How IT can redeem itself from critical outages

In my previous post, I discussed how AppDynamics Application Analytics can rapidly troubleshoot gradually degrading apps. In this blog post, I’ll discuss how IT can redeem themselves from unfortunate outages leveraging ITOA or application analytics.

With so many factors impacting application performance today, it is not surprising that applications frequently are either slow or simply stall entirely. Sometimes it’s the server, sometimes it’s the database, and some days it’s the mobile network carrier to blame. With the complexity of today’s environments, business transactions (such as logging in, adding to a cart, or checking out) have many opportunities to fail. These transactions often fail long before they reach the backend operational databases where all the “committed” transactions are stored. So, as far as the traditional business intelligence (BI) solutions — that provide insights only into committed transaction data — are concerned, these in-flight, yet failed transactions never existed.  

Another trend that makes the equation all the more complex is IT is delivering more and more modular applications at faster and faster rates making the entire application ecosystem very dynamic and highly error-prone. Applications are bound to be slow or stall; It’s how IT redeems itself from such critical outages with their business stakeholders that matter most.

AppDynamics Application Analytics provides real-time visibility to deliver insights into both aggregated or rolled-up metrics, as well as details into individual customer interactions. The breadth of visibility covers the frontend to backend technology stacks — from clients (mobile or browser apps) to the Java or .NET or other language apps, to databases, to servers and infrastructure components. The depth covers code-level visibility into live in-flight transaction data captured as the application processes it, and is not restricted only to those transactions committed to the operational database. 

Let’s say that John is trying to purchase a shirt on his mobile device while commuting back from work. His checkout transaction times out as his mobile device frequently switches cellular towers in the fast moving train. John finally gives up after repeated attempts. Now, this transaction failed on the network carrier and never hit the application inside the data center servicing such requests. So, the application infrastructure has no knowledge of John’s attempt at purchasing the shirt. In another case, Susan was trying to buy a purse from her browser but her order got stuck in a queue due to errors in the messaging queue. Susan’s transaction also never hit the operational database.

In such situations, IT can leverage AppDynamics Application Analytics to not only identify these individuals who had poor experiences, but also to capture what they were trying to accomplish (purchase items, in this case).  

Now rather than treating these as lost revenues and giving up on them, IT can redeem itself by extracting key information into a spreadsheet with a click of a button. This information includes account information of the purchaser (name, email, etc.) as well the exact items being purchased. The marketing team can then use this valuable information and run some win back campaigns that offer 10% discounts to win the heart of the customer. The more sophisticated retailers might go a step further by pre-creating a shopping cart that has all the items pre-loaded, with a discount applied, and ready for one-click checkout from the email itself. Now that’s what I’d call going above and beyond!

IT, typically looked at as a cost prevention insurance method, can actually help drive revenue given the right tools!

Want to check it out for yourself? Download a FREE trial today!

Driving Business Decisions Off Data

There are multiple systems in use for basing business decisions. The popular business intelligence (BI) market focuses on the use of back office data that must be aggregated or otherwise centralized and sliced and diced to make business decisions. While this is clearly critical data, and BI is a $14.4B market according to Gartner (calendar year 2013). Software-defined businesses need a far more real-time view of the front office and systems of engagement. These systems of engagement change far more quickly and require real-time response, similar to running IT Operations versus many other parts of IT. These analytics technologies will not only be used to guide product decisions, but also to enable a fluid organization.

Gartner predicts by 2017, 70 percent of successful digital business models will rely on deliberately unstable processes designed to shift as customer needs morph. (See: )

As a result of a fluid business model, and fluid decision-making to drive innovation, the processes must be adjusted and adapt to this change. The adaptation creates instability in processes, but it’s essential to meet customer preferences and demands. The agile product, agile development, and agile organizations require adaptation and experimentation based on customer interaction. The business transaction marries together the user with the processes customers interact with, making that the discrete focus of monitoring. This monitoring must be captured, typically by APM tools such as ours, or by writing custom instrumentation within the software, and finally sent into analytics engines that can provide the insight.

We still have a significant problem in the industry, which is evident in the monitoring space. We have a dashboard problem, and buyers make decisions on dashboards, not insight. The love of dashboards continues to proliferate tool fragmentation and dashboards.

