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Decryptogen FAQ

Data Engineering & Analytics FAQ

We build intelligent data platforms where engineering, analytics, and machine learning converge. Our architecture combines:
  • Scalable data lakes and warehouses
  • Smart ETL orchestration using AWS Glue
  • ML model embedding (forecasting, classification, anomaly detection)
  • Event-driven and batch processing
  • Compliance-ready data governance with audit controls

We use a robust AWS-native stack, including:
  • AWS Glue – ETL automation, crawlers, schema registry
  • Amazon Redshift – columnar data warehouse
  • AWS Lake Formation – data lake security and access control
  • Amazon S3 – scalable storage for raw and curated data
  • Amazon SageMaker – ML model training and deployment
  • Amazon Athena – interactive SQL on S3-based data
  • EventBridge, Step Functions – for workflow orchestration

  • Real-time: We use Amazon Kinesis, MSK, and Lambda for continuous data flow
  • Batch: AWS Glue, Step Functions, and scheduled jobs power heavy-lift batch processing
  • Our systems can combine both modes for hybrid needs (e.g., real-time alerts + nightly reports)

Absolutely. Decryptogen uses:
  • SageMaker + Lambda to deploy ML models directly into ETL/ELT pipelines
  • Models for image-based grading (tea leaves), waste classification, and student engagement prediction
  • Feature engineering pipelines for predictive analytics
  • GenAI for semantic insights, data enrichment, and classification

We’ve delivered measurable impact in:
  • Agriculture: ML image models for tea grading
  • Manufacturing: GenAI for waste classification
  • Hospitality: Sentiment analysis and experience enhancement
  • Logistics: Supply chain and delivery data optimization
  • Education: Emotion analysis via data and AI-powered learning assistants

We apply a multi-layered approach:
  • Data profiling & validation via AWS Glue
  • Schema evolution tracking with Data Catalog
  • Lake Formation & IAM for fine-grained access
  • Encryption, audit logs, and HIPAA/GDPR/ISO27001 compliance built-in

Yes. We provide end-to-end data migration:
  • Legacy → AWS Glue + Redshift
  • Use AWS DMS for CDC (Change Data Capture) with minimal downtime
  • Convert stored procedures, views, and ETL logic to serverless
  • Ensure full data reconciliation & validation during cutover

Unlike dashboard-only providers, we build AI-native decision engines:
  • GenAI insights layered onto structured datasets
  • Contextual personalization, auto-tagging, alerting
  • Self-optimizing pipelines
  • Predictive + prescriptive analytics in every layer

Yes. We build interoperable architectures:
  • Ingest from Azure, GCP, and on-prem
  • Data replication between Redshift, Snowflake, and BigQuery
  • Lakehouse design patterns that span environments
  • Secure cross-cloud governance frameworks

Email: sales.smith@decryptogen.com
Form: https://decryptogen.com/contact
We offer a free Data Platform Readiness Assessment for qualified enterprises.