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Microsoft releases .NET 7 Preview 1

Microsoft has announced the next major milestone for the .NET platform with the release of the first preview of .NET 7. The language recently celebrated its 20th anniversary and according to Microsoft, this release “marks the first step forward towards the next 20 years of .NET. ”

This release will build off foundations laid in .NET 6, such as a unified set of base libraries, runtime, and SDK; a simplified development experience, and improved productivity for developers.

According to Microsoft, the major areas of focus for .NET 7 include: 1. Improved support for cloud native development 2. Tools that will enable developers to more easily upgrade their legacy projects 3. A simplified experience for working with containers

Features included in .NET 7 Preview 1 include annotations to APIs to support nullability, JIT compiler optimizations, new APIs, and support for hot reload scenarios. .NET MAUI will also be a part of .NET 7. The company released .NET MAUI preview 13 earlier this week and is currently in the process of supporting .NET MAUI in .NET 6. It expects to have a release candidate (RC) for that soon and will continue to ship RCs until it is ready for general availability. Once it is available in .NET 6, it will be included in .NET 7.

People on the move

n Tim Berglund has been announced as the VP of developer relations at StarTree. He will start in his new role in April and will work to deepen the company’s ties with the Apache Pinot community. He is known in the industry as a leading advocate for Apache Kafka, and started off as developer relations advisor when he first joined StarTree in September 2021. Previously he held roles at Confluent and DataStax.

n CData Software has announced two new executive appointments: Manish Patel as chief product officer and Steven Close as chief information security officer. Patel has over 15 years of product management experience and previously served as CEO of Tier1 Financial Solutions and led product management at Valassis Digital and Ipreo. Close previously was a security executive at SolarWinds, and has worked on joint efforts with the FBI, Australia Cyber Crime, and Missing Kids.

n Martha Jenson has joined Sauce Labs as its new chief people officer. In this role she will focus on scaling the company’s operations and culture. She previously established HR operations and people strategy at Ivalua, and has held roles at Facebook, Symantec, HP, VMware, and IBM, as well as having served as an Intelligence Analyst in the U.S. Air Force.

n Pepperdata has announced that Maneesh Dhir is its new chief executive officer, replacing Ash Munshi, who will stay on as executive chairman of the board of directors. Dhir spent five years as managing director of Apple’s India business, where he helped grow the company revenue to over $1 billion in India, which was a 15x increase during his time there. He’s also served as chief product strategy officer at FICO and executive vice president at AOL International.

WSO2 releases Swan Lake update to Ballerina language

The API management company WSO2 announced that its opensource programming language Ballerina’s Swan Lake release is now generally available.

Ballerina is designed specifically for developers interacting with the cloud. It aims to simplify the process of being able to use, combine, and create network services.

According to WSO2, it provides “bidirectional mapping of sequence diagrams and code. ” This allows developers to switch between working with traditional code and low-code as needed. It also abstracts away a lot of complexity in developing cloud-native applications by providing a way to represent network interactions and streamlining functions related to data usage, configurations, and cloud deployments.

Jetpack Compose 1.1 UI toolkit adds touch target sizing

The Android development team has announced the release of UI toolkit Jetpack Compose version 1.1. This release comes with several new features, such as improved focus handling, ImageVector caching, touch target sizing, and support for Android 12 stretch overscroll. Additionally, Compose 1.1 graduates several experimental APIs to stable as well as supports newer versions of Kotlin.

An image vector caching mechanism has been added to painterResource API in order to cache all instances of ImageVectors that are parsed with a certain resource id and theme. The cache will then be invalidated on configuration changes.

With touch target sizing, Material components will expand their layout space in order to meet Material accessibility guidelines. This works to align Compose Material to the same behavior of Material Design Components and brings consistency to behavior when mixing Views and Compose. This feature also works to ensure that minimum requirements for touch target accessibility will be met when creating a UI using Compose Material components.

Kong Enterprise 2.7 released with 25% improved performance

API company Kong announced the general availability of Kong Enterprise 2.7, which delivers 25% faster performance compared to previous versions, improved security, and streamlined workflows.

Kong Enterprise is a service connectivity platform that enables organizations to secure, connect and orchestrate their APIs and services across cloud native, hybrid and on-premise environments.

