Intelligent Data Platforms Built for Real-World Use Cases
At Badaa Data, we design and engineer scalable, cloud-native, and AI-ready data platforms tailored to real business use cases. Our solutions transform complex data into actionable intelligence, enabling faster decision-making, operational efficiency, and measurable growth across analytics, reporting, and AI initiatives.
Build Once, Run Anywhere - Open Hub
The Problem: Growing AWS costs, limited
flexibility, and vendor lock-in make scaling and multi-cloud movement difficult as workloads increase.
Our Solution (Cloud-Agnostic Data Platform)
A fully cloud-agnostic, open-source data platform using Minio, (Spark) (Kubernetes), Trino, Airflow, Kafka, and Kubernetes -removing reliance on AWS services like S3, EMR, Kinesis, and Step Functions.
Business Benefits
30% cost savings, full cloud flexibility, scalable high-performance workloads, multi- cloud readiness, and a future-proof open-source architecture.
Tech Stack
Spark, Kubernetes, Airflow, Trino, Data Lake, RDBMS, APIs, Power BI
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Market Data Analysis (Snowflake) - ELT
The Problem:
Market data ingestion was slow, inconsistent, and not scalable -delaying reporting and real-time insights for trading and risk teams.
Our Solution:
Implemented an ELT pipeline on Snowflake with raw data loaded from S3 via DMS, and all transformations done inside Snowflake using SQL/Snowpark for fast, scalable processing.
Business Benefits
70-80% faster data availability Lower operational cost Clean, unified data improving decision-making across teams
Tech Stack
Python, Airflow, AWS (S3), Snowflake, APIs
Lead pipeline - Intelligent Real-Time
The Problem:
High lead volumes cause delays, inconsistent data quality, and no real-time visibility - resulting in slow lead assignment, missed opportunities, and lower conversions.
Our Solution: Real-Time Leads Streaming Pipeline
A real-time, fault-tolerant leads streaming pipeline using AWS Kinesis, Lambda, Spark, and a custom engine to ingest, clean, enrich, categorize, and instantly route qualified leads.
Business Benefits
40% Faster Reporting: Automation delivers daily analytics-ready data. reduced manual work, and a scalable future-ready streaming setup.
Tech Stack
Spark, Kafka, Data Lake, Trino, APIs, Load Balancer, Power BI, AI/ML
From Raw Data to Insights - ETL Engine
The Problem:
Large daily data volumes lead to slow processing, inconsistent quality, and delayed reporting-making it hard for teams to generate timely insights and slowing down decision-making.
Our Solution: Event-Driven ETL Engine
A scalable, event-driven ETL engine using Spark on EMR, S3, Redshift, and Airflow to automate ingestion, transformation, and loading-delivering clean, analytics-ready datasets every day.
Business Benefits
40% faster reporting, improved data accuracy, reduced operational cost, seamless scalability, and timely insights for stronger, data-driven decisions.
Tech Stack
Spark, Kafka, Kubernetes, Airflow, AWS, Data Warehouse, Power BI
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Product Performance Analysis (Data Engineering + ML)
The Problem:
Manual product metadata tagging was slow (7 days), inconsistent, costly, and unable to handle large daily volumes - causing poor catalog freshness, weak search, and delayed insights.
Our Solution:
Gen AI-based automated tagging + event- driven ETL on GCP (Vertex AI, Pub/Sub, Big Query, Cloud Composer) enabling real-time ingestion, Al attribute extraction, and near real-time data refresh.
Business Benefits
Tagging time reduced 7 days → hours, improved metadata quality, better search/conversions, ~$500K annual savings, daily analytics-ready data, and a fully scalable cloud-native system.
Tech Stack
GCP, Python, BigQuery, Cloud Composer, Vertex AI, Gemini