feat: update quadrant classifications for Databricks, DLT, Snowflake,…

package.json

@@ -1,28 +1,28 @@
 {
-    "name": "info-techradar",
-    "version": "4.0.0",
-    "main": "index.js",
-    "license": "Creative Commons Attribution-NonCommercial-ShareAlike",
-    "scripts": {
-        "build": "techradar build",
-        "serve": "nodemon"
-    },
-    "dependencies": {
-        "aoe_technology_radar": "4.4.0"
-    },
-    "browserslist": {
-        "production": [
-            ">0.2%",
-            "not dead",
-            "not op_mini all"
-        ],
-        "development": [
-            "last 1 chrome version",
-            "last 1 firefox version",
-            "last 1 safari version"
-        ]
-    },
-    "devDependencies": {
-        "nodemon": "^3.1.9"
-    }
+  "name": "info-techradar",
+  "version": "4.0.0",
+  "main": "index.js",
+  "license": "Creative Commons Attribution-NonCommercial-ShareAlike",
+  "scripts": {
+    "build": "techradar build",
+    "serve": "nodemon"
+  },
+  "dependencies": {
+    "aoe_technology_radar": "4.6.1"
+  },
+  "browserslist": {
+    "production": [
+      ">0.2%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  },
+  "devDependencies": {
+    "nodemon": "^3.1.9"
+  }
 }

radar/2024-01-01/contentful.md

@@ -1,5 +1,5 @@
 ---
-title: "Contentful CMS"
+title: 'Contentful CMS'
 ring: adopt
 quadrant: tools
 featured: true
@@ -8,12 +8,14 @@ featured: true
 [Contentful](https://www.contentful.com/) is our preferred headless CMS for managing content across diverse digital platforms. With its robust API-first architecture and user-friendly interface, Contentful streamlines content management and delivery, enabling teams to work efficiently and collaboratively.
 
 ### Why Contentful?
+
 - **Headless Architecture:** Facilitates omnichannel content delivery with a robust set of APIs, ideal for websites, mobile apps, and other digital products.
 - **Developer and Editor Friendly:** Provides easy integration with SDKs for developers and an intuitive UI for content creators, supporting parallel workflows.
 - **Scalability:** Highly scalable for enterprise-level projects, with excellent localization and workflow management capabilities.
 - **Content Modeling:** Enables structured content management, allowing complex and reusable content models that fit various use cases.
 
 ### Best Practices at INFO
+
 - **Adoption Strategy:** Contentful has proven to be a reliable solution, offering our teams the right balance of usability, flexibility, and scalability.
 - **Use Cases:** Used across marketing websites, e-commerce platforms, and complex content-driven applications to manage and deliver content seamlessly.
 

radar/2024-12-01/databricks.md

@@ -1,7 +1,7 @@
 ---
 title: "Databricks"
 ring: trial
-quadrant: tools
+quadrant: platforms-and-operations
 featured: true
 ---
 

radar/2024-12-01/dlt.md

@@ -0,0 +1,30 @@
+---
+title: "Delta Live Tables (DLT)"
+ring: trial
+quadrant: languages-and-frameworks
+featured: true
+---
+
+[Delta Live Tables (DLT)](https://docs.databricks.com/delta-live-tables/index.html) is a declarative framework for building reliable, maintainable, and testable data processing pipelines. It simplifies ETL development by using a simple declarative approach to build and manage data pipelines on the Databricks platform.
+
+### Why Delta Live Tables?
+
+- **Declarative Approach:** Define data transformations using SQL or Python with a simple, declarative syntax that focuses on what rather than how.
+
+- **Automatic Quality Management:** Built-in data quality controls with expectations that can quarantine bad data and provide monitoring insights.
+
+- **Pipeline Orchestration:** Automatically handles dependencies, scheduling, and error recovery without manual intervention.
+
+- **Performance Optimization:** Leverages Delta Lake's ACID transactions and automatic optimization features for reliable and fast data processing.
+
+### Considerations at INFO
+
+- **ETL Modernization:** Being trialed as part of modernizing data pipeline development, particularly for complex transformation workflows.
+
+- **Integration with Databricks:** Evaluating as a complement to our [Databricks](/tools/databricks) platform trials, leveraging its native integration capabilities.
+
+- **Data Quality Focus:** Assessing built-in data quality features for ensuring reliable data pipelines in production environments.
+
+- **Current Focus:** Testing developer productivity improvements, pipeline reliability, and operational simplicity compared to traditional ETL approaches.
+
+Delta Live Tables' declarative pipeline approach and integration with the Databricks ecosystem make it a compelling framework as we trial modern approaches to data pipeline development.

