Latest
Catalog

What Is Data Context? A Practical How-To Guide for Building Trusted, AI-Ready Data

What is Data Context? It is the "who, what, and where" of your data. Learn to master context using metadata, lineage, and observability in this Decube guide.

by

Jatin Solanki

Updated on

January 16, 2026

Other blog articles

Catalog

What is Context Engineering?

Learn what Context Engineering is, why it’s critical for Agentic AI, and how enterprises should prepare their data, lineage, quality, and governance layers for autonomous AI systems.

By

Jatin Solanki

Updated on

January 8, 2026

Governance

Case Study: Data Governance Transformation of a Leading FinTech

Enterprises are increasingly searching for ways to quantify the ROI of data lineage, observability, and data governance initiatives. This case study highlights how Decube’s Data Trust Platform helps organizations save thousands of engineering hours, reduce compliance costs, and build AI-ready pipelines. By unifying metadata, lineage, quality signals, and auditability into a single context layer, Decube delivers measurable operational efficiency and long-term financial value.

By

Richard

Updated on

December 8, 2025

Catalog

New in BETA: Sync Descriptions from Data Warehouse

Align your catalog with your warehouse by enforcing a single source of truth.

By

Nishtha S.

Updated on

December 5, 2025

Catalog

Build a Business-Ready Catalog with Custom Attributes

Evolve your data catalog from a technical inventory into a trusted business asset. Our new Custom Attributes feature provides a governed framework to add, manage, and search your unique business context.

By

Melanie

Updated on

November 13, 2025

Governance

Data Definition: The Missing Foundation of Data Governance

Discover why clear data definitions are essential for building trust, improving data governance, and enabling AI readiness. Learn how organizations can align teams through a single language of data.

By

Jatin

Updated on

November 10, 2025

Catalog

Introducing the new Integrations Hub

We're excited to announce a major upgrade! We've moved Data Sources to a new, streamlined Integrations tab. This new hub centralizes all your connections, making management simpler and faster.

By

Melanie Y

Updated on

October 27, 2025

Data Observability

Lessons Learned in Data Engineering 2025: Do’s, Don’ts & Best Practices

Discover key lessons from 15 years in data engineering. Explore do’s, don’ts, and best practices for 2025, from data lineage and contracts to observability and AI readiness.

By

Jatin Solanki

Updated on

September 30, 2025

Governance

The Future of Data Context in Enterprise AI

Discover why data context—not just models—will define enterprise AI success. Learn how governance, guardrails, and MCP shape the future of AI workflows.

By

Jatin S

Updated on

September 19, 2025

Data Observability

Introducing Data Quality via API

Decube now supports full programmatic control over data quality monitors via its Public API (BETA): create, update, and delete monitors. Monitor types include freshness, volume, field health, custom rules, and more. This allows for flexible scheduling, grouping, thresholds, and alerts/notifications without going through the UI, and ideal for data engineers or governance teams automating monitoring workflows.

By

Melanie Y

Updated on

September 18, 2025

Catalog

Data Trust Strategy for Latin American Financial Services: AI Readiness & Open Banking Compliance

Discover how LATAM banks, fintechs, and neobanks can overcome data silos, poor quality, and regulatory challenges with a Data Trust Strategy. Learn how Decube’s platform enables compliance, AI adoption, and data monetization across Mexico, Brazil, Colombia, and beyond.

By

Richard Hechenbichler

Updated on

August 26, 2025

Data Observability

Announcing Monitors API: Programmatic Monitoring & Alerts

The new Monitors API lets you programmatically manage data monitors and alerts, making it easier to integrate monitoring into your workflows and automate responses to data issues. The API provides endpoints to find, inspect, control, and trigger monitors on-demand.

By

Melanie Y

Updated on

September 30, 2025

Catalog

Data Catalog & Metadata Management: 2025 Guide

Learn what a data catalog is, how metadata management works, and how to implement both in 90 days. Includes checklists, KPIs, and LLM‑ready answers.

By

Jatin

Updated on

August 25, 2025

Catalog

Introducing Catalog Asset & Data Quality APIs

Announcing the release of Decube’s Catalog Asset APIs and Data Quality Scorecard API—empowering teams to automate asset management, synchronize metadata, and monitor data quality programmatically. These new APIs streamline data operations and enable proactive, trustworthy data governance.

By

Melanie Y

Updated on

August 1, 2025

Data Observability

Benefits of Data Observability and Lineage - Improve Data Trust & Pipeline Reliability

Understand how combining data observability and lineage reduces downtime, improves root cause analysis, and enables AI readiness across the modern data stack.

By

Maria

Updated on

July 30, 2025

Catalog

New: Bulk Metadata Made Simple with Export/Import

The new Export/Import module empowers data teams to manage metadata in bulk—enabling fast onboarding, easy catalog updates, and streamlined governance through simple CSV workflows. Save time, reduce errors, and maintain control with secure, auditable bulk operations.

By

Melanie Y

Updated on

August 25, 2025

Catalog

Connector Update: AWS S3 & Azure Data Lake Storage

This release introduces major upgrades to Decube’s S3 and ADLS connectors, enabling data teams to precisely control which files are cataloged, configure file format options, and apply regex-based filters for targeted ingestion.

By

Melanie Y

Updated on

June 26, 2025

Catalog

Semantic Layer in the AI era

Discover why the semantic layer is crucial for AI accuracy, data trust, and LLM success. A must-read for data engineers, VPs of data, and quality leaders.

By

Jatin

Updated on

June 24, 2025

Data Observability

Apache Spark 4 | Comparison with previous version

Discover the core differences between Apache Spark 4.0 and Spark 3.x, including performance improvements, columnar execution, streaming upgrades, and Python UDF enhancements.

By

Jatin

Updated on

June 4, 2025

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