This talk was presented at IP Expo Manchester in May, 2016. the themes discussed are:
- how does machine data relate to devops?
- how can tracking this data lead to better outcomes?
- what types of data are important to track?
Splunk Quick Overview for Emirates Travel HackathonHal Rottenberg
Splunk is an engine for collecting, indexing, querying, and reporting machine data from any source. It allows users to analyze log files, metrics, databases, sensors, and other unstructured or polystructured data. Splunk can serve as a backend, frontend, and middleware platform with an advanced query language for analytics and reporting, and it integrates with other systems through APIs and SDKs.
The document discusses two types of data scientists: those in "the lab" who focus on question-driven and interactive analytics on fixed data, and those in "the factory" who focus on metric-driven and automated analytics on fluid data. It then describes tools for each type, including Apache Spark and related tools for data science in the factory, and Cloudera Impala and related tools for investigative analytics in the lab. The speaker concludes by thanking the audience.
This document contains a graph with semantic information about people and their interests. The graph shows relationships between individuals like Danny being Nezih's brother, and people's interests like Frank liking bicycles and food carts. It then discusses using this semantic graph to infer additional relationships, like which interests two individuals might have in common based on their connections.
This talk focus on what admins need to know about the HTTP Event Collector. Why it exists, how it differs from existing options, and how to configure, manage, deploy and scale it.
This document summarizes a presentation about a company's use of Splunk for log management and analytics. Some key points:
- The company collects 400GB of logs per day across various clusters and forwards them to Splunk.
- They have 1300 user accounts accessing Splunk across development, non-production, and US/EU production environments.
- Splunk is used for tracking errors and volumes, identifying anomalies, network monitoring, and measuring performance.
- In 2013 alone, over 350 issues were identified or resolved using Splunk.
Testing in Production - presentation & webinar by Amber RaceApplitools
** Full webinar recording: https://youtu.be/8-ymeVdNxSE **
"Watch this deep-dive session, where I demonstrate multiple methods you can use for collecting production data, as well as give real-life examples of how production data can help you find bugs, and even enable running tests in production." -- Amber Race
Our software doesn't run in a test environment with mock users — it runs out in the real world, being used by thousands or even millions of real people, with thousands of variations of devices, screen sizes, and operating systems.
Learn why you need to be collecting this data -- and how you can make it actionable for continuous testing, learning and improving, as well as how it can help you make your own services more transparent and user-friendly to your customers.
Watch this webinar by expert Amber Race -- Sr. SDET @ Big Fish Games -- and learn:
-- What monitoring and logging tools are available
-- How to decide what to track
-- Ways to create meaningful reports with monitoring results
-- How to use production data to create more realistic tests
-- Strategies for using real-time monitoring to run tests in production
Take the ideas of DevOps and the notion of a delivery pipeline and combine them for an AppSec Pipeline. This talk covers the open source components used to create an AppSec Pipeline and the benefits we received from its implementation.
Peeling the Onion: Making Sense of the Layers of API SecurityMatt Tesauro
This document provides an overview of API security from multiple perspectives: API security posture, runtime security, and security testing. It discusses the complex API ecosystem involving various stakeholders. The document also outlines common API attack classes like DDoS, data breaches, and abuse of functionality. Finally, it provides key takeaways that APIs have complex interconnected systems, require coordination across teams, and need to be evaluated from different security perspectives.
Boston DevOps Days 2016: Implementing Metrics Driven DevOps - Why and HowAndreas Grabner
How can we detect a bad deployment before it hits production? By automatically looking at the right architectural metrics in your CI/CD and stop a build before its too late. Lets hook up your test automation with app metrics and use them as quality gates to stop bad builds early!
WSO2 Guest Webinar: Building Enterprise Awareness with API Analytics in the A...WSO2
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2015/07/building-enterprise-awareness-with-api-analytics-in-the-api-economy/
An awareness of who is accessing digital resources
How users put these resources to use
Which applications they are using it within
Network level information about where they are accessing the resources from
The document summarizes a company's use of Splunk for log management and analytics. It describes their Splunk environment with multiple clusters, around 1000 users, and applications of Splunk for tracking errors, anomalies, and performance. It discusses lessons learned around query impacts, timestamps, and resources for support. Future plans include search head clustering, heavy forwarders, and apps for visibility and intelligence to enable agility and improved code quality.
Deploying web apis on core clr to dockerGlenn Block
This document provides an overview of .NET Core, ASP.NET 5, and Docker. It introduces .NET Core as a cross-platform version of .NET that allows side-by-side app versioning. ASP.NET 5 is the new version of ASP.NET that is modular and runs on .NET Core, supporting Web UI and APIs. Docker is a tool that allows packaging apps and dependencies into standardized containers to enable portable development environments. The document provides links for additional information on these topics and an example contact API project using these technologies on GitHub.
Automate Application Quality Detection. Use Key Application Quality Metrics (# of SQL, Memory Allocated, CPU & GC Times, ...) captured during Automated Test Executions.
Let these Metrics act as Quality Gates. Leads to better quality software reaching the end of the Pipeline
Improving data interoperability in Python and RWes McKinney
Apache Arrow is a new open source project that aims to establish a common in-memory data representation that can improve interoperability across data science programming languages like Python and R. It provides a standardized columnar memory format that can reduce the CPU overhead of serialization and deserialization between systems by 70-80%. The Feather file format leverages Arrow to provide a fast, language-agnostic binary file format for data frames that enables very fast read/write speeds between Python and R. While Feather has benefits, it still requires data conversion between Arrow storage and each language's native data structures; establishing a common in-memory representation at the C/C++ level could further improve sharing of algorithms and libraries.
- John Bland is a senior software developer at Sabre who is responsible for Splunk implementation and uses it extensively in his work.
- Before Splunk, Sabre struggled to view logs from different locations and applications in a centralized way and had to manually search through logs.
- Sabre now uses Splunk across multiple environments to gain visibility into applications, monitor performance, streamline development processes, and improve support capabilities.
- Splunk has helped Sabre improve uptime, reduce troubleshooting time, and provide better support to internal teams and customers. Sabre continues expanding their use of Splunk across more processes.
Enterprise Search Europe 2015: Fishing the big data streams - the future of ...Charlie Hull
The document discusses the future of search and analytics using streams of data from sources like the Internet of Things. It describes how search technologies can be used to process real-time streams of data by indexing the streams and querying them similar to how searches are currently done on stored data. Examples of searching streams are given, such as searching incoming news stories against stored search profiles to identify matches.
Adopting a security attitude in DevOps via DevOpsSecTapabrata Pal
Adopting a security attitude in DevOps involves implementing security practices and controls at every stage of the software delivery pipeline from coding through testing and deployment. This includes performing security checks and tests during coding like using secure coding practices and static analysis tools, during building like reviewing dependencies and analyzing for vulnerabilities, and during testing and deployment through security testing techniques like penetration testing and configuration reviews. The goal is to shift security left and provide rapid security feedback throughout the development and delivery process.
Building an Open Source AppSec Pipeline - 2015 Texas Linux FestMatt Tesauro
Take the ideas of DevOps and the notion of a delivery pipeline and combine them for an AppSec Pipeline. This talk covers the open source components used to create an AppSec Pipeline and the benefits we received from its implementation.
