Q: What is GraphRAG and why does it matter?

GraphRAG combines knowledge graphs with retrieval-augmented generation for multi-hop reasoning. Traditional RAG retrieves documents; GraphRAG understands relationships between entities across your entire data estate. Example: 'Which customers bought product A, are located in region B, and had support tickets about feature C?' GraphRAG traverses connections to answer complex questions that require synthesis across multiple data sources.

Q: How do you handle data quality and governance?

Data quality through: (1) Schema validation and automated checks, (2) Data profiling and anomaly detection, (3) Deduplication and standardization, (4) Lineage tracking showing data flow. Governance via: (1) Role-based access control, (2) Data cataloging with business glossaries, (3) PII detection and masking, (4) Audit logging for compliance (GDPR, HIPAA, SOC 2). All integrated with tools like AWS Glue Data Catalog, Azure Purview, or GCP Data Catalog.

Q: What's the implementation timeline and cost?

Timeline depends on data volume and sources: (1) MVP with 1-3 sources: 4-6 weeks, (2) Production system with 5-10 sources: 8-12 weeks, (3) Enterprise platform with 10+ sources: 12-16 weeks. Costs include infrastructure (cloud services, typically $2K-10K/month based on volume) and development ($30K-100K based on complexity). ROI typically achieved within 6-12 months through better decisions, reduced manual work, and $200K+ cost savings from optimized infrastructure.

Question 1

How do you handle 20M+ records daily with 99.9% uptime?

Accepted Answer

We use cloud-native data stacks across AWS (Glue, Athena, Redshift, EMR), Azure (Data Factory, Synapse, Databricks), and GCP (BigQuery, Dataflow, Dataproc) with Apache Spark for distributed processing. Auto-scaling infrastructure handles traffic spikes, while multi-region deployment ensures 99.9% uptime. Our pipelines include error handling, dead-letter queues, and automatic retries for resilience.

Question 2

What data sources can you integrate?

Accepted Answer

We integrate with all major data sources: databases (PostgreSQL, MySQL, MongoDB, Cassandra), data warehouses (Snowflake, Redshift, BigQuery), cloud storage (S3, Azure Blob, GCS), APIs (REST, GraphQL, SOAP), streaming platforms (Kafka, Kinesis, Pub/Sub), and SaaS applications (Salesforce, HubSpot, Stripe). Custom connectors built for proprietary systems.

Question 3

How does your data pipeline architecture work?

Accepted Answer

Our lambda architecture combines batch and stream processing: (1) Data ingestion layer with multi-source connectors, (2) Processing layer using Apache Spark for transformations, (3) Storage layer with data lakes (S3, Azure Data Lake, GCS) and warehouses (Redshift, Synapse, BigQuery), (4) Serving layer with vector databases and GraphRAG for instant queries. All orchestrated with Airflow/Prefect with monitoring and alerting.

Question 4

What's the difference between AWS, Azure, and GCP for data engineering?

Accepted Answer

AWS excels at mature services (Glue, Redshift, EMR) with extensive integrations. Azure shines with unified Synapse Analytics combining warehousing, Spark, and Power BI. GCP offers best-in-class BigQuery for serverless analytics and Dataflow for stream processing. We help choose based on your existing cloud footprint, cost optimization goals, and specific use cases. Most enterprises use multi-cloud for resilience.

Question 5

How do you achieve 700% data utilization increase?

Accepted Answer

Most organizations use <10% of their data due to silos, poor quality, and lack of accessibility. We: (1) Consolidate data from scattered sources into unified data lakes, (2) Clean and standardize with automated quality checks, (3) Build semantic layers for business user access, (4) Enable real-time analytics and ML model training, (5) Implement GraphRAG for knowledge synthesis across datasets. Result: data becomes actionable asset driving decisions.

Question 6

What is GraphRAG and why does it matter?

Accepted Answer

GraphRAG combines knowledge graphs with retrieval-augmented generation for multi-hop reasoning. Traditional RAG retrieves documents; GraphRAG understands relationships between entities across your entire data estate. Example: 'Which customers bought product A, are located in region B, and had support tickets about feature C?' GraphRAG traverses connections to answer complex questions that require synthesis across multiple data sources.

Question 7

How do you handle data quality and governance?

Accepted Answer

Data quality through: (1) Schema validation and automated checks, (2) Data profiling and anomaly detection, (3) Deduplication and standardization, (4) Lineage tracking showing data flow. Governance via: (1) Role-based access control, (2) Data cataloging with business glossaries, (3) PII detection and masking, (4) Audit logging for compliance (GDPR, HIPAA, SOC 2). All integrated with tools like AWS Glue Data Catalog, Azure Purview, or GCP Data Catalog.

Question 8

What's the implementation timeline and cost?

Accepted Answer

Timeline depends on data volume and sources: (1) MVP with 1-3 sources: 4-6 weeks, (2) Production system with 5-10 sources: 8-12 weeks, (3) Enterprise platform with 10+ sources: 12-16 weeks. Costs include infrastructure (cloud services, typically $2K-10K/month based on volume) and development ($30K-100K based on complexity). ROI typically achieved within 6-12 months through better decisions, reduced manual work, and $200K+ cost savings from optimized infrastructure.

Data Engineering & Intelligence

What You Get

Why Data Projects Fail

Data Silos & Scattered Systems

Poor Data Quality & Consistency

Slow Queries & Performance Issues

No Scalability or Modern Stack

We've Solved This for 50+ Companies

Multi-Cloud Data Stacks

AWS Data Stack

Azure Data Platform

Google Cloud Services

Processing & Analytics

Proven Results

Complete Data Engineering Ecosystem

Multi-Cloud Data Infrastructure

AWS Data Stack

AWS Glue

Amazon Athena

Amazon Redshift

AWS EMR

Azure Data Platform

Azure Data Factory

Azure Synapse

Azure Databricks

Stream Analytics

Google Cloud Services

BigQuery

Cloud Dataflow

Cloud Dataproc

Vertex AI Integration

Processing & Analytics

Apache Spark

PySpark

Vector Databases

GraphRAG

How We Build It

Discovery & Architecture

Pipeline Development

Infrastructure & Optimization

Testing & Deployment

Frequently Asked Questions

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