Scalytics Connect Blogs With The Topic: Data Architecture

Shift-Left Data Architecture: Transform Data Management

Data quality problems discovered in production cost 10x more to fix than those caught at ingestion. Shift-left architecture embeds validation, governance, and transformation at data sources—reducing pipeline failures by 60% while enabling real-time analytics on clean data.

Author:

Dr. Mirko Kämpf

Breaking AI Data Silos: Problems & Decentralized Solutions

80% of enterprise AI projects fail due to data silos. Centralization projects take years and cost millions. Decentralized alternatives enable AI training across silos without moving data—delivering results in months instead of years while maintaining departmental data ownership.

Author:

Dr. Mirko Kämpf

Gartner AI Hype Cycle 2024: Lakehouses & Federated Learning

Gartner's 2024 AI Hype Cycle positions data lakehouses at the peak of inflated expectations while federated learning approaches the plateau of productivity. Analysis of what this means for enterprise AI investments and data architecture decisions in 2025.

Author:

Dr. Mirko Kämpf

Shift-Left Data Prep: Cut Latency & Costs by 35%

Data preparation consumes 80% of analytics project time. Shift-left moves cleansing, transformation, and feature engineering to source systems—reducing end-to-end latency from hours to minutes and cutting data processing costs by 35% through eliminated redundancy.

Author:

Alexander Alten

ETL vs ELT: Developer's Guide with Code Examples

ETL transforms before loading; ELT transforms after. But the right choice depends on data volumes, transformation complexity, and target platform capabilities. This developer guide includes decision trees and code examples for Spark, dbt, and Wayang implementations.

Author:

Scalytics

Distributed Processing Comparison: Spark vs Flink vs Wayang

Choosing between Spark, Flink, and Wayang depends on your workload profile. This comparison covers batch vs streaming performance, memory management, fault tolerance, and cross-platform scenarios—with benchmark data from TPC-H and real-world ML pipelines.

Author:

Vatsal Shah

Data Maturity Assessment: Measure Your Data Health

Where does your organization stand on data maturity? This assessment framework evaluates data quality, governance, accessibility, and analytics capabilities across five maturity levels—with specific recommendations for advancing from ad-hoc data practices to data-driven decision making.

Author:

Alexander Alten

Data Federation vs Centralization: Decision Framework

Data federation and centralization serve different needs. This decision framework helps you choose based on regulatory requirements (GDPR, HIPAA), latency constraints, data volumes, and total cost of ownership. Includes hybrid patterns for enterprises with mixed requirements.

Author:

Alexander Alten

Data Silos Kill AI: How Federated Processing Fixes It

AI models trained on partial data make partial decisions. Data silos limit training diversity, create bias, and reduce model accuracy by 30-40%. Federated processing enables AI training across all your data sources—improving model performance while respecting data boundaries.

Author:

Alexander Alten

Data Federation ROI: 35% Cost Savings Explained

Data federation eliminates redundant storage, reduces egress fees, and cuts ETL maintenance costs. Our analysis of production deployments shows 35% average cost reduction through eliminated data copies, reduced cloud spending, and lower operational overhead.

Author:

Vatsal Shah

Big Data Analytics: Future of Business Intelligence

Traditional BI delivers historical reports; modern big data analytics enables real-time decisions. This evolution guide covers the shift from data warehouses to streaming analytics, from dashboards to embedded AI, and from IT-controlled to self-service analytics.

Author:

Alexander Alten

Decentralized Data Processing: Architecture & Implementation

Centralized data lakes create bottlenecks, compliance risks, and single points of failure. Decentralized data processing distributes computation across sources—enabling real-time analytics on data that can't be moved while reducing infrastructure costs by 35-50%.

Author:

Dr. Kaustubh Beedkar

Regulation Compliant AI: Federated Processing for GDPR/HIPAA

Regulatory compliance increasingly requires data localization and minimal data movement. Federated processing keeps data in jurisdiction while enabling cross-border analytics and AI—achieving compliance by architecture rather than policy for GDPR, HIPAA, DORA, and the EU AI Act.

Author:

Dr. Kaustubh Beedkar

AI Fabric Explained: Scale AI in Legacy Systems

Legacy systems contain valuable data locked in silos. AI Fabric creates a unified intelligence layer across ERP, CRM, and data warehouses—enabling machine learning without migration projects. Deploy AI on 20-year-old systems while maintaining data governance and compliance.

Author:

Alexander Alten