Mon.Oct 21, 2019

Cloud-Native DevOps: Your World to New Possibilities

Dzone - DevOps

In DevOps, everyone needs to trust that everyone else is doing their best for the business. This can happen only when there is trust between the teams, shared goals, and standard practices.

DevOps Chat: 2019 Accelerate Report with Dr. Nicole Forsgren

DevOps.com

There are many interviews you do as part of the role as editor in chief of DevOps.com. Then there are some that make it all worthwhile. Anytime I have the pleasure of speaking with Dr. Nicole Forsgren, it makes all of the other things I do worthwhile. She has a clarity of vision borne from […].

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Article: The Current and Future State of Testing: a Conversation with Lisa Crispin

InfoQ Culture Methods

Lisa Crispin talks about the current and future state of testing, how testing works in agile environments, the value testers bring to DevOps, testing machine learning and where testing is headed.

10 ways to sabotage your ITSM project

TechBeacon

That IT service management (ITSM) automation project you are about to undertake is big and complicated. Because it is so technical, it's easy to become so focused on the technologies that you might find yourself overlooking other critical considerations that could cause your project to fail.

Tools 81

How AI Can Radically Change Your Business

AI is quickly becoming mainstream, thanks to its value-driving capabilities. Yet, even with such widespread attention, it still is one of the most misunderstood technologies out there. Here's how to make the most out of it and bring a positive change to your company.

How to Align Your Product Strategy using the Product Strategy Canvas (pt 1)

Scrum.org

Hello awesome people. Back with me again with new learning from the trenches. Over the years working with Product Owners mainly who works in large corporations, I see common challenges.

More Trending

DevOps Chat: Service Mesh Tracing, from Envoy, Omnition to Splunk

DevOps.com

As application functions get smaller, containerized, become microservices and combine into service meshes, a new set of challenges crop up. What functions does each service perform? What state constitutes services in trouble? What are the dependencies between services across a complex service mesh?

