devopsy.kinsta.cloud

Category: Enterprise DevOps

  • Debunking 6 Stubborn Public Cloud Myths

    Debunking 6 Stubborn Public Cloud Myths

    A lot of enterprises migrate to the public cloud because they see everyone else doing it. And while you should stay up on the latest and greatest innovations—which often happen in the cloud—you need to be aware of the realities and understand different migration strategies. You need to know why you’re moving to the cloud. What’s your goal? And what outcomes are you seeking? Make sure you know what you’re getting your enterprise into before moving forward in your journey. Here are six stubborn myths that persist about the public cloud and the realities you’re facing.

    1. Cloud Technology is not a Project, it’s a Constant

    Be aware that while there is a starting point to becoming more cloud-native—the migration—there is no stopping point. The migration occurs, but the transformation, development, innovation and optimization is never over.

    There are endless applications and tools to consider; your organization will evolve over time, technology changes regularly and user preferences change even faster. Fueled by your new operating system, cloud computing puts you into continuous motion. While continuous motion is positive for outcomes, you need to be ready to ride the wave regardless of where it goes. Once you get on, success requires that you stay there.

    2. Flex-Agility is Necessary to Survival

    Flexibility + agility = flex-agility, and you need it in the cloud. Flex-agility enables enterprises to adapt to the risks and unknowns occurring in the world. The pandemic continues to highlight the need for flex-agility in business. Organizations further along in their cloud journeys were able to quickly establish remote workforces, adjust customer interactions, communicate completely and effectively and, ultimately, continue running. While the pandemic was unprecedented, more commonly, flex-agility is necessary in natural disasters like floods, hurricanes and tornadoes; after a ransomware or phishing attack; or when an employee’s device is lost, stolen or destroyed.

    3. You Still Have to Move Faster Than the Competition

    Gaining or maintaining your competitive edge in the cloud has a lot to do with speed. Whether it’s the dog-eat-dog nature of your industry, macroeconomics or a political environment, these are the things that speed up innovation. You might not have any control over these factors, but they’re shaping the way consumers interact with brands. Again, when you think about how digital transformation evolved during the pandemic, you saw winning businesses move the fastest. The cloud is an amazing opportunity to meet all the demands of your environment but if you’re not looking forward, forecasting trends and moving faster than the competition, you could fall behind.

    4. People are Riskier than Technology

    In many ways, technology is the easiest part of an enterprise cloud strategy. It’s people that introduce much of the risk involved. You may have a great strategy with clean processes and tactics, but if the execution is poor, the business can’t succeed. A recent survey revealed that 85% of organizations report deficits in cloud expertise, with the top three areas being cloud platforms, cloud-native engineering and security. While business owners acknowledge the importance of these skills, they’re still struggling to attract the caliber of talent necessary.

    In addition to partnering with cloud service experts to ensure a capable team, organizations are also reinventing their technical culture to work more like a startup. This can incentivize the cloud-capable with hybrid work environments, an emphasis on collaboration, use of the agile framework and fostering innovation.

    5. Cost-Savings is Not the Best Reason to Migrate

    Buy-in from executives is key for any enterprise transitioning to the cloud. Budget and resources are necessary to continue moving forward, but the business value of a cloud transformation isn’t entirely about cost savings. Really, it’s about repurposing dollars to achieve other things. At the end of the day, companies are focused on getting customers, keeping customers and growing customers, and that’s what the cloud helps to support.

    By innovating products and services in a cloud environment, an organization is able to give customers new experiences, sell them new things and delight them with helpful customer service and a solid user experience. The cloud isn’t a cost center, it’s a business enabler, and that’s what leadership needs to hear.

    6. Cloud Migration Isn’t Always the Right Answer

    Many enterprises believe that the process of moving to the cloud will solve all of their problems. Unfortunately, the cloud is just the most popular technology operating system platform available today. Sure, it can help you reach your goals with easy-to-use functionality, automated tools and modern business solutions, but it takes effort to use and apply those resources for success.

    For most organizations, moving to the cloud is the right answer, but it could be the wrong time. The organization might not know how it wants to use cloud functionality. Maybe outcomes haven’t been identified yet, the business strategy doesn’t have buy-in from leadership or technicians aren’t aware of the potential opportunities. Another issue stalling migration is internal expertise (or lack thereof). If your technicians aren’t cloud-savvy enough to handle all the moving parts, bring on a collaborative cloud advisor to ensure success.

  • Developer Familiarity is Key to Digital Success

    Developer Familiarity is Key to Digital Success

    For some time, the goal of digital transformation was to replace legacy systems. It became a battle between all that was new and shiny and that which was old, outdated and “had to go.” However, this philosophy has since shifted as companies realized two crucial facts.

