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Killexams : Oracle Implementation test - BingNews Search results Killexams : Oracle Implementation test - BingNews Killexams : Why Continuous Testing Is The Key To Unlocking Your ERP Transformation

Technology leader and co-founder of Opkey — a continuous testing platform redefining test automation for web, mobile and ERP applications.

Many business and technology leaders realize that their digital transformation initiatives can’t be utilized without modernizing their enterprise resource planning (ERP) software. Incorporating new technologies such as artificial intelligence and machine learning is essential to modernizing ERP solutions.

Through a 2019 study of ERP migration and transformation projects, McKinsey revealed that two-thirds of enterprises did not get the ROI they were looking for from their migration project. The common reasons for this dissatisfaction are delays in ERP implementations and misaligned project goals. Intelligent test automation, which powers a continuous testing approach, will help ERP transformation projects run on time and stay within budget.

Continuous testing for ERP applications: Why do you need it?

Next-gen ERPs and digital operations platforms require innovative software to be released rapidly, with minimal business risk. Leading analysts from Gartner, Forrester (paywall) and IDC (registration required) now recognize that software testing in its current form cannot handle the challenges posed by ERP applications. These analysts have concluded that software testing must be aligned with DevOps and AgileOps practices to handle giant ERP transformation projects.

The Agile/DevOps approach is incomplete, inefficient and ineffective without continuous testing. In ERP migration projects where platforms are extended to incorporate new features, functionalities and technologies, continuous testing helps you transparently validate the performance of critical business processes. This significantly reduces the risks associated with a new implementation, along with scheduled software updates. By catching bugs early in the development cycle, continuous testing ensures minimal time and budget overruns while providing advantages in risk reduction.

What are the testing challenges of ERP transformations?

According to a report by Bloor (registration required), more than 80% of migration projects ran over budget in 2007. While I have seen that statistic Strengthen over the years, I know migration projects regularly face issues of running over budget and over time. A 2019 ERP report from Panorama Consulting Group (registration required) shows that 45% of respondents had an average budget overrun of 30%.

Here are some specific testing challenges.

• Unclear Testing Scope: Determining what to test remains a major challenge for QA teams. The business risk grows every time too little testing is done. If you test too much, it wastes the time and resources of your business users.

• Inadequate Test Coverage: There are many moving parts in any ERP migration project. Functional and nonfunctional attributes get added, updated or removed with these migrations. Testing needs to pass various stages, from a unit test to a volume test, and eventually a mock go-live cutover.

• Change Frequency: In a recent Deloitte CIO survey, almost 45% of respondents reported that managing changes in an ERP project scope is one of the top frustrations in planning their ERP journey (pg. 10).

• Testing Fatigue: ERP projects are long and tedious processes. Using a manual testing methodology for ERP transformations can be inefficient and error-prone. Ask yourself: “Can my business users give their full effort to testing?”

Continuous testing for ERP applications: How can I make it work?

To incorporate continuous testing for a digital transformation, leaders must utilize automation. Teams should now focus on next-generation automation platforms that allow them to quickly build test cases, automate them and build the infrastructure to run them in a continuous fashion. Let’s review the four pillars of a continuous testing strategy.

• Know your ideal coverage: Here are some questions to ask yourself: “What’s my current test coverage? Am I testing all of our critical processes? If something goes seriously wrong, is it because I didn’t test enough?”

If the test cases you are automating only cover 30% of your core business processes, the automation might not be good enough. Emphasize knowing your ideal coverage and leverage a process mining technology to validate your ideal coverage. Test mining techniques surface your existing test cases, business processes and configurations from your system process log to determine your existing testing baseline.

• Apply continuous test development: Test assets require considerable reworks to keep pace with the frequent ERP changes typical in an accelerated release cycle. This speed cannot be achieved with continuous testing.

• Monitor changes continually: Ask yourself: “What has changed in the most recent ERP quarterly update? What business processes or test cases are going to be impacted?”

Emphasize the importance of knowing whether you are testing what is needed. Before the updates are pushed to production, use automation tools that give better change visibility to users by alerting them of processes that will be impacted.

• Test execution at scale: Prepare a scalable infrastructure to run thousands of tests on-demand with every change. Opt for a platform that can run your tests continuously on-premises, in the cloud and on mobile seamlessly.

What do you need from a test automation tool?

Three key capabilities must exist in a test automation tool to support an ERP transformation’s continuous testing paradigm.

• Autonomous Configuration Of Tests: Many changes happen at the configuration level for any ERP transformation. Leaders should leverage an automation tool that can autonomously create relevant data sets for test execution.

• Continual Impact Analysis: In the ERP world, updates are rolled out frequently. QA teams can find it difficult to decide the minimum number of test cases that need to be executed to ensure business continuity in post-application updates. AI-based impact analysis recommends a minimum number of test cases that need to be executed based on highlighted risks, keeping business application disruptions at bay.

