Data & Infrastructure

Your Data Is Only Valuable If You Can Use It

Most businesses generate far more data than they use. Customer interactions, transactions, application logs, sensor readings, marketing analytics -- the data exists, but it lives in silos. Your CRM has one picture, your billing system has another, your application database has a third, and nobody has a complete view. Data infrastructure solves this problem by creating automated systems that collect, clean, organize, and deliver data where it needs to go.

We build the plumbing that makes data useful. That means data pipelines that extract information from source systems, transform it into consistent formats, and load it into warehouses or analytics platforms where your team can actually work with it. It also means the cloud infrastructure underneath: the databases, compute resources, networking, and monitoring that keep everything running reliably.

Data Pipeline Development

A data pipeline is an automated workflow that moves data from point A to point B, applying transformations along the way. The simplest pipelines extract data from one system and load it into another. More complex pipelines pull from dozens of sources, clean and normalize the data, apply business logic, join datasets together, and produce the tables and views that power dashboards, reports, and machine learning models.

We build pipelines with Apache Airflow for orchestration, dbt for SQL-based transformations, and custom Python for anything that requires specialized processing. For real-time use cases, we use streaming technologies like AWS Kinesis or Apache Kafka that process data as it arrives rather than on a batch schedule. Every pipeline includes monitoring, alerting, and data quality checks so you know immediately if something goes wrong.

ETL and ELT Workflows

The ETL pattern (Extract, Transform, Load) has been the standard approach for decades, and it still works well when you need to transform data before it reaches the destination. The newer ELT approach (Extract, Load, Transform) takes advantage of the processing power in modern cloud warehouses by loading raw data first and transforming it inside the warehouse. We use both approaches and recommend the one that fits your data volume, latency requirements, and existing tooling.

Regardless of the approach, every pipeline we build handles the messy realities of production data: inconsistent formats, missing fields, duplicate records, schema changes in source systems, and API rate limits. We build pipelines that are resilient to these issues rather than failing silently and producing incorrect downstream results.

Cloud Infrastructure

Data pipelines need reliable infrastructure underneath. We design and build cloud infrastructure on AWS that is secure, scalable, and cost-efficient. This includes VPC networking with proper subnet isolation, managed database clusters, compute environments for data processing, object storage for raw data lakes, and the IAM policies and security groups that keep it all locked down.

Everything is defined as infrastructure as code using Terraform or CloudFormation. This means environments are reproducible, changes are version-controlled, and spinning up a new staging or development environment takes minutes instead of days. We also implement cost monitoring and optimization so your cloud bill stays predictable as data volumes grow.

Data Warehousing

A data warehouse is the central repository where your cleaned, organized data lives. We work with PostgreSQL, Amazon Redshift, Snowflake, and Google BigQuery depending on your requirements. We design warehouse schemas that balance query performance with maintainability, implement partitioning and indexing strategies for large datasets, and set up the access controls that let the right people query the right data.

Monitoring and Observability

Data infrastructure requires monitoring at multiple levels: pipeline execution (did the job run and complete successfully?), data quality (does the output look correct?), infrastructure health (are databases and compute resources performing well?), and cost (are we spending what we expected?). We build dashboards and alerting for all of these so your team has visibility into the health of the entire data platform without checking manually.

Common Use Cases

The most common projects we work on include consolidating data from multiple SaaS tools into a central warehouse for reporting, building real-time dashboards that show business metrics as they happen, creating data feeds for machine learning models, migrating on-premise databases to cloud-managed services, and setting up data lakes for long-term storage and analysis. Whatever the use case, the goal is the same: making your data accessible, reliable, and useful.

Frequently Asked Questions