Data Platform
Introduction
Enterprises operating in regulated or performance-critical environments require on-prem data platforms that deliver:
- Warehouse-grade reliability
- Lake-grade flexibility
- Support for BI, analytics, and Al
- Full control over data, cost, and governance
VigourSoft designed a scalable on-prem Lakehouse architecture to address these needs.
Problem
Typical challenges observed across enterprise customers:
- Fragmented data across systems and teams
- Traditional data warehouses unable to scale or adapt
- Data lakes lacking governance, reliability, and performance
- Separate platforms for BI, analytics, and ML
- Increasing infrastructure and operational costs
Why Traditional Architectures Fail
A. Data Warehouses:
- Rigid schemas
- Expensive to scale
- Poor support for modern analytics
B. Data Lakes
- Lack of ACID guarantees
- Data quality and trust issues
- Difficult governance
Result: High cost, slow insights, limited innovation
Our Solution: On Prem Lakehouse Architecture
We designed and implemented a modern Lakehouse architecture that combines the flexibility of a data lake with the reliability of a data warehouse.
Key Capabilities:
- ACID transactions for reliable reads/writes
- Schema evolution & versioned datasets (time travel)
- Unified platform for BI, analytics, and ML
- Open standards to prevent vendor lock-in
Architecture Overview (High Level)
Storage Layer
- Distributed/object storage (HDFS, Ozone, MinIO, Ceph)
- Encrypted, fault-tolerant, scalable storage foundation
Table Format Layer
- Apache Iceberg (preferred)
- ACID transactions
- Schema evolution
- Time travel & rollback
- Engine-agnostic access
Compute & Query Engines
- Apache Spark – batch processing, ML, streaming
- Trino / Dremio – interactive SQL analytics
- Flink / Spark Streaming – real-time pipelines
Data Engineering & Processing Model
Medallion Architecture
- Bronze – Raw ingested data
- Silver – Cleaned, standardized datasets
- Gold – Business-ready facts & dimensions
Ingestion Patterns
- Batch & micro-batch ingestion
- CDC via Kafka Connect / Debezium
- API and file ingestion using NiFi
Processing Patterns
- Incremental ETL/ELT
- Stateful streaming
- Scalable joins & aggregations
Use Cases
Use Case 1: Integrated Sales, Marketing, and CRM Platform
Challenges
- Disparate Data Sources across CRM, Marketing Analytics, and Databases.
- Inconsistent schemas, formats, and ingestion frequencies
- Poor data quality (duplicates, nulls, mismatched timestamps)
- Manual reporting with slow and unreliable insights
Technologies
- Airflow, Python, PySpark, Hive, MySQL, MixPanel, Google Analytics
Solution
- Centralized Lakehouse to unify all data sources.
- Automated ingestion and orchestration using Airflow
- Bronze-Silver-Gold layered data modelling approach.
- Scalable transformation using PySpark
Benefits
- Single source of truth for customer and sales data
- Clean analytics ready datasets with high data reliability
- Faster insights through automated dashboards and KPIs
- Scalable platform supporting future data growth.
Use Case 2: Fleet, Routing and Delivery Intelligence
Challenges
- High Oracle costs and performance bottleneck for large scale logistics analytics.
- Disparate data across Oracle logistics, web/app systems, and analytics platforms
- Need for near real time visibility into orders, fleet, and delivery status
- Inefficient routing, fleet utilization, and high support load due to siloed data
Technologies
- GoldenGate, Python, PySprak, Hive, Kafka, Spark Streaming, ElasticSearch, HBase
Solution
- Offload Oracle data into a centralized Lakehouse using CDC and Streaming Pipelines
- Implement near real time ingestion with GoldenGate, Kafka, and Spark streaming
- Apply delta detection, deduplication, and upserts for frequently changing logistics Data
Benefits
- Reduced Oracle infrastructure cost and improved system performance
- Near real time delivery visibility improves customer experience and SLA adherence
- Optimized routing and fleet allocation reduce fuel cost and delivery delays
- Chatbot driven insights lower support effort and improve resolution time
Use Case 3: Enterprise Healthcare Data Modernization
Challenges
- Highly fragmented healthcare data across flies, Oracle, S3, NAS, and clinical systems
- Legacy Oracle stewardship limits scalability, governance, and metadata visibility
- Strict HIPPA, PHI/PII security, auditability, and compliance requirements
- Slow reporting and lack of real time operational, financial, and clinical insights
Technologies
- Sqoop, Flume, Kafka, Atlas, Ranger, Kerberos, PySprak, Spark, Spark ML, Hive, Cloudera, Lakehouse, Impala, Solr
Solution
- Build a unified, governed healthcare lakehouse on cloudera integrating all data sources
- Implement secure ingestion and medallion pipelines using spark/PySpark for cleansing and enrichment
- Replace legacy governance with Atlas, Ranger, and Kerberos for lineage, security and Access control
Benefits
- Single, trusted source of truth for patient, clinical, insurance, and financial data
- Faster, automated reporting with real time visibility for leadership and operations