• Autonomous Self-Healing Tests: QA teams often struggle to continuously maintain test scripts with each new release. Through leveraging AI-powered self-healing capabilities, changes can be identified automatically and test scripts can be fixed autonomously.

Continuous Test Automation: A Summary

The key to successful AgileOps is releasing updates as early and as often as possible.

With enterprise application vendors like Oracle, Microsoft and SAP rolling out updates on a weekly, monthly or quarterly basis, enterprises need to embrace those updates as early as possible. However, supporting your software testing initiatives will only be achieved with the right continuous testing strategy.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Wed, 20 Jul 2022 00:15:00 -0500 Pankaj Goel en text/html
Killexams : Anaconda Announces Strategic Cloud Partnership with Oracle

AUSTIN, Texas, Aug. 9, 2022 — Anaconda Inc., provider of the world’s most popular data science platform, today announced a collaboration with Oracle Cloud Infrastructure to offer secure open-source Python and R tools and packages by embedding and enabling Anaconda’s repository across OCI Artificial Intelligence and Machine Learning Services. Customers have access to Anaconda services directly from within OCI without a separate enterprise license.

“We are committed to helping enterprises secure their open-source pipelines through the ability to use Anaconda anywhere, and that includes inside the Oracle Cloud,” said Peter Wang, CEO and co-founder of Anaconda. “By combining Anaconda’s package dependency manager and curated open-source repository with OCI’s products, data scientists and developers can seamlessly collaborate using the open-source Python tools they know and trust – while helping meet enterprise IT governance requirements.”

Python has become the most popular programming language in the data science ecosystem, and for good reason; it is a widely-accessible language that facilitates a variety of programming-driven tasks. Because the velocity of innovation powered by the open-source community outpaces any single technology vendor, more and more organizations are adopting open-source Python for enterprise use.

“Oracle’s partnership to provide data scientists with seamless access to Anaconda not only delivers high-performance machine learning, but also helps ensure strong enterprise governance and security,” said Elad Ziklik, vice president, AI Services, Oracle. “With security built into the core OCI experience, plus the security of Anaconda’s curated repository, data scientists can use their favorite open-source tools to build, train, and deploy models.”

Together, Anaconda and Oracle are looking forward to bringing open-source innovation to the enterprise, helping apply ML and AI to the most important business and research initiatives. For more information on how to use Anaconda in OCI, click here.

About Anaconda

With more than 30 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine learning. We pioneered the use of Python for data science, champion its vibrant community, and continue to steward open-source projects that make tomorrow’s innovations possible. Our enterprise-grade tools are the leading solution for securing and managing commercial uses of Python, and enable corporate, research, and academic institutions around the world to harness the power of open-source for competitive advantage, groundbreaking research, and building a smarter, better world.


Tue, 09 Aug 2022 05:15:00 -0500 text/html
Killexams : Chainlink Confirms Support To The Merge But Not Ethereum Hard Forks; Worries About 'Increased Risk'


  • Ethereum's the Merge could happen sometime in September
  • Chainlink noted it will remain operational during and after the Merge
  • It is preparing to launch the staking mechanism in the second half of 2022

Chainlink, the decentralized node network that utilizes oracles to offer data from off-chain sources to blockchain smart contracts, confirmed it would support Ethereum's transition to proof-of-stake or The Merge, but not its proof-of-work (PoW) hard fork.

In a post about Ethereum's transition from proof-of-work, Chainlink confirmed that it would stay operational during and after the Merge. However, it disclosed that, unlike others, it would not back the forked versions of the blockchain.

"The Chainlink protocol and its services will remain operational on the Ethereum blockchain during and after the Merge to the PoS consensus layer. Users should be aware that forked versions of the Ethereum blockchain, including PoW forks, will not be supported by the Chainlink protocol," the decentralized blockchain noted.

Representation of Ethereum, with its native cryptocurrency ether, is seen in this illustration taken November 29, 2021. Reuters / DADO RUVIC

It also recommended developers and dApps pause smart contract operations if they are not sure of their "migration strategy" about the Merge, not only to protect users but also to "avoid unforeseen incidents." According to Chainlink, decentralized apps on Ethereum's forked versions, including proof-of-work forks, could "behave in unexpected ways" because of app-level problems that might bring "increased risk" for end users.

The decentralized blockchain said its decision is "aligned with both the Ethereum Foundation and broader Ethereum community's decision, achieved via social consensus." Vitalik Buterin, the Canadian programmer and co-founder of Ethereum, has some choice words for people and cryptocurrency platforms supporting the EthereumPoW hard fork.

Over the weekend, the crypto genius said Tron founder Justin Sun, Huobi and Poloniex were "simply trying to make a quick buck." Like Chainlink, Buterin believed that the fork would have some issues and that "people responsible must mitigate those problems."