The new version achieved 52,250 transactions per second (TPS) maximum throughput with a 100% success rate (up from 40,625 TPS in 2021), performing 2,886% faster than Apigee X, which achieved 1,750 TPS maximum throughput with 100% success, according to Kong.

Additional features include

Google Identity Services update makes it easier to implement authentication

Last year, Google announced Google Identity Services (GIS), which is a set of APIs that consolidated several identity offerings from the company. Included in the GIS development kit are the Sign in with Google button and the authentication prompt One Tap.

Now, Google is adding an authorization feature to GIS to bolster the offerings of the SDK and make it easy for developers to implement secure authentication into their apps.

The new authentication library acts as a “one-stop-shop’ for authentication and authorization, according to the company. Authentication allows for new user sign ups and returning user sign ins, and authorization provides developers with access tokens to call Google APIs with a user’s consent.

The SDK provides clear separation between these two functions in order to provide developers with greater control. Developers can make calls as two separate flows based on app needs.

the ability to bulk-apply policies to APIs and developers automatically, an OpenID Connect Configuration wizard for faster authentication setup, and support for new real-time and event-based use cases with Kafka and webhooks.

OpenSSF announces new project for improving supply chain security

OpenSSF announced the Alpha-Omega Project to improve the security posture of open-source software by working together with software security experts.

Microsoft and Google are supporting the project, which aims to improve global OSS supply chain security by working with project maintainers to systematically look for new, as-yet-undiscovered vulnerabilities in open source code with a $5 million investment.

The project is being split into two sides, Alpha and Omega. Alpha will work with the most critical open source projects to improve their security posture. The projects will include standalone projects and core ecosystem services that will be selected based on the work by the OpenSSF Securing Critical Projects working group.

Omega will identify at least 10,000 widely deployed OSS projects where it can apply automated security analysis, scoring, and remediation guidance to their open source maintainer communities.

Android Studio update introduces new Device Manager

The Android development team has announced that the latest version of its IDE, Android Studio, is now available. Android Studio Bumblebee 2021.1.1, which is the codename for the release, improves functionality for building and deploying, profiling and inspection, and design.

One new feature for building and deploying is the new Device Manager, which makes it easier to manage virtual and test devices. This new tool has both Virtual and Physical features. Virtual features include creating a new device, reviewing device details, and deleting a device. Physical features include pairing to a new device using ADB Wi-Fi to see details and inspecting a device’s file system using Device File Explore.

In addition, the Android Gradle Plugin Upgrade Assistant, which helps developers keep their projects current with the latest version, now checks for and offers to update domainspecific languages (DSLs) to avoid developers using deprecated APIs in their apps.

Other new features related to the build and deploy theme include a simplified flow for connecting devices over Wi-Fi for deployment using ADB, the ability to run instrumented tests using Gradle, nontransitive R classes are now turned on by default, the Emulator tool window is enabled by default, and updated support for Apple Silicon.

ShiftLeft CORE gets new vulnerability identification features

Security company ShiftLeft today announced the new release of its ShiftLeft CORE platform with the Velocity Update that has new features for identifying and addressing potential vulnerabilities earlier in the software development life cycle.

New features and capabilities include the ability to perform code analysis for Kotlin apps for mobile development, which is an early-stage beta release, and Intelligent SCA for Python and Golang, which is also a beta release, that allows developers to identify attackable open-source vulnerabilities in their code.

The release also includes workflow enhancements like improved build rules that allow for automatic detection and interception of attacker reachable open-source vulnerabilities, interactive remediation that enables developers to specify custom validation for the tool to recognize in scan results, enhanced vulnerability descriptions, branch selection, and richer data flow visualizations.

Microsoft gives sneak peek at C# 11 features

In Microsoft’s Visual Studio 17.1, users will get a sneak peek at features coming to C# 11. These features will also be available in .NET SDK 6.0.200.

A feature available in early preview is parameter null checking, which verifies at runtime if a null has been passed to code. This is separate from Nullable Reference Types (NRT), which identifies at design time if a null is possible.