- Strong compliance posture with full lineage, audit logs, and PHI/PII protection
- Scalable foundation for predictive analytics, fraud detection, and ML driven insight
Use Case 4: On Prem Lakehouse AI/LLM Ops
Challenges
- Unstructured enterprise data is not analytics ready & difficult to operationalize for LLM’s
- Managing end to end RAG lifecycle is complex at scale
- Lack of traceability from LLM responses back to data, embeddings, prompts, and models
- Strict security, governance, and efficient on prem GPU scaling requirements
Technologies
- Nifi, Kafka, Hive, Iceberg, Milvus/ pgvector, LangChain, LlamaIndex, PySprak, Spark, Hugging Face/Llama models, VLLM, Ranger, Kerberos, Atlas, Mlflow, Prometheus
Solution
- Use of governed on prem Data Lakehouse to store versioned data, embeddings, prompts, and metadata
- Build scalable text processing, chunking, and embedding pipelines using Spark PySpark
- Deploy production grade inference with vLLM and full observability across data
- Standardize RAG worflows with Langchain/Llamalndex and vector search using Milvus or pgvector
Benefits
- Trusted, traceable, and auditable LLM responses grounded in enterprise data
- Reduced hallucinations and better answer quality trhough versioned RAG pipelines
- Scalable, cost efficient on prem LLM platform without vendor lock in
- Strong governance, security and compliance alignment for regulated environments
Challenges We Solve At Scale
Technical
- Small files & metadata bloat
- Performance degradation
- Upserts at scale
- Multi-engine consistency
Operational
- Schema drift
- Broken pipelines
- Compute cost spikes
Architectural
- Legacy warehouse migrations
- Hybrid connectivity
- Unified lineage & governance
Why On Prem LakeHouse
- Lower long-term cost vs cloud
- Predictable infrastructure spend
- No vendor lock-in
- Optimized hardware utilization
- Proven savings at scale
Vigoursoft’s Differentiation
- Deep experience with data platforms
- Strong open-source expertise
- End-to-end ownership: architecture → pipelines → governance
- Designed for scale, performance, and future-proofing
VigourSoft Office Locations & Contact Details
- Locations: HQ, California (USA); Partner Delivery Center (POLAND); Delivery Center (Pune & Bengaluru, INDIA)
- Email: info@vigoursoft.com
- Phone: +1-669-238-5102, +91-84466-31706
- Contact Person: Prasad Kulkarni, VigourSoft Global Solutions || www.vigoursoft.com | prasad.kulkarni@vigoursoft.com
Breaking Free: The Journey from Oracle to PostgreSQL
Imagine this: Your company has been running on Oracle for years. It’s familiar, it works, but the licensing costs make you question - is this really worth it?
Your engineering team is frustrated with the rigid structure, scalability isn’t seamless, and making changes feels like jumping through hoops.
Enter PostgreSQL - the powerful, open-source database that’s flexible, cost-effective, and cloud-ready. It’s becoming the go-to choice for enterprises making the shift.
Why move away from Oracle?
- It’s expensive: Rising licensing fees make it costly.
- Vendor lock-in: Oracle’s proprietary ecosystem restricts flexibility.
- Scaling is complex: Performance tuning and cloud deployment are challenging.
The tech landscape is evolving - businesses now seek more control, flexibility, and cost savings. This is where PostgreSQL shines.
Why PostgreSQL? The Logical Next Step
- Open-source & free: No more licensing headaches.
- Feature-packed: Advanced indexing, replication, partitioning, and security.
- Cloud-native: Integrates seamlessly with AWS, GCP, and Azure.
- AI & Analytics-ready: Supports AI-driven insights and vector search.
PostgreSQL isn’t just an alternative - it’s an upgrade.
How VigourSoft Helps in PostgreSQL Migration
Migrating from Oracle to PostgreSQL isn’t a simple copy-paste job. It requires careful planning and expertise. At VigourSoft, we ensure a smooth transition with minimal disruption.
Our Migration Process:
- Step 1: Assessment & Strategy – Evaluate database structure, dependencies, and build a roadmap.
- Step 2: Schema & PL/SQL Conversion – Optimize schemas, convert stored procedures.
- Step 3: Data Migration & Validation – Use tools like Ora2Pg for seamless migration.
- Step 4: Query & Application Optimization – Performance tuning and query refactoring.
- Step 5: Testing & Go-Live – Ensure a flawless transition before deployment.
Common Challenges & Our Solutions
- PL/SQL Compatibility? → We optimize and rewrite procedures for PostgreSQL.
- Performance Issues? → Fine-tune indexing, caching, and partitioning.
- Data Integrity Concerns? → Rigorous testing ensures accuracy.
Migrations don’t have to be painful - they just need the right partner.
Businesses That Made the Leap
Companies across industries are moving away from Oracle and embracing PostgreSQL - cutting database costs by up to 80%, improving performance, and gaining flexibility.
At VigourSoft, we’ve helped enterprises make this switch seamlessly, optimizing databases for high performance, scalability, and long-term savings.
Thinking About Migration? Let’s Talk!
If you’re still on Oracle and considering PostgreSQL, now is the time. Contact Us Today!