Taking the Best of Agile, DevOps and CI/CD into securityMatt Tesauro
Software development continues to move faster with the rise of Agile, DevOps, and CI/CD, while traditional AppSec continues with slow delivery and failure to scale. In this talk, we’ll discuss lessons learned from forward thinking software development at a multitude of companies, and show you how to apply them to your org. By taking the best of DevOps, CI/CD and Agile, you can iteratively up your AppSec program and ascend out of traditional AppSec pitfalls.
My talk from Secure Coding Virtual Summit (2021-03-24)
In this talk, we shared some of our highlights of the GraphQL Europe conference.
You can see the full coverage of the conference here: https://www.graph.cool/talks/
OOP 2016 - Building Software That Eats The WorldAndreas Grabner
According to VC and web pioneer Marc Andreessen software is eating the world. Evidence proves he is right. Uber, the biggest taxi company, has no cars, AirBnB, the biggest hotel service, has no rooms and there are many more examples. Looking at these success stories there is a clear blueprint how to build software that eats the world. Just a quick heads up: It is not about building your typical web application any more.
Data Science Challenges in Personal Program AnalysisWork-Bench
This document discusses personal program analysis (PPA) using data science to provide developers with alerts about potential bugs and coding issues in their code. PPA analyzes source code and queries it to find alerts, which are issues like defining the same key twice in a dictionary. The service aims to help developers improve by making analysis personal and comparing developers' contributions and improvements over time. It also demonstrates using PPA on various open source projects. The presentation argues that personalizing feedback encourages developers to improve their skills for better code and software.
APIdays Paris 2018 - Deliver API Updates in Real Time with Mercure.rocks Kévi...apidays
The document discusses live updates using server-sent events (SSEs) and the Mercure protocol. It begins by outlining several use cases for push notifications and real-time updates. It then compares different protocols for live updates like polling, websockets, and SSEs. The bulk of the document describes the Mercure protocol which is built on SSEs and supports features like authentication, reconnection, and publishing/subscribing. It provides details on using the reference implementation including starting the hub, subscribing/publishing, and integrating with frameworks like Symfony and React.
GraphQL Munich Meetup #1 - How We Use GraphQL At CommercetoolsNicola Molinari
The document describes commercetools' experience with adopting GraphQL for their merchant center application. They were initially fetching category data with multiple REST requests, but were able to fetch it in one GraphQL query along with additional fields like number of subcategories and products. This solved a performance issue and allowed them to easily extend their API. They also migrated other parts of their client to use GraphQL, reducing network requests and complexity. Overall, GraphQL allowed them to optimize queries and extend their API with minimal effort.
The document discusses challenges that arise when trying to scale analytics teams by building business intelligence (BI) tools, and proposes an alternative approach of hiring "Analysis Developers" to help analysts scale their work using R. Some key points made include:
- Building BI tools often leads to dysfunction as product and engineering teams compete to build the simplest tools.
- Analysis Developers would develop reusable R packages and help all analysts work more efficiently through skills training.
- This avoids issues like static tools becoming unstable as requirements change, and allows for flexible, reproducible analyses.
- Promoting skills acquisition rather than deliverables helps analysts progress in their careers.
Taking AppSec to 11: AppSec Pipeline, DevOps and Making Things BetterMatt Tesauro
This document summarizes Matt Tesauro's presentation on improving application security (AppSec) through the use of AppSec pipelines and DevOps strategies. The key points are:
1. AppSec pipelines are designed to optimize AppSec personnel by automating tasks and increasing consistency, tracking, flow and visibility of work. This allows AppSec teams to focus on custom work rather than setup.
2. Integrating AppSec tools and workflows into development pipelines can help drive up consistency, reduce friction with developers, and increase the number of assessments an AppSec team can complete without increasing headcount.
3. Continual experimentation and optimizing the critical resource - in this case AppSec personnel - is important for
Purple is the New Black: Modern Approaches for Application SecurityTanya Janca
Tanya Janca hosted a discussion on modern application security approaches called the "purple team". She defined red, blue, and purple teams and explained that purple teams collaborate between security and developers. Janca discussed purple team tactics like advocacy, automation, and continuous learning. She then covered modern security topics for applications like zero trust, serverless workflows, third-party components, and containers. Janca emphasized automating security work through DevSecOps and provided additional resources.
AppSec Pipelines and Event based SecurityMatt Tesauro
Matt Tesauro discusses moving application security (AppSec) beyond traditional security testing towards event-based security using continuous integration/continuous delivery (CI/CD) pipelines and automation. Key points include:
- Implementing AppSec pipelines that automate security tasks using tools like Docker to increase efficiency and consistency while reducing friction between AppSec and development teams.
- Treating individual security findings as tests that are run continuously via tools like Jenkins to quickly determine when issues are fixed.
- With increased automation and efficiency, one company increased the number of application assessments from 44 in 2014 to over 400 in 2016 while reducing AppSec staffing levels.
Eduketing 2015- Antes y después de Eduketing: Camino de la salvación. El caso...EDUKETING
Este documento describe la transformación de la Escuela de FP Montserrat después de adoptar estrategias de marketing educativo. Antes tenía menos del 30% de capacidad debido a bajo número de estudiantes. Después de asistir a un evento de EdukeTing, la escuela implementó nuevas herramientas de marketing como redes sociales, mejoró su imagen y procesos de ventas, y ganó compromiso de la comunidad educativa. Como resultado, aumentó su matrícula en un 40% en dos años, salvando la escuela de cerrar.
Boston DevOps Days 2016: Implementing Metrics Driven DevOps - Why and HowAndreas Grabner
How can we detect a bad deployment before it hits production? By automatically looking at the right architectural metrics in your CI/CD and stop a build before its too late. Lets hook up your test automation with app metrics and use them as quality gates to stop bad builds early!
WSO2 Guest Webinar: Building Enterprise Awareness with API Analytics in the A...WSO2
To view recording of this webinar please use below URL:
http://wso2.com/library/webinars/2015/07/building-enterprise-awareness-with-api-analytics-in-the-api-economy/
An awareness of who is accessing digital resources
How users put these resources to use
Which applications they are using it within
Network level information about where they are accessing the resources from
The document summarizes a company's use of Splunk for log management and analytics. It describes their Splunk environment with multiple clusters, around 1000 users, and applications of Splunk for tracking errors, anomalies, and performance. It discusses lessons learned around query impacts, timestamps, and resources for support. Future plans include search head clustering, heavy forwarders, and apps for visibility and intelligence to enable agility and improved code quality.
Deploying web apis on core clr to dockerGlenn Block
This document provides an overview of .NET Core, ASP.NET 5, and Docker. It introduces .NET Core as a cross-platform version of .NET that allows side-by-side app versioning. ASP.NET 5 is the new version of ASP.NET that is modular and runs on .NET Core, supporting Web UI and APIs. Docker is a tool that allows packaging apps and dependencies into standardized containers to enable portable development environments. The document provides links for additional information on these topics and an example contact API project using these technologies on GitHub.
Automate Application Quality Detection. Use Key Application Quality Metrics (# of SQL, Memory Allocated, CPU & GC Times, ...) captured during Automated Test Executions.
