Data architecture design
A plan for how your data moves, is stored, and connects across your systems.
Data Engineering
Vectrel's Data Engineering service builds the pipelines, warehouses, and integration layers that make your data usable for reporting and AI. We handle architecture, validation, and legacy migration, and we agree what good data means up front instead of assuming it.
- Data architecture design
- ETL/ELT pipeline development
- Data warehouse & lake implementation
- API integration layer development
Overview
What your reporting and AI can do depends on how good and how available your data is. This work covers the pipelines, warehouses, migration tooling, and integration layers that pull your sources together. We define the architecture, the quality checks, who can access what, and how a migration cuts over, based on the systems and results in scope.
What the work can include.
ETL/ELT pipeline development
Pipelines that move and check your data on the schedule you need.
Data warehouse or lake implementation
A central store built for your sources, access rules, and the questions you ask of it.
API integration layer development
The layer that lets your apps and pipelines exchange data cleanly.
Data quality and validation frameworks
Automatic checks for missing fields, bad values, and anomalies in your data.
Migration from legacy systems
A phased move off the old system, with reconciliation, rollback, and a safe cutover.
Documentation and data dictionaries
Plain definitions of your fields, where they come from, and who owns them.
Illustrative use cases
Where this can apply.
Unified Client Data Platform
Illustrative example: consolidate selected CRM and spreadsheet sources into one governed warehouse for the reporting and analysis you need.
Legacy System Migration
Illustrative example: plan a phased move off a legacy production system, with reconciliation, rollback, cutover, and acceptance checks you sign off on.
Teams whose data is scattered across systems and needs to be organized before AI can use it.
Technologies
Common options.
PythonSQLPostgreSQLSnowflakeBigQuerydbtApache AirflowAWSMicrosoft AzureGoogle Cloud
FAQ
Frequently asked questions
01
What is data engineering?
It is designing the pipelines, warehouses, and integration layers that move and prepare data for a specific use. The work can include architecture, ETL or ELT pipelines, validation, legacy migration, lineage, and documentation. What counts as reliable and done is agreed for the sources, destinations, and consumers in scope.
02
Who needs data engineering?
It helps teams whose reporting or product depends on scattered CRMs, spreadsheets, SaaS tools, and databases. It is also often the first step for an AI use case when the source data is not clean, accessible, or well labeled enough. Discovery decides whether a focused fix or a bigger platform change is warranted.
03
How long does a data engineering project take?
It depends on data access, volume and quality, how complex the schema is, any migration, and how much validation is needed. The proposal sets out the phases, the dependencies, the reconciliation and acceptance steps, and the timeline once the systems are assessed.
04
Why is data engineering a prerequisite for AI?
AI behavior depends partly on the quality, coverage, access, and lineage of the data it trains on, retrieves from, or is tested against. When those foundations are weak, data engineering usually comes first. How much work that is varies by use case and is settled in discovery.
05
What technologies does Vectrel use?
Options can include Python, SQL, PostgreSQL, Snowflake, BigQuery, dbt, Apache Airflow, AWS, Microsoft Azure, and Google Cloud. We choose based on your existing setup, the data volume, the analytical workload, security, who will run it, and budget, once the requirements are clear.
Start a Data Engineering project
Every project starts with a conversation. Tell us what you are working on and we will take it from there.