Building a Production-Ready Lakehouse on AWS with Apache Iceberg
A practical blueprint for an S3 + Iceberg lakehouse on AWS: layers, services, and the operational details that decide whether it succeeds.
AWS Data Engineering · AI/ML · GenAI
Turn your data into production-grade AI
Praitna helps companies design, build and staff modern data platforms on AWS and put machine learning and LLMs to work, with the engineering rigor to run them reliably.
Data Lakes & LakehousesAmazon Bedrock & RAGSageMaker MLOpsStaff Augmentation
What we do
End-to-end data and AI capability, delivered your way
From the first pipeline to LLM-powered applications, we bring hands-on practitioners who have built these systems in production.
AWS Data Engineering
Scalable batch and streaming pipelines, data lakes, and lakehouses built on AWS-native services and open table formats.
Learn moreGenerative AI & LLM Solutions
Take LLM ideas from prototype to production: retrieval-augmented generation, agents, and evaluation, with security built in.
Learn moreMachine Learning & MLOps
Repeatable paths from notebook to production: training, deployment, monitoring and retraining on Amazon SageMaker.
Learn moreData Platform Modernization
Move legacy warehouses and Hadoop workloads to AWS with a clear plan, without disrupting the business.
Learn moreStaff Augmentation
Vetted data, ML and cloud engineers who join your team quickly and contribute from the first sprint.
Learn moreData & AI Strategy
Focused assessments that turn "we should do something with AI" into a prioritized, fundable roadmap.
Learn moreHow we engage
Flexible models that fit your stage
Need an extra engineer next week, a team to own a platform build, or an expert second opinion? Pick the model that matches the problem.
Staff Augmentation
Add skilled engineers to your team, working in your tools and rituals under your direction.
Best when: You have a roadmap and need hands.
Project Delivery
We own a defined outcome end to end, from architecture to production, with a clear scope and milestones.
Best when: You need a capability built and handed over.
Advisory & Assessment
A short, focused engagement to review architecture, find risks and cost savings, and plan next steps.
Best when: You need clarity before committing budget.
Why Praitna
Practitioners, not slideware
We are engineers first. Every recommendation we make is one we would be comfortable building, operating and being on call for.
About usProduction-first engineering
Infrastructure as code, testing, observability and documentation are part of the job, not extras.
Deep AWS expertise
Glue, EMR, Redshift, Kinesis, SageMaker, Bedrock and more, used the way AWS intends, with cost in mind.
Security & confidentiality
Least-privilege access, data protection and strict confidentiality for your systems and information.
Knowledge transfer built in
We leave your team stronger, with runbooks, pairing and clean handover so you are never locked in.
Our approach
A simple, transparent delivery process
01
Discover
Understand your goals, data, constraints and what success looks like.
02
Design
Agree on architecture, scope, team and a delivery plan you can trust.
03
Build
Ship in short iterations with demos, tests, documentation and IaC.
04
Operate & Transfer
Monitor, optimize and hand over knowledge so your team owns it.
Technology
The stack we work in every day
Amazon S3AWS GlueAmazon EMRAmazon RedshiftAmazon AthenaAmazon KinesisAmazon MSKAWS LambdaStep FunctionsLake FormationAmazon SageMakerAmazon BedrockOpenSearchApache SparkApache IcebergApache AirflowdbtSnowflakeDatabricksKafkaPythonTerraformAWS CDKLangChain
Insights
All articles From the blog
RAG on AWS: Taking LLM Prototypes to Production
Retrieval-augmented generation demos are easy. Production RAG is not. Here is what changes between the two, and how to build it on Amazon Bedrock.
Staff Augmentation vs. Project Delivery: Choosing the Right Model for Data & AI Work
When should you add engineers to your team, and when should you hand off an outcome? A practical guide for data and AI leaders.
Have a data or AI initiative in mind?
Tell us what you are trying to achieve. We will respond within one business day with practical next steps, whether that is a project, an assessment, or the right engineer for your team.