Let these Metrics act as Quality Gates. Leads to better quality software reaching the end of the Pipeline
Improving data interoperability in Python and RWes McKinney
Apache Arrow is a new open source project that aims to establish a common in-memory data representation that can improve interoperability across data science programming languages like Python and R. It provides a standardized columnar memory format that can reduce the CPU overhead of serialization and deserialization between systems by 70-80%. The Feather file format leverages Arrow to provide a fast, language-agnostic binary file format for data frames that enables very fast read/write speeds between Python and R. While Feather has benefits, it still requires data conversion between Arrow storage and each language's native data structures; establishing a common in-memory representation at the C/C++ level could further improve sharing of algorithms and libraries.
- John Bland is a senior software developer at Sabre who is responsible for Splunk implementation and uses it extensively in his work.
- Before Splunk, Sabre struggled to view logs from different locations and applications in a centralized way and had to manually search through logs.
- Sabre now uses Splunk across multiple environments to gain visibility into applications, monitor performance, streamline development processes, and improve support capabilities.
- Splunk has helped Sabre improve uptime, reduce troubleshooting time, and provide better support to internal teams and customers. Sabre continues expanding their use of Splunk across more processes.
Enterprise Search Europe 2015: Fishing the big data streams - the future of ...Charlie Hull
The document discusses the future of search and analytics using streams of data from sources like the Internet of Things. It describes how search technologies can be used to process real-time streams of data by indexing the streams and querying them similar to how searches are currently done on stored data. Examples of searching streams are given, such as searching incoming news stories against stored search profiles to identify matches.
Adopting a security attitude in DevOps via DevOpsSecTapabrata Pal
Adopting a security attitude in DevOps involves implementing security practices and controls at every stage of the software delivery pipeline from coding through testing and deployment. This includes performing security checks and tests during coding like using secure coding practices and static analysis tools, during building like reviewing dependencies and analyzing for vulnerabilities, and during testing and deployment through security testing techniques like penetration testing and configuration reviews. The goal is to shift security left and provide rapid security feedback throughout the development and delivery process.
Building an Open Source AppSec Pipeline - 2015 Texas Linux FestMatt Tesauro
Take the ideas of DevOps and the notion of a delivery pipeline and combine them for an AppSec Pipeline. This talk covers the open source components used to create an AppSec Pipeline and the benefits we received from its implementation.
Taking the Best of Agile, DevOps and CI/CD into securityMatt Tesauro
Software development continues to move faster with the rise of Agile, DevOps, and CI/CD, while traditional AppSec continues with slow delivery and failure to scale. In this talk, we’ll discuss lessons learned from forward thinking software development at a multitude of companies, and show you how to apply them to your org. By taking the best of DevOps, CI/CD and Agile, you can iteratively up your AppSec program and ascend out of traditional AppSec pitfalls.
My talk from Secure Coding Virtual Summit (2021-03-24)
In this talk, we shared some of our highlights of the GraphQL Europe conference.
You can see the full coverage of the conference here: https://www.graph.cool/talks/
OOP 2016 - Building Software That Eats The WorldAndreas Grabner
According to VC and web pioneer Marc Andreessen software is eating the world. Evidence proves he is right. Uber, the biggest taxi company, has no cars, AirBnB, the biggest hotel service, has no rooms and there are many more examples. Looking at these success stories there is a clear blueprint how to build software that eats the world. Just a quick heads up: It is not about building your typical web application any more.
Data Science Challenges in Personal Program AnalysisWork-Bench
This document discusses personal program analysis (PPA) using data science to provide developers with alerts about potential bugs and coding issues in their code. PPA analyzes source code and queries it to find alerts, which are issues like defining the same key twice in a dictionary. The service aims to help developers improve by making analysis personal and comparing developers' contributions and improvements over time. It also demonstrates using PPA on various open source projects. The presentation argues that personalizing feedback encourages developers to improve their skills for better code and software.
APIdays Paris 2018 - Deliver API Updates in Real Time with Mercure.rocks Kévi...apidays
The document discusses live updates using server-sent events (SSEs) and the Mercure protocol. It begins by outlining several use cases for push notifications and real-time updates. It then compares different protocols for live updates like polling, websockets, and SSEs. The bulk of the document describes the Mercure protocol which is built on SSEs and supports features like authentication, reconnection, and publishing/subscribing. It provides details on using the reference implementation including starting the hub, subscribing/publishing, and integrating with frameworks like Symfony and React.
GraphQL Munich Meetup #1 - How We Use GraphQL At CommercetoolsNicola Molinari
The document describes commercetools' experience with adopting GraphQL for their merchant center application. They were initially fetching category data with multiple REST requests, but were able to fetch it in one GraphQL query along with additional fields like number of subcategories and products. This solved a performance issue and allowed them to easily extend their API. They also migrated other parts of their client to use GraphQL, reducing network requests and complexity. Overall, GraphQL allowed them to optimize queries and extend their API with minimal effort.
The document discusses challenges that arise when trying to scale analytics teams by building business intelligence (BI) tools, and proposes an alternative approach of hiring "Analysis Developers" to help analysts scale their work using R. Some key points made include:
- Building BI tools often leads to dysfunction as product and engineering teams compete to build the simplest tools.
- Analysis Developers would develop reusable R packages and help all analysts work more efficiently through skills training.
- This avoids issues like static tools becoming unstable as requirements change, and allows for flexible, reproducible analyses.
- Promoting skills acquisition rather than deliverables helps analysts progress in their careers.
Taking AppSec to 11: AppSec Pipeline, DevOps and Making Things BetterMatt Tesauro
This document summarizes Matt Tesauro's presentation on improving application security (AppSec) through the use of AppSec pipelines and DevOps strategies. The key points are:
1. AppSec pipelines are designed to optimize AppSec personnel by automating tasks and increasing consistency, tracking, flow and visibility of work. This allows AppSec teams to focus on custom work rather than setup.
2. Integrating AppSec tools and workflows into development pipelines can help drive up consistency, reduce friction with developers, and increase the number of assessments an AppSec team can complete without increasing headcount.
3. Continual experimentation and optimizing the critical resource - in this case AppSec personnel - is important for
Purple is the New Black: Modern Approaches for Application SecurityTanya Janca
Tanya Janca hosted a discussion on modern application security approaches called the "purple team". She defined red, blue, and purple teams and explained that purple teams collaborate between security and developers. Janca discussed purple team tactics like advocacy, automation, and continuous learning. She then covered modern security topics for applications like zero trust, serverless workflows, third-party components, and containers. Janca emphasized automating security work through DevSecOps and provided additional resources.
AppSec Pipelines and Event based SecurityMatt Tesauro
Matt Tesauro discusses moving application security (AppSec) beyond traditional security testing towards event-based security using continuous integration/continuous delivery (CI/CD) pipelines and automation. Key points include:
- Implementing AppSec pipelines that automate security tasks using tools like Docker to increase efficiency and consistency while reducing friction between AppSec and development teams.
- Treating individual security findings as tests that are run continuously via tools like Jenkins to quickly determine when issues are fixed.
- With increased automation and efficiency, one company increased the number of application assessments from 44 in 2014 to over 400 in 2016 while reducing AppSec staffing levels.