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused…

Netflix TechBlog

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused Applications By Jeff Chao on behalf of the Mantis team Today we’re excited to announce that we’re open sourcing Mantis , a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We believe the challenges we face here at Netflix are not necessarily unique to Netflix which is why we’re sharing it with the broader community. As a streaming microservices ecosystem, the Mantis platform provides engineers with capabilities to minimize the costs of observing and operating complex distributed systems without compromising on operational insights. Engineers have built cost-efficient applications on top of Mantis to quickly identify issues, trigger alerts, and apply remediations to minimize or completely avoid downtime to the Netflix service. Where other systems may take over ten minutes to process metrics accurately, Mantis reduces that from tens of minutes down to seconds, effectively reducing our Mean-Time-To-Detect. This is crucial because any amount of downtime is brutal and comes with an incredibly high impact to our members?—?every second counts during an outage. As the company continues to grow our member base, and as those members use the Netflix service even more, having cost-efficient, rapid, and precise insights into the operational health of our systems is only growing in importance. For example, a five-minute outage today is equivalent to a two-hour outage at the time of our last Mantis blog post. Mantis Makes It Easy to Answer New Questions The traditional way of working with metrics and logs alone is not sufficient for large-scale and growing systems. Metrics and logs require that you to know what you want to answer ahead of time. Mantis on the other hand allows us to sidestep this drawback completely by giving us the ability to answer new questions without having to add new instrumentation. Instead of logs or metrics, Mantis enables a democratization of events where developers can tap into an event stream from any instrumented application on demand. By making consumption on-demand, you’re able to freely publish all of your data to Mantis. Mantis is Cost-Effective in Answering Questions Publishing 100% of your operational data so that you’re able to answer new questions in the future is traditionally cost prohibitive at scale. Mantis uses an on-demand, reactive model where you don’t pay the cost for these events until something is subscribed to their stream. To further reduce cost, Mantis reissues the same data for equivalent subscribers. In this way, Mantis is differentiated from other systems by allowing us to achieve streaming-based observability on events while empowering engineers with the tooling to reduce costs that would otherwise become detrimental to the business. From the beginning, we’ve built Mantis with this exact guiding principle in mind: Let’s make sure we minimize the costs of observing and operating our systems without compromising on required and opportunistic insights. Guiding Principles Behind Building Mantis The following are the guiding principles behind building Mantis. We should have access to raw events. Applications that publish events into Mantis should be free to publish every single event. If we prematurely transform events at this stage, then we’re already at a disadvantage when it comes to getting insight since data in its original form is already lost. We should be able to access these events in realtime. Operational use cases are inherently time sensitive by nature. The traditional method of publishing, storing, and then aggregating events in batch is too slow. Instead, we should process and serve events one at a time as they arrive. This becomes increasingly important with scale as the impact becomes much larger in far less time. We should be able to ask new questions of this data without having to add new instrumentation to your applications. It’s not possible to know ahead of time every single possible failure mode our systems might encounter despite all the rigor built in to make these systems resilient. When these failures do inevitably occur, it’s important that we can derive new insights with this data. You should be able to publish as large of an event with as much context as you want. That way, when you think of a new questions to ask of your systems in the future, the data will be available for you to answer those questions. We should be able to do all of the above in a cost-effective way. As our business critical systems scale, we need to make sure the systems in support of these business critical systems don’t end up costing more than the business critical systems themselves. With these guiding principles in mind, let’s take a look at how Mantis brings value to Netflix. How Mantis Brings Value to Netflix Mantis has been in production for over four years. Over this period several critical operational insight applications have been built on top of the Mantis platform. A few noteworthy examples include: Realtime monitoring of Netflix streaming health which examines all of Netflix’s streaming video traffic in realtime and accurately identifies negative impact on the viewing experience with fine-grained granularity. This system serves as an early warning indicator of the overall health of the Netflix service and will trigger and alert relevant teams within seconds. Contextual Alerting which analyzes millions of interactions between dozens of Netflix microservices in realtime to identify anomalies and provide operators with rich and relevant context. The realtime nature of these Mantis-backed aggregations allows the Mean-Time-To-Detect to be cut down from tens of minutes to a few seconds. Given the scale of Netflix this makes a huge impact. Raven which allows users to perform ad-hoc exploration of realtime data from hundreds of streaming sources using our Mantis Query Language (MQL). Cassandra Health check which analyzes rich operational events in realtime to generate a holistic picture of the health of every Cassandra cluster at Netflix. Alerting on Log data which detects application errors by processing data from thousands of Netflix servers in realtime. Chaos Experimentation monitoring which tracks user experience during a Chaos exercise in realtime and triggers an abort of the chaos exercise in case of an adverse impact. Realtime Personally Identifiable Information (PII) data detection samples data across all streaming sources to quickly identify transmission of sensitive data. Try It Out Today To learn more about Mantis, you can check out the main Mantis page. You can try out Mantis today by spinning up your first Mantis cluster locally using Docker or using the Mantis CLI to bootstrap a minimal cluster in AWS. You can also start contributing to Mantis by getting the code on Github or engaging with the community on the users or dev mailing list. Acknowledgements A lot of work has gone into making Mantis successful at Netflix. We’d like to thank all the contributors, in alphabetical order by first name, that have been involved with Mantis at various points of its existence: Andrei Ushakov, Ben Christensen, Ben Schmaus, Chris Carey, Cody Rioux, Daniel Jacobson, Danny Yuan, Erik Meijer, Indrajit Roy Choudhury, Jeff Chao, Josh Evans, Justin Becker, Kathrin Probst, Kevin Lew, Neeraj Joshi, Nick Mahilani, Piyush Goyal, Prashanth Ramdas, Ram Vaithalingam, Ranjit Mavinkurve, Sangeeta Narayanan, Santosh Kalidindi, Seth Katz, Sharma Podila, Zhenzhong Xu. Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused… was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story. observability stream-processing reliability realtime microservices

FlexDeploy Loves APEX: Deploy the Application and Supporting Objects

Flexagon

FlexDeploy is a DevOps platform for CI/CD with out of box support Oracle APEX build & deploy automation. Oracle Application Express (APEX) is a common low code development framework, which is also used to develop extensions for Oracle E-Business Suite.

How to Avoid Pigeon Holing Yourself For Greater Career Success

Let's Grow Leaders

Do you ever feel like you’re pigeon-holed at work? You’re nailing your role and have a track record of career success. In fact, you’re known as the best there is. So, how do you get others to recognize that you have […].

Indistractable: How to Control Your Attention and Choose Your Life with Nir Eyal

Speaker: Nir Eyal

Ever get the feeling the world is full of too many distractions? Research shows the ability to stay focused is a competitive advantage, in work and in life. However, in an age of ever-increasing demands on our attention how do we stay productive and stay sane? In this webinar, Nir provides research-backed, practical advice, and memorable strategies for managing distraction and our time. Nir Eyal shares findings of his five years of research into how to master what he calls, "the skill of the century," the power to be "Indistractable."