Today’s analytics are user driven, where the user of the tool or product is driving the analysis and making decisions. With advances in machine learning (See: we are starting to see these new algorithms being applied to IT Operations data and ITOA which will allow for machine driven analytics and insight. This significant shift will have repercussions across IT Operations Management.

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Executive Summary Flipping to Digital Leadership: The 2015 CIO Agenda

Further reading:

Rapid troubleshooting of gradually degrading apps with Application Analytics

In my last blog post, I made a case for the powerful combo of APM and Analytics that you shouldn’t miss out on. Today, I’m going to explain what I mean by that with an example on how you can build better apps and write better code leveraging this powerful combination.

There is a common misperception that the only way a developer can write better code is by running profilers and by painstakingly collecting every call graph of their application to learn how its code is actually behaving. Cutting edge developers have started using application performance management (APM) to understand code performance and identify bottlenecks. The problem with both these approaches is that this often results into testing just the happy-path of your code. Negative or failure testing will catch exception scenarios but that is rarely coupled with stress testing scenarios. Rarely do testers test combine the two (negative and stress) to uncover scenarios that develop over time with cumulative or sequential failures.

Any DevOps professional will attest to the fact that no test plan can replicate the actual action that happens in the production system. DevOps can spend hours, if not days, attempting to reproduce scenarios for debugging and resolving issues. In today’s fast changing application environments where dev → test → prod cycles are getting shorter and faster, application behavior in production environments is where the proverbial rubber meets the road. And developers and testers are willing to give an arm and a leg to gain insights into their production apps without introducing large overhead.

There is a better way…

Application Analytics in conjunction with APM can provide you perspectives that ensure production readiness earlier in the development cycle.

A decent APM tool will alert you that a certain set of transactions are not performing per expectations. A great APM tool will go a step further and provide you automatically captured snapshots with detailed call graphs to help you better investigate that particular transaction. In some cases the challenge is to not only determine the symptoms of how performance is degrading, but also how the user journey, or series of steps that resulted into the problem. An easy view of the cumulative set of preceding activities that resulted into this condition can prove very beneficial. This is where an analytics solution that captures performance and business data of every transaction over the life of the application will come to your rescue. It will help you easily string together a series of related and seemingly unrelated transactions and analyze the cumulative impact on application performance. This multidimensional view on your application performance and behavior can prove to be priceless particularly in today’s day and age of fast moving, agile development methodologies.

A couple of simple and easy to relate to code bugs would be thread contention, thread exhaustion, or connection handle leaks. The symptoms of these issues don’t show up for each transaction but can accumulate over time to create devastating results. For instance, if a DB connection handle is not returned back to the connection pool in some corner case erroneous condition, the pool may get exhausted to result into poor performance or complete stalls. An APM solution will alert you that threads are waiting on getting a connection out of the connection pool and hence performance has slowed down or stalled.

Troubleshooting with an analytics-based approach

Let’s walk through an example and then watch my colleague Steve Sturtevant demonstrate the solution using both AppDynamics APM and AppDynamics Application Analytics.

Wrapping up, you’ll see that the combo of APM and Analytics can be a very powerful one, especially in today’s fast moving world of DevOps.

For more information on how AppDynamics Application Analytics solution can help you run your software and your business better, download this white paper today.

Priorities for Application Operations Teams in 2015 [INFOGRAPHIC]

As we start 2015, IT and Application Operations teams prioritize their goals to improve the overall efficiency, migrate to the cloud, place importance on big data, among other department goals.

In case you’ve been living under a rock, and haven’t heard about the monumental success of AppDynamics AppSphere™ 2014, well now you have some required reading. As part of the event, we surveyed all those present on their IT priorities and and the results were quite surprising.

Here are a few noteworthy stats:

  • Docker and other containers are growing. 25% said they plan to use a container solution in the next year.
  • Nearly half of respondents (46%) listed “Improving Operational Efficiency” as their number one priority.
  • Enterprises still prefer private cloud to public.

Check out the full infographic below…


Interested in attended the next AppDynamics AppSphere? You can pre register now and save your seat!

Want to see how AppDynamics can help your IT and Application Operations teams? Download a FREE trial now!


Is Your Intelligence Failing You in One Critical Area?

How’s your Business Intelligence software working out for you?

If your experience has been like that of many BI users, your answer is probably a bit of a mixed bag. That’s because most BI users have experienced a combo of great insights and extreme frustration from their BI software.

Change is in the air. The reason I want to discuss this today is because as with many things IT, intelligence is up for disruption as well.