Eduketing 2015- Antes y después de Eduketing: Camino de la salvación. El caso...EDUKETING
Este documento describe la transformación de la Escuela de FP Montserrat después de adoptar estrategias de marketing educativo. Antes tenía menos del 30% de capacidad debido a bajo número de estudiantes. Después de asistir a un evento de EdukeTing, la escuela implementó nuevas herramientas de marketing como redes sociales, mejoró su imagen y procesos de ventas, y ganó compromiso de la comunidad educativa. Como resultado, aumentó su matrícula en un 40% en dos años, salvando la escuela de cerrar.
Security testing has traditionally been siloed from development, operations, and quality assurance teams. However, a new approach called "Security as a Code" aims to break down these barriers by integrating security directly into coding practices similar to how unit testing, infrastructure as code, and acceptance testing have previously integrated other functions. This would allow security to work in parallel with development rather than as a separate phase, improving feedback cycles and collaboration between teams.
IT Operation Analytic for security- MiSSconf(sp1)stelligence
IT Operation Analytic: Using Anomaly Detection , Unsupervised Machine Learning, to distinct normal and abnormal behavior and enhance efficiency of SIEM detection and alert capability.
Accelerate Troubleshooting and Reinvent Monitoring with Interactive Visualiza...Splunk
Time is money, especially when it’s downtime impacting your services. Splunk IT Service Intelligence (ITSI) introduces an enhanced troubleshooting and monitoring experience that enables you to visually investigate issues and prevent outages, faster than ever before.
Thiis demo-rich webinar helps you to understand how you can use Splunk ITSI to:
- Speed up investigations by organizing and correlating relevant metrics and events into “swim lanes”
- Compare performance over time with real-time performance to understand trends and identify systemic issues
- Identify and alert on notable events by creating predefined correlation searches
This document discusses using machine data for security insights through big data analytics. It begins by explaining the importance of understanding the types of machine data, such as firewall, IPS, and Windows logs, and the context around that data. It then discusses where insights can come from, such as anomalies, trends, and deficiencies. Measures for detecting common attacks are proposed. The document outlines a security analysis life cycle of detecting incidents, verifying them, and reacting. It concludes by providing examples of implementing such a system using various open source and commercial tools.
Secure code review is probably the most effective technique to identify security bugs early in the system development lifecycle.
When used together with automated and manual penetration testing, code review can significantly increase the cost effectiveness of an application security verification effort. This presentation explain how can we start secure code review effectively.
This document discusses how Splunk can be used for DevOps. It defines DevOps as integrating development and operations. It then discusses some common DevOps metrics like culture, process, quality, systems, activity, and impact metrics. It explains that machine data from across the development lifecycle and IT operations is a critical source of DevOps metrics. The document provides examples of how Splunk can provide visibility and collect machine data from various parts of the development and operations environments, like code review, version control, CI/build servers, testing, releases, and infrastructure systems. It discusses how Splunk can be used to increase delivery velocity, improve code quality, and enable data-driven continuous delivery for DevOps teams.
Listen to Your Machines: DevOps Analytics for Better Feedback LoopsSplunk
Effective DevOps Practices for collecting, correlating and analyzing DevOps data. First presented by Splunk's Andi Mann at DevOps Summit at Cloud Expo.
Data-Driven DevOps: Mining Machine Data for 'Metrics that Matter' in a DevOps...Splunk
IT organizations are increasingly using machine data - including in DevOps practices - to get away from 'vanity metrics' and instead to generate 'metrics that matter'. These metrics provide visibility into the delivery of new application code and the business value of DevOps, to both IT and business stakeholders.
Machine data provides DevOps teams and others - including QA, secops, CxOs and LOB leaders - with meaningful and actionable metrics. This allows stakeholders to monitor, measure, and continuously improve the velocity and quality of code throughout the software lifecycle, from dev/test to customer-facing outcomes and business impact.
In this session Andi Mann, chief technology advocate at Splunk, will share core methodologies, interesting case studies, key success factors and 'gotcha' moments from real-world experience with mining machine data to produce 'metrics that matter' in a DevOps context.
Data-Driven DevOps: Improve Velocity and Quality of Software Delivery with Me...Splunk
Much of the value of DevOps comes from a (renewed) focus on measurement, sharing, and continuous feedback loops. In increasingly complex DevOps workflows and environments, and especially in larger, regulated, or more crystallized organizations, these core concepts become even more critical.
This session will show how, by focusing on 'metrics that matter,' you can provide objective, transparent, and meaningful feedback on DevOps processes to all stakeholders. Learn from real-life examples how to use the data generated throughout application delivery to continuously identify, measure, and improve deployment speed, code quality, process efficiency, outsourcing value, security coverage, audit success, customer satisfaction, and business alignment.
Agile & DevOps - It's all about project successAdam Stephensen
The document provides information on DevOps practices and tools from Microsoft. It discusses how DevOps enables continuous delivery of value through integrating people, processes, and tools. Benefits of DevOps include more frequent and stable releases, lower change failure rates, and empowered development teams. The document provides examples of DevOps scenarios and recommends discussing solutions and migration plans with Microsoft.
This document discusses DevOps concepts and how Splunk can be used to power DevOps initiatives. It defines key DevOps terms like continuous deployment, continuous delivery, push vs. pull deployments. It also outlines how Splunk provides visibility across the application development lifecycle from coding to testing to production. Example use cases are presented that leverage Splunk data and analytics to improve developer productivity, deployment health, and operational efficiency. The document promotes transforming organizations to DevOps using Splunk to provide a unified platform for data-driven insights.
This document discusses how Splunk can help with DevOps challenges by providing visibility across the product development lifecycle. It notes common challenges like lack of visibility, pressure to increase velocity, and limited insights from application logs. The document outlines how Splunk collects log data from various DevOps tools and environments to enable searching, visualizing, and monitoring logs. This allows issues to be identified and resolved faster, improving agility and time to market. Key benefits are listed as reduced time to market, increased agility, and insights into application performance from centralized log access.
This document discusses how Splunk can help organizations with DevOps practices through concise 3 sentence summaries:
Splunk allows organizations to increase application delivery velocity by providing continuous insights to devops teams from planning through monitoring. It also improves code quality by enabling code quality scans, security scans, and automated acceptance tests across the development lifecycle. Splunk further drives impact by providing visibility across infrastructure, applications, services, and tools to give insights across the entire IT environment.
Creating a DevOps Practice for Analytics -- Strata Data, September 28, 2017Caserta
Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand.
Topics include:
- The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics
- Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business
- An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem
- The challenges that come with transforming into an analytics-driven company and how to overcome them
- Practical use cases from Caserta clients
This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.
This document discusses DevOps concepts and how Splunk can be used to power DevOps initiatives. It defines key DevOps terminology like continuous deployment versus continuous delivery and deploy versus release. It also outlines how Splunk provides visibility across the development lifecycle and operations environment. Finally, it provides examples of use cases for how Splunk can be leveraged in areas like application development, testing, automation/deployment, and collaboration to help transform organizations to DevOps.
