Data Engineering & Intelligence
Achieve significant data utilization increase with intelligent pipelines.
Turn scattered, messy data into AI-ready assets. Our battle-tested pipelines deliver enterprise-scale performance with production-ready reliability.
What You Get
Why Data Projects Fail
Most organizations use only a fraction of their data. Here's what's blocking you:
Data Silos & Scattered Systems
Data trapped in multiple disconnected systems. Sales in Salesforce, operations in ERP, support in Zendesk. No single source of truth, causing duplicated work and conflicting reports.
Poor Data Quality & Consistency
Missing fields, duplicates, format inconsistencies. Many decisions based on bad data. Manual cleaning burns hours weekly but problems persist.
Slow Queries & Performance Issues
Reports take hours to run. Databases crash under load. Business users wait days for analytics team. Real-time insights are impossible with current infrastructure.
No Scalability or Modern Stack
Legacy systems can't handle growth. Adding new data sources takes months. No streaming capabilities, no ML integration, no cloud benefits. Technical debt compounds daily.
We've Built Production-Ready Solutions
Battle-tested pipelines delivering enterprise-scale performance. Significant data utilization increase with substantial cost savings.
Multi-Cloud Data Stacks
Complete data engineering on AWS, Azure, or Google Cloud with Apache Spark processing.
AWS Data Stack
Complete AWS ecosystem with Glue for serverless ETL, Athena for SQL on S3, Redshift for petabyte-scale warehousing, S3 data lakes, and EMR for Spark clusters. Automated schema discovery and Parquet/ORC optimization.
- AWS Glue with Data Catalog & crawlers
- Athena serverless SQL with Presto engine
- Redshift MPP with Spectrum for S3 queries
- EMR managed Hadoop/Spark at scale
Azure Data Platform
Unified Azure platform with Data Factory for cloud ETL with extensive connectors, Synapse Analytics combining warehousing and Spark, Databricks with Delta Lake, and Stream Analytics for real-time processing with low latency.
- Data Factory with drag-and-drop pipelines
- Synapse unified analytics with Power BI
- Databricks lakehouse with MLflow
- Stream Analytics with windowing & temporal joins
Google Cloud Services
Serverless GCP stack with BigQuery for separated storage/compute analytics, Dataflow for unified batch/streaming Apache Beam, Dataproc with Lightning Engine for faster processing, and native ML integration with Vertex AI.
- BigQuery petabyte-scale SQL analytics
- Dataflow auto-scaling Apache Beam
- Dataproc second-by-second billing
- Cloud Storage with BigQuery integration
Processing & Analytics
Apache Spark unified engine with significantly faster processing than Hadoop, PySpark Python API, Spark SQL with Catalyst optimizer, vector databases for semantic search, and GraphRAG for knowledge synthesis and multi-hop reasoning across data.
- Apache Spark batch/streaming processing
- PySpark with DataFrames & RDD operations
- Vector databases for instant semantic search
- GraphRAG for knowledge synthesis
Proven Results
Built by Cognilium - proven data engineering capabilities.
Complete Data Engineering Ecosystem
AWS, Azure, GCP with unified orchestration and monitoring
Lambda architecture for both streaming and batch processing
RBAC, PII masking, audit logs, GDPR/HIPAA compliance
Multi-Cloud Data Infrastructure
Complete data stacks on AWS, Azure, or Google Cloud - choose based on your existing infrastructure.
AWS Data Stack
AWS Glue
Serverless ETL with Data Catalog, automated schema discovery & crawlers
Amazon Athena
Serverless SQL on S3 with Presto engine & federated queries
Amazon Redshift
Petabyte-scale MPP warehouse with Spectrum for S3 queries
AWS EMR
Managed Hadoop/Spark clusters for big data at scale
Azure Data Platform
Azure Data Factory
Cloud ETL with extensive connectors & event-driven triggers
Azure Synapse
Unified warehousing, Spark pools & Power BI integration
Azure Databricks
Apache Spark with Delta Lake & lakehouse architecture
Stream Analytics
Real-time processing with low latency
Google Cloud Services
BigQuery
Serverless data warehouse with separated storage/compute
Cloud Dataflow
Unified batch/streaming Apache Beam with auto-scaling
Cloud Dataproc
Managed Spark/Hadoop with Lightning Engine for faster processing
Vertex AI Integration
ML with distributed training & Jupyter notebooks
Processing & Analytics
Apache Spark, vector databases, and GraphRAG for intelligent data processing.
Apache Spark
Unified engine with significantly faster speed over Hadoop. Batch/streaming processing, SQL analytics, MLlib machine learning.
PySpark
Python API for Spark with DataFrames, RDD operations, integration with S3, BigQuery, Azure Blob Storage.
Vector Databases
Instant semantic search with Pinecone, Weaviate, Qdrant, ChromaDB for embedding storage and similarity search.
GraphRAG
Knowledge synthesis & multi-hop reasoning across data sources. Answer complex questions requiring entity relationships.
How We Build It
Proven structured process from scattered data to production-ready intelligent pipelines.
Discovery & Architecture
Map data sources, define ETL requirements, design pipeline architecture, and select optimal cloud platform.
- Data source inventory
- Pipeline architecture diagram
- Cloud platform selection
- Cost & timeline estimate
Pipeline Development
Build data ingestion, implement transformations with Spark, set up data quality checks, and create orchestration workflows.
- Working data pipelines
- Spark transformations
- Data quality framework
- Orchestration setup
Infrastructure & Optimization
Deploy cloud data stack, configure auto-scaling, implement monitoring, and optimize query performance.
- Cloud infrastructure deployed
- Auto-scaling configured
- Monitoring dashboards
- Query optimization
Testing & Deployment
Validate data accuracy, conduct performance testing, train team on operations, and deploy to production.
- Data validation complete
- Performance benchmarks
- Team training completed
- Production deployment
Frequently Asked Questions
Everything you need to know about our data engineering solutions.
We architect distributed data pipelines with Apache Spark for parallel processing across cluster nodes, implement auto-scaling infrastructure that adjusts compute resources based on data volume, use managed cloud services (AWS EMR, Azure Databricks, GCP Dataproc) with built-in fault tolerance, and deploy comprehensive monitoring with DataDog/CloudWatch for real-time alerting and automatic recovery mechanisms.
Ready to Transform Your Data Infrastructure?
Join enterprises achieving enterprise-scale data processing with production-ready reliability. Get a custom data architecture blueprint in our discovery call.
✓ No commitment required • ✓ Initial consultation • ✓ Custom architecture blueprint