A Brief History of BI

In the early days, BI revolved around the following process:

  •      A business user would define a need, and submit a ticket to the IT department
  •      The IT department would gather the relevant data, often from data warehouses and cubes (Cognos, Business Objects, etc.), and deliver it to a business analyst
  •      The business analyst would then analyze the data using spreadsheets or some form of dashboards, and then create reports for the business user

Sounds like a cumbersome process, doesn’t it?

Even worse, it’s a very s-l-o-w process. A lag time of weeks or even months between the initial request and final delivery were common. Or perhaps I should say are common – many companies are still stuck in this “early days” process.

And data was often stale before it was even loaded into the data warehouses, since data was often collected from production databases on a weekly basis. So by the time business analysts finally got their hands on the reports, they weren’t exactly looking at up-to-date information.

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Eventually, companies like Tableau and Qlik (formerly QlikView) arose to fill the growing demand for visualization and dashboarding. Business analysts finally had the ability to slice and dice data to their own needs. That was great progress. But business users were still working with stale data.

But early on, BI processes revolved strictly around structured data. Data warehouses – 1990s technology – did not have the ability to capture unstructured data.

All data captures were from relational-based data storage. Only data that conformed to a conventional relational schema was captured; all other data was untouched.

Unstructured Data Gets Unlocked

Large companies like Yahoo and Google were crawling trillions of web pages, amassing massive amounts of data, and indexing the unstructured information for rapid searchability. They built Hadoop-like technologies to capture and analyze large volumes of unstructured data. And so the open-source technology Hadoop became quite popular for storing vast quantities of unstructured information – though not nearly as efficiently as structured data storage.

To facilitate the process of storing unstructured data, Hadoop created its own file system: HDFS (Hadoop Data File System). And Hadoop provided MapReduce technology, which allowed analytics to run on top of all that unstructured data.

The advent of Hadoop inspired companies to begin capturing ever-increasing quantities of unstructured data.

But the MapReduce technology has its flaws. It’s slow. It takes lots of time to run jobs that must sift through massive amounts of data. The time lag between asking a question and getting an answer can be substantial – and in many cases, entirely unacceptable.

Currently, many companies are using Hadoop to store huge quantities of unstructured data. And they’re combining the text data analytics with structured data analytics from data warehouses, and using applications such as Tableau to analyze the resulting amalgamation of data.

Traditional Business Intelligence solutions – based both on structured and unstructured data – have evolved to be of great value. They provide companies with a wealth of decision-making support that simply wasn’t available not so long ago.

But there’s a problem…

A Gap Between Capabilities and Needs

More and more, the business world is running on software. In many cases, software-based business models have even toppled long-entrenched business dynamos.

Netflix vs. Blockbuster is a classic example.

Netflix, enjoying the benefits and economies of operating a software-based business model, contributed greatly to putting Blockbuster and it’s huge empire of physical stores essentially out of business.

But as the business world becomes more software-oriented, companies increasingly need a way to gather insights into software operations. And traditional BI tools are failing companies in a very critical way. Software provides businesses flexibility to their operations. Code changes can easily alter how businesses are operating. The DevOps culture is resulting in multiple application updates per day, and BI tools and their huge latencies are simply not getting the job done.

Let’s Go Shopping

To illustrate the problem with traditional BI tools, let’s imagine that you’re logged-on to one of your favorite eCommerce websites to do some shopping – something that you likely do very frequently.

There’s a particular product you want to buy. But during the process, you’ll probably do some browsing around. Read some customer reviews. Consider alternatives.

And then once you’ve fulfilled your mission, and added your must-have item to your shopping cart, you’re likely to do some more browsing. Just some fun shopping. Some wish-listing.

Then, finally, you go through the checkout process and leave the site. The classic BI tool has only captured the end result of your interaction with the site – your purchase. What has the merchant company learned about you, their customer? Probably not as much as they could have or should have.

Opportunity Lost…

If the company is using only traditional BI tools, they’ve not learned nearly as much about you as opportunity offered. Sure, they’ve collected some data relating to your purchase.

But they could have learned so much more about you than what the mere transaction records offer.

They could have learned more about your interests. They could have learned how to serve you better. They could have learned ways to engage you far beyond your single purchase.

All invaluable data – and right there for the taking. But many companies don’t take it. Intentionally or not, many companies are turning up their noses at this unprecedented opportunity.

