100ms feature retrieval.
Common situation: pipelines that worked fine at 100 GB now fail at 10 TB. Or: nightly jobs that randomly fail twice/week. Or: silent data corruption discovered 6 months late. Engagement: 4-6 weeks to diagnose, fix, add observability. USD 35-70K typical. Output: stable pipeline + observability + runbook.
Monthly retainer: USD 10-22K with locked engineer hours + on-call rotation. Use case: data teams without 24/7 on-call coverage. We take primary on-call for production pipeline incidents, escalate to your team only when business-context decision needed.
Senior data engineer joins. Review current pipeline architecture OR greenfield requirements. Top-3 friction points (or failure modes) identified.
Singapore-law MSA + scoped SOW + fixed-fee quote. Detailed reliability targets (uptime %, max latency, recovery time). Lead engineer named.
Weekly demo + written progress report. Milestone-based payment. Production handoff: architecture diagrams, runbook, on-call playbook, Datadog/OpenTelemetry dashboards.
30-day warranty (we fix any bugs free). Optional monthly retainer for ongoing on-call ownership. Quarterly architecture + reliability reviews if retainer engaged.
Real-time transaction warehouses for fraud scoring + AML monitoring. CDC from Postgres via Debezium → Kafka → Snowflake/BigQuery. Sub-second latency, exactly-once semantics, audit-trail preservation. Engagement USD 80-200K.
Product events at scale (Segment, Mixpanel, Amplitude integration). Customer 360 unification, retention modeling features, A/B test event capture. Common: Segment → Snowflake → dbt → ML feature store. USD 50-120K.
Real-time inventory across multi-marketplace (Shopify + Shopee + Amazon + Tokopedia). Stream processing for stock-out alerts, dynamic pricing feeds. Tested at >10K events/sec during peak (Black Friday, 11.11).
Real-time bid streams (Kafka), attribution windows (1d/7d/28d) via Flink, multi-touch attribution models. Cookie deprecation + first-party data pipelines. USD 100-250K.
HL7 v2 / FHIR R4 ingestion from EHR systems, de-identification stream, OMOP-formatted output. HIPAA-compliant infra via BAA + BYO-VPC pattern. USD 80-180K.
Sensor data ingestion (MQTT + Kafka), edge-aggregation, predictive maintenance feature pipelines, multi-plant rollup. Connects to PrismLab AI/AR Japan factory CV track record. USD 100-280K.
Greenfield: Dagster (asset-based model, better observability, faster iteration). Existing Airflow at scale: stay on Airflow (migration cost rarely worth it). Lightweight Python-first: Prefect 2.x. We're experienced on all three. The choice usually matters less than execution quality.
Yes — pipeline rescue is a common engagement. Common findings: missing idempotency, no retry budget, untested edge cases (DST transitions, leap days, vendor outages), no SLA monitoring. 4-6 week engagement USD 35-70K. Output: stable pipeline + observability + on-call runbook.
Yes — Kafka + Kinesis + Pub/Sub all supported. We've built sub-second latency pipelines for fraud detection, dynamic pricing, real-time inventory. Stream processing via Flink or Kafka Streams. Exactly-once semantics where required (financial), at-least-once with idempotent downstream where acceptable (analytics).
Yes — Debezium-based CDC from Postgres / MySQL / MongoDB / DynamoDB / SQL Server. Schema evolution, transaction boundaries preserved. Common destination: Snowflake / BigQuery / Iceberg lake. Engagement USD 40-90K depending on source count + schema complexity.
Three layers: (1) Schema enforcement at ingestion (Avro/Protobuf with Schema Registry, reject malformed events to DLQ). (2) In-flight expectations (Great Expectations / Soda Core checks on critical columns). (3) Post-load observability (Monte Carlo / Anomalo for anomaly detection on freshness, volume, schema, value distributions).
Two options: (a) Handoff complete — your team owns on-call. We provide runbook + Datadog dashboard + 30-day warranty for bug fixes. (b) Optional retainer USD 10-22K/month — we take primary on-call rotation, your team escalation point for business decisions.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Tony's team understood our legacy PHP system faster than our internal team. Zero downtime migration, exactly as promised. The bilingual PM made communication seamless.
We went from 15 hours/week of manual prospecting to fully automated lead gen in 8 weeks. ROI in 60 days as Tony promised.
NKKTech delivered our LLM document processing pipeline on time and exactly on budget. The tech lead was available on Slack daily. First offshore team that actually worked the way we expected.
Last updated: · Reviewed quarterly for accuracy.
30-minute free discovery call with a senior NKKTech engineer (not a sales rep). We'll review your requirements, scope an engagement, and tell you honestly whether we're the right fit.
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