Scriptless Testing Is Not Just Record and Playback: Top 10 Scriptless Testing Approaches

Dzone - DevOps

Scriptless testing is bigger than just the push of a button. In traditional software development, testing professionals manually tested the developed software but the need to test redundant scenarios necessitated the use of testing tools that would allow them to execute the same tasks automatically.

How Leaders Quench the Service Thirst of Customers

thoughtLEADERS, LLC

We’ve all heard the idea of being customer-centric, but how you utilizing your leadership to best serve your customers. Today’s post is by Chip R. Bell, author of Kaleidoscope (CLICK HERE to get your copy). The very thirsty crow came upon a pitcher of water.

Test Automation: Seamless Integration of Tools and Frameworks

Dzone - DevOps

In this article, I want to give you an overview of how well the latest technologies can be integrated seamlessly into a test automation framework.

Making Data Readily Available for Developers

The New Stack

VMware sponsored this podcast. The technology industry is undergoing a data revolution in which the unlimited storage and compute power of the cloud is changing how data is stored, processed and managed.

Building Healthy Innovation Ecosystems for Your Projects

Speaker: Nick Noreña, Innovation Coach and Advisor, Kromatic

In this webinar, Nick Noreña will walk through an Innovation Ecosystem Model that he and his team at Kromatic have developed to help investors, heads of product, teachers, and executives understand how they can best support innovation in their own ecosystem. He'll also go over metrics we can use to measure the health of our ecosystems as we build more resources for innovators.

Truly Becoming Agile by Piping in Automation Testing

Dzone - DevOps

Automation testing is essential to good Agile practices. If you have worked in the technology industry during the last few decades, you have heard the term "Agile" more times than you can count. Agile, DevOps–the industry tosses these buzzwords around like free candy.

Version 1 Software: Don’t Go Chasing Waterfall

CTOvision

I’ve been managing software development projects since the pre-internet digital dark ages. Over time I’ve seen software development process change as much as software languages and tools.

Selecting a Programming Language for Selenium Automation Testing

Dzone - DevOps

So many language to learn, so little time. As people are shifting to automation from manual testing, they prefer to go with the best-suited testing framework for them. When we talk about a popular automation testing framework, most people immediately think about Selenium.

PHP 60

Teradata is Moving the Cloud Forward

Teradata

With four new offerings, Teradata is helping companies move from analytics to answers wherever they are on their cloud journey. Read more

BI Buyers Guide: Embedding Analytics in Your Software

The business intelligence market has exploded. And as the number of vendors grows, it gets harder to make sense of it all. Learn how to decide what features you need and get an evaluation framework for every technical and non-technical requirement you could imagine.

