Explore what we offer

We offer expert data, cloud, and AI engineering for companies that need reliable data systems. Bryla Data helps organizations design data pipelines, automate cloud infrastructure, and adopt practical AI workflows with senior-level technical expertise.

Sample Problem Statement

Many organizations have valuable data trapped across client systems, operational databases, and shared platforms, but the data is not consistently available in a clean, governed, and AI-ready format.

Teams often struggle with manual data exports, inconsistent data quality, limited monitoring, unclear data ownership, and disconnected handoffs between engineering, analytics, and data science teams. As a result, reporting takes longer, AI initiatives move slowly, and business teams lack confidence in the data they depend on.

Reference Architecture Description

This reference architecture shows how Bryla Data helps organizations build a secure, automated, and AI-ready data platform using Snowflake, dbt, Jenkins, and AWS.

The solution securely replicates client data through Snowflake Secure Data Sharing, lands it in a governed Snowflake environment, transforms it with tested dbt models, and exports curated datasets to Amazon S3 using Snowflake COPY INTO. From there, the data can be consumed by data science teams, machine learning pipelines, analytics platforms, and AI applications.

The architecture is designed to demonstrate three core capabilities: modern data engineering, DevSecOps automation, and AI enablement. It combines reliable data movement, automated deployment, governance, observability, security controls, and downstream access patterns into a simple platform that is easy to maintain and scale.

How This Solution Adds Value

This architecture creates a repeatable, production-grade data pipeline that turns shared client data into trusted, usable data products.

Snowflake Secure Data Sharing removes the need for fragile file transfers and enables controlled access to source data. Snowflake Streams and Tasks automate replication and change processing, while dbt creates modular, tested, and documented transformation logic. Jenkins adds DevSecOps discipline by automating testing, deployment, promotion, and monitoring of Snowflake and dbt assets.

By exporting curated datasets to Amazon S3 in analytics-friendly formats, the platform gives data science and AI teams reliable access to clean, governed, feature-ready data. This reduces manual preparation work, improves trust in downstream models and reports, and shortens the path from raw data to business insight.

In practical terms, the solution helps clients:

  1. Reduce manual data movement and recurring operational work

  2. Improve data quality, lineage, and governance

  3. Automate deployment of data pipelines and transformations

  4. Create secure, scalable data products for analytics and AI

  5. Give business, data science, and engineering teams a shared foundation they can trust