Opportunity Maximized…

Our Application Intelligence Platform offers companies a means of turning all of this disregarded or neglected data into golden opportunity. It provides:

  •      Real-time information about every interaction flowing through the software system
  •      Business context for every transaction type – logging transactions; add-to-cart transactions; checkout transactions; etc.
  •      End-to-end visibility of all transaction streams, front-end to back-end
  •      All information presented in a single dashboard

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Application Intelligence fills the gap that other BI tools ignore. As more companies adopt a software-defined model, customer experience becomes one of the most valuable commodities. Understanding your customer to give them a seamless experience is vital to long term success. With Application Intelligence, you can understand your customer better. It helps you to serve your customer better.

It helps to maximize the benefits of the hard-earned relationships you’ve established with your customers. And in the end, isn’t that what Business Intelligence is all about?

Start understanding your customer better and gaining insightful metrics. Try out AppDynamics for FREE today!

What is the business impact? – ITOA use case #4

In the first three parts of this series I’ve covered common ITOA use cases we’ve seen across our customer base:

Analyze the business impact

The fourth use case, analyzing business impact, is a way to quantify the value of all the work IT Operations professionals put in to making application environments run as smoothly as possible.  Most of the time, IT is viewed as a cost center because when a revenue generating application has an outage for example, IT Ops is an easy team to point a finger at.  However, by leveraging our new offering, Application Analytics, Operations teams now have a solution that enables them to have a more intelligent discussion about operational data and the associated business outcomes.  This enables Ops to have more positive conversations that revolve around the added value that the team provides to the business.

Consider the example below; the Ops team was able to build a dashboard showing the dollar amount associated with normal, slow, very slow, and error transactions.  The error transactions are the requests that did not complete as expected and resulted in no revenue.  This is the money the business is leaving on the table due to an application that isn’t performing well.  If the dollar value associated with these error transactions ($161K) is half of what it was during the same time period yesterday due to the work the Ops team completed by finding and fixing performance bottlenecks… they’ve added a very quantifiable number to the company’s top line.

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Suppose there was an unplanned outage that affected a large number of users – wouldn’t it be great if marketing could get a report showing all the users that were affected during the checkout process and the exact items that were in their cart at the time so they could kick off a ‘win back’ campaign?  With Application Analytics, Ops teams can make that wish a reality.

By filtering the set of transactions to only those with errors during a checkout process for a particular time range, and adding in the user IDs, product category, and product names, Ops teams can come out of a negative situation (like an outage) looking like a hero to their line of business peers.

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Not only can Ops teams analyze the business impact of the troubleshooting they do on a regular basis, they can also use Application Analytics to identify anomalies and use this information to start the triage and troubleshooting process instead of only analyzing the results of their work.  For example, Ops teams can leverage Application Analytics to discover the answers to these kinds of questions and proactively address issues:

  • Why is the number of TVs sold in the past 15 minutes far below normal?
  • Why are there a large number of slow transactions associated with trying to add a book to an online shopping cart?
  • Why did a particular customer’s package not get delivered?

Application Analytics provides a real-time view into how the business is correlated with the operational metrics they interact with on a daily basis, all with automatic propagation of business context in the form of business transactions and no application code changes required.

In the next post we’ll talk about the fifth common ITOA use case, creating an action plan.  In the meantime I encourage you to sign up for a free trial of AppDynamics to try these use cases out for yourself.

Prioritize with business context – ITOA use case #3

In the first two posts of this series we’ve taken a look at two common ITOA use cases:

I recommend spending some time familiarizing yourself with the first three use cases I’ve highlighted, they lay some important groundwork for the rest of this series. With that, let’s dive into use case #3 — prioritizing issues and opportunities.

Prioritizing issues and opportunities

Once operations teams isolate the root-cause of an application performance issue, the next step is to determine how to rank and prioritize these different issues. Oftentimes these fixes are simply prioritized in the order in which they came into the support team. However, what if the most recent root-cause that’s been discovered is one that is directly impacting revenue, not the support ticket that was filed first?

Let’s look at an example to illustrate this point. Put yourself in the shoes of an application support person and look at this short list of open issues that are in your queue:

  • November 1, 2014 – Issue #1075 – Open for 14 days
  • November 10, 2014 – Issue #1091 – Open for 4 days
  • November 12, 2014 – Issue #1087 – Open for 2 days

Which one do you tackle first? Most likely the one that has been open the longest, right? Prioritizing that ticket will help you, as an application support person, improve the KPIs you get measured on like average time taken to close out a ticket.