Another new addition to the language is the ability to allow newlines in holes in non-verbatim interpolated strings. Holes, or interpolation expressions, are contained inside curly braces and supply runtime values. Previously newlines were allowed in verbatim interpolated strings, but in non-verbatim strings escapes, like \r or \n, were required instead. z

BY GEORGE TILLMANN

A project champion is focused on the project, while a mentor is focused on the project manager

Like all U.S. presidents, Andrew Jackson had an official cabinet, confirmed by the Senate, in charge of the various government departments. However, Jackson tended to ignore the official cabinet members in favor of an informal group of advisors dubbed the “kitchen cabinet. ” Since then, many U.S. presidents have relied more on an informal and unconfirmed list of advisors for counsel and help in making decisions, than on the official cabinet secretaries. Titles can be misleading. Lobbyists soon learn that cozying up to the unofficial in-crowd is often more fruitful than courting official dignitaries.

Corporations can be similarly led. Sometimes the power rests within the chain of command. In other organizations, the CEO might rely on an unofficial team of employees of various titles and positions, perhaps not even in the chain of command. It is these insiders who make or influence corporate decisions.

What does this have to do with IT and project management? Well, put your project planning books aside. The critical decisions about your project might have been made months ago, before you were even assigned to the project, and kept private by a group of people you don ’t know, sitting around a table in some unnamed conference

George Tillmann is a retired programmer, analyst, systems and programming manager, and CIO. This article is adapted from his book, Project Management Scholia: Recognizing and Avoiding Project Management’ s Biggest Mistakes (Stockbridge Press, 2019). He can be reached at georgetillmann@gmx.com.

room you never heard of, who know little to nothing about IT.

If you are a project manager, even the best project manager in the world, it is unlikely you will be sitting with that group anytime soon. Even the CIO might be a stranger to that assemblage.

If your project is building an application to manage IT’ s bowling scores, then you can skip this article. Even the project manager of a more corporate relevant though small and not revenue significant project might be able to breeze though the following pages. However, if your project is mission critical, meaning that it plays a major role in the success of your organization, then read on, because, know it or not, your project needs senior representation.

Projects are like wolves, they are useful, but they also have people gunning for them. For whatever reasons, someone, somewhere, will be out to get your project. He might think it’ s a waste of money; poorly led, planned, or executed; not needed by the business; or better alternatives are available. Whatever the rationale, he will do all he can to bring your project to a halt. Project naysayers are not evil people, just convinced that a mistake is being made and that they have a responsibility to point it out if not correct it.

If the naysayer is a junior member of the organization, then there is probably no problem; however, if the project critic is a senior executive, then beware. Somewhere, around some bend or detour you have to take, the critic waits ready to spring on any perceived misstep or error.

When will the ambush occur? Well, there are a few fairly predictable points.

First sign of weakness. Drop the ball, or even bobble it, and you are in trouble. A slipped schedule, budget issues, a vendor not able to deliver what or when resources were promised, or staffing problems are all reasons the viability of the project could be questioned.

Wrong place, wrong time. When a project kicks off, there is considerable enthusiasm and energy aimed at the undertaking. The project team is charged, users are thinking of how things will be when the systems is in, and managers everywhere are looking to bask in the credit they will take, deserved or not. But enthusiasm will wane. It doesn ’t matter that the plan says that the project will not show tangible user results for 8 months, or that the users were told, time and again, that there is a deliverable desert between months one and seven. Sometime, after 3 or 4 months, with nothing end-user oriented to show for all the work besides bills, even the most ardent supporters experience the mid-project blues and start to second-guess their decision. Now add in the naysayers whispering “I told you so ” in their ears, and even the strongest supporters start “ re-thinking the project. ”

There are two ways to avoid the midproject blues. First, keep the drought short. Try to have some user-focused deliverables that will keep the users happy as soon and as often as possible. Never go more than 6 months before some functionality is installed, and never more than 3 months without some kind of demo.

Second, get a project champion. A project champion is a senior executive, usually from the business side of the organization, who has the respect of peers and the ears of the very top echelons. More specifically, the project champion either sits around that decision-making conference room table or routinely works with those who do. He or she knows what that body is thinking or can influence what they do. Moreover, this senior executive, who truly believes in the system and the benefits it will deliver to the company, is willing to forcefully campaign for the project.

