Your data is piling up in S3, Redshift is timing out on every report, and your analysts are still waiting three days for a query that should run in three minutes. Flentas designs and builds your end-to-end data engineering stack — from ingestion and transformation to warehousing, BI, and ML-ready pipelines — so your business runs on insight, not gut feel.
Your data is piling up in S3, Redshift times out on every report, and analysts wait days for a query that should take minutes.
Nobody owns the pipeline, so a question that should run in three minutes waits three days in a queue.
Overlapping stacks and a spreadsheet nobody trusts mean every metric has three answers.
Raw, processed and aggregated data all hit the same cluster, and full-table scans burn the budget.
Unoptimized clusters and unpartitioned tables make every dashboard refresh a line on the invoice.
Models trained in Jupyter never reach production because there's no pipeline to carry them.
Personal data scattered across dozens of tables with no catalog is a DPDP audit waiting to happen.
Reports that should take seconds are taking days because nobody owns the pipeline. Production-grade data pipelines on AWS — ingestion to dashboard — give every team accurate, real-time answers without raising a ticket.
Three ETL jobs, two data warehouses, and a spreadsheet nobody trusts. Flentas consolidates your data architecture onto a unified, scalable AWS stack — one source of truth your analysts, engineers, and executives all agree on.
Unoptimised Redshift clusters and full-table scans are burning your cloud budget. Flentas right-sizes your warehouse, partitions your data lake, and configures serverless query execution — up to 60% reduction in compute costs.
Models trained in notebooks that never reach production. SageMaker-powered ML pipelines with automated retraining, feature stores, and inference endpoints — so your models actually drive decisions, not quarterly slide decks.
Most teams don't know what data they have, where it lives, or why three systems disagree on the same metric. A data architecture audit catalogues sources, maps flows, and scores data quality — so we fix the right problems first.
Brittle cron jobs and manual ETL scripts fail silently until a stakeholder notices the dashboard is stale. Event-driven ingestion pipelines using AWS Glue, Kinesis, and Lambda — with automated alerting and retry logic so data flows reliably.
Raw, processed, and aggregated data all queried from the same place is why your Redshift cluster times out. Flentas designs your storage architecture — S3 data lake, Redshift warehouse, and Athena for serverless ad-hoc queries — giving each workload the right engine.
BI tools nobody trusts because the numbers change on every refresh. Semantic layer definitions, certified datasets, and QuickSight or Tableau dashboards with row-level security — finance, operations, and product all see the same truth.
Data science prototypes living in Jupyter notebooks that never ship. SageMaker pipelines with feature stores, model registries, A/B testing endpoints, and automated retraining — your models run in production, not in a slide deck.
PII scattered across 14 tables with no lineage documentation is a DPDP audit waiting to happen. AWS Lake Formation access controls, Glue Data Catalog lineage, and data masking policies make your data estate governed, documented, and defensible.
Fintech
60% Reduction in Incident TAT
Loan origination data siloed across five systems with no single risk view — a serverless analytics pipeline on AWS delivers real-time credit decisioning dashboards, cutting analyst query time by 80%.
Gaming
35% In-App Purchase Conversion Uplift
Millions of game events per second with no monetisation visibility — a Kinesis-to-S3-to-Athena pipeline enabled real-time player behaviour analytics and a 35% uplift in in-app purchase conversion.
SaaS
4 hrs Reporting Time, Down from 3 Days
Churn prediction model trained quarterly on stale data — automated SageMaker retraining with daily feature refresh reduced monthly churn by 18% within two quarters.
Daily sales reports delivered the following afternoon via emailed spreadsheets — an automated QuickSight pipeline gives merchandising teams live inventory and revenue visibility across 12 product categories.
“Our analysts were spending three days every week just preparing data for reports that were already out of date by the time they landed in inboxes. Flentas redesigned our entire data pipeline on AWS — Glue, Redshift, QuickSight — in under eight weeks. Reports that used to take three days now run in under four hours. The dashboards update every 15 minutes. We made better pricing decisions in the first month than we had in the previous two quarters combined.”
VP of Data & AnalyticsLeading E-Commerce Platform, India
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One governed copy of your data, ready for BI and AI.
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Explore Agentic Cloud OperationsBook a free data architecture audit. We'll catalogue your sources, score your data quality, and show you the path from broken pipelines to dashboards your board actually trusts.