How LakeStack works inside your aws environment
See how data moves from source systems to governed datasets and AI workloads, all within your AWS account.
From raw data to production-ready AI enablement
LakeStack operates as a continuous data system. Each stage is pre-configured to work together, so data flows from ingestion to consumption without manual intervention or rework.
Ingest data from enterprise-grade systems without building or maintaining custom pipelines.
- Pre-built connectors for SaaS and databases
- Change data capture for real-time updates
- Automated schema mapping and updates
Convert raw data into structured, analytics-ready datasets automatically.
- Continuous transformation pipelines
- Schema standardization across domains
- Built-in data validation and cleansing
Apply controls, policies, and visibility across all datasets.
- Fine-grained access control
- Data lineage and audit logs
- Policy enforcement at ingestion
Deliver trusted datasets to tools, teams, and systems.
- Integrate with BI tools and applications
- Serve data through APIs and query engines
- Maintain consistency across use cases
Use production-ready data for machine learning and AI use cases.
- Direct integration with AWS AI services
- No additional data preparation required
- Support for feature engineering and model workflows
Go live in weeks, not months
Eliminate pipeline engineering effort
End-to-end real-time data processing
ROI realized fast
Where teams unlock value from their data
Data modernization
Replace legacy systems with a modern data foundation that reduces complexity, improves reliability, and scales with your business.
Data connectivity
Unify data across systems and sources so your teams always work with complete, up-to-date information.
AI readiness
Move from fragmented data to production-ready datasets that power real AI and machine learning use cases.
Pipeline automation
Remove manual pipeline work and keep data flowing reliably with automated ingestion, transformation, and updates.
Self-service analytics
Give business teams direct access to trusted data so they can explore, analyze, and act without engineering bottlenecks.
Designed for data-intensive industries
From fragmented data pipelines to governed data foundations
- 80% reduction in ingestion and reporting workload
- Reporting cycles reduced from days to minutes
- 9-12 months of engineering effort avoided
- Operational workflows are 40–50% faster
- Delivered in four weeks
- 80% reduction in manual data prep and file processing
- 70% faster clinician and operational visibility
- 9-12 months of engineering effort avoided
- Operational workflows are 40–50% faster
- Delivered in four weeks
Simple, transparent pricing
Choose a plan based on your data volume, use cases, and deployment needs. No hidden costs or complex licensing.
- No per user, per query, or consumption pricing
- Deployed inside your AWS account with full data ownership
- Predictable costs aligned to long-term data strategy
- Clear ROI before expanding scope
Frequently asked questions
LakeStack automatically detects and adapts to schema changes during ingestion and transformation. Pipelines continue running without manual fixes, preventing data breaks and rework.
LakeStack stores data in Apache Iceberg tables on Amazon S3. This provides versioning, time travel, and compatibility with multiple query engines without locking you into a proprietary format.
Transformations are pre-configured and run continuously as data is ingested. LakeStack standardizes schemas, applies data quality checks, and prepares datasets for analytics and AI without manual pipeline development.
LakeStack supports fine-grained access control, including role-based permissions, column-level restrictions, and policy enforcement using AWS-native governance services.
LakeStack prepares structured, governed datasets that can be directly used with AWS AI services like SageMaker and Bedrock, without additional data preparation steps.
LakeStack includes built-in monitoring and self-healing capabilities. Pipelines continue running even when schema changes occur, reducing the need for manual intervention.
LakeStack includes automated detection of sensitive data and applies governance policies to control access, ensuring compliance with security and regulatory requirements.
See how this would work in your environment
We’ll map your current systems, data flows, and use cases, then show exactly how LakeStack would be deployed inside your AWS account.