A good project champion will keep the true believers believing and the naysayers quiet, giving the project team the time it needs/deserves to build the application and deliver the goods. Project champions are worth their weight in gold.

A good project champion can support a project in at least four ways.

Represent the project at the high-

est corporate levels. As a member of the inner circle, the champion can either advocate for the project (budget, schedules, resources, etc.) at executive decision-making meetings or sometimes make the decisions unilaterally.

Can commit corporate resources.

The champion can directly make or, as an executive conduit, influence organizational binding resource decisions.

Keep the firm focused on the

endgame. If the champion is sufficiently senior in the organization, then he or she can cut through corporate red tape, thwart naysayer interference, and clear organizational obstacles.

Mentors and champions play different roles

Mentor. A mentor is a senior and respected expert in one or more areas of the organization. He or she is knowledgeable, not just in the official procedures and processes of the organization, but also in the culture and unofficial — unwritten — rules of corporate engagement.

There should be no line responsibility between mentor and mentee. Rather the relationship is informal and advisory. The objective is not to have a buddy, but rather someone who can advise the mentee on when he or she is doing something right or when they are on the wrong track. Mentors are not a cheerleading squad — call mom if constant encouragement is needed — they are there to listen to mentee questions and provide factual and practical advice. They are not there to intervene with the mentee’s managers to “fix things. ” A good mentor will resist talking to the mentee’s boss, if at all possible, to avoid interfering in the employee-manager relationship or second guessing management decisions.

Everyone in an organization should have a mentor. Some organization’s assign an official mentor; others allow employees to pick their own. Even if there are official mentors, everyone should also have one or more unofficial mentors.

Champion. The project champion is a senior member of the organization who takes a personal interest in the project. He or she regularly attends or is at least a guest at the highest level corporate governance meetings. The champion works closely with the senior executives in the organization (and is often their peer) and can represent and advocate for the project with senior executives. The champion often has the power to influence, if not modify, budgets and project plans, and commit organizational resources. The champion can speak for the organization at project meetings and reviews. The champion is an excellent audience for the project manager to practice meetings and presentations with senior users and project oversight groups. The champion can recommend strategies and tactics to improve communication and the likelihood of favorable outcomes. z

< continued from page 7

Clear the decks for the project.

The champion is not a member of the project team but rather an important resource to allow the team to do its job. Like a snowplow on a train, the champion clears the way for those who follow behind.

Both Six Sigma (a set of process improvement techniques and rules) and the Project Management Institute (PMI) recognize the role and recommend that projects have a project champion.

However, even the best project champions need the help of a project manager. A smart project manager will become quite familiar with the project champion and solicit his or her advice in dealing with, and presenting to, senior management. The champion can then float ideas with senior executives, identifying and clearing potential objections and obstacles, before the project team recommends them.

Do not confuse a project champion with a mentor

Every project manager should have one or more mentors (official or unofficial) who can help them navigate corporate waters. Mentors provide staff with advice and insight regarding their corporate careers, focusing on individual performance and success. Mentors can help an employee with development and training choices, positioning for promotions, interacting with other staff, and more.

A mentor ’ s focus is on the project manager ’ s career. The project champion ’ s focus is the project, not the project manager.

Finding a project champion

Finding a project champion can be a challenging task. Luckily, a champion might have existed before the project even kicked off, and have played an instrumental role in its creation. However, some champions decide to take a back seat once the project is underway. This is unfortunate since the champion is often needed more after project kickoff than before. Job one for the project manager is keeping the project champion engaged during the entire project. Explain to a reticent champion the tasks and the challenges facing the project. This needs to be one of the project managers best per-

formances.

If there was no pre-project champion, then the project manager ’ s options are limited. With the support of the IT organization, meet with likely candidates and convince them to take on the task. The project manager should learn all he or she can about those high level pre-project meetings. Who spoke up for the project and its budget? Who opposed it? Meet with the candidates and ask for their help. With some luck you might be pleasantly surprised.

