Data & Data Pipelines

Turn Raw Data Into Reliable, Queryable Assets

Your data is one of your most valuable assets — but only if it’s accessible, consistent, and trustworthy. Most growing companies hit a wall where ad-hoc scripts, manual exports, and duct-taped integrations can’t keep up. Dashboards go stale. Reports conflict. Engineers spend more time wrangling data than building product.

Sharper Cloud designs and builds data infrastructure that moves data reliably from where it lives to where it needs to be — whether that’s a warehouse for analytics, a real-time stream for operational decisions, or a clean dataset for your AI and ML workloads.

The Problem: Data Chaos Slows Everything Down

Data problems compound quietly until they become urgent:

  • Multiple teams pulling from the same source get different numbers
  • ETL scripts break silently and nobody notices for days
  • Analysts wait hours (or days) for engineers to prepare datasets
  • Real-time data needs are served by batch jobs that run overnight
  • Database performance degrades as data volume grows
  • Data quality issues propagate downstream and erode trust in reporting

These aren’t just technical problems — they’re business problems. Bad data infrastructure means bad decisions, slow product development, and missed opportunities.

Our Solution: Modern Data Infrastructure That Scales

We build data pipelines and infrastructure that are reliable, observable, and maintainable by your team:

Data Pipeline Architecture

  • ETL/ELT pipeline design for batch and streaming workloads
  • Source-to-warehouse data flows with schema management
  • Change Data Capture (CDC) for real-time database replication
  • Event-driven architectures with Kafka, Kinesis, or Pub/Sub
  • Idempotent, retry-safe pipeline design

Database Architecture & Optimization

  • PostgreSQL, MySQL, and managed database tuning
  • Read replica strategies and connection pooling
  • Database schema design for performance at scale
  • Migration strategies between database engines
  • Time-series data architecture (TimescaleDB, InfluxDB)

Data Warehouse & Analytics Infrastructure

  • Warehouse setup and optimization (BigQuery, Redshift, Snowflake)
  • Data modeling and dimensional design
  • dbt for transformation layer management
  • Query performance optimization
  • Cost management for warehouse compute

Data Quality & Observability

  • Data validation and quality checks at every pipeline stage
  • Schema drift detection and alerting
  • Pipeline monitoring with Grafana dashboards
  • SLA tracking for data freshness
  • Lineage tracking so you know where every number comes from

Scope of Work: What’s Included

Assessment & Strategy

  • Audit of current data flows, sources, and consumers
  • Identification of reliability gaps and quality issues
  • Architecture recommendation based on your scale and needs
  • Technology selection with cost/benefit analysis

Pipeline Development

  • Pipeline implementation with proper error handling and retries
  • Schema management and migration tooling
  • Testing frameworks for data pipelines
  • Deployment automation (CI/CD for data)
  • Monitoring and alerting setup

Infrastructure & Operations

  • Database deployment and tuning
  • Warehouse setup and optimization
  • Streaming infrastructure (Kafka, Kinesis) if needed
  • Backup, recovery, and disaster planning
  • Documentation and runbooks

Knowledge Transfer

  • Team training on pipeline operations
  • Troubleshooting guides for common failure modes
  • Architecture documentation
  • Handoff for ongoing maintenance

Tools & Technologies

Pipeline Orchestration: Apache Airflow, Dagster, Prefect, n8n

Streaming: Apache Kafka, AWS Kinesis, GCP Pub/Sub

Transformation: dbt, custom Python/Go, Apache Spark

Databases: PostgreSQL, MySQL, TimescaleDB, InfluxDB, Redis

Warehouses: BigQuery, Redshift, Snowflake, ClickHouse

Infrastructure: Docker, Kubernetes, Terraform

Monitoring: Grafana, Prometheus, custom data quality dashboards

CDC: Debezium, AWS DMS, logical replication

Why Sharper Cloud for Data Pipelines

Justin Sharp has built data infrastructure at companies processing millions of events daily. At Divvy, he architected the data pipelines that supported real-time financial transaction processing at scale. He understands both the infrastructure layer (where pipelines run) and the data layer (what they move and transform) — which means pipelines that are reliable, cost-efficient, and actually maintainable.

He doesn’t over-engineer. If a cron job and a Python script solve the problem, that’s the recommendation. If you need Kafka and a streaming architecture, he’ll build it right.

Typical Engagement Results

  • Reliable data flows with automated monitoring and alerting
  • Hours to minutes reduction in data freshness for analytics
  • Single source of truth for metrics and reporting
  • Self-service analytics so engineers aren’t bottlenecked by data requests
  • Pipeline observability so you know immediately when something breaks
  • Scalable architecture designed for 10x your current data volume

Real example: A Series B fintech company replaced 23 fragile cron-job ETL scripts with a managed Airflow pipeline architecture. Data freshness improved from “whenever someone notices it’s stale” to under 15 minutes with automated quality checks. The team went from spending 20+ hours/week on data firefighting to near-zero.

Frequently Asked Questions

Do we need a data warehouse?

It depends on your analytics needs. If you're running complex queries against production databases, a warehouse will improve both analytics performance and production stability. If your data needs are simple, we might recommend a read replica instead. We'll recommend the right solution for your scale.

Should we use Kafka or something simpler?

Kafka is powerful but operationally complex. If you need true real-time streaming with high throughput, it's the right choice. For many use cases, simpler tools like managed queues (SQS, Pub/Sub) or CDC with Debezium work just as well at a fraction of the operational cost. We'll match the tool to the actual problem.

Can you work with our existing database setup?

Yes. We work with what you have and improve incrementally. Whether you're on RDS, self-managed PostgreSQL, or a mix of databases, we'll design pipelines that work with your current infrastructure while planning for future growth.

How do you handle data quality?

Data quality checks are built into every pipeline stage — not bolted on after the fact. We implement schema validation, row count checks, freshness monitoring, and anomaly detection. When something breaks, you know within minutes, not days.

Ready to Fix Your Data Infrastructure?

Reliable data pipelines aren’t optional — they’re the foundation for good decisions. Let’s build data infrastructure your team can trust.

Book a Free 30-Minute Consultation to discuss your data challenges, map out your current pipeline landscape, and identify the highest-impact improvements.

Related services: Data pipelines pair well with Monitoring & Observability for end-to-end visibility and AI & Intelligent Automation for building ML-ready data infrastructure.