AWS Data Platforms Built to Last

We build data lakes and warehouses on AWS that stay maintainable after the first project ends. That means partitioned open formats on S3, catalogued so anyone can find them, with cost controls in place before the bill teaches you the lesson.

Our team holds four AWS certifications including Data Engineer Associate, Solutions Architect Associate, Data Analytics Specialty and Database Specialty. Several of the client testimonials on this site come from AWS migration work.

What We Do On AWS

S3 Data Lakes

Open table formats on S3, partitioned and catalogued for real query patterns.

Partitioning and file sizing strategy
Parquet, Iceberg and Delta formats
Lifecycle and storage tiering
Lake Formation permissions

AWS Glue

Serverless ETL, cataloguing and crawlers wired into a coherent framework.

Glue ETL job development
Data Catalog and crawler design
Glue Studio and workflows
Job bookmarking and incremental loads

Amazon Redshift

Warehouse modelling and tuning for concurrency and cost.

Distribution and sort key design
RA3 and Serverless sizing
Redshift Spectrum over S3
Workload management queues

Amazon EMR

Managed Spark and Hadoop for the workloads that need it.

EMR cluster and EMR Serverless design
Spark tuning on EMR
Spot and instance fleet strategy
Step orchestration

Amazon Athena

Ad hoc SQL directly over the lake without standing infrastructure.

Athena workgroup design
Query cost controls
Federated queries
View and UDF patterns

Cost and Security

Guardrails so the platform stays affordable and compliant.

Cost allocation tagging and budgets
IAM least privilege design
KMS encryption at rest and in transit
VPC endpoints and network isolation

Oracle & SQL Server Database Migration to AWS

Legacy commercial databases carry licence costs that grow faster than the data inside them. We move them onto managed PostgreSQL on AWS in stages, with a parallel run against real traffic before anything is switched over.

Oracle to AWS RDS PostgreSQL Migration

Schema conversion with AWS SCT plus manual review of what it cannot translate
PL/SQL packages, triggers and sequences rewritten for PostgreSQL
AWS Database Migration Service for full load and change data capture
Amazon RDS and Aurora PostgreSQL sizing, parameter groups and failover
Parallel run and staged cutover with a documented rollback path

SQL Server Migration and Deadlock Tuning

SQL Server to RDS PostgreSQL or RDS for SQL Server, chosen on workload
Deadlock analysis from extended events with fixes to the access order
Stored procedure tuning and execution plan review
Index consolidation so writes stop paying for reads nobody runs

What You Get

An S3 data lake partitioned and catalogued for the queries you run
Glue or EMR pipelines with incremental loading built in
Redshift modelled and tuned against real concurrency
Athena available for ad hoc analysis without new infrastructure
IAM, KMS and network controls documented for audit
Cost tagging and budgets so spend is attributable

AI & GenAI on AWS

AWS Bedrock gives you managed foundation models without leaving your account. The engineering that decides whether it works is upstream: what gets indexed, how often embeddings refresh and who is allowed to retrieve what. We build that layer on the lake you already have.

Bedrock model access, Knowledge Bases and Guardrails
OpenSearch Serverless vector engine and k-NN indexes
pgvector on Amazon RDS and Aurora PostgreSQL
Embedding pipelines driven by Glue and Step Functions
Lake Formation permissions carried into retrieval
KMS encryption and VPC endpoints for private inference

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AWS Data Engineering & Database Migration