Scriptix
Data Engineering
Pipelines, warehouses, and real-time streaming architectures that turn scattered, inconsistent data into something your team (and any AI system you build) can actually rely on.
What's Included
How We Approach It
Most "we need AI" conversations are actually data problems in disguise: models and dashboards are only as good as the data feeding them, and inconsistent, siloed, or poorly governed data undermines both long before the AI layer is even a factor.
We start by mapping where your data actually lives and how it moves today, then design ingestion and transformation pipelines around that reality rather than an idealized architecture diagram. Data quality checks and governance get built into the pipeline itself, not bolted on as a manual review step later.
Whether you need batch ETL feeding a warehouse for reporting, or real-time streaming for operational decisions, we match the architecture to the actual latency and volume requirements. Over-engineering a real-time system for a report that runs once a day just adds cost and complexity.
See how this approach applies in practice: an illustrative scenario on building a recommendation engine →
Built With
Apache Airflow for orchestration, dbt for transformation, Snowflake and Databricks for warehousing and compute, Apache Kafka for streaming, and Fivetran for ingestion.
Common Questions
Our data is scattered across a lot of different tools: where do we start? expand_more
With an audit of what exists and how it's currently used. Centralizing everything at once is rarely the right first move. We usually start with the data that feeds your highest-priority use case and expand from there.
Do we need real-time data, or is batch processing enough? expand_more
Usually batch is enough, and it's simpler and cheaper to run. Real-time streaming is worth the added complexity when decisions genuinely need to happen within seconds or minutes of an event, not for routine reporting.
Is this a prerequisite for the AI Automation work you do? expand_more
Not always, but often. If your data is inconsistent or hard to access, cleaning that up first tends to make any AI work faster and more reliable, and we'll tell you honestly if that's the case for your project.
Struggling to trust your own data?
Tell us where it currently lives and what you're trying to do with it.
Talk to Us