radar/2024-12-01/snowflake.md

@@ -1,7 +1,7 @@
 ---
 title: "Snowflake"
 ring: assess
-quadrant: tools
+quadrant: platforms-and-operations
 featured: true
 ---
 

radar/2024-12-01/spark.md

@@ -0,0 +1,30 @@
+---
+title: "Apache Spark"
+ring: trial
+quadrant: languages-and-frameworks
+featured: true
+---
+
+[Apache Spark](https://spark.apache.org/) is an open-source unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general computation graphs for data analysis.
+
+### Why Apache Spark?
+
+- **Unified Engine:** Provides a single framework for batch processing, real-time streaming, machine learning, and graph processing.
+
+- **High Performance:** In-memory computing capabilities significantly speed up data processing compared to traditional disk-based systems.
+
+- **Multi-Language Support:** Native support for Java, Scala, Python (PySpark), and R (SparkR), enabling diverse development teams to work with familiar languages.
+
+- **Extensive Ecosystem:** Rich ecosystem of libraries including Spark SQL, MLlib for machine learning, GraphX for graph processing, and Structured Streaming.
+
+### Considerations at INFO
+
+- **Big Data Processing:** Being trialed for large-scale data processing workloads that require distributed computing capabilities.
+
+- **Integration Assessment:** Evaluating integration with existing data infrastructure and cloud platforms, particularly in conjunction with [Databricks](/tools/databricks).
+
+- **Performance Evaluation:** Testing performance improvements over traditional data processing approaches, especially for complex analytical workloads.
+
+- **Current Focus:** Assessing developer productivity, scalability benefits, and operational complexity in production environments.
+
+Apache Spark's unified analytics capabilities and performance characteristics make it a promising framework as we trial its potential for enterprise-scale data processing initiatives.

radar/2025-09-01/contentful.md

@@ -0,0 +1,30 @@
+---
+title: 'Contentful CMS'
+ring: adopt
+quadrant: tools
+featured: true
+---
+
+[Contentful](https://www.contentful.com/) is our preferred headless CMS for managing content across diverse digital platforms. With its robust API-first architecture and user-friendly interface, Contentful streamlines content management and delivery, enabling teams to work efficiently and collaboratively.
+
+### Why Contentful?
+
+- **Headless Architecture:** Facilitates omnichannel content delivery with a robust set of APIs, ideal for websites, mobile apps, and other digital products.
+
+- **Developer and Editor Friendly:** Provides easy integration with SDKs for developers and an intuitive UI for content creators, supporting parallel workflows.
+
+- **Scalability:** Highly scalable for enterprise-level projects, with excellent localization and workflow management capabilities.
+
+- **Content Modeling:** Enables structured content management, allowing complex and reusable content models that fit various use cases.
+
+### Considerations at INFO
+
+- **Proven Solution:** Contentful continues as our adopted SaaS-based CMS solution, demonstrating consistent value across multiple projects and use cases.
+
+- **Strategic Choice:** Selected as our primary SaaS CMS over other alternatives, providing the right balance of features, usability, and enterprise capabilities.
+
+- **Production Success:** Successfully deployed across marketing websites, e-commerce platforms, and content-driven applications with reliable performance and user satisfaction.
+
+- **Current Focus:** Maintaining our investment in Contentful while leveraging its advanced features for complex content workflows and multi-channel publishing strategies.
+
+Contentful's continued adoption reinforces our commitment to efficient, scalable, and user-friendly content management solutions at INFO.

