Get your data flowing. Keep it governed.
A scoped consulting engagement for data ingestion, ETL pipelines and data migration on AWS. Automated, repeatable, privacy-first. Built natively so you own it without third-party licensing overhead.
We assess your data sources, design the pipelines, build on AWS-native services and hand over documentation your team can operate and extend.
What you get
Data Source Assessment
Every source catalogued: databases, APIs, flat files, event streams and third-party feeds. Volume, frequency and sensitivity assessed before design begins.
ETL Pipeline Design
AWS-native pipeline architecture using Glue, Lambda, Step Functions and EventBridge. Built for repeatability, observability and cost efficiency from the first deployment.
Privacy and Anonymisation
PII and sensitive fields identified, masked or anonymised before data lands in your warehouse. Policy enforced in code so it cannot be bypassed as the pipeline evolves.
Schema Management
Schema evolution handled with versioning and compatibility checks. Source changes do not silently break downstream consumers.
Monitoring and Alerting
CloudWatch dashboards, pipeline failure alerts and data quality checks configured. Your team knows about issues before your customers do.
Handover and Documentation
Runbooks, architecture diagrams and operational guides delivered as part of the engagement. Your team can operate, extend and debug without us in the room.
Scoped engagement. Everything built in your AWS account.
Discovery
- Data source catalogue
- Sensitivity and volume assessment
- Compliance requirements review
- Schema inventory
- Success criteria agreed
Design
- AWS-native architecture
- Pipeline topology diagram
- Privacy and anonymisation policy
- Schema management approach
- Monitoring and alerting plan
Delivery
- Pipelines deployed in your account
- Data quality checks running
- Dashboards and alerts configured
- Runbooks and architecture docs
- Signed-off handover session
How it works
Data integration work has natural phases. Each one produces something concrete before the next begins.
Discover
Source catalogue, compliance review, sensitivity assessment and success criteria. Clear input to design.
Design
AWS-native architecture, pipeline topology, privacy policy, schema strategy and monitoring plan. Reviewed before build starts.
Build
Pipelines built in your AWS account. Data quality checks and alerts configured. Your engineers paired in throughout.
Handover
Pipelines running, documentation delivered, team trained. Your engineers operate and extend from day one.
Audited and certified
AWS DevOps Competency
ISO 27001 Certified
AWS SaaS Competency
Tell us about your data integration challenge.
Walk us through your sources, your destination and your compliance requirements. We will scope an engagement on the first call.
Data Integration FAQ
What data sources do you support?
We work with relational databases, NoSQL stores, REST and GraphQL APIs, flat file feeds, event streams such as Kinesis and Kafka and SaaS platform exports. If your data moves, we can build a pipeline for it.
How do you handle sensitive data?
Sensitive fields are identified during discovery and anonymised or masked before they reach your destination. Policy is enforced in code, not just in documentation, so it cannot drift as the pipeline evolves. We align to GDPR, CCPA and APRA requirements where relevant.
Do you use AWS-native tools?
Yes. We build on Glue, Lambda, Step Functions, EventBridge, S3 and Athena depending on the workload. The goal is pipelines you pay for on usage with no third-party per-seat licensing and no quota limits imposed by an external tool.
How long does it take?
A focused engagement covering one or two data domains typically runs four to eight weeks from discovery through handover. More complex migrations with many sources take longer. We scope before we start so you know the timeline upfront.
What do we own afterwards?
Everything. All code lives in your AWS account and your source control from day one. We do not host anything on your behalf. When the engagement ends, your team operates it with the runbooks and documentation we produced together.
How does this differ from your AI data integration service?
Our AI-focused data integration service at base2services.com/artificialintelligence/data/ is scoped specifically to preparing data for AI workloads: vector stores, embedding pipelines, retrieval systems. This engagement covers general-purpose ETL, data migration and integration regardless of whether AI is involved.