Now let’s take another look at that same list but this time we will add in one data point that indicates the business context of the particular issue.

  • November 1, 2014 – Issue #1075 – Open for 14 days – Related to ‘update profile’ function
  • November 10, 2014 – Issue #1091 – Open for 4 days – Related to ‘search products’ function
  • November 12, 2014 – Issue #1087 – Open for 2 days – Related to ‘checkout’ function

Now which one would you prioritize? A segment of users not being able to update their profiles or search for certain products is obviously not good for the end user experience, but if people are having issues checking out of your application, that means revenue is being directly impacted. Updating preferences in a profile is not nearly important to the business as making sure money is flowing in. That business context allows operations teams to focus their efforts on the issue with the largest potential business impact. In the Application Intelligence Platform, that business context is what we refer to as a business transaction.
Simply put, a business transaction is a type of user request. AppDynamics automatically buckets these different types of user requests and baselines their normal behavior out of the box. Actions like ‘submit order’, ‘get quote’, ‘login’, ‘add to cart’ are all examples of business transactions.

Getting this business context, and automatically correlating it with the different performance issues that occur in the application, give operations teams that additional data point they need to stack rank remediation effort and tackle the large problems that are impacting the business first. And because AppDynamics dynamically baselines each business transaction separately, you’ll know when a certain business transaction breaches what we’ve defined as ‘normal’ performance. This allows Operations teams to create alerts and other automated actions when a business transaction has abnormal performance. For example, you could automatically create an incident in your ticketing system (ServiceNow, PagerDuty, VictorOps, Jira, etc.) anytime a really important business transaction deviates from the baseline, ensuring it gets proper prioritization within your team so they can proactively address the issue before it affects other end users.

By leveraging business transactions, Operations teams can easily understand how their technical perspective of the health of the environment relates to what really matters — the health of the business.

In the next post we’ll explore the fourth common IT Operations Analytics (ITOA) use case, analyzing business impact. In the meantime, discover how customers like Cisco, Edmunds, Fox News, OpenTable, and have leveraged the power of business transactions by signing up for a free trial of AppDynamics and trying it out for yourself.

Stop troubleshooting based on hunches and intuition – ITOA use case #2

In the first installment of this series, I talked about why analytics matters for IT Operations teams and drilled into the first of five ITOA use cases — visualizing your environments. I described our flow map feature at length, which automatically maps the topology of the application environment so customers can understand the dependencies between application components.

In this post I’m going to focus on the second common ITOA use case, rapid troubleshooting.

Rapid troubleshooting

Once you can visualize the inter-dependencies inside your environment, the second common ITOA use case involves leveraging this visibility to solve operational problems. Being able to pinpoint root cause of an issue using data generated from your application environment is of the utmost importance for businesses whose livelihood depends on these types of applications. Typically Operations teams troubleshoot issues that arise using their instincts and knowledge of past events, however with AppDynamics’ unified monitoring approach, Operations teams can triage and isolate issues faster than ever before using a common view of application performance. Quickly knowing what an issue ISN’T is almost as valuable as knowing what it IS in these situations where every second counts.

AppDynamics offers multiple ways for customers to isolate and identify the root-cause of application performance issues in real-time. I’ll walk through some examples of how customers can discover root cause using several different views inside the Application Intelligence Platform starting the investigation on the front end, going through to the server side, and wrapping things up with the database backends.

Real-User Monitoring (RUM):

Customers can view the high-level real-user load and health by geography:
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By clicking on a geo, one can filter the data associated with just that geography.
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When viewing Browser RUM data, one can right click and view all browser snapshots from a particular region:
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These browser snapshots provide a wealth of information including the performance of the different components of the browser (like pages, AJAX requests, and iFrames), associated business transactions, and resource timing for the snapshot.
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When looking at Mobile RUM data, you can view network request snapshots to analyze what happened when the device called out to the application over the network…
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…and you can also view the crash dashboard to analyze crash trends…
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…and crash snapshots to see what was happening on the device at this particular point in time.
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If the issue doesn’t appear to be related to the browser or device, AppDynamics gives users the ability to automatically drill downstream into the associated server side business transaction snapshots.
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By drilling down into the call graph, one can isolate the exact class, method, or web service that is a performance bottleneck for a particular user request:
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We also isolate the hot spots of this particular snapshot:
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And show things like top SQL calls that could potentially be slowing down the application from a database perspective:
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What was going on in the hardware and memory during this time? We automatically correlate and show those metrics too:
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Database Monitoring:

What if all signs point to an issue in the database as the root cause? AppDynamics also offers a database monitoring product module that provides granular visibility inside your database environment.