The Ethereum co-founder hopes "whatever happens doesn't lead to people losing money," adding that he is not expecting that it will "have substantial, long-term adoption."

Ethereum developers have already scheduled the final test for the completion of the Merge and the community is anticipating the full implementation sometime in September. The team only needs the Goerli/Prater testnet deployment to complete the transition, which according to developers will take place sometime between Aug. 6 and 12.

Developers hope to launch the Merge on Sept. 19 if the Goerli testnet runs smoothly. When this happens, all Ethereum activities will be transferred from the proof-of-work Beacon chain.

Chainlink's position about Ethereum's hard forks came on the heels of the company's announcement on its plans to grow its oracle network and reinforce its security through a new token staking system, which could come out sometime in the second half of 2022. The staking system is almost similar to proof-of-stake blockchains, which after its implementation, would lock up LINK tokens as collateral.

With the staking mechanism, "crypto rewards and penalties are applied to help further incentivize the network's proper operation," Chainlink explained. The LINK tokens can be taxed or slashed if a node misreports data and these slashed tokens will be distributed to honest validators as rewards.

Staking is a crucial moment for the company to indicate the start of the Chainlink Economy 2.0 evolution for the decentralized blockchain's "long-term security and network economics." The mechanism is designed to build a strong foundation and reduce the risk for participants.

The team, however, anticipates that its long-term benefit would revolve around scaling Chainlink "into a global standard with a growing and sustainable user base, which in turn offers the greater opportunity of rewards for stakers who increase the network's crypto economic security and user assurances."


Mon, 08 Aug 2022 15:48:00 -0500 Nica Osorio en text/html
Killexams : Anaconda Announces Strategic Cloud Partnership with Oracle to Enable Seamless, Secure Open-Source Innovation in the Cloud

Anaconda Inc., provider of the world’s most popular data science platform, today announced a collaboration with Oracle Cloud Infrastructure to offer secure open-source Python and R tools and packages by embedding and enabling Anaconda’s repository across OCI Artificial Intelligence and Machine Learning Services. Customers have access to Anaconda services directly from within OCI without a separate enterprise license.

“We are committed to helping enterprises secure their open-source pipelines through the ability to use Anaconda anywhere, and that includes inside the Oracle Cloud,” said Peter Wang, CEO and co-founder of Anaconda. “By combining Anaconda’s package dependency manager and curated open-source repository with OCI’s products, data scientists and developers can seamlessly collaborate using the open-source Python tools they know and trust – while helping meet enterprise IT governance requirements.”

Python has become the most popular programming language in the data science ecosystem, and for good reason; it is a widely-accessible language that facilitates a variety of programming-driven tasks. Because the velocity of innovation powered by the open-source community outpaces any single technology vendor, more and more organizations are adopting open-source Python for enterprise use.

“Oracle’s partnership to provide data scientists with seamless access to Anaconda not only delivers high-performance machine learning, but also helps ensure strong enterprise governance and security,” said Elad Ziklik, vice president, AI Services, Oracle. “With security built into the core OCI experience, plus the security of Anaconda’s curated repository, data scientists can use their favorite open-source tools to build, train, and deploy models.”

Together, Anaconda and Oracle are looking forward to bringing open-source innovation to the enterprise, helping apply ML and AI to the most important business and research initiatives. For more information on how to use Anaconda in OCI, click here.

Tue, 09 Aug 2022 03:44:00 -0500 en-US text/html
Killexams : UK Government signs procurement memo of understanding with Salesforce, but are more needed to prevent a cloud oligopoly?

The UK Government’s Crown Commercial Service (CCS) procurement body has signed a Memorandum of Understanding (MoU) with Salesforce to make it easier and cheaper for public sector organizations to buy from the supplier.

According to Philip Orumwense, Commercial Director and Chief Technology Procurement Officer at CCS:

The agreement will further ensure increased collaboration and aggregation of government and wider public sector spend to achieve increased automation, forecasting, reporting and customer engagement management tools.

The main items on the Salesforce MoU are:

  • A discount on licences (Salesforce, Mulesoft, Tableau & Slack) and services for eligible UK public sector bodies, including health bodies.
  • Free experimentation projects, so that eligible bodies can test and learn how Salesforce solutions can be used to meet their requirements.
  • Direct access to a panel of Salesforce’s SME implementation partners.
  • Discounted training and support.
  • A discounted trial of Salesforce’s Net Zero Cloud, supporting the UK government’s drive towards Net Zero.

Salesforce has a number of UK public sector customers, including the Health Service Executive, Department for Works & Pensions, various local authorities and CCS itself.