Continuous Testing: A Key to DevOps SuccessTechWell
As IT organizations adopt a DevOps strategy, continuous testing (CT) becomes a key ingredient of the DevOps ecosystem. CT enables faster release cycles, more changes per release, upfront isolation of risks, and reduced operations costs. The approach to scale the traditional automation testing infrastructure, test environments, and test data management requires a culture shift using new tools and techniques. Sujay Honnamane discusses a CT strategy for aspiring and already implemented DevOps organizations. Sujay shares examples of tools, techniques, and practical solutions that include continuous integration using the Jenkins CI server, service virtualization through CA Lisa tools, automated code coverage analysis to create impact-based tests, automated test script load balancing for effective use of test environments, and faster test cycles, providing a holistic approach/workflow for CT. Sujay and his teams have successfully implemented CT for several clients in their DevOps journey to achieve a repeatable and highly predictable software delivery process.
Keys to Continuous Delivery Success - Mark Warren, Product Director, Perforc...Perforce
Many well-known SaaS vendors use Continuous Delivery to update production sites at mind-bogglingly short intervals and non-SaaS organizations are increasingly looking to CD to increase customer satisfaction and be more competitive.This session will review case studies by Perforce customers, including NYSE, CCP Games, salesforce.com and others, to identify key success factors and provide recommendations for starting, or accelerating, the adoption of CD in your organization.
Pivotal korea transformation_strategy_seminar_enterprise_dev_ops_20160630_v1.0minseok kim
devops has been popular in IT ever since emerging cloud technology. to make IT more agile, we need to keep setup goal and measure performance with adopting new cloud native tools.
Keys to continuous testing for faster delivery euro star webinar TEST Huddle
Your business needs to deliver faster. To accommodate, Development needs to introduce fewer changes but in a much more frequent cadence. This creates a challenge for test teams to keep up with the rapid pace of change without compromising on quality. Automation is paramount to the success or failure of Continuous Delivery, and Continuous Testing enables early and frequent quality feedback throughout the CI/CD pipeline.
In this webinar, Eran & Ayal will explore how to implement Continuous Testing to ensure high quality releases in a Continuous Delivery environment; including what to test and when to automate new functionality in order to optimize your efforts.
DevOps For Everyone: Bringing DevOps Success to Every App and Every Role in y...Siva Rama Krishna Chunduru
Understand DevOps and it's fitment to various types of applications.
Understand various Organization Roles after Org-restructure.
Understand the way to measure the success.
All you need is fast feedback loop, fast feedback loop, fast feedback loop is...Nacho Cougil
Join us in exploring the importance of having an agile feedback loop in software development.
By minimizing the time between code changes and receiving feedback, teams can accelerate bug detection, improve software quality, enhance collaboration, and increase happiness.
We'll discuss key components like continuous integration, automated testing, monitoring, and best practices and strategies. Discover the benefits of implementing DORA metrics, running experiments, using feature flags, and saving costs. Prepare to be inspired by real-life cases and understand how a fast feedback loop can revolutionize your development process.
Participants will be encouraged to adopt a "fast feedback loop" mentality, emphasizing the transformative impact it can have on the efficiency and effectiveness of software development and, at the end, be prepared to sing in chorus (emulating a famous band): "Fast feedback loop, fast feedback loop, fast feedback loop is all you need!" 😉
---
Presentation shared at WeAreDevelopers World Congress '24
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How Azure DevOps can boost your organization's productivityIvan Porta
Azure DevOps can boost productivity through collaboration and automation. DevOps aims to continuously deliver value to users through practices like continuous integration, delivery, and deployment. Microsoft tools like Azure Boards, Pipelines, and Repos support the DevOps process. Azure Pipelines automates building, testing, and deploying code. Branching workflows and pull requests enable collaboration. Automation reduces errors and speeds up the release process. DevOps has helped organizations like Fidelity and Amica reduce costs and deployment times.
Enterprise Devops Presentation @ Magentys Seminar London May 15 2014Jwooldridge
Thanks to Liam and the crew from Magentys for arranging a fantastic evening of presentations on all things DevOps.
Attached is my presentation from the event on Enterprise Devops.
For those of you who missed it:
“Join the crowd of 100 industry leaders across the Retail, Finance and Digital sectors for an exciting evening of talks in London’s Tech City on DevOps. Enjoy networking with a chilled beer alongside the experts who are making DevOps work and those who want to make it work.
Whether you’re a corporate or start-up, DevOps should be a hot topic so listen to how the experts are achieving great things, hear their views on the trends and discuss the future of DevOps.”
Jonny
enterprisedevops.com
In Data Engineer’s Lunch #68, Will Angel, Technical Product Manager at Caribou Financial, will provide an introduction to DevOps practices and tooling including testing, deployment automation, logging, monitoring, and DevOps principles. Additionally, we will discuss some of the ways that DevOps for data engineering is different from conventional application development.
Accompanying Blog: Coming Soon!
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Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
.
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Visual Effects:
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Video Compositing:
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Animation:
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Post-Production Tool:
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After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
Why Orangescrum Is a Game Changer for Construction Companies in 2025Orangescrum
Orangescrum revolutionizes construction project management in 2025 with real-time collaboration, resource planning, task tracking, and workflow automation, boosting efficiency, transparency, and on-time project delivery.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik
This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements.
Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows.
We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure.
The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.
Who Watches the Watchmen (SciFiDevCon 2025)Allon Mureinik
Tests, especially unit tests, are the developers’ superheroes. They allow us to mess around with our code and keep us safe.
We often trust them with the safety of our codebase, but how do we know that we should? How do we know that this trust is well-deserved?
Enter mutation testing – by intentionally injecting harmful mutations into our code and seeing if they are caught by the tests, we can evaluate the quality of the safety net they provide. By watching the watchmen, we can make sure our tests really protect us, and we aren’t just green-washing our IDEs to a false sense of security.
Talk from SciFiDevCon 2025
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"YouTube by Click" likely refers to the ByClick Downloader software, a video downloading and conversion tool, specifically designed to download content from YouTube and other video platforms. It allows users to download YouTube videos for offline viewing and to convert them to different formats.
Microsoft AI Nonprofit Use Cases and Live Demo_2025.04.30.pdfTechSoup
In this webinar we will dive into the essentials of generative AI, address key AI concerns, and demonstrate how nonprofits can benefit from using Microsoft’s AI assistant, Copilot, to achieve their goals.
This event series to help nonprofits obtain Copilot skills is made possible by generous support from Microsoft.
What You’ll Learn in Part 2:
Explore real-world nonprofit use cases and success stories.
Participate in live demonstrations and a hands-on activity to see how you can use Microsoft 365 Copilot in your own work!
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
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Adobe Master Collection CC (Creative Cloud) is a comprehensive subscription-based package that bundles virtually all of Adobe's creative software applications. It provides access to a wide range of tools for graphic design, video editing, web development, photography, and more. Essentially, it's a one-stop-shop for creatives needing a broad set of professional tools.
Key Features and Benefits:
All-in-one access:
The Master Collection includes apps like Photoshop, Illustrator, InDesign, Premiere Pro, After Effects, Audition, and many others.
Subscription-based:
You pay a recurring fee for access to the latest versions of all the software, including new features and updates.
Comprehensive suite:
It offers tools for a wide variety of creative tasks, from photo editing and illustration to video editing and web development.
