From 908e7936c1fd2d5d0c072fa3af0f4f1a2215f7b6 Mon Sep 17 00:00:00 2001 From: Whisker Jones Date: Thu, 30 May 2024 09:58:55 -0600 Subject: [PATCH] added new docs --- tech_docs/observability.md | 166 +++++++++++++++++++++++++++++++++++++ work/digital.md | 43 ++++++++++ 2 files changed, 209 insertions(+) create mode 100644 tech_docs/observability.md create mode 100644 work/digital.md diff --git a/tech_docs/observability.md b/tech_docs/observability.md new file mode 100644 index 0000000..e695e6e --- /dev/null +++ b/tech_docs/observability.md @@ -0,0 +1,166 @@ +The ELK stack (Elasticsearch, Logstash, and Kibana) is another popular open-source solution for managing and analyzing log data. Let's compare the ELK stack with Prometheus, Splunk, and Datadog to understand its strengths and limitations. + +### ELK Stack (Elasticsearch, Logstash, Kibana) + +**Components:** +- **Elasticsearch:** A search and analytics engine. +- **Logstash:** A server-side data processing pipeline that ingests data from multiple sources, transforms it, and sends it to a "stash" like Elasticsearch. +- **Kibana:** A visualization layer that provides a user interface for Elasticsearch, allowing you to create dashboards and visualizations. + +**Strengths:** +- **Logs and Unstructured Data:** The ELK stack excels at ingesting, storing, and analyzing log data and other unstructured data. +- **Powerful Search:** Elasticsearch provides powerful search capabilities, enabling complex queries on large datasets. +- **Visualization:** Kibana offers robust visualization tools, allowing you to create detailed and interactive dashboards. +- **Scalability:** Elasticsearch can scale horizontally to handle large amounts of data and queries. +- **Flexibility:** Logstash provides a flexible way to parse and transform incoming data, making it easy to integrate with various data sources. + +**Limitations:** +- **Complex Setup:** Setting up and managing the ELK stack can be complex and may require significant expertise. +- **Resource Intensive:** Elasticsearch can be resource-intensive, requiring careful tuning and management, especially at scale. +- **Focused on Logs:** While it can handle metrics (especially with integrations like Metricbeat), it is primarily focused on log data and may not be as efficient for time-series metrics as Prometheus. +- **Alerting:** Kibana's alerting features are not as advanced as those provided by specialized monitoring tools like Prometheus or Datadog. + +### Comparison to Prometheus, Splunk, and Datadog + +**Use Case Focus:** +- **Prometheus:** Best for metrics and time-series data, especially in cloud-native environments. Requires additional tools for logs and traces. +- **ELK Stack:** Best for log data and unstructured data. Can be extended to handle metrics but not as natively optimized for time-series data. +- **Splunk:** Comprehensive observability platform for logs, metrics, and traces. Powerful search and analytics capabilities but can be costly. +- **Datadog:** Unified observability platform for metrics, logs, and traces. Easy to set up with strong cloud-native support, but can be expensive. + +**Setup and Maintenance:** +- **Prometheus:** Moderate setup complexity. Requires integration with Grafana for visualization and other tools for logs and traces. +- **ELK Stack:** High setup complexity. Requires careful tuning and management, especially at scale. +- **Splunk:** High setup complexity. Powerful features but can be expensive and complex to manage. +- **Datadog:** Low setup complexity. Easy to use with many pre-built integrations, but can become expensive. + +**Cost:** +- **Prometheus:** Free and open-source, with community support. Costs may arise from managing infrastructure and additional tools. +- **ELK Stack:** Free and open-source, but can incur costs related to infrastructure and management. Commercial support is available from Elastic. +- **Splunk:** Proprietary and can be very expensive, especially for large volumes of data. +- **Datadog:** Proprietary with a subscription-based pricing model. Costs can increase with scale and additional features. + +**Integration and Extensibility:** +- **Prometheus:** Integrates well with Kubernetes and cloud-native environments. Requires additional tools for full observability. +- **ELK Stack:** Highly flexible and extensible with Logstash and Beats for data ingestion. Integrates well with many data sources. +- **Splunk:** Extensive integrations and capabilities, suitable for enterprise environments. +- **Datadog:** Many pre-built integrations and a unified platform for comprehensive observability. + +### Summary + +- **Prometheus** is best suited for metrics and time-series data, particularly in cloud-native environments. It requires additional tools for logs and traces. +- **ELK Stack** excels at log data and unstructured data, with powerful search and visualization capabilities. It can handle metrics but is not optimized for time-series data. +- **Splunk** provides a comprehensive observability platform with powerful search and analytics but at a high cost and complexity. +- **Datadog** offers an easy-to-use, all-in-one observability platform with strong cloud-native support, but it can be expensive. + +The choice depends on your specific needs and environment. If your primary focus is on logs and you need powerful search and analysis