More MoUs

CCS has signed a number of such MoUs in recent years with cloud suppliers, including the likes of Oracle, Google and Microsoft. Oracle’s agreement was first signed as far back as 2012 with an updated  and expanded deal signed last year. At that time, Orumwense commented:

This enhanced Memorandum of Understanding will continue to deliver savings and benefits for new and existing public sector customers using Oracle's cloud based technologies. It will continue delivering value for money whilst supporting public sector customers' journey to the cloud.

Expanding the list of suppliers offering cloud services has become a political agenda item in the UK as legislators have queried the amount of business that has gone to Amazon Web Services (AWS). As of February last year, some £75 million of contracts had been awarded in the previous 12 months.

Lord Maude, who previously ran the UK Cabinet Office where he waged a war on excessively priced tech contracts and essentially began the MoU process in earnest as part of his reforms, was quoted as warning:

When it comes to hosting, we've regressed into allowing a small group, and one vendor, in particular, to dominate. If you take a view of the government as simply as a customer, it makes absolutely no sense for the government to be overly dependent on one supplier. No one would sensibly do that.

The Salesforce MoU looks well-timed as CCS recently launched a tender for a range of cloud services in a set of deals that could be worth up to £5 billion in total. Procurement notices have been issued under the G-Cloud 13 framework, covering cloud hosting, cloud software and cloud support, with a further lot for migration and set-up services to follow. Contracts can last for 3 years with an option to extend by a further year.

Eligible suppliers must be able to offer services in the following capabilities:

  • Planning - the provision of planning services to enable customers to move to cloud software and/or hosting services;
  • Setup and Migration- the provision of setup and migration services which involves the process of consolidating and transferring a collection of workloads. Workloads can include emails, files, calendars, document types, related metadata, instant messages, applications, user permissions, compound structure and linked components.
  • Security services - Maintain the confidentiality, integrity and availability of services and information, and protect services against threats.
  • Quality assurance and performance testing - Continuously ensure that a service does what it’s supposed to do to meet user needs efficiently and reliably.
  • Training
  • Ongoing support - Support user needs by providing help before, during and after service delivery.

My take

Having a wider range of potential providers operating under such MoUs is crucial for government to deliver value for taxpayers money.

Those of us who lived through the crusading days of Maude insisting that tech vendors - mostly large US systems houses and consultancies - come back to the negotiating table, tear up their existing contracts and start from scratch, have been dismayed, but not surprised, that the so-called ‘oligopoly’ simply had to sit it out and wait for a change of government/minister to get things back to ‘normal’.

There were successes that linger. The UK’s G-Cloud framework was a triumph when set up and continues to do good work. As an aside, and given this article has been triggered by a Salesforce announcement, I do remember talking to CEO Marc Benioff in London prior to the formal announcement of G-Cloud and how it would work.   

At the time there was a heavy push from certain quarters to make G-Cloud all about virtualization and private cloud rather than the public cloud push it was to become. I asked Benioff if he thought this was the right direction of travel and got a very firm rebuttal as he told me:

The UK government is way behind in this, and way too much into virtualization…Government needs to stop hiding behind the private cloud.

I was in good company - Benioff had been in at the Cabinet Office the previous day and given Maude the same message.  Thirteen years on, the Public Cloud First policy that was shaped later that year still stands, but progress hasn’t been made at the rate that was promised back in those heady launch days and which needs to be achieved.

In 2022, there’s the risk of a different sort of oligopoly, as the concern around AWS' grip on government contracts suggests - and not just in the UK -  but unfortunately there’s no sign of a Maude to take charge this time and bang the negotiating table.

Instead the Secretary of State with responsibility for digital thinks the internet has been around for ten years and retweets memes of politicians being stabbed. Meanwhile a putative, unelected new Prime Minster has just announced that she (somehow) intends to redesign the internet into adults-only and kid-friendly versions. Sigh. 

Mon, 01 Aug 2022 23:26:00 -0500 BRAINSUM en text/html
Killexams : Datadog: What You Need To Know Before The Earnings On Thursday
Shot of a young woman using a digital tablet while working in a server room

Charday Penn/E+ via Getty Images

(Disclaimer: before we start, I'm not a developer or a software engineer. So, if despite my efforts, there are still mistakes in this article, please let me know!)

Datadog? What?

Datadog log


Datadog (NASDAQ:DDOG) is not an easy company to understand if you don't work in software (hence the disclaimer above). I will try to dissect the company and hopefully, at the end of the article, you understand what it does and how it makes money. I also take a brief look at the earnings.

An introduction to Datadog and its history

Logo Datadog


Datadog was founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, who are still leading the company, Pomel as the CEO, Lê-Quôc as the CTO (Chief Technology Officer). The two Frenchmen are long-time friends and colleagues. They met in the Ecole Centrale in Paris, where they both got computer science degrees.

Olivier Pomel CEO Datadog


(Olivier Pomel, from the company's website)

Olivier Pomel is an original author of the VLC Media Player, which a lot of you will know or recognize the logo.