Cloud integration:
Creative Cloud provides cloud storage, asset sharing, and collaboration features.
Comparison to CS6:
While Adobe Creative Suite 6 (CS6) was a one-time purchase version of the software, Adobe Creative Cloud (CC) is a subscription service. CC offers access to the latest versions, regular updates, and cloud integration, while CS6 is no longer updated.
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Adobe Photoshop: For image editing and manipulation.
Adobe Illustrator: For vector graphics and illustration.
Adobe InDesign: For page layout and desktop publishing.
Adobe Premiere Pro: For video editing and post-production.
Adobe After Effects: For visual effects and motion graphics.
Adobe Audition: For audio editing and mixing.
Exceptional Behaviors: How Frequently Are They Tested? (AST 2025)Andre Hora
Exceptions allow developers to handle error cases expected to occur infrequently. Ideally, good test suites should test both normal and exceptional behaviors to catch more bugs and avoid regressions. While current research analyzes exceptions that propagate to tests, it does not explore other exceptions that do not reach the tests. In this paper, we provide an empirical study to explore how frequently exceptional behaviors are tested in real-world systems. We consider both exceptions that propagate to tests and the ones that do not reach the tests. For this purpose, we run an instrumented version of test suites, monitor their execution, and collect information about the exceptions raised at runtime. We analyze the test suites of 25 Python systems, covering 5,372 executed methods, 17.9M calls, and 1.4M raised exceptions. We find that 21.4% of the executed methods do raise exceptions at runtime. In methods that raise exceptions, on the median, 1 in 10 calls exercise exceptional behaviors. Close to 80% of the methods that raise exceptions do so infrequently, but about 20% raise exceptions more frequently. Finally, we provide implications for researchers and practitioners. We suggest developing novel tools to support exercising exceptional behaviors and refactoring expensive try/except blocks. We also call attention to the fact that exception-raising behaviors are not necessarily “abnormal” or rare.
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Wayland is revolutionizing the way we interact with graphical interfaces, offering a modern alternative to the X Window System. In this webinar, we’ll delve into the architecture and benefits of Wayland, including its streamlined design, enhanced performance, and improved security features.
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5. Hal really didn’t like
the first two titles,
thought way too long
on a third one, then
gave up and decided
that what really
mattered was the next
slide anyway
6. This is what I’m selling
“DEVOPS IS A SUPERSET, NOT A SUBSET”
15. Yeah, but …
… what are you
achieving?
I’m gonna need you
to come in Sunday.
16. Some DevOps Metrics that Might Matter
Culture
e.g.
• Retention
• Satisfaction
• Callouts
Process
e.g.
• Idea-to-cash
• MTTR
• Deliver time
Quality
e.g.
• Tests passed
• Tests failed
• Best/worst
Systems
e.g.
• Throughput
• Uptime
• Build times
Activity
e.g.
• Commits
• Tests run
• Releases
Impact
e.g.
• Signups
• Checkouts
• Revenue
17. From every tool, every process, every component, on-prem or off
Machine Data Is A
Critical Source Of
DevOps Metrics
18. Industry Leading Platform for Machine Data
Any Machine Data
Online
Services Web
Services
Servers
Security GPS
Location
Storage
Desktops
Networks
Packaged
Applications
Custom
ApplicationsMessaging
Telecoms
Online
Shopping
Cart
Web
Clickstreams
Databases
Energy
Meters
Call Detail
Records
Smartphones
and Devices
RFID
Datacenter
Private
Cloud
Public
Cloud
Enterprise
Scalability
Search and
Investigation
Proactive
Monitoring
Operational
Visibility
Real-time
Business
Insights
Operational Intelligence
19. Visibility Across the Ops Environment
API
SDKs UI
Server, Storage.
N/W
Server
Virtualization
Operating
Systems
Infrastructure
Applications
Mobile
Applications
Cloud Services
Other Tools
Ticketing/Help
Desk
No rigid schemas – add in data from any other source.
Custom
Applications
API Services
20. Visibility Across the Dev Lifecycle
API
SDKs UI
Other Tools
Escalation/
Collaboration
No rigid schemas – add in data from any other source.
Plan Code Build Test/QA Stage Release Config Monitor
30. CI / Build
Server
Code
Review
Task
Tracking
What Data Can You Splunk?
Which code has already been reviewed for this release/sprint? Who has
completed the most code reviews? What code has NOT been reviewed?
Who is changing files? What kinds of files are being changed? What branches are
most active? What types of activities are occurring for a branch?
Version
Control
How many builds completed today/this week/this month? Which check-in kicked
off this build? Which tests ran against this failed build?
Which tasks are assigned to which developers? What progress is being made to
complete assigned tasks? What tasks remain for this release/sprint?
32. Industry Leading Platform for Machine Data
Any Machine Data
Online
Services Web
Services
Servers
Security GPS
Location
Storage
Desktops
Networks
Packaged
Applications
Custom
ApplicationsMessaging
Telecoms
Online
Shopping
Cart
Web
Clickstreams
Databases
Energy
Meters
Call Detail
Records
Smartphones
and Devices
RFID
Datacenter
Private
Cloud
Public
Cloud
Enterprise
Scalability
Search and
Investigation
Proactive
Monitoring
Operational
Visibility
Real-time
Business
Insights
Operational Intelligence
40. curl -k https://<host>:8088/services/collector -H 'Authorization: Splunk <token>' -d
'{"event":"Hello Event Collector"}'
Applications IoT Devices
Agentless, direct data onboarding via a standard API
HTTP Event Collector
Scales to Millions of Events/Second
42. Splunk App for Stream
Enables real-time
insights into private,
public and hybrid
cloud infrastructures
Delivers rapid
deployment, easy
scale out and efficient
wire data capture
Capture and analyze
critical events not
found in logs or with
other collection
methods.
