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.
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.
Learn moreThree 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.
Learn moreUnoptimised 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.
Learn moreModels 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.
Learn moreMost 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.
Kinesis Data Streams · Kinesis Firehose · AWS DMS · Apache Kafka (MSK) · AppFlow · AWS Glue · Lambda · Debezium CDC
Learn moreAmazon S3 · Lake Formation · Hadoop (HDFS) · DynamoDB · ElastiCache · Glacier · S3 Intelligent-Tiering
Learn moreAmazon Redshift · Athena · Glue ETL · EMR (Spark, Hive) · Snowflake · BigQuery · dbt · Redshift Spectrum
Learn moreQuickSight · Tableau · Power BI · Apache Superset · Metabase · Grafana · Looker · OpenSearch (Kibana)
Learn moreSageMaker · Feature Store · SageMaker Pipelines · Rekognition · Forecast · Fraud Detector · Comprehend · MLflow · Bedrock
Learn moreLake Formation · Glue Data Catalog · Macie · Airflow (MWAA) · Great Expectations · Monte Carlo · Python (PySpark) · Spark · SQL · Scala
Learn moreLoan 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%.
Learn moreMillions 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.
Learn moreChurn prediction model trained quarterly on stale data — automated SageMaker retraining with daily feature refresh reduced monthly churn by 18% within two quarters.
Learn moreDaily 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.
Learn moreA UAE BNPL platform had no analytics visibility on loan performance post-MVP. Flentas built a serverless analytics pipeline — S3 data lake, Athena, Glue ETL, QuickSight — with anomaly detection and infrastructure costs cut through serverless-first architecture.
A gaming studio generated petabytes of event data on Firebase with no monetisation analytics. Flentas migrated the stack to AWS — Kinesis Firehose, partitioned S3 data lake, Athena, and SageMaker player behaviour models — handling 40x data volume growth.
A high-growth SaaS platform had manual monthly reporting and zero ML in production. Flentas built automated dbt pipelines, a Redshift warehouse, and SageMaker churn prediction — the model was live in production within 6 weeks.
“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
Your new data platform needs 24x7 monitoring and incident response — so pipelines don't break at 2AM without anyone noticing.
Explore Cloud Managed ServicesStill running your data warehouse on-premise? Flentas migrates your legacy data estate to AWS before we build the analytics layer on top.
Explore Cloud MigrationData pipelines that ship manually break manually. CI/CD automation for your data workflows — automated testing, deployment, and rollback.
Explore DevSecOps & Automation