AWS Services

AWS Data & Analytics

AQBEE designs and builds enterprise data platforms on AWS that unify CRM, operational, and third-party data for advanced analytics. From S3 data lakes and Redshift warehouses to real-time streaming with Kinesis and ETL with Glue, we create the data infrastructure that powers AI, BI, and data-driven decision making across your organisation.

Capabilities

What We Deliver

S3 Data Lake Architecture

Design multi-layer data lake architectures on Amazon S3 with bronze/silver/gold zones, data cataloging with AWS Glue, and lake formation governance.

Redshift Data Warehouse

Build performant data warehouses on Amazon Redshift Serverless with Salesforce data replication for cross-functional analytics and executive reporting.

Real-Time Streaming with Kinesis

Implement real-time data streaming pipelines using Kinesis Data Streams and Firehose for live event processing from Salesforce and operational systems.

ETL with AWS Glue

Build automated ETL pipelines using AWS Glue for data transformation, deduplication, and enrichment across Salesforce and enterprise data sources.

QuickSight & Athena Analytics

Deploy interactive dashboards on Amazon QuickSight with Athena-powered queries across your data lake — complementing Tableau and CRM Analytics.

Real-World Applications

How We Apply It

Customer 360 Data Platform

A retail company builds a unified customer data platform on S3 and Redshift, combining Salesforce CRM data with web analytics, POS transactions, and loyalty program data for complete customer intelligence.

Key Highlight

SageMaker customer segmentation models run on the unified dataset, with segment assignments pushed back to Salesforce Data Cloud for personalised marketing.

Real-Time Revenue Dashboard

A SaaS company streams subscription events through Kinesis, joins with Salesforce pipeline data in Redshift, and builds real-time revenue dashboards that update every minute.

Key Highlight

SageMaker forecasting models predict monthly recurring revenue trends based on pipeline velocity and historical patterns.

Compliance Data Archive

A healthcare organisation archives Salesforce Health Cloud records to S3 with lifecycle policies, encryption, and audit trails for HIPAA-compliant long-term retention and regulatory queries.

Key Highlight

Amazon Comprehend Medical extracts clinical entities from archived records for retrospective analysis and compliance reporting.

Case Studies

AWS Data & Analytics Success Stories

See how we've delivered measurable results with AWS Data & Analytics.

Serverless Data Pipeline & Real-Time Analytics on AWS

8 hours → 30 seconds

Data Latency

Technology

Serverless Data Pipeline & Real-Time Analytics on AWS

Velocity Analytics Inc.

Velocity, a B2B analytics platform processing 500M+ events daily, was running a fragile on-premise ETL pipeline that took 8 hours to produce daily reports. Data latency meant customers were making decisions on stale information. Infrastructure costs were escalating with every new client onboarding, and the engineering team spent 40% of their time on pipeline maintenance rather than product development. The company needed a scalable, real-time data architecture that could grow with their client base while integrating processed insights back into Salesforce for their customer success team.

AWS LambdaAmazon KinesisAmazon RedshiftAmazon S3

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Legacy Data Warehouse to BigQuery: Real-Time Analytics for 200M+ Records

24 hrs → Real-time

Data Freshness

Logistics & Supply Chain

Legacy Data Warehouse to BigQuery: Real-Time Analytics for 200M+ Records

TransGlobal Logistics

TransGlobal's legacy on-premise data warehouse was hitting capacity limits with 200M+ shipment records, overnight ETL jobs running 14 hours, and analysts waiting until noon for yesterday's data. The BI team maintained 400+ reports in an ageing Cognos installation, with no self-service capability. Real-time visibility into shipment status, fleet utilisation, and supply chain disruptions was impossible, leaving operations teams reactive instead of proactive.

Google BigQueryDataflowLooker StudioVertex AI

Real-Time Retail Analytics Platform on Google Cloud

MegaMart Analytics

MegaMart operated 1,200 stores nationwide but lacked a unified analytics platform. Merchandising decisions relied on week-old batch reports, and the data team spent 80% of their time preparing data rather than generating insights. The absence of real-time inventory analytics was leading to both stockouts on high-demand SKUs and overstock write-downs exceeding $18M annually.

BigQueryVertex AILookerPub/Sub

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