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused…

Netflix TechBlog

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused Applications By Cody Rioux, Daniel Jacobson, Jeff Chao, Neeraj Joshi, Nick Mahilani, Piyush Goyal, Prashanth Ramdas, Zhenzhong Xu Today we’re excited to announce that we’re open sourcing Mantis , a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We believe the challenges we face here at Netflix are not necessarily unique to Netflix which is why we’re sharing it with the broader community. As a streaming microservices ecosystem, the Mantis platform provides engineers with capabilities to minimize the costs of observing and operating complex distributed systems without compromising on operational insights. Engineers have built cost-efficient applications on top of Mantis to quickly identify issues, trigger alerts, and apply remediations to minimize or completely avoid downtime to the Netflix service. Where other systems may take over ten minutes to process metrics accurately, Mantis reduces that from tens of minutes down to seconds, effectively reducing our Mean-Time-To-Detect. This is crucial because any amount of downtime is brutal and comes with an incredibly high impact to our members?—?every second counts during an outage. As the company continues to grow our member base, and as those members use the Netflix service even more, having cost-efficient, rapid, and precise insights into the operational health of our systems is only growing in importance. For example, a five-minute outage today is equivalent to a two-hour outage at the time of our last Mantis blog post. Mantis Makes It Easy to Answer New Questions The traditional way of working with metrics and logs alone is not sufficient for large-scale and growing systems. Metrics and logs require that you to know what you want to answer ahead of time. Mantis on the other hand allows us to sidestep this drawback completely by giving us the ability to answer new questions without having to add new instrumentation. Instead of logs or metrics, Mantis enables a democratization of events where developers can tap into an event stream from any instrumented application on demand. By making consumption on-demand, you’re able to freely publish all of your data to Mantis. Mantis is Cost-Effective in Answering Questions Publishing 100% of your operational data so that you’re able to answer new questions in the future is traditionally cost prohibitive at scale. Mantis uses an on-demand, reactive model where you don’t pay the cost for these events until something is subscribed to their stream. To further reduce cost, Mantis reissues the same data for equivalent subscribers. In this way, Mantis is differentiated from other systems by allowing us to achieve streaming-based observability on events while empowering engineers with the tooling to reduce costs that would otherwise become detrimental to the business. From the beginning, we’ve built Mantis with this exact guiding principle in mind: Let’s make sure we minimize the costs of observing and operating our systems without compromising on required and opportunistic insights. Guiding Principles Behind Building Mantis The following are the guiding principles behind building Mantis. We should have access to raw events. Applications that publish events into Mantis should be free to publish every single event. If we prematurely transform events at this stage, then we’re already at a disadvantage when it comes to getting insight since data in its original form is already lost. We should be able to access these events in realtime. Operational use cases are inherently time sensitive by nature. The traditional method of publishing, storing, and then aggregating events in batch is too slow. Instead, we should process and serve events one at a time as they arrive. This becomes increasingly important with scale as the impact becomes much larger in far less time. We should be able to ask new questions of this data without having to add new instrumentation to your applications. It’s not possible to know ahead of time every single possible failure mode our systems might encounter despite all the rigor built in to make these systems resilient. When these failures do inevitably occur, it’s important that we can derive new insights with this data. You should be able to publish as large of an event with as much context as you want. That way, when you think of a new questions to ask of your systems in the future, the data will be available for you to answer those questions. We should be able to do all of the above in a cost-effective way. As our business critical systems scale, we need to make sure the systems in support of these business critical systems don’t end up costing more than the business critical systems themselves. With these guiding principles in mind, let’s take a look at how Mantis brings value to Netflix. How Mantis Brings Value to Netflix Mantis has been in production for over four years. Over this period several critical operational insight applications have been built on top of the Mantis platform. A few noteworthy examples include: Realtime monitoring of Netflix streaming health which examines all of Netflix’s streaming video traffic in realtime and accurately identifies negative impact on the viewing experience with fine-grained granularity. This system serves as an early warning indicator of the overall health of the Netflix service and will trigger and alert relevant teams within seconds. Contextual Alerting which analyzes millions of interactions between dozens of Netflix microservices in realtime to identify anomalies and provide operators with rich and relevant context. The realtime nature of these Mantis-backed aggregations allows the Mean-Time-To-Detect to be cut down from tens of minutes to a few seconds. Given the scale of Netflix this makes a huge impact. Raven which allows users to perform ad-hoc exploration of realtime data from hundreds of streaming sources using our Mantis Query Language (MQL). Cassandra Health check which analyzes rich operational events in realtime to generate a holistic picture of the health of every Cassandra cluster at Netflix. Alerting on Log data which detects application errors by processing data from thousands of Netflix servers in realtime. Chaos Experimentation monitoring which tracks user experience during a Chaos exercise in realtime and triggers an abort of the chaos exercise in case of an adverse impact. Realtime Personally Identifiable Information (PII) data detection samples data across all streaming sources to quickly identify transmission of sensitive data. Try It Out Today To learn more about Mantis, you can check out the main Mantis page. You can try out Mantis today by spinning up your first Mantis cluster locally using Docker or using the Mantis CLI to bootstrap a minimal cluster in AWS. You can also start contributing to Mantis by getting the code on Github or engaging with the community on the users or dev mailing list. Acknowledgements A lot of work has gone into making Mantis successful at Netflix. We’d like to thank all the contributors, in alphabetical order by first name, that have been involved with Mantis at various points of its existence: Andrei Ushakov, Ben Christensen, Ben Schmaus, Chris Carey, Danny Yuan, Erik Meijer, Indrajit Roy Choudhury, Josh Evans, Justin Becker, Kathrin Probst, Kevin Lew, Ram Vaithalingam, Ranjit Mavinkurve, Sangeeta Narayanan, Santosh Kalidindi, Seth Katz, Sharma Podila. Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused… was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story. observability stream-processing reliability realtime microservices

The computer understands only in binary numbers

I'm Programmer

Programmer Humor. 1 of 3. ART OF PROGRAMMING! ART OF PROGRAMMING! Everytime I code in C! Everytime I code in C! We all know which youtube tutorial gives the correct answers We all know which youtube tutorial gives the correct answers.

