Amazon SageMaker Lakehouse

Make your data lakehouse the foundation for enterprise-scale AI

Enterprises are pressured to unify fragmented data stored across lakes, warehouses, and operational systems, without driving up integration costs, duplicating storage, or compromising governance. AWS and N-iX are here to implement the new generation of open lakehouse architecture built on Apache Iceberg: Amazon SageMaker Lakehouse. It unifies data across Amazon S3 data lakes and Redshift warehouses, enabling analytics and AI/ML workloads to run on a single, consistent copy of data and using zero-ETL integrations and federated access to operational systems.

Through Lakehouse Enablement and Acceleration Program (LEAP), we provide expert implementation services to seamlessly transition to this open, scalable, and cost-efficient platform. Partnering with N-iX means building a secure, future-proof lakehouse architecture tailored to your enterprise workloads.

56%+

plan to reduce over 50% cost savings by eliminating data copies and ETL pipelines

Dremio

81%

expect to use lakehouses for AI model development

The Business Research Company

51%

plan to scale platforms to support 20-100+ data sources

Dremio

70%

of organizations plan to adopt a lakehouse architecture within the next three years

The Business Research Company

Why Amazon SageMaker Lakehouse: key features

Unified data management

Consolidate data from S3, Redshift, operational systems, and third-party sources into a single logical layer. With zero-ETL ingestion, fine-grained access controls, and open Apache Iceberg compatibility, it simplifies data governance, reduces duplication, and ensures secure, consistent access across all engines.

Built for performance and scale

Achieve up to 7× faster Redshift queries, 50% faster Spark reads, and 10× higher throughput on S3 tables. Engineered to support petabyte-scale data analytics and sub-second response times for large, distributed workloads.

Enterprise-grade governance and security

Enforce fine-grained access control (row, column, cell, SQL view) across all engines. Benefit from centralized policy management with AWS Lake Formation and consistent enforcement across BI and AI workloads.

Fully open and interoperable architecture

Leverage Apache Iceberg for open table formats and standard REST APIs. Ensure compatibility with leading engines like Spark, Presto, Flink, and SQL while avoiding vendor lock-in and enabling hybrid deployments.

AI/ML and GenAI ready by design

Build, train, and deploy ML and foundation models directly on unified lakehouse data. LEAP accelerates AI adoption by embedding Bedrock integration and self-service analytics capabilities into the lakehouse.

Unified development experience

Bring together the full AI/ML development lifecycle, from data preparation to model deployment within one environment. It integrates Redshift, EMR, Athena, Glue, and Bedrock tools, allowing data engineers, scientists, and analysts to collaborate on a shared platform.

N-iX Amazon SageMaker Lakehouse implementation services

Enterprises partner with N-iX to design, build, and scale custom lakehouse solutions using Amazon SageMaker. We provide full-cycle services to help you maximize the impact of your data and AI investments.

Data strategy and platform advisory

We collaborate with enterprise stakeholders to define a clear, scalable strategy for lakehouse adoption. Our process contains architectural blueprints, integration planning with existing Redshift and S3 environments, and decision support for leveraging Apache Iceberg, Zero-ETL pipelines, and federated cataloging. All recommendations are grounded in cost-performance tradeoff analysis, operational risk mapping, and future-state data productization models.

Lakehouse readiness assessment

As an initial phase of LEAP, we conduct a comprehensive assessment of the data estate, covering ingestion architecture, latency points, workload distribution, metadata management, and governance maturity. Based on these findings, targeted PoCs validate lakehouse use cases such as federated analytics, ML model training, or Iceberg-based table management.

Architecture design

Our architects design fault-tolerant, high-performance lakehouse platforms combining Amazon S3, Redshift Managed Storage, EMR, Glue, and SageMaker Studio. We define cataloging hierarchies, transactional consistency rules, access policies, and data lake zoning strategies: workload profiling, compliance requirements, and long-term total cost of ownership drive platform selection.

End-to-end system integration

We engineer robust integrations across diverse data sources into a unified lakehouse fabric. These pipelines are architected for low-latency data sharing, transactional CDC, and streaming ingestion. Our teams also optimize performance through compaction, Iceberg snapshot retention policies, and Spark–Redshift read/write enhancements.

Data governance enablement

We implement fine-grained governance models leveraging tag-based and role-based access control (TBAC/RBAC), column- and row-level permissions, and secure multi-tenant catalog access. Our approach aligns technical enforcement with enterprise data governance mandates, ensuring data is accessible and protected where required.

Managed lakehouse operations

Post-deployment, we provide complete lifecycle management: monitoring data workflows, tuning query performance, managing Iceberg metadata operations, and enabling continuous compliance. We also support changing use cases by integrating new engines, user enablement, and cross-org data product sharing via a secure, zero-copy architecture.

How we implement Amazon SageMaker Lakehouse

N-iX implements Amazon Sagemaker through LEAP (Lakehouse Enablement and Acceleration Program), a proven framework that moves enterprises from fragmented, high-maintenance data landscapes to a unified, governed, and AI-ready foundation. Every phase is engineered to ensure technical precision, operational resilience, and a direct line of sight to measurable business outcomes.

Strategic assessment

This phase engages senior business, technology, and compliance stakeholders to define the lakehouse’s role in achieving organizational objectives, from advanced analytics to AI/ML enablement. We establish the scope of integration with existing Redshift and S3 investments, align governance requirements with regulatory mandates, and quantify the expected business impact.

Data estate readiness

We conduct a comprehensive audit of the current data estate to identify enablers and constraints to lakehouse adoption. This includes evaluating storage structures, data processing reliability, governance maturity, and integration readiness, allowing us to map high-value opportunities where zero-ETL analytics, federated querying, and ML integration can deliver measurable gains in performance and efficiency.

