Generated from prompt:
Generate a 15-slide presentation on 'Modern Metadata Platform' covering the following: introduction, importance of metadata, key challenges, evolution from traditional systems, architecture overview, data cataloging, metadata ingestion, governance, automation, AI and ML integration, scalability, use cases, best practices, tools comparison, and future trends.
Explores modern metadata platforms: intro, importance, challenges, evolution, architecture, cataloging, ingestion, governance, automation, AI/ML, scalability, use cases & best practices. (148 chars)
This title slide is titled "Modern Metadata Platform." Its subtitle provides a comprehensive overview of modern metadata platforms, covering their evolution, components, and future.
Comprehensive overview of modern metadata platforms, their evolution, components, and future.
This agenda slide outlines a presentation on metadata platforms, starting with an introduction to fundamentals like overview, importance, challenges, and evolution. It proceeds through core architecture and components, advanced features and scalability, applications with best practices, and future trends.
Overview, importance, challenges, and evolution of metadata platforms.
Architecture overview, data cataloging, ingestion, and governance.
Automation, AI/ML integration, and scalability considerations.
Use cases, best practices, and tools comparison.
Emerging trends in modern metadata platforms. Source: Modern Metadata Platform
Metadata platforms centralize data about data and serve as an integral part of data management ecosystems. They enhance asset discoverability, enforce governance and compliance, and enable advanced analytics capabilities.
Metadata is essential for enabling data discovery, search, compliance, and lineage tracking. It also drives analytics, AI, business value, and reduces data silos.
The "Key Challenges" slide presents stats on data issues: 90% unstructured data from volume explosion, 70% siloed metadata inaccessible across silos, 60% governance gaps causing non-compliance, and 80% manual processes wasting time. These figures highlight major barriers to effective data management.
Explosion in data volume
Inaccessible across silos
Non-compliant practices
Time wasted on tasks
The slide timelines the evolution of data systems from 1990s basic metadata catalogs for organization, through 2000s ETL pipelines for integration and 2010s big data lakes for raw storage. It culminates in the 2020s shift to AI- and ML-powered real-time metadata platforms.
1990s: Basic Catalogs Emerge Simple metadata catalogs introduced for basic data organization and discovery. 2000s: ETL Processes Dominate Focus on Extract, Transform, Load pipelines for data integration and movement. 2010s: Big Data Lakes Rise Massive data lakes store raw data, challenging traditional metadata management. 2020s: AI-Driven Real-Time Platforms Shift to real-time, intelligent metadata platforms powered by AI and ML.
The slide titled "Architecture Overview" depicts a layered system for metadata management. It includes Ingestion for collecting data from diverse sources, Catalog for centralized discovery, Governance for policies and compliance, and Consumption for AI/ML integration and queries.
Source: data architecture
The Data Cataloging slide outlines automated scanning and classification, plus semantic tagging and relationships for data. It also provides a searchable inventory with previews and integration with BI tools.
Metadata Ingestion supports batch and streaming sources while ingesting CDC, logs, and APIs. It handles schema evolution and validates data quality on ingest.
The Governance slide outlines key features including robust policy enforcement and end-to-end lineage tracking. It also emphasizes granular access controls with auditing, plus compliance with GDPR and industry standards.
Automation orchestrates workflows for efficient operations and analyzes metadata change impacts. It provides self-service user portals and implements CI/CD for pipelines.
The slide "AI and ML Integration" highlights ML fundamentals, including auto-tagging of metadata for quick discovery and real-time anomaly detection for proactive data quality flagging. It also covers AI enhancements like predictive lineage mapping for future data flows and recommendation engines for suggesting relevant datasets and metadata.
| ML Fundamentals | AI Enhancements |
|---|---|
| Machine Learning enables auto-tagging of metadata assets for quick discovery and real-time anomaly detection to flag data quality issues proactively. | AI powers predictive lineage mapping for future data flows and recommendation engines to suggest relevant datasets and metadata intelligently. |
The Scalability slide highlights 1000x data growth scaling via horizontal support, 99.99% uptime guarantee with high availability SLA, and catalog capacity exceeding 1PB. These stats emphasize petabyte-scale handling for robust performance.
Horizontal scaling support
High availability SLA
Petabyte-scale handling
The slide outlines metadata use cases in two columns: data marketplaces for discovery, lineage, quality scoring, and monetization, plus compliance reporting for automated audits and governance visualization. The right column covers ML Ops for model versioning, experiment tracking, reproducibility, and monitoring, alongside personalized analytics for tailored insights and dashboards.
| Data Marketplaces & Compliance Reporting | ML Ops & Personalized Analytics |
|---|---|
| Data marketplaces thrive on metadata for discovery, lineage, quality scoring, and secure monetization. Compliance reporting automates audits, regulatory filings, and proof of governance through comprehensive metadata tracking and visualization. | ML Ops uses metadata for model versioning, experiment tracking, reproducibility, and deployment monitoring. Personalized analytics delivers tailored insights, recommendations, and dashboards based on user-specific metadata profiles and preferences. |
The "Best Practices" slide presents a quote from Gartner likening metadata to "data's GPS." It recommends centralizing, automating, governing metadata, and fostering a culture of stewardship.
> Metadata is data's GPS. Centralize, automate, govern, and foster a culture of metadata stewardship.
— Gartner (Leading Research Firm)
Source: Gartner
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