radar/2025-09-01/databricks.md

@@ -0,0 +1,30 @@
+---
+title: "Databricks"
+ring: adopt
+quadrant: platforms-and-operations
+featured: true
+---
+
+Databricks is a unified analytics platform that accelerates innovation by unifying data science, engineering, and business analytics. Built by the original creators of Apache Spark™, it offers a cloud-based environment for processing large-scale data, running machine learning models, and enabling real-time analytics.
+
+### Why Databricks?
+
+- **Unified Platform:** Combines data engineering, data science, and business analytics in one collaborative workspace.
+
+- **Scalable Processing:** Optimized for big data with Apache Spark™, allowing for efficient processing of large datasets.
+
+- **Collaborative Notebooks:** Features interactive notebooks with real-time co-authoring, version control, and support for multiple programming languages.
+
+- **Machine Learning Integration:** Provides seamless integration with popular ML frameworks like TensorFlow, PyTorch, and scikit-learn.
+
+### Considerations at INFO
+
+- **Production Ready:** After successful trials, Databricks has proven its value in production environments for complex data processing and machine learning workflows.
+
+- **Enhanced Collaboration:** The platform has significantly improved collaboration between data engineers, data scientists, and business analysts across teams.
+
+- **Integration Success:** Successfully integrated with existing data pipelines and cloud infrastructure, demonstrating reliability and scalability.
+
+- **Current Focus:** Now adopted as our primary platform for advanced analytics, big data processing, and machine learning model development and deployment.
+
+Databricks' comprehensive analytics capabilities and proven performance in production make it our adopted solution for enterprise-scale data science and analytics initiatives.

radar/2025-09-01/dlt.md

@@ -0,0 +1,32 @@
+---
+title: "Delta Live Tables (DLT)"
+ring: adopt
+quadrant: languages-and-frameworks
+featured: true
+---
+
+[Delta Live Tables (DLT)](https://docs.databricks.com/delta-live-tables/index.html) is a declarative framework for building reliable, maintainable, and testable data processing pipelines. It simplifies ETL development by using a simple declarative approach to build and manage data pipelines on the Databricks platform.
+
+### Why Delta Live Tables?
+
+- **Declarative Approach:** Define data transformations using SQL or Python with a simple, declarative syntax that focuses on what rather than how.
+
+- **Automatic Quality Management:** Built-in data quality controls with expectations that can quarantine bad data and provide monitoring insights.
+
+- **Pipeline Orchestration:** Automatically handles dependencies, scheduling, and error recovery without manual intervention.
+
+- **Performance Optimization:** Leverages Delta Lake's ACID transactions and automatic optimization features for reliable and fast data processing.
+
+### Considerations at INFO
+
+- **Production Ready:** After successful trials, Delta Live Tables has proven its value for building reliable and maintainable data pipelines in production environments.
+
+- **ETL Transformation:** Successfully adopted as our standard approach for modern data pipeline development, significantly improving developer productivity.
+
+- **Quality Assurance:** Built-in data quality features have enhanced reliability and monitoring of our data pipelines, reducing manual oversight requirements.
+
+- **Platform Integration:** Seamlessly integrated with our adopted [Databricks](/platforms-and-operations/databricks) platform and [Apache Spark](/languages-and-frameworks/spark) framework.
+
+- **Current Focus:** Now adopted as our preferred framework for declarative data pipeline development, enabling teams to focus on business logic rather than infrastructure complexity.
+
+Delta Live Tables' declarative approach and proven reliability make it our adopted solution for modern, maintainable data pipeline development across the organization.

radar/2025-09-01/sanity.md

@@ -0,0 +1,30 @@
+---
+title: 'Sanity CMS'
+ring: assess
+quadrant: tools
+featured: true
+---
+
+[Sanity](https://www.sanity.io) stands out for its customizable studio, real-time collaboration, and schema-driven content management. After initial trials, we continue to assess its features and potential fit within our content management ecosystem.
+
+### Why Sanity?
+
+- **Highly Customizable Studio:** Studio can be tailored to fit diverse editorial needs, with custom inputs, plugins, and preview features.
+
+- **Real-Time Collaboration:** Supports simultaneous editing and comprehensive version control, enhancing productivity in content teams.
+
+- **API and GraphQL Support:** Provides flexible content delivery solutions that adapt to varying project requirements.
+
+- **Structured Content:** Schema-driven approach allows for creating highly organized and reusable content.
+
+### Considerations at INFO
+
+- **Extended Assessment:** Following initial trials, Sanity remains under assessment as we evaluate its long-term fit alongside our adopted [Contentful](/tools/contentful) solution.
+
+- **Specialized Use Cases:** Being evaluated for specific scenarios requiring extensive customization and advanced editorial workflows that may complement our existing CMS strategy.
+
+- **Technical Evaluation:** Assessing integration complexity, development overhead, and maintenance requirements compared to our current content management approach.
+
+- **Current Focus:** Determining whether Sanity's advanced customization capabilities justify adoption for specialized content management needs.
+
+Sanity's powerful customization and collaboration features continue to be assessed for specific use cases where advanced editorial capabilities might provide additional value beyond our primary CMS solution.