VLC Media Player

VLC Media Player

(The VLC Media Player Icon)

Pomel and Lê-Quôc both worked at Wireless Generation, a company that built data systems for K-12 teachers. For those who don't know the American educational system, K-12 stands for all years between kindergarten and the 12th grade, from age 5 to 18. K-12 has three stages: elementary school (K-5), middle school (K6-8) and high school (K9-12).

Wireless Generation is now called Amplify and it offers assessments and curriculum sources for education to schools. Wireless Generation was sold to Newscorp in 2010, which was the sign for the two friends to go and found their own company. Pomel was VP of Technology for Wireless Generation and he built out his team from a handful of people to almost 100 of the best engineers in New York.

Yes, you read that right, New York. Because Pomel and Lê-Quôc knew many people in the developer scene in New York, Datadog is one of the few big tech companies not based in Silicon Valley. The company's headquarters are still in Manhattan today, on 8th Avenue, in the New York Times building, close to Times Square and the Museum Of Modern Art.

Before Wireless Generation, Pomel also worked at IBM Research and several internet startups.

Alexis Lê-Quôc is Pomel's co-founder, friend, and long-time colleague. He is the current CTO of Datadog.

Alexis Lê-Quôc, CTO Datadog


(Alexis, Lê-Quôc, from the company's website)

Alexis Lê-Quôc served as the Director of Operations at Wireless Generation. He built a team as well there and a top-notch infrastructure. He also worked at IBM Research and other companies like Orange and Neomeo.

DevOps, Very Important For Datadog

He has been a long-time proponent of the DevOps movement and that's important to understand Datadog. The DevOps movement tried to solve the problem that many developers and operators worked next to and even against each other, almost acting as enemies. DevOps focuses on how to put them together to make everything more frictionless. Developers often blamed the operational side if there was a problem (for example, the database that was not up-to-date) and operators blamed developers (a mistake in the code). By working together in teams, good communication and even as much integration between the teams as possible, DevOps tried to solve that problem.

The problem was that there was no software for a unified platform for DevOps and Datadog helped solve that problem. If you want to know where the problem is, it's good to have a central observability platform for DevOps and Datadog set it as its task to make that.

As a tech company in New York, Datadog had quite a lot of trouble raising money initially. But once it had secured money, it started building, releasing the first product in 2012, a cloud infrastructure monitoring service, just ten years ago. It had a dashboard, alerting, and visualizations.

In 2014, Datadog expanded to include AWS, Azure, Google Cloud Platform, Red Hat OpenShift and others.

Because of the French origin of the founders, it was natural for them to think internationally from the start. The company set up a large office and R&D center in France to conquer Europe quite early in its history, in 2015 already, just three years after the launch of its first product.

Also in 2015, it acquired Mortar Data, a great acquisition. Up to then, Datadog just aggregated data from servers, databases and applications to unify the platform for application performance. That was already revolutionary at the time. Datadog already had customers like Netflix (NFLX), MercadoLibre (MELI) and Spotify (SPOT). But Mortar Data added meaningful insights to Datadog's platform. This allowed Datadog's customers to Strengthen their applications constantly.

Datadog really needed this as companies like Splunk (SPLK) and New Relic (NEWR) had done or were in the process of doing the same. Datadog was seen as a competitor of New Relic at the time. To a certain extent, that is still the same today.

In 2017, Datadog did a French acquisition with, which specialized in searching and visualizing logs. It made Datadog the first to have APM (application performance monitoring), infrastructure metrics and log management on a single platform.

In 2019, Datadog bought Madumbo, another French company. It's an AI-based application testing platform. In other words, because of the self-learning capabilities, the platform becomes more and more powerful in finding weak links and reporting them without the need to write additional code. Instead, it interacts with the application in a way that is as organic as possible, through test e-mails, password testing, and many other interactions while testing everything for speed and functionality. The bot can also detect JavaScript weaknesses. The capability was immediately added to the core platform of Datadog.

Also in 2019, Datadog founded a Japanese subsidiary and in September of 2019, Datadog went public.

Datadog IPO September 2019


(The Datadog IPO, source)

Before it had its IPO, Cisco (CSCO) tried to buy Datadog above the range of its IPO price. Pomel about how he thought about this $8B offer:

Wow this is a lot of money! But at the same time I see all this potential and everything else in front of us and there’s much more we can build

Datadog decided not to sell and on the first day that the company traded, it jumped to a valuation of almost $11B.

The name Datadog is a remarkable one. None of the founders had or particularly liked dogs. In Wireless Generation, Pomel and Lê-Quôc, named their production servers "dogs”, staging servers "cats" and so on. “Data dogs” were production databases. There were dogs to be afraid of. Pomel:

“Data Dog 17” was the horrible, horrible, Oracle database that everyone lived in fear of. Every year it had to double in size and we sacrificed goats so the database wouldn’t go down.