1 2 3
Enhance Operational Intelligence With Wire Data Capture
43. Splunk MINT for Mobile Data
Deliver Better
Performing, More
Reliable Apps
Deliver Real-Time
Analytics
Achieve End-to-End
Visibility
46. Machine Data To Enable Continuous
Improvement
Defect
Information
Capacity
Planning
Quality
Standards
Enhancement
Requests
Integration
Requirements
Acceptance
Metrics
Service Levels
and KPIs
Application Development Test and Acceptance Production
BuildCodePlan Test/QA Stage Release Config Monitor
Infrastructure
Dependencies
47. Increase Delivery Velocity
DevOps Teams Iterate with
Continuous Insights
Product Managers
identify new
opportunities
Code Continuously delivered
to market
Auditors
have visibility
Customers
are happy
48. Improve Code Quality
Code quality scans Static security scans
White BoxDevelopers
check in code
Automated
Acceptance Tests
Dynamic Security
Scans
Black Box
“Chaos Monkey”
tests
Test Fail:
Return
Test Fail:
Return
X
X
Production
QA Prod Pattern
QA Pattern Library
Test Pass:
Promote
Test Pass:
Promote to
Production
Pattern
library used
for test and
QA
53. Where to go for more Info
● DevOps Videos, Customer Stories, Whitepapers
‣ http://splunk.com/DevOps
– Developer Tutorials, Code Samples, Downloads
– http://dev.splunk.com
● Splunk Apps and Plugins
– https://splunkbase.splunk.com
● Blogs for Dev, Ops, and DevOps
– http://blogs.splunk.com
53
57. Real-time dashboards show error rate
in production and impact of pushing
new builds
Developers can search and visualize
web logs, Java logs, eventlogs etc;
trace tx without complex
instrumentation
Alerts notify developers as soon as a
problem arises
57
Find and Fix Issues Faster
58. Gain end-to-end visibility to make
informed decisions
Analytics insights without the need for
additional analytics tools
Ask questions while exploring and
collecting data
58
Push Better Code Using Analytics
59. Powerful Platform for Enterprise Developers
59
REST API
Build Splunk Apps Extend and Integrate Splunk
Simple XML
JavaScript/CSS Extensions C#
JavaScript
Python
Ruby
Java
PHP
Data Models
Search Extensibility
Modular Inputs
SDKs
KV Store
60. Splunk Developer Guidance
Splunk Reference Apps
Complete, working real-world Splunk solutions
built together with partners (Conducive; Auth0)
̶ 2 (pseudo-) production releases
̶ entire code & test repos on GitHub
̶ under Apache 2.0
Associated Guidance
I. Start-to-Finish Journey Documentary
II. Essentials
dev.splunk.com/goto/devguide
63. 63
Improved DevOps Agility
63
Key Customer Benefits
• Increased success rate of deployments
• Detect issues before they affect broad production
• Monitoring deployment process several times per day
-Robert Gonsalves,
Web Operations
“It’s like we were working
without peripheral vision
before and now we have it.”
64. 64
Deliver Better Code Quality
Key Customer Benefits
-Principal Engineer,
Apollo Group
“Developers are now able to look
for errors and troubleshoot issues
five to ten times faster by having
all their event data centralized in
Splunk.”
• Provide full visibility into QA sanity and load testing
before production
• Exceed SLA thresholds with full visibility and benchmark
key infrastructure metrics and errors
• Easier troubleshooting if tests do not contain the
expected results
65. 65
Enable Data-driven Continuous Delivery
-Alison Perkins,
Senior Systems Engineer
“ Dump all the logs into Splunk,
and it starts looking like one big
system, instead of a bazillion
teeny ones that hate each
other.”
Key Customer Benefits
• Quickly validate and troubleshoot code pushes to
production
• Ensure that new code does not negatively impact
performance or user experience
• Reduced one application’s error rate by 2 orders of
magnitude in a matter of weeks
Editor's Notes
#11: - You’ve heard the culture story before, let’s go over that briefly
#17: I promised you FIVE. Check it out – this is SIX.
#19: Splunk Enterprise is fully featured, platform for collecting, searching, monitoring and analyzing machine data and getting operational intelligence. You can monitor both real-time (as the data is streaming) and historical data. Splunk collects machine data securely and reliably from wherever it’s generated in any formant. It stores and indexes the data in real time in a centralized location and protects it with role-based access controls. You can troubleshoot your network problems and investigate security incidents in minutes (not hours or days). Monitor your end-to-end infrastructure to avoid service degradation or outages. Gain real-time visibility and critical insights into customer experience, transactions and behavior.
#21: Splunk can provide insight across the entire application delivery lifecycle. Developers can search and visualize data from entire build pipeline and production environments without needing to access production machines.
#22: When using Splunk to mine machine data our customers and prospects can
1) INCREASE APP DELIVERY VELOCITY
2) IMPROVE CODE QUALITY
3) INCREASE BUSINESS IMPACT OF APPLICATION DELIVERY
#30:
Pro-actively identify business issues
Visualize the source of the issue
Collect & Analyze diagnostics data automatically
Take pro-active actions to mitigate the problem
Engage the right people immediately
Proactively Identify:
Stuck deals are identified before the call is made to support
Visualize: Business Activity Monitoring tracks deals; Yellow indicates where the deal is stuck. Red would indicate a catastrophic system failure.
#33: Splunk Enterprise is fully featured, platform for collecting, searching, monitoring and analyzing machine data and getting operational intelligence. You can monitor both real-time (as the data is streaming) and historical data. Splunk collects machine data securely and reliably from wherever it’s generated in any formant. It stores and indexes the data in real time in a centralized location and protects it with role-based access controls. You can troubleshoot your network problems and investigate security incidents in minutes (not hours or days). Monitor your end-to-end infrastructure to avoid service degradation or outages. Gain real-time visibility and critical insights into customer experience, transactions and behavior.
#36: The new app collects and analyzes performance data from Puppet Enterprise. Customers get visibility into critical services, such as PuppetDB, the Puppet Serve, and console services. The app also helps to reduce troubleshooting times and proactively fix health issues in the Puppet environment, and includes the following insights:
Console services response times to benchmark and actively plan console resources
The number of request errors by Puppet clients to help recognize potential code or infrastructure issues
Role-based access control dashboards to monitor user activity, including authentication errors to help with potential security issues
Requests from PuppetDB to identify commonly executed or failed queries—isolating potential infrastructure bottlenecks
PuppetDB node deactivation activity for isolation of security or automation issues
Commonly submitted PuppetDB commands from client IPs to assist in pinpointing potential security issues
Puppet Server compilation metrics that help teams evaluate the health of their automation environment and appropriately assign resource
#37: The Chef Analytics App for Splunk is available for free on Splunkbase Splunk app marketplace and provides Chef users with visibility into metrics such as success / failure rates, most active users and most active organizations. The Chef Analytics App for Splunk also helps you understand the frequency of the details of errors across infrastructure so that you can catch and troubleshoot high impact issues, like a major bug in a cookbook or an infrastructure issue like network connectivity, in real time
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#38: Why Splunk for AWS?
Security Intelligence (Cloudtrail, Config Cloudwatch, VPC)
Operational Intelligence (Cloudwatch, Config, ELB, Cloudfront)
DevOps Intelligence (Cloudwatch, Lambda)
Big Data Insights (Kinesis, EMR, IoT, S3)
Data sources in Splunk App for AWS
AWS Cloudtrail
Service that delivers logs of admin activity on AWS infrastructure
Examples:
Start/Stop/Create instance
Change of User roles/rights
Modification of Network Configuration
Delivers log files to customers; no UI, display, analysis, search
AWS Config
Provides resource inventory
Provides configuration history & change information
Enables security & governance
Amazon Cloudwatch Metrics
IP traffic information to/from VPC network interfaces
Data stored and accessible from AWS Cloudwatch Logs
Amazon Cloudwatch VPC Flow Logs
IP traffic information to/from VPC network interfaces
Data stored and accessible from AWS Cloudwatch Logs
AWS Access Logs
Elastic Load Balancing (ELB)
Cloudfront CDN
S3
AWS Billing
Current Month via Cloudwatch metrics
Monthly Detailed Billing
#40: Secure - Supports TLS / SSL
Support for configurable data collection to simplify data classification and access control
index
sourcetype
source
Support for collection of container labels and env keys which can further help with data classification
Simple
Easy to setup e.g., no need to deploy Splunk Universal Forwarder (UF) and scale it
Much easier to collect data in Splunk Cloud deployments
Scalable built on top of HTTP Event Collector (HEC)
Support encryption via SSL as long as Splunk has a TCP SSL endpoint opened. The difference I would say is that with syslog over SSL there is no auth model other than the cert itself and you can only have one SSL cert for all of Splunk. With HEC you have fine grained control of which servers can send data and which indexes they can access as that can be configured per token.