capabilities, the ELK stack is a strong contender. For metrics-focused monitoring, Prometheus is a better fit. If you need a comprehensive, easy-to-use solution and are willing to pay for it, Datadog or Splunk might be more appropriate. + +--- + +Sure, let's structure this information in a comprehensive and logical manner for someone looking to understand and compare observability platforms. + +--- + +# Overview of Observability Platforms + +Observability is crucial for understanding the health and performance of your systems. This guide provides a comparison of four major observability platforms: Prometheus, ELK Stack, Splunk, and Datadog, highlighting their strengths, limitations, and use cases to help you choose the right tool for your needs. + +## 1. Prometheus + +### Overview +Prometheus is an open-source monitoring system and time-series database developed by SoundCloud. It is designed for reliability and scalability in cloud-native environments. + +### Strengths +- **Time-Series Data:** Efficiently handles metrics and time-series data. +- **Open Source:** No licensing fees and strong community support. +- **Pull-Based Model:** Prometheus scrapes metrics from targets, offering flexible and secure monitoring. +- **Integration:** Seamlessly integrates with Kubernetes and other cloud-native technologies. + +### Limitations +- **Limited to Metrics:** Primarily focused on metrics, not logs or traces. +- **No Built-In Visualization:** Requires Grafana or other tools for advanced visualization. +- **Storage:** Challenges with long-term storage and high cardinality without additional tools like Thanos or Cortex. + +### Ideal Use Case +Best suited for metrics monitoring in cloud-native environments. Pair with Grafana for visualization and consider additional tools for logs and traces. + +## 2. ELK Stack (Elasticsearch, Logstash, Kibana) + +### Overview +The ELK stack is a collection of three open-source projects: Elasticsearch for search and analytics, Logstash for data processing, and Kibana for visualization. + +### Strengths +- **Logs and Unstructured Data:** Excels at handling and analyzing log data. +- **Powerful Search:** Advanced search capabilities through Elasticsearch. +- **Visualization:** Robust visualization with Kibana dashboards. +- **Scalability:** Can scale horizontally to handle large datasets. +- **Flexibility:** Logstash provides versatile data ingestion and transformation. + +### Limitations +- **Complex Setup:** Requires significant expertise to set up and manage. +- **Resource Intensive:** Elasticsearch can be resource-heavy. +- **Focused on Logs:** Not optimized for time-series metrics compared to Prometheus. +- **Alerting:** Kibana's alerting features are less advanced. + +### Ideal Use Case +Best for log management and analysis. Suitable for environments where log data is critical, with capabilities to extend for metrics. + +## 3. Splunk + +### Overview +Splunk is a proprietary platform for searching, monitoring, and analyzing machine-generated data. + +### Strengths +- **Comprehensive Data Types:** Handles logs, metrics, and traces. +- **Advanced Search and Analysis:** Powerful search language (SPL) for data analysis. +- **Visualization:** Includes robust built-in visualization tools. +- **Alerting and Reporting:** Strong alerting and reporting features. +- **Enterprise Features:** Extensive features for user management and compliance. + +### Limitations +- **Cost:** Can be very expensive, especially for large data volumes. +- **Complexity:** Requires significant expertise to manage. +- **Proprietary:** Dependency on Splunk for support and updates. + +### Ideal Use Case +Ideal for enterprises needing comprehensive observability and willing to invest in a premium solution for deep insights and extensive features. + +## 4. Datadog + +### Overview +Datadog is a cloud-native monitoring and analytics platform providing observability for metrics, logs, and traces. + +### Strengths +- **Unified Platform:** Single platform for metrics, logs, and traces. +- **Easy Setup:** User-friendly with many pre-built integrations. +- **Visualization:** Strong visualization capabilities with customizable dashboards. +- **Alerting and Anomaly Detection:** Advanced alerting features. +- **Cloud-Native:** Designed for seamless integration with cloud environments. + +### Limitations +- **Cost:** Can become expensive as data volumes increase. +- **Data Retention:** Limited retention periods based on the pricing plan. +- **Proprietary:** Vendor lock-in with a subscription-based model. + +### Ideal Use Case +Suitable for organizations needing an easy-to-use, all-in-one observability platform with strong cloud-native support, prepared for potential higher costs. + +## Summary + +### Choosing the Right Tool +- **Prometheus** is ideal for metrics and time-series data, especially in cloud-native environments. Requires Grafana for visualization. +- **ELK Stack** excels at log data and unstructured data with powerful search and visualization. Suitable for log-centric environments. +- **Splunk** provides a comprehensive observability platform for logs, metrics, and traces, best for enterprises needing deep insights and extensive features. +- **Datadog** offers a unified, easy-to-use observability platform with strong cloud-native support, suitable for those willing to invest in