Sold by: Automat-it LTD
Tired of your data team spending more time maintaining pipelines than building them? Automat-it Agentic Lakehouse solves this. It is an AWS-native Apache Iceberg data platform with three Amazon Bedrock AgentCore agents that detect schema drift, keep your catalog current, and answer business questions in plain language. Every agent-proposed change arrives as a pull request a human approves. This solution helps mitigate proposed data changes and sign-off before data ships.
Overview
Data platforms rarely fail outright. They quietly consume the team that built them. Every upstream schema change, every new column, every dataset that lands uncatalogued generates manual remediation work, and that work is usually discovered downstream when a report breaks rather than at the point the change occurred. The maintenance burden scales with the number of sources connected, so the platform's own team ends up funding its upkeep instead of advancing the roadmap.
Automat-it's Agentic Lakehouse offers an AWS-native alternative that puts agents on the finding, reading and drafting, and helps users make better decisions. Data stays in your own S3 buckets in an open table format, governed by AWS Lake Formation, with nothing held in a proprietary store and no seat-based pricing on analytical access.
The solution delivers a production-grade medallion lakehouse without enterprise overhead, built using standardized, production-tested infrastructure-as-code patterns and leveraging native AWS services:
- Amazon S3 with Apache Iceberg v2 for open, governed Bronze, Silver, Golden and Audit zones
- Amazon MWAA for orchestration, batch identity and watermark management AWS Glue Spark and transient Amazon EMR on Spot for configuration-driven transformation
- Amazon Bedrock AgentCore for the Discovery, Pipeline Maintenance and Talk-to-Your-Data agents
- Amazon Athena and Amazon QuickSight for SQL access and self-service BI
All deployed within your AWS account, infrastructure you own and control, to deliver a governed and operationally consistent data platform.
The Discovery Agent profiles newly landed data and proposes catalog updates before it becomes an undocumented liability. The Pipeline Maintenance Agent detects drift between your source schemas and your pipeline code, and proposes the specific change that absorbs it. The Talk-to-Your-Data Agent answers natural-language questions from your governed serving layer, citing the data behind each answer, with no write path at all. Every proposal arrives as a pull request carrying a diff, an owner and an audit trail, so automation stays reviewable and adoptable in regulated and business-critical environments.
Highlights
- Reduced engineering burden: pipelines that maintain themselves. Schema discovery, catalog updates and drift remediation are drafted by agents and reviewed as a short pull request, converting open-ended investigation into a bounded review task. Onboarding a new table after handover is configuration and SQL, not a code change, so the cost of growth stays low and predictable.
- Risk mitigation: governed automation with a reconcilable audit trail. All agents operate propose-and-wait, with no autonomous write path to production. Agent tool access is default-deny under explicit policy. Three independent records reconcile for audit: AWS CloudTrail, an immutable Iceberg audit zone recording every batch, and full Git history of every pipeline and configuration change.
- Open format, consumption-based cost, predictable delivery. Serverless-first economics on Glue, Athena and Lambda, with no idle analytical clusters. Data stays in Apache Iceberg v2 on your own S3, readable by Athena, Spark, EMR, Trino or Redshift, so no migration cost is embedded in a future engine decision. Production deployment in 3–4 weeks for a single-domain scope.
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