    First, legacy systems are often too important to be phased out entirely for traditional organizations—given SQL’s flexibility and familiarity among developers, for example. As a result, many of these legacy applications and systems are instead being converted into virtual versions of their former selves.

    Also, while it’s not impossible for some organizations to reinvent themselves as greenfield businesses that rely entirely on new, cutting-edge technologies, that is a route that is usually resource-intensive and time-consuming for many companies, at least for the foreseeable future.

    Second, tossing out legacy systems disrupts existing skills—and not in a good way—especially given that 64% of digital architects report being locked into using them because they have invested heavily in the relevant skills. Furthermore, overtly focusing on new talent excludes many IT professionals with well-honed skills and considerable knowledge about business processes and systems relationships. And, at the same time, digital skills are expensive—not just to acquire but also to upskill. Even startups—which often have a literal “system blank check”—are facing skills constraints, and many are considering bringing more established skills into emerging tech environments.

    The Developer Skills Problem

    Digital transformation is now in the spotlight thanks to the COVID-19 pandemic, and project managers have consequently accrued valuable experience on what makes transformation projects successful. Recent surveys conducted by 451 Research revealed that 44% of companies are well into the execution stages of their transformation strategies and of that group, 66% are starting to see success. Another 10% declared their transformation projects to be a complete success.

    Archimedes said he could move the planet with a sufficiently large lever. For digital transformation goals, skills are that tectonic lever. According to a report from WorldSkills UK, 60% of businesses believe that their reliance on advanced digital skills will increase. Pressure relating to skills requirements is not going anywhere, and the competition between legacy and new technologies only compounds it further.

    As you might expect, developers are caught in the middle. As the world digitizes—witness the acceleration of digital projects over the past two years—developers have emerged as the heroes. Perhaps unsurprisingly, 92% of respondents to a Couchbase survey agreed with that statement. Around 63% also revealed that a proper development culture allowed them the flexibility to change their goals pertaining to digital transformation outcomes.

    Yet whenever digital projects get stuck, developers are often blamed for the delays. According to the same research, at least one-fifth of developers do not have the skills or access to technology that they need. Many also aren’t given the support or space to deliver and an astounding 49% say they are being asked to do too much in too little time.

    Digital transformation projects have been increasingly successful, and we can thank developers for much of that success. But those successes haven’t alleviated the pressures or problems surrounding skills, and many projects consequently still get stuck. Furthermore, a recent report by Gartner found that IT executives are struggling to source skills that enable cloud and edge adoption.

    Legacy is Not the Enemy

    If we combine these points (the need for continual support for legacy, emerging modernization demands, increasing developer skills shortages and the fate of established technology experts), they appear to be at odds with each other. Something must give, right?

    Not necessarily. There is growing acknowledgment that orphaning legacy skills is a mistake. Doing so can cannibalize skill sets and business knowledge that should be retained. There’s also the realization that we don’t need to juxtapose legacy and newer technologies. Modern systems can incorporate and even absorb legacy environments, coding languages and processes.

    Modern multi-model databases provide a good example. At a high level, we can group databases into relational (SQL) and non-relational (NoSQL) systems. SQL databases cannot provide the agility NoSQL systems are known for, yet it’s a technology approach that still delivers many benefits. Multi-model database systems, where a SQL developer can use SQL coding on a NoSQL system, accomplish both.

    A New Place for Established Developer Skills

    Creating this familiarity is essential for successful digital projects. Focusing exclusively on broad reskilling or continually hunting for new talent aren’t the only ways to meet the demands required by modern enterprises. Instead of asking if developers can go to the mountain, vendors should ask: Can we bring the mountain to the developers?

    Deploying new technologies that can leverage existing developer skills is becoming increasingly popular. Besides certain databases, we also see this with mainframes connected to web services via APIs and using microservices to accentuate specific parts of legacy environments.

    Consider Lehman’s famous laws of software evolution, among which are familiarity and organizational stability. Most modernization projects ignore these two laws in favor of brute force progress. But digital modernization is a journey, and every successful journey needs all hands on deck—not dead weight and stowaways. Providing developers that which they already know best in a new environment is the key to digital success.

  • Microsoft Expands Azure DevOps Portfolio

    Microsoft Expands Azure DevOps Portfolio

    Microsoft has made available a bevy of updates to its DevOps portfolio that, collectively, are intended to increase its share of a wave of applications that will be rolled out across the extended enterprise.