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused…

Netflix TechBlog

Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused Applications By Cody Rioux, Daniel Jacobson, Jeff Chao, Neeraj Joshi, Nick Mahilani, Piyush Goyal, Prashanth Ramdas, Zhenzhong Xu Today we’re excited to announce that we’re open sourcing Mantis , a platform that helps Netflix engineers better understand the behavior of their applications to ensure the highest quality experience for our members. We believe the challenges we face here at Netflix are not necessarily unique to Netflix which is why we’re sharing it with the broader community. As a streaming microservices ecosystem, the Mantis platform provides engineers with capabilities to minimize the costs of observing and operating complex distributed systems without compromising on operational insights. Engineers have built cost-efficient applications on top of Mantis to quickly identify issues, trigger alerts, and apply remediations to minimize or completely avoid downtime to the Netflix service. Where other systems may take over ten minutes to process metrics accurately, Mantis reduces that from tens of minutes down to seconds, effectively reducing our Mean-Time-To-Detect. This is crucial because any amount of downtime is brutal and comes with an incredibly high impact to our members?—?every second counts during an outage. As the company continues to grow our member base, and as those members use the Netflix service even more, having cost-efficient, rapid, and precise insights into the operational health of our systems is only growing in importance. For example, a five-minute outage today is equivalent to a two-hour outage at the time of our last Mantis blog post. Mantis Makes It Easy to Answer New Questions The traditional way of working with metrics and logs alone is not sufficient for large-scale and growing systems. Metrics and logs require that you to know what you want to answer ahead of time. Mantis on the other hand allows us to sidestep this drawback completely by giving us the ability to answer new questions without having to add new instrumentation. Instead of logs or metrics, Mantis enables a democratization of events where developers can tap into an event stream from any instrumented application on demand. By making consumption on-demand, you’re able to freely publish all of your data to Mantis. Mantis is Cost-Effective in Answering Questions Publishing 100% of your operational data so that you’re able to answer new questions in the future is traditionally cost prohibitive at scale. Mantis uses an on-demand, reactive model where you don’t pay the cost for these events until something is subscribed to their stream. To further reduce cost, Mantis reissues the same data for equivalent subscribers. In this way, Mantis is differentiated from other systems by allowing us to achieve streaming-based observability on events while empowering engineers with the tooling to reduce costs that would otherwise become detrimental to the business. From the beginning, we’ve built Mantis with this exact guiding principle in mind: Let’s make sure we minimize the costs of observing and operating our systems without compromising on required and opportunistic insights. Guiding Principles Behind Building Mantis The following are the guiding principles behind building Mantis. We should have access to raw events. Applications that publish events into Mantis should be free to publish every single event. If we prematurely transform events at this stage, then we’re already at a disadvantage when it comes to getting insight since data in its original form is already lost. We should be able to access these events in realtime. Operational use cases are inherently time sensitive by nature. The traditional method of publishing, storing, and then aggregating events in batch is too slow. Instead, we should process and serve events one at a time as they arrive. This becomes increasingly important with scale as the impact becomes much larger in far less time. We should be able to ask new questions of this data without having to add new instrumentation to your applications. It’s not possible to know ahead of time every single possible failure mode our systems might encounter despite all the rigor built in to make these systems resilient. When these failures do inevitably occur, it’s important that we can derive new insights with this data. You should be able to publish as large of an event with as much context as you want. That way, when you think of a new questions to ask of your systems in the future, the data will be available for you to answer those questions. We should be able to do all of the above in a cost-effective way. As our business critical systems scale, we need to make sure the systems in support of these business critical systems don’t end up costing more than the business critical systems themselves. With these guiding principles in mind, let’s take a look at how Mantis brings value to Netflix. How Mantis Brings Value to Netflix Mantis has been in production for over four years. Over this period several critical operational insight applications have been built on top of the Mantis platform. A few noteworthy examples include: Realtime monitoring of Netflix streaming health which examines all of Netflix’s streaming video traffic in realtime and accurately identifies negative impact on the viewing experience with fine-grained granularity. This system serves as an early warning indicator of the overall health of the Netflix service and will trigger and alert relevant teams within seconds. Contextual Alerting which analyzes millions of interactions between dozens of Netflix microservices in realtime to identify anomalies and provide operators with rich and relevant context. The realtime nature of these Mantis-backed aggregations allows the Mean-Time-To-Detect to be cut down from tens of minutes to a few seconds. Given the scale of Netflix this makes a huge impact. Raven which allows users to perform ad-hoc exploration of realtime data from hundreds of streaming sources using our Mantis Query Language (MQL). Cassandra Health check which analyzes rich operational events in realtime to generate a holistic picture of the health of every Cassandra cluster at Netflix. Alerting on Log data which detects application errors by processing data from thousands of Netflix servers in realtime. Chaos Experimentation monitoring which tracks user experience during a Chaos exercise in realtime and triggers an abort of the chaos exercise in case of an adverse impact. Realtime Personally Identifiable Information (PII) data detection samples data across all streaming sources to quickly identify transmission of sensitive data. Try It Out Today To learn more about Mantis, you can check out the main Mantis page. You can try out Mantis today by spinning up your first Mantis cluster locally using Docker or using the Mantis CLI to bootstrap a minimal cluster in AWS. You can also start contributing to Mantis by getting the code on Github or engaging with the community on the users or dev mailing list. Acknowledgements A lot of work has gone into making Mantis successful at Netflix. We’d like to thank all the contributors, in alphabetical order by first name, that have been involved with Mantis at various points of its existence: Andrei Ushakov, Ben Christensen, Ben Schmaus, Chris Carey, Danny Yuan, Erik Meijer, Indrajit Roy Choudhury, Josh Evans, Justin Becker, Kathrin Probst, Kevin Lew, Ram Vaithalingam, Ranjit Mavinkurve, Sangeeta Narayanan, Santosh Kalidindi, Seth Katz, Sharma Podila. Open Sourcing Mantis: A Platform For Building Cost-Effective, Realtime, Operations-Focused… was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story. observability stream-processing reliability realtime microservices

