Data, Analytics & Cost Optimisation

Data Management#

Modern data architecture for storage, processing, and analytics at enterprise scale.

What Aezona builds and manages:

Data Lake Implementation#

Design and deploy a scalable data lake on AWS S3, Azure Data Lake Storage, or GCP Cloud Storage. Includes:

• Landing zone, curated zone, and consumption zone architecture
• Access control and encryption per zone
• Lifecycle policies for cost management
• Integration with downstream analytics tools

ETL Pipeline Development#

Build and maintain extract, transform, and load pipelines using:

• AWS: Glue, Lambda, Step Functions, Kinesis
• Azure: Data Factory, Event Hubs, Synapse Pipelines
• GCP: Dataflow, Pub/Sub, Cloud Composer (Airflow)
• Open source: Apache Spark, dbt, Airbyte

Pipelines are version-controlled, monitored, and alerting on failure.

Data Governance Framework#

Implement policies and tooling to ensure data quality, lineage, and access control:

• Data catalogue (AWS Glue Data Catalog, Apache Atlas, or Collibra)
• Column-level access controls for sensitive data
• Data lineage tracking from source to consumption
• GDPR and CCPA compliance controls — right-to-erasure workflows

Quality Management#

Automated data quality checks built into your pipelines:

• Schema validation and type checking
• Row count and null-value thresholds
• Statistical anomaly detection on key metrics
• Alerting and pipeline halt on quality failures

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Analytics & Business Intelligence#

Transform raw data into actionable insights with powerful analytics and BI solutions.

Data Visualisation#

Design and build dashboards in your chosen BI tool:

Tool	Use case
Tableau	Complex enterprise dashboards, embedded analytics
Power BI	Microsoft-stack organisations
Looker / Looker Studio	Google Cloud, self-service analytics
Grafana	Operational metrics and infrastructure observability
Metabase	Lightweight open-source BI

Predictive Analytics#

Apply machine learning to your business data:

• Demand forecasting (sales, capacity, inventory)
• Churn prediction and customer lifetime value modelling
• Anomaly detection on business metrics
• Recommendation systems

Models are trained, validated, and deployed as managed endpoints. Drift detection and retraining pipelines are included.

ML Integration#

Integrate ML models into your data pipelines and applications:

• SageMaker, Azure ML, or Vertex AI model deployment
• Feature store design and management
• A/B testing frameworks for model comparison
• Monitoring for prediction quality and data drift

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Cost Optimisation#

Maximise your cloud investment through continuous cost management and waste elimination.

What's included:

Spend Analysis and Reporting#

Monthly cloud spend reports broken down by:

• Service (EC2, RDS, S3, networking, etc.)
• Team / cost centre (using resource tagging)
• Environment (production, staging, development)
• Region

Reports include month-over-month trends and anomaly flagging for unexpected spend increases.

Reserved Instance and Savings Plan Planning#

Analysis of your on-demand usage patterns to identify:

• EC2 and RDS Reserved Instance opportunities (1-year or 3-year)
• Compute Savings Plans for flexible coverage
• Spot Instance opportunities for fault-tolerant workloads

Aezona handles the purchase recommendation and tracks utilisation to ensure you're not over-committed.

Waste Identification#

Monthly scan of your environment for:

• Unattached EBS volumes and unused Elastic IPs
• Stopped EC2 and RDS instances incurring charges
• Over-provisioned instance classes
• S3 buckets without lifecycle policies
• Idle NAT Gateways and underused load balancers

Budget Forecasting#

Set monthly and quarterly budgets per team, environment, or service. Aezona:

• Configures cloud provider budget alerts at 80% and 100% thresholds
• Provides a 30-day rolling forecast updated weekly
• Flags projects trending over budget before the billing period ends