Alexander Alten

CTO & founder

Alexander Alten is a software architect with more than two decades of experience in distributed systems and large-scale data processing. His career includes leading technical roles at Cloudera, Healthgrades and E.ON, where he also collaborated with Google on IoT initiatives. He is a PMC member of Apache Wayang and one of the initiators who brought the project to the Apache Software Foundation. At Scalytics he focuses on federated data processing and AI that runs where the data lives.

Streaming Data Becomes Storage-Native

Apache Kafka established the distributed commit log as the foundation for real-time data processing. The protocol is now commodity infrastructure. The constraint is the storage architecture. This article examines how storage-native streaming separates analytical workloads from broker infrastructure, following the same disaggregation pattern that moved batch processing from HDFS to S3. We introduce KafScale, an open source implementation with a documented storage format that enables direct S3 access for batch and AI workloads without broker involvement. The architecture draws on research from Apache Wayang, Apache Flink (FLIP-531), and production deployments at organizations adopting diskless Kafka alternatives.

Alexander Alten

Apache Hadoop vs Apache Spark vs Apache Wayang: The Evolution of Big Data Processing

Apache Wayang became an Apache Top-Level Project in December 2025, reflecting a shift in how modern data architectures are designed. This article explains how big data processing evolved from Hadoop’s batch-oriented MapReduce model to Spark’s in-memory execution, and why cross-platform optimization is now necessary as analytics spans databases, streaming systems, and distributed engines. Drawing on peer-reviewed research and benchmark results, it outlines how Apache Wayang addresses the growing fragmentation of enterprise data platforms.

Alexander Alten

Scalytics Connect Community Edition: Free Open Source AI

Enterprise AI shouldn't require enterprise budgets. Scalytics Connect Community Edition delivers production-ready AI infrastructure under Apache 2.0: vLLM inference, vector database, semantic search, and OpenAI-compatible APIs. Deploy on your hardware with zero licensing costs.

Alexander Alten

Scalytics 3.5.0: vLLM Streaming & Deep Search Updates

Scalytics 3.5.0 delivers enterprise-ready AI inference: vLLM streaming for real-time responses, improved PyTorch model stability, and enhanced deep search with graph-based reasoning. Plus: fine-tune models on your private data without cloud exposure.

Alexander Alten

Deep Search AI: Grounded, Trustworthy Enterprise Search

Enterprise search needs more than semantic similarity. Our Deep Search Optimization (DSO) Agent delivers grounded results with source citations, trust scores based on source authority, and relevance ranking tuned to your domain—eliminating hallucinations in high-stakes knowledge retrieval.

Alexander Alten

SynthLink Benchmark: Testing Enterprise AI Accuracy

How accurate is your enterprise AI on complex queries? SynthLink benchmark tests multi-hop reasoning, evidence chain construction, and fact verification across 60 carefully designed questions. Evaluate Llama, Mistral, GPT-4, and Claude on real enterprise knowledge tasks.

Alexander Alten

Enterprise LLM Runtime: Deploy Open Source Models Securely

Deploying Llama or Mistral in production requires more than a GPU. Enterprise LLM runtime needs request queuing, prompt injection protection, context window management, token budgeting, and audit logging. Scalytics Connect provides the missing operational layer for self-hosted AI.

Alexander Alten

AI Agent Framework: Build Agentic RAG with Data Control

This article explains why AI agents must operate at the edge and why enterprises need a control layer to govern their workflows. It introduces the Agent Context Protocol as the foundation for secure, auditable multi agent systems that work on real time data without centralization.

Alexander Alten

DORA Compliance: Federated AI for Financial Services

Financial institutions face rising regulatory pressure and operational risks from cloud centric data processing. DORA exposes the weaknesses of centralized architectures and demands stronger control over data, infrastructure, and third party dependencies. Scalytics Federated provides a federated execution model that keeps sensitive data at the source, reduces exposure, and aligns with DORA's core requirements for resilience, security, and operational continuity.

Alexander Alten

LLMs and Federated Learning in Real Operations: A Practical Architecture for Modern Enterprises

This article explains how enterprises can deploy AI across distributed and regulated environments using LLMs and Federated Learning. It shows how Scalytics Federated executes workloads where data resides, enabling compliance, lower cost, and better operational performance without building new data platforms.