So it was really the name of fear and pain and so when we started the company we used Datadog 17 as a code name, and we thought we’d find something nicer later. It turns out everyone remembered Datadog so we cut the 17 so it wouldn’t sound like a MySpace handle and we had to buy the domain name, but it turned out it was a good name in the end.

What Datadog does

Datadog describes what it does as 'Modern monitoring & security'. I could give you the explanation of what that means myself, but if founder and CEO Olivier Pomel does a really good job in explaining from a high level what Datadog does here why would I not let him do it, right?

Whenever you watch a movie online or whenever you buy something from a store, in the back end, there’s ten thousand or tens of thousands of servers and applications and various things that basically participate into completing that – either serving the video or making sure your credit cards go through with the vendor and clears everything with your bank.

What we do is actually instrument all of that, we instrument the machines, the applications, we capture all of the events – everything that’s taking place in there, all of the exhausts from those machines and applications that tell you what they’re doing, how they’re doing it, what the customers are doing.

We bring all that together and help the people who need to make sense of it understand what’s happening: is it happening for real, is it happening at the right speed, is it still happening, are we making money, who is churning over time. So we basically help the teams – whether they are in engineering, operations, product or business – to understand what these applications are doing in real time for their business.

In the old days, you had a development team that made an application and it took maybe six months before it was operational. For the next few years, that was it, no changes could be made. If the developers regretted a weakness, they had to wait for a few years, until the next upgrade.

That changed with the cloud. You could now constantly upgrade and developers can easily make changes without going through a whole administrative and technological drag of a process. If you implement a certain code and you think there is a better solution the next day, no problem. Moreover, Datadog will show you what doesn't really work well.

Olivier Pomel gives a few examples of issues Datadog can help its customers with:

There’s a number of things our customers can’t do on their own. For example they don’t know what’s happening beyond their account on a cloud provider. One thing we do for them is we tell them when we detect an issue that is going to span across different customers on the cloud provider. We tell them “hey you’re having an issue right now on your AWS and it’s not just you. It’s very useful because otherwise they have no way to know and they see your screen will go red and they have to figure out why that is.

Other things we do is we‘re going to watch very large amounts of signals that they can’t humanly watch, so we’re going to look at millions of metrics and we’re going to tell them the ones that we know for sure are important and not behaving right now, even if they didn’t know that already, if they didn’t know “I should watch this”, “I should put an alert on that”, “I should go and figure out if this changes”. These are examples of what we do for them.

The problems that Datadog solves

Datadog helps with observability and this in turn limits downtime, controls the development and implementation, finds and fixes problems and provides insight into every detail necessary on a unified platform.

But to make it even more like real life and where Datadog can make a difference, Olivier Pomel has a good way of explaining what problem Datadog exactly solves. He talks about Wireless Generation, where he and Alexis Lê-Quôc were the head of development and operations.

I was running the development team, and he was running the operation team. We knew each other very well, we had worked together, we were very good friends. We started the teams from scratch so we hired everyone, and we had a “no jerks” policy for hiring, so we were off to a good start. Despite all that, we ended up in a situation where operations hated development, development hated operations, we were finger pointing all day.

So the starting point for Datadog was that there must be a better way for people to talk to each other. We wanted to build a system that brought the two sides of the house together, all of the data was available to both and they speak the same language and see the same reality.

It turns out, it was not just us, it was the whole industry that was going this way.

Datadog covers what it calls 'the three pillars of observability': metrics, traces and logs (next to other things).

A metric is something that is a data point that is measured and tracked over time. It's used to assess, compare and track code production or performance.

Traces are everything that has to do with a program's execution, the metadata that connects everything. When you clicked on this article, that took you from the link in your mail to here but this is being retrieved from a database. Those connections can be found in traces. Traces are often used for debugging or making the software better.

Logs are events that are being generated by any of the participants in any of the systems. There are system logs (which have to do with the operating system), application logs (which have to do with the activity in the application, the interactions), or security logs (which log access and identity).

Companies used to have several software solutions for each separately. For metrics, companies had monitoring software like Graphite. For metrics, developers needed other software, APM or application performance monitoring. This was New Relic (NEWR), for example. And then for logs, there used to be log management software like Splunk (SPLK).

These platforms didn't talk to each other and developers or operators had to open them all separately and compare the silos manually. That didn't make sense, of course. Problems often went across borders; therefore, it makes sense to unify everything on one platform and that's exactly what Datadog did.

This allows observability teams to act much faster, especially because Datadog also provides the context of why something unexpected happens.

The solutions that companies use, are more and more complex, weaving together more applications, more multiple cloud hosting, more APIs, bigger or more teams working on separate projects simultaneously, edge cloud computing and so on. More than ever, there is a need for 'the one to rule them all' when it comes to observability, which is the fight that Datadog seems to have won.