With our database monitoring product module, ops teams can now drill into the databases themselves to gain information about:

Detailed query analysis:
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Top query analysis:
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As you can see, the Application Intelligence Platform provides multiple ways for customers to leverage operational analytics to isolate issues and drill down to root cause from multiple dimensions. No matter where you start in the troubleshooting process, AppDynamics provides the flexibility to iterate quickly and drill up, down, and across the application environment, with all data automatically correlated, so Operations teams can find the root-cause as fast as possible.

In the next post, we’ll take a look at the third common ITOA use case — prioritizing issues and opportunities. If you’d like to try out the powerful troubleshooting capabilities of the Application Intelligence Platform, sign up for your free trial today and get started in minutes.

Fall 2014 Release Enables Modern Collaborative Troubleshooting

Today, I am very pleased to announce our Fall 2014 Release which is packed with exciting new features and platform enhancements. We gave a sneak peak of some new features at AppSphere back in November, but for those who couldn’t make it to Las Vegas I wanted to highlight what our team has been working on. At a high level, the features and capabilities can be grouped into four themes:

  • Cutting edge collaboration capabilities
  • Deep visibility into complex applications
  • Application Analytics
  • Platform collection & processing enhancements

Modern collaborative troubleshooting for rapid resolution

The Application Intelligence platform has several new features that make it easier for DevOps teams to collaborate in full context to reduce mean-time-to-resolution (MTTR) and learn from the past to better manage future issues.

Virtual war room

In traditional war room scenarios, there is a mix of chat windows, online meeting rooms, teleconference calls, emails, and different dashboards on everyone’s screens which make the process painful and really difficult for remote employees to participate in. To add to that, each person in the room is focused on Mean-Time-To-Innocence metric than MTTR. After resolution, this information is usually lost and cannot be accessed again in the future.

With the troubleshooting virtual war room capabilities, AppDynamics now provides customers with a powerful ad-hoc virtual space that gives Ops & Dev teams one place they can chat, make system changes in real time, view and annotate events on second-interval charts so everyone (even non-AppDynamics users) can collaborate in a view that unites the different aspects of troubleshooting to provide common context and common data to all. The entire decision-making process can be indexed in knowledge management systems.

By leveraging a common view and context of the environment during troubleshooting exercises, AppDynamics reduces the time it takes to resolve application issues, reduces the business impact of unplanned outages, and allows organizations to learn from past troubleshooting situations.

Scheduled & shareable reports

One important aspect of improving collaboration is making sure key stakeholders have access to necessary information whenever they need it. In order to get that information, usually someone had to log into a monitoring system, configure a report, run it, then manually send it over to a colleague for consumption each time they wanted to share data.

With the new emailed reports feature, DevOps teams can setup and schedule reports to be shared via email with anyone they’d like.

Now, AppDynamics customers can foster a more open and transparent culture by making it extremely easy to share reports across an organization.

Companion iOS app (beta)

IT Ops teams typically have some kind of alerting & ticketing system already set up, but what happens when they aren’t at the office or don’t have access to their laptop?

With the introduction of AppDynamics’ native mobile app, customers can be sent push notifications when alerts are triggered and can access KPIs for their mission-critical applications straight from their iOS device.

Customers can now easily access AppDynamics notifications and application metrics while on the go!

Deep visibility into complex applications

AppDynamics has introduced functionality to make it easy for ops teams to monitor and troubleshoot the most complex interactions and transactions in modern application environments.

Business flow

Large enterprise business architectures are typically built on multiple large applications such as a fulfillment application, inventory application, etc., service oriented architectures that oftentimes are shared across different business processes. Getting both a macro level view of the dependencies and drilling down into a particular service are required for proper performance management.

The Business flow feature allows customers to be flexible in terms of how they define and visualize their applications and dependencies, and enables app to app metrics and flow map visualizations, snapshot drill-down to a different app, and role-based access control so IT Ops teams can see how their apps are interacting with shared services at a high level, without needing drill-down access into the service itself. Separation of concerns is used in this context.

