AI Infrastructure Implementation Guides

Private AI Prompting Guide: Optimize Scalytics Connect

Generic prompting advice fails for enterprise AI. This guide covers model selection for different tasks (Mistral for speed, Llama for reasoning), prompt templates for structured outputs, context window optimization, and advanced techniques like chain-of-thought for complex analysis.

Author:

Scalytics

MultiContext: Cross-Cluster Data Processing in Scalytics

MultiContext in Scalytics Connect enables one ETL pipeline to run across multiple clusters and data sources without moving raw data. It supports in situ execution, privacy requirements, and heterogeneous environments such as Spark and Flink.

Author:

Dr. Zoi Kaoudi

Scalytics FAQ: Data Platform Questions Answered

Get answers to common questions about Scalytics Connect: deployment options, supported data sources, integration patterns, pricing models, and comparison with alternatives. Essential reading before starting your federated data platform evaluation.

Author:

Dr. Zoi Kaoudi

Building Custom Execution Plugins for Scalytics Federated

Extend Scalytics Connect with custom functionality. This developer tutorial covers the plugin architecture, API interfaces, and implementation patterns for building new data source connectors, transformation operators, and output sinks. Part 1 of our developer series.

Author:

Scalytics

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.

Author:

Alexander Alten

TagSniff Tutorial: Online & Post-Hoc Data Debugging

Hands-on guide to implementing TagSniff in your data pipelines. Covers online debugging (inspecting data during execution), post-hoc analysis (debugging from logs), and integration patterns for Spark, Flink, and Apache Wayang applications with code examples.

Author:

Dr. Jorge Quiané

TagSniff: Debugging Model for Distributed Data Processing

TagSniff introduces two simple primitives—tag and sniff—for debugging distributed data processing. Tag marks tuples with metadata; sniff identifies tuples needing attention. Together they enable data breakpoints, lineage tracking, and anomaly detection across Spark and Flink jobs.

Author:

Dr. Jorge Quiané

Big Data Debugging: Why Traditional Tools Fail

Step-through debugging fails when your job runs across 100 nodes processing terabytes. This analysis explains why traditional debugging approaches break down for big data applications and what new primitives are needed for effective distributed data debugging.

Author:

Dr. Jorge Quiané