If you look at the company's timeline, you see that initially, it only had infrastructure monitoring, so metrics. Datadog added logs and traces but other things too along the way.

Datadog timeline

Datadog's S-1

As you can see, when Datadog added the "three pillars of observability" it didn't rest on its laurels.

In 2019, it introduced RUM or real-user monitoring. It's a product that allows the Datadog customer to see the interactions of real users with their products (a site, for example, or a game). Think about how many people who have downloaded a game click on the explanation of the game before playing and which mistakes they still make, how many immediately start playing, if they can find the play button fast enough, and so on. Or think about new accounts. If there is a steep drop, Datadog will flag this and engineers can investigate this. Maybe the update had a bug that doesn't allow users to use logging in through their Apple account anymore, for example.

I'm returning to synthetics in a minute, but I first want to mention security, which is not on the roadmap above yet. As we all know, security has become much more important than just a few years ago and therefore it's important to also integrate security people into the DevOps team and make it a DevSecOps team. Datadog has already adapted for the new DevSecOps movement.

It introduced the Datadog Cloud Security Platform in August of 2021, which means that it now offers a full-stack security context on top of Datadog’s observability capabilities. Again, just like with DevOps, the company is early in what is clearly a developing trend (pun not intended) in software, the integration of security specialists into the core team of DevOps. Datadog offers a unified platform for all three and security issues can be coupled to data across the infrastructure, the network and applications. It allows security teams to respond faster and gain much more granular insights after the breach has been solved.

Again, Datadog solves a real problem here. As more and more data move to the cloud, security teams often had less and less visibility, while the attacks become more and more sophisticated. That's why it's important to give back that visibility to these teams and give them a tool to implement security. Developers and operations can implement security into all levels of software, applications and infrastructure.

Datadog also added synthetics in 2019, as I already mentioned before. Synthetics are the simulation of user behavior to see if everything works as it should, even if no users are on the system yet. That was added through Datadog's acquisition of Madumbo, as we saw earlier. Pomel about synthetics:

There is an existing category for that. It’s not necessarily super interesting on its own. It tends to be a bit commoditized and it’s a little bit high churn, but it makes sense as part of a broader platform which we offer. When you have a broader platform, the churn goes away and the fact it is commoditized can actually differentiate by having everything else on the platform.

And then Pomel adds a short but very interesting sentence:

There’s a few like that, and there’s more we’re adding.

So, you shouldn't expect the expansion of Datadog to stop anytime soon.

How Datadog makes money

In short, Datadog makes money through a SaaS model, Software-as-a-Service. That means that customers have to pay monthly. But let's look at how this works in more detail.

Datadog uses a land-and-expand model. It uses a free tier that is limited in volume. Basically, you can get the infrastructure observability for free if you have less than five servers. You will have to pay if you have more servers, as it makes no sense to not add certain servers.

Datadog pricing



This is how Datadog defines a host:

A host is any physical or virtual OS instance that you monitor with Datadog. It could be a server, VM, node (in the case of Kubernetes) or App Service Plan instance (in the case of Azure App Services).

This is what you get in the different plans:

Datadog what you get in the different plans


Datadog what you get in the different plans



It's important to know that this is just the infrastructure module. Datadog is a master in cross-selling and upselling existing customers and sells them several of these modules:

Datadog different modules


This is another example, for APM & Continuous Profiler.

Datadog APM and Continuous Profiler pricing


Other modules, like log management, are usage-based pricing:

Datadog log management usage based pricing


I won't list all the pricing possibilities for all modules here. You can go to this page if you want to see them all.

Datadog's Sales Approach

The sales approach the company takes is really aimed at developers. When I hear Olivier Pomel talk about the approach, it reminds me so much of Twilio's founder and CEO Jeff Lawson's approach to sales and doing business in general, summarized in the title of his book: "Ask Your Developer." It means that the sales strategy is bottom-up: after having convinced developers, they convince their CIO or CTO and then the big contracts are made.

For large enterprises, Datadog works a bit differently, but not that much. They first talk to the CIO and they let their teams test the software (with the free tier) to get feedback. After a certain time, Datadog comes back and it often results in an order form that is being signed.

Olivier Pomel about this approach:

Small company or large company – the product is adopted the same way. The users in the end are very similar. When you’re a developer at a very large enterprise you don’t think of yourself differently as a developer at a start-up or smaller company. There’s more and more communities between those.

There are four types of sales teams in Datadog. The enterprise sales team obviously sells to large companies, the customer success sales team takes care of the onboarding and cross-selling to existing customers. The partner team works with reseller, referral partners, system integrators and other outside sellers. The inside sales team is the team that focusses on bringing in new customers.