    Additions to the Microsoft DevOps portfolio were announced at the Microsoft Ignite 2021 conference and include DevOps Workflow Generator, a free tool designed to help DevOps teams visualize their toolchain, and updates to Azure Automanage, a framework for automating the management of virtual machines on Windows Server and, in preview, on Linux.

    At the same time, Microsoft has added support for open source OpenTelemetry agent software to Azure Monitor and made available a preview of Azure Chaos Studio, a service for testing application resiliency.

    Microsoft is also making available a preview of Windows Server 2022 that adds secure-code capabilities for workloads deployed on the platform. Microsoft also previewed updates to backed databases such as SQL Server 2022, Cosmos and its implementation of the Redis database created by Redis Labs.

    Finally, Microsoft has also made available a preview of integration between GitHub Actions for automating software development tasks and Azure Active Directory (Azure AD) using Open ID Connect, an authentication protocol advanced by the OpenID Consortium.

    Microsoft CEO Satya Nadella told conference attendees the next 10 years will witness more digitization than the previous four decades as IT shifts from an era defined by mobile and cloud computing to one defined by ubiquitous computing and ambient intelligence.

    Applications built in everything from procedural code to the Microsoft Power Fx, a low-code tool that Microsoft created using the programming language it originally built for users of the Microsoft Excel spreadsheet. Microsoft envisions those applications will primarily be built on its cloud and distributed everywhere. On the backend, a range of database and artificial intelligence (AI) services will be invoked primarily via application programming interfaces (APIs).

    Of course, every major cloud service provider has similar ambitions. Most organizations today are not standardizing on a single cloud. Rather, they are determining which class of workloads run best on which cloud computing platform regardless of whether that approach increases the total cost of IT. Each additional platform adopted typically requires IT teams to master separate tools. However, Microsoft is now also making a case for a cloud-native framework that can be deployed anywhere to build, deploy and manage applications.

    In the meantime, the number of workloads deployed on cloud services continues to grow. The market research firm Canalys reported worldwide spending on cloud infrastructure services reached $49.4 billion in the third quarter of 2021, a 35% increase. Microsoft currently trails Amazon Web Services (AWS) in overall market share but continues to steadily make gains. Microsoft and AWS collectively account for roughly half of all cloud infrastructure service consumption.

    DevOps best practices will need to evolve to meet the challenges of multi-cloud computing. In addition to cloud platforms and on-premises IT environments, applications are now starting to be deployed at the edge in greater numbers. DevOps teams need to be able to continuously deploy applications and their associated updates across a wide range of platforms. The challenge, of course, is that all those platforms have unique attributes that make truly automating the entire process on an end-to-end basis an elusive goal.

  • How Retailers Can Prosper in the Age of eBay and Prime

    How Retailers Can Prosper in the Age of eBay and Prime

    It feels like a long time since the beginning of the pandemic, where once malls and city centers were alive with the footfall of customers and any service could be found across a counter. The landscape of consumer services has now changed irreparably. Online marketplaces, which have increasingly dominated much of consumer purchasing habits since the 2000s, became increasingly monopolistic. Amazon, eBay and other e-commerce platforms are now a one-stop shop for pretty much everything—made possible by the creation of complex global supply chains and unmatched economies of scale, in combination with nationwide distribution centers and an army of delivery drivers. 

    But these tech-enabled big retailers cannot offer everything to a customer. In many cases, they lack the expertise that smaller, independent vendors can offer—and this is where an advantage can be found. 

    A USP for Specialists on Par With UPS

    Amazon, as large as they are, cannot offer someone to, say, fix your car or mow your lawn and eBay cannot offer a service to unblock your sink or walk your dog. These tasks require skills and services that are still vital in the post-internet age. On my last getaway, I experienced this when renting sports equipment from a waterfront shopkeeper at a remote beach resort. His experience and know-how was first-class. So, the challenge these specialists face is how to offer bespoke services to customers in a convenient, substantial and scalable way. 

    Technology Takes Services to Customers Like Never Before

    And now, prompted by the effects of the pandemic lockdown, an increasing number of retailers are realizing the benefits of augmenting their physical estate with services that bring the shop to the customer’s front door. This is where the rise of mobile retail services has taken hold, providing pre-booked services with minimal contact and disruption to the customer. There are multiple benefits to the customer: Professional services delivered at their convenience, backed up by digital communications to keep them looped in on slot times, stock levels and arrival expectations. 

    But Mobile Services Need to be Able to Service!

    From a retailer’s perspective, a move to mobile services can be as simple as procuring a fleet of vehicles and putting their experts on the road. But on the back end, they must ensure their software infrastructure is up to the challenge. When slots are promised and services are often vital for the customer, missing an appointment is not an option. 