Alexander Alten

The Modern AI Stack: A Data Execution Problem, Not a Model Problem

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.

Alexander Alten

Hadoop, Spark and Wayang: Cross Platform Execution and Why It Matters

Spark and Flink perform well in their domains, but modern workloads span multiple systems. This comparison introduces Apache Wayang as the cross platform optimizer and includes benchmark insights from TPC-H and production pipelines.

Alexander Alten

Data Maturity Through Federated Execution

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.

Alexander Alten

Federated Data Processing vs Traditional ETL: How Scalytics Enables In Situ Execution

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.

Alexander Alten

In-Situ Processing: Train AI Where Data Lives

Moving petabytes to train AI is expensive, slow, and often illegal under GDPR. In-situ federated processing brings computation to data—training models across distributed sources without centralization. Reduce cloud egress costs by 60% while achieving compliance by design.

Alexander Alten

Federated Data Management: Engineer's Complete Guide

Federated data management is becoming essential for data engineers. This technical guide covers federation patterns, query optimization across distributed sources, consistency models, and implementation with Apache Wayang—skills increasingly required for enterprise data platforms.

Alexander Alten

Data Democratization: Build a Data Culture for AI Success

Data democratization puts analytics capabilities in business users' hands. Learn how to build a data culture that enables self-service while maintaining governance—the foundation for successful AI adoption and faster time-to-insight across departments.

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.

Alexander Alten

Earth Observation AI: Climate Analytics with Federated Learning

Earth observation generates petabytes of satellite imagery across international agencies. Federated processing enables collaborative climate analysis—tracking deforestation, ice sheet changes, and urban growth across jurisdictional boundaries while respecting data sovereignty.

Alexander Alten

Distributed Processing for LLM Training: Unify Your Data Lakes

Large language models require diverse training data scattered across your organization. Distributed data processing with Apache Wayang lets you unify data lakes for LLM fine-tuning without moving sensitive data—enabling compliant AI training across healthcare, finance, and government sectors.

Alexander Alten

The Scalytics Story: Our Mission & Founding Team

Scalytics was founded by the creators of Apache Wayang with a mission to democratize enterprise AI. Learn about our founding team—researchers from Max Planck Institute and IT University of Copenhagen—and our vision for federated data intelligence.

Alexander Alten

Scalytics: Gartner Best of Breed Recognition

Scalytics Connect (formerly Blossom Sky) earned Gartner Digital Markets recognition for Best of Value and Best of Use in data integration. See why analysts recognize our federated approach as a leader in the enterprise data platform category.

Alexander Alten

Reduce AI Bias with Federated Learning

AI trained on centralized data inherits the biases of whoever collected it. Federated learning enables model training across diverse populations and regions—reducing demographic bias while maintaining privacy. Essential for fair AI in healthcare diagnostics, lending decisions, and hiring systems.

Alexander Alten

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.

Alexander Alten

Digital Twins + Generative AI: Predictive Industrial AI

Digital twins simulate physical systems; generative AI predicts their futures. Combined with federated edge processing, this architecture enables predictive maintenance, process optimization, and scenario planning for manufacturing plants, power grids, and smart cities.

Alexander Alten

Apache Wayang: The Complete Guide

Apache Wayang enables unified cross-platform data processing across Spark, Flink, and Java in a single pipeline. Created by Scalytics founders, now an Apache TLP. Learn how to reduce processing costs by 40% while eliminating vendor lock-in with this comprehensive implementation guide.

Alexander Alten

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.

Alexander Alten

Federated Learning Adoption: Industry Trends & Frameworks

Federated learning adoption is accelerating across regulated industries. This analysis covers adoption patterns in healthcare (cross-hospital AI), finance (fraud detection consortiums), and manufacturing (predictive maintenance networks)—plus framework comparison of Flower, PySyft, and TensorFlow Federated.

Alexander Alten

Federated Learning Guide: Privacy-Preserving AI Training

Federated Learning enables AI training across hospitals, banks, and distributed systems without moving data. Technical guide covering FL architectures, secure aggregation, differential privacy, and HIPAA/GDPR compliance from the Apache Wayang team. This guide draws on our team's experience building Apache Wayang, the cross-platform data processing framework and deploying federated systems for healthcare networks, financial institutions, and government agencies.

Alexander Alten

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