a premium solution. + +### Recommendations +- **Start with Prometheus and Grafana** if your focus is on metrics and time-series data. +- **Consider the ELK Stack** for detailed log analysis and visualization. +- **Evaluate Splunk** if you need a comprehensive, enterprise-grade solution and have the budget for it. +- **Explore Datadog** for an integrated observability solution with quick setup and strong cloud support. + +By understanding the strengths and limitations of each platform, you can make an informed decision that best fits your observability needs and environment. + +--- + +This structure provides a logical and comprehensive overview, helping you understand the capabilities and use cases of each observability platform, making it easier to choose the right tool for your specific needs. diff --git a/work/digital.md b/work/digital.md new file mode 100644 index 0000000..c517069 --- /dev/null +++ b/work/digital.md @@ -0,0 +1,43 @@ +For Panera Bread, WWT delivered a next-gen restaurant experience, shifting the way consumers engage with the Panera brand and its chain of bakery-cafés across the United States and Canada. The challenge was to deliver unparalleled digital experiences, reducing line length and transaction friction inside their bakery-cafés. WWT’s approach was strategic, utilizing their deep roots in hardware distribution integration and R&D, innovative software development capabilities, and core competency as a strategic digital advisor. The result was a 360-degree approach and a five-star consumer-rated kiosk experience. This approach encompassed store transformation, omni-channel integrity, and the evolution of in-store, mobile app, and website commerce to unified commerce [1]. + +For Wingstop, WWT helped them evolve their digital and technology platforms to become a global digital leader. The foundation created by WWT included e-commerce, Data Analytics and Machine Learning, and Identity Management [2]. WWT also helped Wingstop set up a digital platform to enable them to be a true digital commerce company, supporting their journey towards 100% digital commerce [5]. + +In terms of technical capabilities, WWT’s digital services include research and insights, design, development and integration, and more. They also offer a variety of services such as AI & data services, automation services, cloud services, digital services, digital workspace services, and software services [8]. + +In addition to these specific projects, WWT has also conducted several Proof of Concepts (POCs) with both Panera Bread and Wingstop, focusing on areas such as server infrastructure, software-defined WAN, and digital transformation initiatives [ServiceNow data]. + +--- + +For Panera Bread, WWT delivered a next-gen restaurant experience, shifting the way consumers engage with the Panera brand and its chain of bakery-cafés across the United States and Canada. The challenge was to deliver unparalleled digital experiences, reducing line length and transaction friction inside their bakery-cafés. WWT’s approach was strategic, utilizing their deep roots in hardware distribution integration and R&D, innovative software development capabilities, and core competency as a strategic digital advisor. The result was a 360-degree approach and a five-star consumer-rated kiosk experience. This approach encompassed store transformation, omni-channel integrity, and the evolution of in-store, mobile app, and website commerce to unified commerce [1]. + +For Wingstop, WWT helped them evolve their digital and technology platforms to become a global digital leader. The foundation created by WWT included e-commerce, Data Analytics and Machine Learning, and Identity Management [2]. WWT also helped Wingstop set up a digital platform to enable them to be a true digital commerce company, supporting their journey towards 100% digital commerce [5]. + +In terms of technical capabilities, WWT’s digital services include research and insights, design, development and integration, and more. They also offer a variety of services such as AI & data services, automation services, cloud services, digital services, digital workspace services, and software services [8]. + +In addition to these specific projects, WWT has also conducted several Proof of Concepts (POCs) with both Panera Bread and Wingstop, focusing on areas such as server infrastructure, software-defined WAN, and digital transformation initiatives [ServiceNow data]. + +--- + +strategic approach, store transformation, omni-channel integrity, e-commerce foundation, data analytics and machine learning, identity management, proof of concepts (POCs), and digital services. + +--- + +https://www.wwt.com/case-study/embedding-seamless-service-at-panera-bread + +https://www.wwt.com/video/atsm-04-08-2022-wingstop-case-study + +https://www.wwt.com/event/616dda3c187c3e008374eee6 + +https://www.wwt.com/video/2024-sko-digital-employee-experience-breakout-session + +https://www.wwt.com/video/atsm-10-07-2022-unified-commerce + +https://www.wwt.com/article/revolutionizing-the-restaurant-technology-ecosystem + +https://www.wwt.com/video/atsm-04-01-2022-wwt-digital-solutions + +https://www.wwt.com/service/wwt-services/overview + +https://www.wwt.com/digital-workspace-atsm + +https://www.wwt.com/briefing/customer-experience-digital-capabilities-briefing \ No newline at end of file