    This means making sure they have a software system to match the end-to-end mobile customer journey and expectations. That means taking data and putting this journey together, from offering available slots online through to order processing, stocking vans, route optimization and last-mile delivery. 

    With a robust software system in place, scale and efficiencies can be quickly realized.

    Stores Need a Digital Makeover to Ensure Service Delivery

    Back in the physical store, manual spreadsheets and creaking retail platforms for ePOS, stock management and appointment bookings cannot provide the level of consistent service and support required by both employees and customers in today’s ultra-competitive retail environment.

    Retailers must augment colleagues’ personal expertise with digital means to ensure a more consistent and customer-centric shopping experience. Tablets and mobile devices should be on the shop floor to allow employees to quickly answer customer queries, check stock levels and even execute a purchase. 

    Even when employees are carrying out services on a customer’s equipment, they can use tablets to photograph or film work done and deliver this straight to the customer to ensure the highest possible standards of quality and safety. 

    This assisted selling model makes sure employees across the retail business, regardless of their location or skill level, have access to the information they need to ensure a customer-centric interaction.

    Adopting Online Innovation to Satisfy Customer Needs

    A recent PwC report underlines the vital role technology will play in driving a more customer-centric retail future as consumer expectations continue to rise. The report found that speed, convenience, knowledgeable help and friendly service are prioritized by consumers. These qualities were highlighted by nearly 80% of all survey respondents as being the most important elements to ensure a positive customer experience.  

    The report states: “Those who get it right prioritize technologies that foster or provide these benefits over adopting technology for the sake of being cutting edge.” It continues: “[C]ustomers expect technology to always work (and are unlikely to take note of new technology unless it malfunctions or interrupts the seamless, friendly experience). They want the design of websites and mobile apps to be elegant and user-friendly; they want automation to ease experience. But these advances are not meaningful if speed, convenience and the right information at the right time are lacking.”

    Retailers Can Keep Their Digital Hands on the Customer

    In a volatile post-pandemic world where more transactions will become digitized, specialist retailers still have an exciting opportunity to interact with a customer base that no longer frequents their stores on a daily basis. They can tap into ever-expanding technology to not only retain customers but truly grow and prosper in ways that were previously not feasible. To do this, they need to shed traditional practices and embrace the new dawn of the customer-focused digital age—in a way that Amazon and eBay can’t!

  • Driving Software Delivery Automation With AI/ML

    Driving Software Delivery Automation With AI/ML

    What if we could use an artificial intelligence (AI) engine to predict the next lines of software code to build? What if machine learning (ML) could trigger the creation of automated test cases to validate our solutions?

    Automation has been a focus for the information and communications technology (ICT) industry for years, and there are some lessons that could apply to the software developer community, too, as well as other verticals. Today, more streamlined execution of operational and software delivery processes, such as continuous integration, continuous delivery and continuous deployment (CI/CD) in app development, continue to drive new levels of cost reductions, allowing products to get to market and update faster. 

    However, with the increased amount of data comes more complex ecosystems and the need for the next level of automation. Hyperautomation, or the use of AI/ML to further streamline processes, could build on these CI/CD processes, revolutionize the way multiple industries deliver software and optimize operational processes.

    Today’s Network Complexities and Challenges

    In the telecommunications industry, disruptions from hyperscalers and other innovative players, as well as increasing levels of investment in 5G technology, are forcing the hand of communications service providers (CSPs). This is causing them to turn to automation to improve their levels of operational efficiency and innovation to keep up with the need to push frequent updates. 

    However, challenges remain in the form of:

    • Total cost of ownership and operating costs: With more frequent needs for upgrading existing networks, additional levels of automation including AI/ML and hyperautomation are required to manage and maintain today’s complex networks. 
    • Time to market: In an increasingly competitive market, the CSP’s ability to react to market needs and/or new marketing ideas and to rapidly launch those needs and ideas to their end users and enterprise customers is a key competitive differentiator. 
    • Customer experience: Digitalizing business processes, providing an increased level of self-service and seeking ways to optimize customers’ overall journey remains an important need.
    • Employee productivity: In an effort to increase revenue, more efficiently drive their networks and improve end user customer experience, CSPs also need to increase employee productivity. They can do this by engaging their employees to solve bigger problems. As a result, the simplest and most repetitive low-level tasks can be automated.

    The Power of AI and ML

    The automation journey started with breaking down complex processes into smaller parts and automating discrete parts using scripts, runbook automation (RBA) and/or robotic process automation (RPA). 

    With the introduction of AI emulating human intelligence through mechanical and computational processes and ML using algorithms that learn from data to make predictions and decisions, CSPs can now bring automation and hyperautomation to the next level. 