Project champion: a resource, not a friend

Every project manager needs to remember that the project champion represents the organization and the project and not the project manager. For example, a champion might decide that the current project manager is the wrong person to lead the project and needs to be replaced. Both the champion and the project manager need to jointly understand the champion ’ s goals and responsibilities and the potential consequences of both. z

While the amount of data in the world is infinite, our attention span is not. That’ s why AI is becoming a valuable tool for data integration to create concise analysis from data and to make it more accessible to everyone throughout an organization.

According to SnapLogic ’ s Ultimate Guide to Data Integration, AI and ML capabilities are increasingly being built into data integration platforms to significantly improve integrator productivity and time to value.

Companies are also making sure that no data slips through the cracks. They realize that they have to be more sensitive and careful with user data in the wake of large data breaches and resulting regulations that followed.

They can rely on AI and ML capabilities to identify what data should be masked or anonymized, and also discern what is useful and what isn ’t. AI is able to do this automatically to help ensure compliance with HIPAA, GDPR, and other regulations.

The process of adding AI to analyze and transform massive data sets into intelligent data insight is often referred to as data intelligence, according to an article by data analytics platform provider OmniSci.

Five components of intelligence

There are five major components of data-driven intelligence, including descriptive data, prescriptive data, diagnostic data, decisive data, and predictive data. Applying AI to these areas helps with understanding data, developing alternative knowledge, resolving issues, and analyzing historical data to predict future trends.

“AI is being used across multiple functions in data integration, but I would say it is being used most effectively in providing intelligence about data, automating the collection and curation of metadata, so that organizations can gain control over highly distributed, diverse, and dynamic modern data environments, ” said Stewart Bond, the research director of IDC’ s Data Integration and Intelligence service.

Data intelligence is effective at gathering the data from various sources, which is often necessary within a company ’ s data integration initiatives, and then it creates a uniform identity model across the data sources.

This intelligence can leverage business, technical, relational, and behavioral metadata to provide transparency of data profiles, classification, quality, location, lineage, and context.

“To take an example from our world at LinearB: to effectively integrate data from disparate dev systems such as Git or Jira, one needs to be able to map the identities such as developer usernames between these systems. That’ s a great task for some ML models. As more systems are involved, the problem gets tougher but you have more data to assist your AI/ML to solve it, ” said Yishai Beeri, the CTO at LinearB.

Organizations that are looking to infuse AI into their data integration are primarily looking at three things: how to minimize human effort, reduce complexity, and cost optimization, according to Robert Thanaraj, senior principal analyst who is part of the data management team at Gartner.

“Number one, I’ m looking at improved productivity of users, the

BY JAKUB LEWKOWICZ

technical experts, citizen developers, or business users. Secondly, if complexities are solved, it opens up for business users to carry out integration tasks almost without any support from a central IT team, or your integration specialist, such as a data engineer, ” Thanaraj said. “Lastly, ask yourself, can we get rid of any duplicated copies of data? Can we recommend an alternative source for good quality trusted data? Those are the kind of the typical benefits that enterprises are looking to prototype and then to experiment with integrating AI into data integration. ”

AI is being used to improve data quality

AI is now not only turning out to be pivotal in business use cases, but it can also quickly solve problems that have to do with data quality.

Specifically, AI is making it possible to achieve improved consistency of data and allows for improved master data management, according to Chandra Ambadipudi, senior vice president at EXL, a provider of data services.

Dan Willoughby, a principal engineer at Crowdstorage, described how his company used AI/ML to tackle data quality problems in a proactive rather than reactive fashion.

The company would continually write 15 petabytes of data to over 250,000 devices in people ’ s homes every month and AI was used for both predicting when a device would go offline and to detect malicious devices.

“Since a device could go offline at any time for any reason, our system had to detect which data was becoming endangered, ” Willoughby explained. “If it was in trouble, that data would be queued up to be repaired and placed elsewhere. The idea was that if we could predict a device would go offline soon by observing patterns of other devices we ’d stop sending data to it, so we could save on repair costs. ”

Also, since the company had no control over what people could do to their devices, they needed to have protections in place beyond encryption to see anomalies in a device ’ s behavior.

“ML is perfect for this because it can average out the “ normal” behavior and easily determine a bad actor, ” Willoughby said.

LinearB’ s Beeri said another common example of AI weeding out bad data is in detecting and ignoring Git work done by scripts and bots.

