diff --git a/package-lock.json b/package-lock.json index bf5584b..1cdde64 100644 --- a/package-lock.json +++ b/package-lock.json @@ -9,7 +9,7 @@ "version": "4.0.0", "license": "Creative Commons Attribution-NonCommercial-ShareAlike", "dependencies": { - "aoe_technology_radar": "4.4.0" + "aoe_technology_radar": "4.6.1" }, "devDependencies": { "nodemon": "^3.1.9" @@ -29,9 +29,9 @@ } }, "node_modules/aoe_technology_radar": { - "version": "4.4.0", - "resolved": "https://registry.npmjs.org/aoe_technology_radar/-/aoe_technology_radar-4.4.0.tgz", - "integrity": "sha512-r/6kWsT5FOJ4nf/aRu7hhqL06TntBGi5JM72GvoD30LAi3Qz4DFnOIAeCoCzxW/UovrY6Ko9PsS227QxzZ1VeA==", + "version": "4.6.1", + "resolved": "https://registry.npmjs.org/aoe_technology_radar/-/aoe_technology_radar-4.6.1.tgz", + "integrity": "sha512-pHnmNfqiVm+UhT2WSjMLL/+gyqGkCXenAULCGy7XNPJPyyx/aeP1s3g1WETVseYami3XMVoZ/JPNrgQRA6msjA==", "hasInstallScript": true, "bin": { "techradar": "bin/techradar.js" @@ -380,9 +380,9 @@ } }, "aoe_technology_radar": { - "version": "4.4.0", - "resolved": "https://registry.npmjs.org/aoe_technology_radar/-/aoe_technology_radar-4.4.0.tgz", - "integrity": "sha512-r/6kWsT5FOJ4nf/aRu7hhqL06TntBGi5JM72GvoD30LAi3Qz4DFnOIAeCoCzxW/UovrY6Ko9PsS227QxzZ1VeA==" + "version": "4.6.1", + "resolved": "https://registry.npmjs.org/aoe_technology_radar/-/aoe_technology_radar-4.6.1.tgz", + "integrity": "sha512-pHnmNfqiVm+UhT2WSjMLL/+gyqGkCXenAULCGy7XNPJPyyx/aeP1s3g1WETVseYami3XMVoZ/JPNrgQRA6msjA==" }, "balanced-match": { "version": "1.0.2", diff --git a/package.json b/package.json index 12e3c91..5b9914b 100644 --- a/package.json +++ b/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" + } } diff --git a/radar/2024-01-01/contentful.md b/radar/2024-01-01/contentful.md index 2895b5e..2593572 100644 --- a/radar/2024-01-01/contentful.md +++ b/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. diff --git a/radar/2024-12-01/databricks.md b/radar/2024-12-01/databricks.md index 470b00e..c7fb66c 100644 --- a/radar/2024-12-01/databricks.md +++ b/radar/2024-12-01/databricks.md @@ -1,7 +1,7 @@ --- title: "Databricks" ring: trial -quadrant: tools +quadrant: platforms-and-operations featured: true --- diff --git a/radar/2024-12-01/dlt.md b/radar/2024-12-01/dlt.md new file mode 100644 index 0000000..4e05c63 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2024-12-01/snowflake.md b/radar/2024-12-01/snowflake.md index abd92b3..a8d1ab3 100644 --- a/radar/2024-12-01/snowflake.md +++ b/radar/2024-12-01/snowflake.md @@ -1,7 +1,7 @@ --- title: "Snowflake" ring: assess -quadrant: tools +quadrant: platforms-and-operations featured: true --- diff --git a/radar/2024-12-01/spark.md b/radar/2024-12-01/spark.md new file mode 100644 index 0000000..00c442f --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2025-09-01/contentful.md b/radar/2025-09-01/contentful.md new file mode 100644 index 0000000..4fe2128 --- /dev/null +++ b/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. diff --git a/radar/2025-09-01/databricks.md b/radar/2025-09-01/databricks.md new file mode 100644 index 0000000..331d615 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2025-09-01/dlt.md b/radar/2025-09-01/dlt.md new file mode 100644 index 0000000..670c41a --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2025-09-01/sanity.md b/radar/2025-09-01/sanity.md new file mode 100644 index 0000000..02f50f3 --- /dev/null +++ b/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. diff --git a/radar/2025-09-01/snowflake.md b/radar/2025-09-01/snowflake.md new file mode 100644 index 0000000..b9b4301 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2025-09-01/spark.md b/radar/2025-09-01/spark.md new file mode 100644 index 0000000..b128c42 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/radar/2025-09-01/strapi.md b/radar/2025-09-01/strapi.md new file mode 100644 index 0000000..8c9a05e --- /dev/null +++ b/radar/2025-09-01/strapi.md @@ -0,0 +1,40 @@ +--- +title: 'Strapi CMS' +ring: adopt +quadrant: tools +featured: true +--- + +[Strapi](https://strapi.io/) is our preferred open-source CMS at INFO. It offers a headless architecture that provides the flexibility and control needed for modern content management. Known for its robust API, ease of customization, and TypeScript support, Strapi enables teams to build and manage content-rich applications efficiently. + +### Why Strapi? + +- **Open-Source and Headless:** Strapi's open-source nature allows full access to the source code, giving developers complete control over customization and scalability. + +- **Developer-Friendly:** Provides an intuitive admin panel, customizable APIs, and support for both RESTful and GraphQL, making it highly adaptable to various tech stacks. + +- **Extensible Content Models:** Allows easy creation and management of complex content structures with reusable components. + +- **Community and Enterprise Editions:** While the free community edition meets most needs, enterprise editions offer additional features such as role-based access control, SSO, and enhanced security for larger projects. + +- **TypeScript Support:** Native TypeScript support enhances code quality and developer productivity, making it easier to build scalable, type-safe applications. + +### Use Cases + +- **API-Driven Content Delivery:** Ideal for headless setups, delivering content seamlessly to web, mobile, and other digital platforms. + +- **Custom Content Solutions:** Highly customizable to fit unique business requirements, from simple blogs to complex data-driven applications. + +- **Flexible Integrations:** Integrates well with various databases, front-end frameworks, and other services, enhancing overall development agility. + +### Considerations at INFO + +- **Dual CMS Strategy:** Strapi continues as our adopted open-source CMS solution, complementing [Contentful](/tools/contentful) as our SaaS-based option, providing flexibility for different project requirements. + +- **Technical Control:** Preferred for projects requiring extensive customization, self-hosting capabilities, or specific technical constraints that benefit from open-source flexibility. + +- **Development Excellence:** TypeScript support and robust API capabilities continue to deliver significant value for complex, developer-focused content management scenarios. + +- **Current Focus:** Maintaining Strapi as our primary open-source CMS while leveraging its latest features for projects requiring maximum customization and technical control. + +Strapi's flexibility, extensibility, and TypeScript support make it our continued choice for open-source content management, providing technical teams with the tools needed for complex, customizable content solutions.