Simplified classification by source and labels, you can do that in syslog driver as well. See labels and env in https://docs.docker.com/engine/admin/logging/overview/
Additional configurations on Splunk logging driver to see “process” field, https://docs.docker.com/engine/admin/logging/log_tags/. Available by default in Syslog.
Splunk Cloud: HEC is fully supported in Splunk Cloud. You cannot however arbitrarily open a TCP/UDP port in Splunk Cloud which would be required if you needed to forward Syslog data to a UF running in Splunk Cloud. Need to confirm how to scale HEC in Splunk Cloud.
https://docs.docker.com/engine/admin/logging/splunk/
#41: HTTP Event Collector is easy way to send data to Splunk Enterprise. Notably, the EC enables you to send data over HTTP/ HTTPS directly to Splunk Enterprise from your application. The EC was developed with application developers in mind, so that all it takes is a few lines of code added to an app for the app to send data. Also, the EC is token-based, so you never need to hard-code your Splunk Enterprise credentials in your app or supporting files. HTTP Event Collector provides a new way for developers to send application logging and metrics directly to Splunk Enterprise via HTTP in a highly efficient, scalable and secure manner
#42: AWS Lambda is an Amazon Web Services compute service that runs your back-end code in response to events, and manages compute resources for you. In cooperation with Amazon, Splunk is pleased to provide a built-in AWS Lambda Node.js blueprint for HTTP Event Collector. The blueprint makes it easy to get started quickly, sending events from AWS Lambda to HTTP Event Collector running on Splunk Enterprise or Splunk Cloud. You can also write a Lambda function from scratch, either in JavaScript using Node.js or in Java. AWS Lambda can receive event data from Amazon Kinesis, Amazon DynamoDB, Amazon S3, and other Amazon services, and then send it on to HTTP Event Collector. You can collect the data using HTTP Event Collector in Splunk Cloud, which also runs on AWS, or in Splunk Enterprise on-premises.
#43: Splunk App for Stream is a free App that enables you to capture, visualize and analyze data in much more granular way then ever before. You can see everything – ALL user and applications behavior ],response times from every layer, DNS information, storage traffic, network traffic, your websites content, connections. Once this data is in Splunk you can correlate it with other data for much more comprehensive visibility. First Splunk App for Stream is a way of get wire data into Splunk Enterprise. By adding this comprehensive source of machine data, it enables you to extend Operational Intelligence use cases across IT security and the business. It is a software only solution with the ability that can be installed on VM on any host, it enables real-time insights into multi-cloud environments. And as such, it is easy to install anywhere on most of standard machines, it is a passive very efficient way to capture data.
#44: To address the needs of developers, operations and product management, you need Operational Intelligence for your mobile apps. This is what we call mobile intelligence. Mobile intelligence provides real-time insight on how your mobile apps are performing, and can correlate with and enhance Operational Intelligence.
Splunk software enables organizations to search, monitor, analyze and visualize machine-generated data from websites, applications, servers, networks, sensors and mobile devices. Splunk MINT helps organizations monitor mobile app usage and performance, gain deep visibility into mobile app transactions and accelerate development
Deliver better performing, more reliable apps
When a user has a problem with a mobile app, the issue could be isolated or spread across all app versions, handsets and OS types. With Splunk MINT, you can see issues with app performance or availability in real time. Bugs can be addressed quickly, and app developers can gain a head start in creating and delivering valuable app updates.
Achieve End-to-End visibility
When mobile apps fail, there are many potential sources of failure. With Splunk MINT, you can analyze overall transaction performance. And using Splunk MINT, you can correlate this data with information from back-end apps to gain detailed insight on transaction problems. As a result, operations can reduce MTTR and better anticipate future mobile app back-end requirements.
Deliver real-time analytics
Mobile apps give enterprises new ways of conducting digital business. With mobile app information in Splunk Enterprise, you can correlate usage and performance information— some call this omni-channel analytics—to better understand how users are engaging all aspects of your organization.
#48: Fast feedback obtained with analytics improves app delivery velocity. From code definition to production, having insight for all the teams leads to less bugs, faster testing, faster releases, less production issues and accelerated innovation.
Deliver end-to-end visibility across every DevOps toolchain component
Iterate faster with correlated insight across the application delivery lifecycle
Improve DevOps team efficiency by measuring and benchmarking release contributions
#49: Pinpoint and resolve code issues before they impact customers .Find and fix production issues faster Use objective metrics to ensure code is operational and meets quality SLAs
#52: When using Splunk to mine machine data our customers and prospects can
1) INCREASE APP DELIVERY VELOCITY
2) IMPROVE CODE QUALITY
3) INCREASE BUSINESS IMPACT OF APPLICATION DELIVERY
#58: Order Flow, message queues, Garbage Collection, Java Heap
identify errors by java class, thread
alert actions - jira ticket, service now ticket, webhook
#59: PM’s love to look at feature usage; are new features being used?
How do we allocate developer time to create/enhance features
#64: The NBC Universal’s Web Operations team is using Splunk platform to track performance of their releases from pre-production, QA and in production. Splunk software frees up their DevOps teams and enables them to focus on innovation while pushing code to production daily.
The way they test these deployments is to release to half of our servers first while they are out of production rotation but still accessible through other non-public DNS paths for testing. Their “Gamma” environment is a subset of production servers that have been released to for testing, but aren’t taking public traffic. Theyuse Splunk to monitor new errors on the servers after releasing changes to them, which is easy to do since just before the servers were released to they already had an established error volume baseline from being in production. If any new errors appear or the volume of traffic for 400/500 errors suddenly increases after the release, They immediately see the spike in our monitoring. Alerting on thresholds for event log errors - roll-back problematic deployments or diagnose a build in a subset of the production environment
#65: Splunk is Used Enterprise-wide at Apollo Group
Provide full visibility into sanity and Load testing in QA environments before deploying to production
Using the Splunk SDK for Java to gather key machine metrics (CPU, memory, disk utilization) and log file errors metrics are compared to benchmarks and SLA thresholds
Helps troubleshooting if tests do not contain the expected results
Product Lifecycle Monitoring
Custom Application Monitoring
Mobile App Monitoring/Analytics
Customer Analytics
Security and Forensics/Analytics
Network Monitoring/Analytics
AWS Billing Monitoring/Analytics
Cloud and Physical Server Monitoring/Analytics
Issue resolution from days to minutes in both QA/staging and production environments – significant productivity/quality impact
ROI in the millions - Engineering/IT teams are significantly more productive & Fewer outages
#66: Challenges:
No single place to access and visualize machine data
Manual diagnosing and searching through data generated by servers and applications
To retrieve information, sysadmins have to ssh into production machines before sending off to developers to grep through the logs
With Splunk:
Quickly validate and troubleshoot code pushes to production
Ensure that new code does not negatively impact performance or user experience
Reduced one application’s error rate by 2 orders of magnitude in a matter of weeks