Data & Analytics

Eleks builds and runs data platforms on AWS for organizations whose information sits scattered across ERP, CRM, SaaS APIs and on-premises databases, with no single trusted source and reporting that arrives days late. It also suits teams whose on-premises Oracle, SQL Server, Teradata or Hadoop platform has become expensive or unsupportable, whose AWS analytics estate works but costs more every quarter with no clear explanation why, or whose AI initiative has stalled for want of a governed data foundation. Where a lean data team can run a platform but cannot build one, we build it and hand it over.

We engage at any point in the lifecycle. Each track is scoped and priced independently and can be delivered on its own or in sequence:

Assessment and architecture - A measured baseline of what your platform costs and how it performs today, a review against the AWS Well-Architected Data Analytics Lens, a target architecture, and a prioritized roadmap carrying a fixed price for the build.

Warehouse, lakehouse or data lake build - Whichever fits the workload, on Amazon S3 with Apache Iceberg tables, AWS Glue, Amazon EMR Serverless, Amazon Redshift Serverless and Amazon Athena, orchestrated with Amazon MWAA or AWS Step Functions. Every team works from the same figures, and cost tracks query volume rather than provisioned peak.

Data integration - Sales, finance, operations and customer data brought into one trusted view through automated AWS Glue pipelines, with change data capture from your operational databases via AWS DMS. Adding a new system or an acquired business takes days.

Data quality - AWS Glue Data Quality rules enforced in-pipeline check completeness, duplication and freshness before a figure reaches a report. What fails is held back and its owner alerted through Amazon CloudWatch, against a stated freshness threshold.

Data governance and security - Access becomes provable at column level with AWS Lake Formation, catalogued in Amazon DataZone, with Amazon Macie finding personal data, AWS KMS customer-managed keys and AWS CloudTrail audit trails - what regulatory review requires, and what lets data be shared across domains without over-exposing it.

Legacy warehouse and Hadoop migration - Old and new run side by side until reconciliation proves the outputs match, using AWS DMS Schema Conversion and AWS DMS onto Amazon EMR or an AWS Glue lakehouse. Cutover risk goes, and so do license, hardware and support costs.

Business intelligence and self-service - Metric definitions live in a semantic layer rather than in individual reports, so the same figure means the same thing in every Amazon QuickSight dashboard. Amazon Q in QuickSight answers questions in plain language, and business users extend the model themselves.