Now, customer applications are more manageable without loss of control.

Asynchronous transactions & improved visualizations

Async transactions are notoriously difficult to trace and monitor because tracking the true end-to-end latency of these requests is nearly impossible. When IT Ops teams don’t have visibility into the most complex transactions they are effectively disadvantaged at monitoring their environments due to blind spots. Most real world apps have async transactions in them.

AppDynamics automatically traces and monitors all async requests to give visibility into these complex distributed environments. With our new release we’ve taken this to the next level, giving you a model to easily track overall latency and the ability to customize when long running async business processes start and stop.

IT Ops teams now receive powerful granular visibility into how these transactions impact overall application performance and the end user experience.

WebSocket support

Within traditional monitoring solutions, WebSocket requests usually appear to be very slow / stalled transactions since they have long running/open connections. So, very often they may receive what is essentially a ‘false positive.’

Customers now have the ability to understand the actual performance of WebSocket connections from the end user perspective, identify the business transaction using the websocket URL, and monitor the true latency of requests.

With this visibility into WebSocket requests, AppDynamics delivers a comprehensive view of the performance of these requests to better arm IT Ops teams with the real-time view they need to uncover abnormal WebSocket behavior.

Application Analytics

Today’s software-driven businesses operate in highly distributed environments where each business function is a modular standalone application. These applications are generating humongous amounts of structured and unstructured data that are siloed in nature as well. AppDynamics Application Analytics eliminates these silos by automatically propagating end-to-end business transaction context across analytics datasets without the need for any application code changes. By leveraging Application Analytics, business and IT users can now quickly unlock actionable business insights to answer more meaningful questions than ever before, and all in real time.


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Platform collection & processing enhancements

We’ve added native support for a number of different technologies that are prevalent across our customer base. With the Fall 2014 release, AppDynamics has added support for:

  • WebMethods
  • Cassandra backends
  • SQL Azure databases
  • Single page apps
  • C/C++ (currently in beta)

We’ve also eliminated the pain of collecting and storing large amounts of metrics generated from complex enterprise applications. AppDynamics announces an infinitely scalable metrics store, available via on-premise or SaaS deployments. Now, we enable customers to capture and analyze trillions of events with ease!

Try these features for yourself, download AppDynamics for FREE today!

What’s new in the Summer 2014 release for the Exec Team

As part of our recent release announcement, AppDynamics has improved the functionality for application owners that are concerned with the high-level impact of performance on the applications they oversee.  In this blog we’ll take a look at some exciting functionality of the Application Intelligence platform that delivers value for application owners.

Application analytics (beta)

With the announcement of our application analytics beta program, customers now receive unprecedented visibility into the business data that is contained inside business transactions and log files.  Go deeper than simply monitoring performance. Explore the details of each and every transaction including user experience, operational metrics, and correlate that with business data associated with that particular business transaction.  Best of all, there is no administration overhead because there are no changes to application code or infrastructure necessary.

View revenue figures in real-time so you can be notified the moment something abnormal happens.

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Understand the customer journey so you can optimize your business.

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If you are interested in signing up for the Application analytics beta program, click here.  You can also sign up for a free trial of the platform here.

Smart dashboards

Managing and monitoring large deployments with thousands of nodes and tiers can be overwhelming for the APM professional. Creating a separate dashboard for each individual node and tier that allows them to monitor them individually is a near impossible task.

In this release AppDynamics introduces a powerful dashboard templating engine that auto-generate dashboards based on predefined characteristics of the nodes or tiers. This feature enhances monitoring productivity by making dashboards reusable without having to manually create individual dashboards for each node.

Create & associate dashboards with tiers / nodes once…

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… and use across any tier / node in your environment

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Real-time business metrics

Today’s leaders are tasked with making decisions on business data that is typically batch processed and often days, sometimes even weeks old.  Not having real-time visibility into the business affects application owners’ ability to make data-driven decisions.

AppDynamics allows customers to collect business metrics from your applications in real-time.  Metrics such as revenue, orders, deposits, signups, etc. can be easily extracted from production applications and displayed in real-time in an easy to digest way.

View operational metrics correlated automatically with business metrics like revenue to see how application performance affects the business.

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Test drive these new features yourself by signing up for a free trial today.