Analytics & BI
Trusted marts and metrics definitions so leadership stops arguing about numbers in meetings.
NASBIX TECHNOLOGIES
Big data pipelines that feed analytics and AI
Ingestion, lakehouse architecture, Spark and dbt transforms, streaming and batch jobs—reliable data platforms with quality, lineage, and cost controls from our Karachi delivery center.
Batch & streaming ingest
Lakehouse & warehouse
Spark, dbt & orchestration
Quality & lineage
Big data engineering
Data engineers unblock ML and BI—not another siloed CSV
Platforms fail when ingestion is fragile, nobody owns schema changes, and finance opens a cloud bill nobody can explain. NASBIX builds data foundations: sources connected, bronze/silver/gold layers documented, transforms tested, and SLAs analytics and ML teams can rely on.
We deliver on AWS, GCP, or Azure—from Kafka and Spark streaming to dbt models on Snowflake, BigQuery, or Redshift. Pair with BI for semantic layers and dashboards, and with AI when RAG and features need fresh, governed corpora.
- Architecture fit: batch-only, lambda/kappa streaming, or lakehouse—trade-offs written down
- Idempotent pipelines, backfills, and disaster recovery paths ops can run
- Data contracts between product, ops, and warehouse consumers
- FinOps habits: partition strategy, job sizing, and storage lifecycle policies
Who we help
Built for teams outgrowing spreadsheets and ad hoc SQL
Greenfield platforms, migrations, and pipeline rescue when jobs fail silently every night.
What you get
Big data capabilities that matter
Production pipelines with observability—not one-off ETL scripts on a forgotten VM.
Ingestion
APIs, databases, files, CDC, and event streams—batch and real time with retry and dead-letter handling.
Storage & lakehouse
S3/GCS/ADLS layers, Iceberg/Delta patterns, and warehouse sync with clear retention rules.
Transformation
Spark, dbt, SQL, and Python jobs—modular, tested, and scheduled with Airflow, Dagster, or cloud native.
Streaming
Kafka, Kinesis, or Pub/Sub consumers with windowing, joins, and delivery guarantees where required.
Governance
Schema registry, data quality checks, lineage metadata, and access policies for sensitive fields.
Ops & cost
Monitoring, alerting, runbooks, and FinOps reviews so spend scales with value—not surprise.
Platform depth
Data platforms that survive org change
We document ownership, on-call expectations, and upgrade paths—so the platform still runs when the first contractor leaves.
- Migration from legacy ETL, on-prem Hadoop, or tangled cron to modern lakehouse
- Reverse ETL and operational analytics when warehouses must push back to SaaS tools
- PII tokenization, column masking, and audit logs for regulated datasets
- Integration with app squads on Java, Node.js, and event producers
- Mobile and web analytics export pipelines for product teams
- Security reviews aligned with secure SDLC and cloud IAM best practice
How we deliver
From data chaos to governed platform
Start with highest-value sources and marts—expand coverage without big-bang cutovers.
- 01 · DiscoverSources, consumers, SLAs, compliance, cloud constraints, and current pain points.
- 02 · ArchitectLakehouse/warehouse design, tooling choices, security model, and migration waves.
- 03 · BuildPipelines live, tests in CI, observability wired, and stakeholder marts validated.
- 04 · OperateHandoff, on-call docs, cost tuning, and optional managed data engineering pod.
Engagement models
Buy the way you scale data
Platform assessment
Architecture and pipeline review with prioritized roadmap and cost/quality findings.
Source-to-mart delivery
Fixed-scope ingestion and transformation for one domain or product line.
Migration program
Phased move from legacy stack to cloud lakehouse with parallel run and cutover plan.
Data platform squad
Embedded engineers owning pipeline backlog, on-call rotation, and platform upgrades.
Explore more
Related AI & analytics paths
ML, BI, and innovation programs built on trustworthy data.