As you may guess, there is a lot of training for the salespeople, so they stay on top of their industry. They also have to translate a customer's problems to one or several product offerings.

Affordability is important to Datadog. Founder and CEO Olivier Pomel:

In terms of pricing philosophy though, we had to be fair in what we wanted to achieve with the price. And the number one objective for us was to be deployed as widely as possible precisely so we could bring all those different streams of data and different teams together. I wanted to make sure we were in a situation where customers were not deploying us in one place and then forgetting the rest because it can’t afford it.

Pomel also gives an interesting insight into how the company decided on its pricing for the company's customers:

We looked at the overall share, what it would get, how much they would pay for their infrastructure, we decided which fraction of that we thought they could afford for us , then we divided that by the salary and infrastructure so we could actually get a price that scales.

Now the most important thing about pricing as we’ve been scaling it – and customers send us more and more data – is to make sure that customers have the control and they can align what they pay with the value of what they get.

This customer-centricity of the pricing model is an important point of differentiation.

The Earnings: What I Pay Attention To

Datadog is to report its earnings on Thursday. Very important to me is revenue growth. The consensus estimate for revenue is $381.28M, up 63.25% YoY. But Datadog has beaten the consensus in every single quarter since it became a public company.

Datadog revenue beats

Seeking Alpha Premium

The consensus for EPS (on an adjusted base) stands at $0.15 but in the previous quarter, Datadog blew away the estimates too. The consensus was $0.13 but the company brought in $0.24.

When you look at free-cash flow margins, you see that Datadog is very profitable. This is revenue and free cash flow.

DDOG Free Cash Flow data by YCharts

$335.95M on total revenue of $1.193B means there is an FCF margin of 28%. But that still improves. In the previous quarter, Q1 2022, the company had an FCF margin of almost 36%, very impressive. Especially if you look at how little the company invests in sales, compared to other high-growth companies. And SG&A (sales, general and administrative costs) continues to go down as a percentage of revenue, despite the very high revenue growth of what could be 70%.

Datadog SG&A going down

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Of course, with a forward PS of 20 and a forward PE of 134, the stock is not cheap. But if you are a long-term investor, and for me that means at least three years, preferably longer, I think Datadog is a very exciting stock to own and well worth the premium, especially if you look at the high free cash flow. Let's see if the company keeps executing when it announces its the earnings on Thursday.

Some of you might wonder if they should buy before earnings. I'm not a market timer but I invest for the long term. Every two weeks, I add money to my portfolio and I often scale into positions over years. So, investing for me is a constant process, not a point in time. For me, the best situation is that Datadog has great earnings but the stock drops anyway for a small detail that doesn't really matter. In that case, I would definitely add a bit more than usual.

In the meantime, keep growing!

Mon, 01 Aug 2022 08:28:00 -0500 en text/html
Killexams : Salesforce targets UK public sector with MoU

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Killexams : Sizewell C gets green light in boost for Britain’s nuclear power push No result found, try new keyword!Kwasi Kwarteng has overruled officials and granted planning permission for the Sizewell C nuclear power plant despite concerns it will reduce water supplies available for households. Wed, 20 Jul 2022 05:09:49 -0500 en-gb text/html Killexams : Literacy Act implementation test scores show improvement; nearly 12,000 students still falling behind

HUNTSVILLE, Ala. (WAFF) - If an Alabama law were fully implemented, 12,000 students across the state wouldn’t be moving on to the next grade. They’d be held back.

Last year, a portion of The Alabama Literacy Act went into effect it was created to help Strengthen practicing in Alabama public schools. It was also created to ensure students are practicing on grade level by the end of the 3rd grade.

After one year, there has been only a small improvement in test scores. There is still a long way to go.

“Without that skill at the end of the third grade, they are four times more likely not to complete high school,” said Senior Research Associate for Public Affairs Research Council of Alabama Thomas Spencer.

Spencer says the 2022 Alabama Comprehensive Assessment Program test scores show that 22 percent of third graders are not practicing at a proficient level.

During the 2021 school year, Alabama implemented the Literacy Act curriculum to sharpen the focus on early grades reading.

“Particularly, students with learning disabilities and also students from economically disadvantaged backgrounds end to not come into school with quite the level of preparation and exposure to literature and practicing that other kids get,” Spencer said.

According to the test scores, Wilcox County had the lowest test scores, with 58% of third graders falling behind, and the highest test scores were from Mountain Brook City, with just three percent.

“Parents, teachers, and communities need to work together and identify those students who are struggling in practicing and wrap the services around them as early as kindergarten,” Spencer said.

Originally, part of the act was to hold back any 3rd-grade student, not at a proficient practicing level, but that portion of the act has been delayed until the 2023-24 school year.

You can find a link to the full study here.

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Mon, 25 Jul 2022 19:12:00 -0500 en text/html
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