Stop Reporting on Yesterday's Data
Batch pipelines refreshing once a day mean your business is always running blind. Flentas migrates to event-driven streaming on AWS — decisions backed by data measured in seconds, not overnight jobs.
Learn moreMigrate & Modernize
Data Modernization & Analytics
Your data warehouse hasn't been touched in four years, your analysts run queries on stale exports, and your executives make decisions from dashboards nobody trusts. Flentas eliminates the fragmented pipelines, siloed data lakes, and manual reporting that stall enterprise intelligence — modernising your entire data estate on AWS with AI automating schema migration, pipeline orchestration, and real-time analytics.
Key Benefits
Stop Wrangling Data. Start Using It.
End the Spreadsheet Archaeology
When analysts spend 60% of their week cleaning exports and reconciling conflicting numbers, they're not doing analysis. AI-automated pipelines and a governed semantic layer mean one version of truth — trusted by every team.
Learn moreKill the Warehouse That's Costing a Fortune
Legacy on-premise warehouses compound cost with every terabyte. AI-driven migration to Amazon Redshift or Databricks on AWS cuts storage and compute spend by up to 60% — while quadrupling query performance.
Learn morePut ML Models into Production. Finally.
Most data science teams build models that never leave a notebook. End-to-end MLOps on AWS — automated retraining, versioned deployments, and monitored inference — so your ML investment stops sitting in a Jupyter file.
Learn moreProof Points
60%Average reduction in data infrastructure spend
10xFaster time-to-insight
100%Of ML models delivered reach production
How It Works
How We Modernise Your Data Estate
1
Map What Your Data Actually Is
You can't modernise a data estate you don't understand. AI discovery agents profile every source system, schema, and pipeline — surfacing quality issues, undocumented dependencies, and compliance gaps before a single byte moves.
2
Choose the Architecture That Fits Reality
One architecture does not fit every workload. Flentas maps your query patterns, latency requirements, and budget to the right stack — Amazon Redshift, Databricks on AWS, AWS Glue, or a hybrid lakehouse — so you build once and don't rebuild in 18 months.
3
Migrate Schemas Without Breaking Reports
Schema migrations break downstream dashboards silently. AWS Schema Conversion Tool combined with AI validation automatically maps and tests every transformation — catching breaking changes before analysts notice anything has moved.
4
Replace Fragile Pipelines With Governed Flows
Handcrafted SQL scripts and cron jobs fail quietly and take hours to debug. Orchestrated, monitored pipelines on AWS Glue and Apache Airflow — every flow documented, lineage-tracked, and self-healing when upstream sources change.
5
Deliver Analytics Your Board Can Trust
Conflicting numbers across dashboards destroy analytical credibility. A governed semantic layer ensures every metric is defined once, calculated consistently, and traceable to source — no more "which number is right?" in the boardroom.
6
Ship ML Models That Actually Run in Production
Models that can't reach production have zero business value. MLOps infrastructure on Amazon SageMaker — automated pipelines, model registries, A/B testing, and drift monitoring — so your data science team spends time on models, not DevOps.
Technology Stack
Technologies & Tools We Use
Warehousing & Lakehouse
Amazon Redshift · Databricks on AWS · Lake Formation · Glue Data Catalog · Apache Iceberg · Delta Lake · S3 data lake
Learn morePipeline & Orchestration
AWS Glue (ETL/ELT) · Kinesis · Apache Airflow (MWAA) · EventBridge · Step Functions · dbt
Learn moreMigration & Schema Conversion
AWS Schema Conversion Tool · DMS · DataSync · Snow Family · S3 Transfer Acceleration
Learn moreAnalytics & BI
QuickSight · Athena · OpenSearch · Redshift Serverless · Tableau · Power BI (AWS integrated)
Learn moreAI/ML & MLOps
SageMaker · Bedrock · Amazon Q · SageMaker Pipelines · Model Registry · Feature Store · Comprehend · Rekognition
Learn moreGovernance & Security
AWS Macie · IAM · KMS · Audit Manager · Amazon DataZone · Glue Data Quality · Apache Atlas · Great Expectations
Learn moreUse Cases
Industries & Scenarios We Serve
Fintech / Lending
Credit decisioning running on stale overnight batch data — real-time Kinesis pipelines on AWS cut decisioning latency from 24 hours to under 90 seconds.
Learn moreNBFC / Regulatory Reporting
Regulators demanding audit trails no existing system could produce — a governed data lake on AWS Lake Formation delivers lineage-tracked, RBI-compliant reporting without manual reconciliation.
Learn moreE-Commerce / Retail
Recommendation engine stuck in a notebook for eighteen months — a SageMaker MLOps pipeline shipped the model to production in six weeks; personalisation revenue lifted 23% in 90 days.
Learn moreSaaS / Product Analytics
Product team navigating five conflicting dashboards — a unified semantic layer on Amazon Redshift eliminated dashboard conflicts; product decisions accelerated measurably.
Learn moreCase Studies
Proof, Not Promises
60Days to a Full Lakehouse + ML Pipelines
Cassbana's legacy data infrastructure was blocking the ML roadmap. Flentas delivered a full-stack Databricks-on-AWS migration plus production-ready ML pipelines in under 60 days — data science team shipping models within the first sprint.
ZeroAudit Findings, Full Lineage
Moneyfellows had no audit-ready data lineage for three compliance frameworks. A governed data lake with full AWS Glue cataloguing and automated PCI DSS compliance reporting made every configuration traceable.
45 daysTo a Live Personalisation Engine
Ludo King needed real-time player behaviour data for live personalisation at scale. An event-streaming pipeline on Amazon Kinesis processes tens of millions of events per hour.
“Our analysts were spending three days a week reconciling numbers from five different systems. Nobody trusted the dashboards. Flentas moved our entire data estate to Redshift in eleven weeks, built a semantic layer our BI tools actually connect to, and automated the pipelines that were breaking every Friday night. First board meeting after go-live, the CFO asked why the numbers finally matched. That's the outcome we needed.”
Head of Data & AnalyticsLeading NBFC, India
Related Services
What's Next After This Engagement
Application Modernization
Your data estate is modern but your applications still write to legacy schemas. Modernizing the application layer unlocks the full value of your new data infrastructure.
Explore Application ModernizationCloud Migration
Data modernization on-premise is a ceiling. Moving your workload estate to AWS first removes the infrastructure constraints that limit what your data platform can do.
Explore Cloud MigrationCloud Managed Services
Your new data platform is live. Now who monitors the pipelines at 3am and acts on SageMaker drift alerts before they affect reporting? Flentas 24x7 ops keeps it running.
Explore Cloud Managed Services