AI can address many of the common data integration challenges

The introduction of AI and ML to data integration is still a relatively new phenomenon, but companies are realizing that handling data integration tasks manually is proving especially difficult.

One of the challenges is the absence of intelligence about the data when handled manually.

According to the Data Culture Survey that IDC ran in December 2020, 50% of the respondents said they felt there was too much data available and they couldn ’t find the signal for the noise, and the other 50% said there wasn ’t enough data to help them make data-driven decisions, which is the outcome of data integration and analytics.

“If you don ’t know where the best data is related to the problem you are trying to solve, what that data means, where it came from, how clean or dirty

< continued from page 11 it is — it can be difficult to integrate and use in analytical pipelines, ” IDC’ s Bond said. “Manual methods of harvesting and maintaining intelligence about data are no longer effective. Many still use spreadsheets and Wikis and other forms of documentation that cannot be kept up to date with the speed at which data is moved, consumed, and changed. ”

As for getting started with AI and ML in data integration, companies should see if the solutions fit the requirements of their work, Bond added. And many of these industries with the greatest need for data intelligence include cybersecurity, finance, health, insurance, and law enforcement.

Companies should look at how data intelligence factors into the solution, whether it is part of the vendor ’ s platform, or whether the technology supports integration with data intelligence solutions.

“As organizations try to understand how data integration and intelligence tasks are automated, they should understand what is truly AI-driven and what is rules-driven, ” Bond said. “Rules require maintenance, AI requires training. If you have too many rules, maintenance is difficult. ”

Gartner ’ s Thanaraj recommends embarking on the data fabric design, which utilizes continuous analytics over existing, discoverable, and inferenced metadata assets. This model can support the design, deployment, and utilization of integrated and reusable data across all environments, including hybrid and multi-cloud platforms.

This method leverages both human and machine capabilities and continuously identifies and connects data from disparate applications to discover unique, business-relevant relationships between the available data points.

It uses Knowledge Graph technologies that are built on top of a solid data integration backbone. It also uses recommendation engines, orchestration of AI, and data capabilities, primarily driven with metadata.

“Metadata will be a game-changer of the future, and AI will take advantage of the metadata, ” Thanaraj said. z

How does the introduction of AI/ML affect the data engineering role

AI and ML will vastly improve the speed at which data integration is handled, but the role of data engineering is constantly in demand and even more so to work with AI in an augmented way.

AI can help in making recommendations about the best way to join multiple data sets together, the best sequence of operations on the data, or the best ways to parse data within fields and standardize output, according to IDC’s Bond.

“If we consider data quality work, people will shift from writing rules for identifying and cleansing data to training machines on whether or not anomalies that are detected are really data quality issues, or if it represents valid data, ” Bond said. “If we consider data classification efforts for governance and business context, again the person becomes the supervisor of the machine — training the machine about what are the correct associations or classifications, and what are not correct assumptions made by the machine. ”

The AI capabilities will help people working on data integration with the mundane tasks, which both frees them up to do more important work and helps them avoid burnout when dealing with data, a common problem today.

“It takes easily between 18 to 24 months before data engineers are fully productive and then in another year or so, they are burnt out because of lack of automation, ” Thanaraj said. “So one of the key things I recommend to data and analytics leaders is you should create a social structure where you’re celebrating automation. ”

Data engineers can’t do everything by themselves, and this has resulted in various roles that specialize in various aspects of handling data.

In a blog post, IDC listed these roles as data integration specialists that blend data for analytics and reporting or data architects who bridge business and technology with contextual, logical and physical data models and dictionaries. On top of that, there are data stewards, DataOps managers, and business analysts, and data scientists.

“Data engineers are our critical role for any enterprise to succeed today. And it is in the hands of data engineers, you’re going to build these automation capabilities at the end of the day, ” Thanaraj said. “The AI bots or AI engines are going to do the core repetitive scanning for filing, classifying, and standardizing all these tasks with data. ”

On top of that, you need business experts and domain experts to be validating whether the data is being used the right way and to have the final say. As a result, AI and ML are then learning from these human decisions.

“This is why humans become the number one custodians; the ones who monitor and avoid any deviation of models done by AI, ” Thanaraj said. z