Architecture design and technical blueprint

The architecture blueprint is designed to ensure interoperability, scalability, and governance from the outset. We define the storage and compute strategy, integration paths for AI/ML workloads, and a security model that maintains compliance while supporting multi-engine access, subjecting the design to rigorous performance and resilience validation before implementation.

Implementation and data integration

Deployment is executed with minimal disruption, maintaining data integrity and continuity across systems. We establish zero-ETL pipelines, integrate Iceberg-based table formats, enable federated access across query engines, and ensure operational workloads are seamlessly connected to the lakehouse.

Continuous value delivery

Once operational, the focus shifts to optimization, workload scaling, and value realization. To guarantee that the lakehouse keeps up with business priorities and technological developments, we adjust performance metrics, expand data sharing capabilities throughout the organization, and add new analytics and AI workloads.

How we can help: custom solutions built with SageMaker Lakehouse

Through the LEAP, we design and implement custom Lakehouse-based solutions. Our engineering teams enable enterprise clients to unify analytics, simplify architecture, and accelerate ML adoption without reinventing existing infrastructure.

Unify data across S3 and Redshift with secure, shared access

We help enterprises manage a single, governed copy of data across Amazon S3 and Redshift using Apache Iceberg-compatible catalogs. Our approach eliminates data duplication and sync issues, while our engineers design fine-grained access controls (RBAC, TBAC) to meet compliance needs in regulated environments.

Enable near real-time analytics via zero-ETL

Our experts architect zero-ETL pipelines that allow operational data to be queried in place, whether stored in Aurora, RDS, DynamoDB, or third-party sources. This feature empowers near real-time decision-making for AI workloads, BI reporting, and event-based applications.

Build multi-warehouse Redshift architectures

N-iX enables enterprises to build multi-warehouse architectures on Amazon Redshift, combining siloed Redshift clusters and workgroups into a unified Lakehouse layer. Our solutions allow teams to query and join datasets across warehouses, run scalable ETL pipelines, and support cross-team analytics.

Tailored approaches for different data starting points

N-iX works with enterprises at every stage of their data maturity, from companies running Redshift at scale to those managing complex S3 data lakes or modernizing legacy on-prem systems.

For Redshift-centric workloads
Leverage existing Redshift data without migration, using Apache Iceberg-compatible engines while maintaining performance and avoiding costly transfers.
For S3 data lake environments
Unify siloed data across diverse sources. Connect AWS and non-AWS data sources using zero-ETL pipelines with near real-time ingestion from operational databases.
For Greenfield or on-prem deployments
Build an open, flexible lakehouse from scratch. Create a modern lakehouse on AWS using open table formats (Iceberg), gaining interoperability, performance, and full control over your toolset.

When additional expertise is required, we involve other experienced N-iX professionals to support your project with the right knowledge.

Why choose N-iX for SageMaker Lakehouse implementation

120+

AWS-based projects delivered , including enterprise-grade data platform implementations under the LEAP framework.

150+

AWS-certified and accredited experts with deep Redshift, S3, and SageMaker knowledge.

60+

successful data initiatives covering lakehouse architecture, zero-ETL integration, and ML pipelines.

Proven experience

with Iceberg-based solutions and cross-engine data access on AWS.

Data governance

and compliance expertise, including GDPR, HIPAA, and ISO standards.

Trusted by

global enterprises such as Gogo, Redflex, and Fluke for complex AWS data solutions.

FAQ

Can we use SageMaker Lakehouse without replacing our existing Redshift and S3 setup?

Amazon SageMaker Lakehouse is designed to extend your current Redshift and S3 environments. It is used as a unified logical layer on your existing infrastructure, allowing seamless access to S3-based Iceberg tables and Redshift-managed storage. You can continue using both services independently while gaining a consistent data governance model and enhanced interoperability between tools and workloads.

What security and compliance capabilities does SageMaker Lakehouse offer?

Amazon SageMaker Lakehouse enforces fine-grained data access controls through AWS Lake Formation and IAM, supporting row-, column-, and cell-level permissions. These controls are applied consistently across query engines such as Redshift, Athena, and Spark. The platform supports strict compliance requirements, including GDPR, HIPAA, and SOC 2, and integrates with AWS CloudTrail and Amazon DataZone for auditing and governance traceability.

Can SageMaker Lakehouse support complex enterprise workloads with diverse data sources?

Yes, it is designed to operate at enterprise scale, supporting petabyte-level storage and 20–100+ data sources. It allows ingestion from operational databases, SaaS systems, and legacy sources through zero-ETL and federated query capabilities. Under LEAP, N-iX architect lakehouse environments that unify analytics and governance for complex, multi-format datasets without re-platforming or duplicating assets.

Can we still benefit from Amazon SageMaker Lakehouse if we use Snowflake?

Absolutely. SageMaker Lakehouse is built on the open-source Apache Iceberg format, which enables compatibility with Iceberg-supporting platforms like Snowflake. This functionality allows enterprises to maintain their Snowflake investments while centralizing governance, reducing data duplication, and simplifying architecture using SageMaker Lakehouse.

What are the cost implications of adopting Amazon SageMaker Lakehouse?

SageMaker Lakehouse is a cost-optimized service by design. You only pay for the AWS resources used, storage on S3, compute on Redshift Serverless, and metadata management via Glue Data Catalog. Automated table optimization and zero-ETL ingestion reduce operational overhead and long-term infrastructure costs. N-iX applies LEAP methodologies to assess cost-performance trade-offs early, preventing budget overruns and maximizing ROI.

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