radar/2025-09-01/snowflake.md

@@ -0,0 +1,28 @@
+---
+title: "Snowflake"
+ring: assess
+quadrant: platforms-and-operations
+featured: true
+---
+
+[Snowflake](https://www.snowflake.com/en/data-cloud/platform/) is a cloud-based data warehousing platform that offers scalable storage and compute resources for big data processing and analytics. Designed specifically for the cloud, it provides a flexible and efficient solution for data warehousing, data lakes, and data sharing.
+
+### Why Snowflake?
+
+- **Scalable Architecture:** Separates compute and storage, allowing independent scaling and cost optimization based on workloads.
+
+- **Multi-Cloud Availability:** Operates across major cloud providers like [AWS](/platforms-and-operations/aws), [Azure](/platforms-and-operations/azure), and Google Cloud, offering flexibility and avoiding vendor lock-in.
+
+- **Data Sharing and Collaboration:** Enables secure and instant data sharing within and outside the organization without the need to move or copy data.
+
+- **Performance Optimization:** Automatically manages performance tuning, indexing, and partitioning, reducing administrative overhead.
+
+### Considerations at INFO
+
+- **Continued Assessment:** As a cloud-native data platform, Snowflake remains under evaluation for enterprise data warehousing needs, particularly in comparison to our adopted [Databricks](/platforms-and-operations/databricks) solution.
+
+- **Platform Positioning:** Recognized as a platforms and operations solution rather than just a tool, given its infrastructure-level capabilities and cloud-native architecture.
+
+- **Current Focus:** Evaluating specific use cases where Snowflake's data warehousing capabilities might complement our existing data infrastructure, including integration with cloud platforms and cost optimization strategies.
+
+Snowflake's cloud-native data platform continues to be assessed for specific enterprise data warehousing scenarios where its unique architecture might provide additional value to our data operations.

radar/2025-09-01/spark.md

@@ -0,0 +1,32 @@
+---
+title: "Apache Spark"
+ring: adopt
+quadrant: languages-and-frameworks
+featured: true
+---
+
+[Apache Spark](https://spark.apache.org/) is an open-source unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python, and R, and an optimized engine that supports general computation graphs for data analysis.
+
+### Why Apache Spark?
+
+- **Unified Engine:** Provides a single framework for batch processing, real-time streaming, machine learning, and graph processing.
+
+- **High Performance:** In-memory computing capabilities significantly speed up data processing compared to traditional disk-based systems.
+
+- **Multi-Language Support:** Native support for Java, Scala, Python (PySpark), and R (SparkR), enabling diverse development teams to work with familiar languages.
+
+- **Extensive Ecosystem:** Rich ecosystem of libraries including Spark SQL, MLlib for machine learning, GraphX for graph processing, and Structured Streaming.
+
+### Considerations at INFO
+
+- **Production Success:** After successful trials, Apache Spark has proven its value for large-scale data processing workloads in production environments.
+
+- **Platform Integration:** Successfully integrated with our [Databricks](/platforms-and-operations/databricks) platform, providing a robust foundation for distributed computing.
+
+- **Performance Delivered:** Demonstrated significant performance improvements over traditional data processing approaches, particularly for complex analytical workloads.
+
+- **Developer Adoption:** Teams have successfully adopted Spark's APIs across multiple programming languages, enhancing productivity and flexibility.
+
+- **Current Focus:** Now adopted as our standard framework for big data processing, streaming analytics, and distributed machine learning workloads.
+
+Apache Spark's proven performance and successful integration make it our adopted solution for enterprise-scale distributed data processing and analytics.