How to Keep Company IoT Networks Secure

CTOvision

Read Grayson Kemper explain how companies can keep the Internet of Things networks secure on IoT for All : As employees increase how often they use smart devices as part of their daily jobs, businesses are investing in building IoT networks. While IoT networks make information accessible and protected from hardware errors, businesses should invest in […].

Business Agile: A Roadmap for Transforming Your Management & Adapting to the VUCA Environment

Speaker: Peter Taylor, Speaker/Author, The Lazy Project Manager

Businesses everywhere are trying to “get business agile”—but it’s not easy to adapt to becoming this adaptive. How can conventional organizations succeed in this transformation? In this webinar, Peter Taylor will walk through the change process step by step, and look at a tried and tested transformation roadmap: benefits are outlined, solutions to common challenges offered, and tried and tested methods and tools provided. It will be a guide towards a decentralized and management style that offers more successful decision making through collaboration.

How Mozilla Festival 2019 Will Highlight Neurodiversity

The New Stack

Neurodiversity spans a variety of experiences, and there is no “one size fits all,” approach to accessibility.

Alphabet’s Wing starts drone deliveries to US homes

CTOvision

This afternoon, in a first for drone delivery in the U.S., Wing is delivering packages, over-the-counter medication, snacks and gifts to residents of Christiansburg, Virginia. With an expanded Air Carrier Certificate from the Federal Aviation Administration (FAA), Wing today became the first company to operate a commercial air delivery service via drone directly to homes […].

Avalanche Cloud Data Warehouse Launches for Azure with Free Storage Offer

Actian

Today we announced the availability of Actian’s industry-leading Avalanche Cloud Data Warehouse Managed Service for Azure. Actian Avalanche is the only hybrid data warehouse solution on Azure and provides significant performance gains at a lower cost compared to alternatives. We are also pleased to announce a special offer of $0 cloud storage on Azure for customers of Avalanche. . Details of the offer: .

Azure 40

7 Critical Identity Management Questions Your Enterprise Needs to Ask

CTOvision

Read list seven critical identity management questions every company should ask on Solutions Review : What are the 7 critical identity management questions your enterprise needs to ask itself? Where can you go to find the answers to these questions? Also, how should these questions influence or direct your identity security solution search? Cybersecurity presents a […].

2019 State of Engineering Performance Management Report

More than 100 software leaders were asked how they manage and measure engineering performance. How do the findings stack up to your own experience? The report includes the top engineering challenges and the most used performance metrics.