    The potential use cases for the delivery and maintenance of telco software solutions are unlimited, and many of these lessons can be applied to other sectors. Some examples include:

    Autonomous Fault and Anomaly Detection for Cloud-Native Solutions

    Today’s network solutions are deployed on containers and microservices. The identification of issues and assurance of service quality becomes increasingly challenging with thousands of workloads running in parallel. Container failure can cause severe service disruptions. In fact, a single service failure or abnormal behavior could have a butterfly effect on the overall system and negatively affect customer satisfaction. 

    A proactive, AI-based monitoring solution can continuously search for anomalous behavior in the environment. Different container metrics and logs can automatically analyze and detect problems proactively and help find the right corrective actions for the application workflows. This is based on identifying anomalies in the container orchestration metrics related to pods, workloads and namespace. These techniques enable a more reliable and predictable application runtime environment with higher service quality and faster identification and resolution of system faults and, ultimately, a better overall customer experience

    Machine Learning-Triggered Closed-Loop Automation

    Large clusters of compute, storage and networking resources are managed throughout a data center to enable the deployment of custom workloads. Due to the inherent nature of applications running on the virtualized and cloud infrastructure, there are usage highs and lows which need constant infrastructure component deployment or reconfiguration of the cluster to ensure cost-efficient service availability. 

    A machine learning-based solution can detect anomalies in the infrastructure. Specifically, with closed-loop automation, enterprises can bring in a layer of intelligence over the current setup to monitor application performance and infrastructure metrics and identify anomalies that require immediate system attention. If those are found, they can trigger the automated addition of capacity to the infrastructure. When anomalies have subsided and once the utilization is capable of being handled by the original capacity, the solution can consistently monitor metrics to roll back the virtual or cloud-native functions. This allows for an efficient and fully automated use of network capacity, minimizing human oversight.

    Smart Code Recommendations

    One of the most clear-cut ways tools can reduce the effort of software development for programmers is through code completion. AI-powered code completion is a convenient way to access descriptions of functions and their parameter lists. These solutions also speed up software development by reducing keyboard input and the necessity of name memorization. They also allow users to refer less frequently to external documentation, as interactive documentation appears dynamically in the form of tooltips. AI-powered code completion can also use an automatically generated in-memory database of classes, variable names and other constructs or references based on machine learning to open code repositories.

    AI-Driven Test Recommendation

    Significant time is spent understanding solution requirements and identifying critical scenarios for testing. This is usually followed by a manual test plan creation and execution. To solve this, AI/ML techniques can be applied to identify what tests will need to be done during each cycle of the software development stage, including automating the analysis of required documentation. This can be used to increase overall test-cycle time and improve productivity. 

    AI-Enabled Test Automation

    API testing is critical for validating application logic and needs to go beyond the GUI layer to validate the core of an application. This implies complexity because the protocols are usually difficult to understand, the API inventory is usually large and sequencing of the API calls is critical to a successful test specification and execution. With AI/ML and natural language processing techniques, the analysis of the solution test plan can be automated to obtain recommendations on what test frameworks to utilize, identify and suggest the parameters for each test case, identify the API call sequence and automate the development of test cases based on reusable functions. This can increase overall quality of service, simplify change management, improve the productivity of the testing team and offer more reliable test cases.

    Test Failure Analytics

    During execution of automated test cases, multiple failures can be observed in various logs. Manually analyzing the logs and correlating them with the application issues can be time-consuming. Also, the identification of unreliable tests impacting system stability is complex. 

    AI/ML techniques such as vectorization, K-means clustering or topic modeling can be applied to automate the collection of test logs across applications and label failures including the type of cluster failed tests. The test subject matter expert (SME) is provided with a failure analysis report summarizing issues and problems automatically from many logs across applications, simplifying the identification of issues in test automation areas or scripts. These techniques can significantly increase test productivity and overall troubleshooting time.

    Accelerated Root Cause Identification and Analysis (RCI&A)

    When a problem occurs in a telco network, it tends to cause a ripple effect, affecting other integrated services and creating an alert storm. In such scenarios, root cause identification can become complex and cumbersome. Event aggregation and synchronization are needed to reduce environment complexity and variance. 

    Machine learning weighted analysis techniques can be used to determine the influence individual IT metrics have in affecting the overall health and performance of hosted applications and services so that pinpointing the root cause of any service impact in real-time can become easier for the team operating the network. Efficient AI/ML techniques for accelerated RCI&A can lead to significant timeline reductions. 

    AI/ML Driving Operational Innovation and Market Agility

    In telco, there is great potential for AI/ML-driven use cases to further improve the cloud-native journey and enable an IT-like software life cycle management process for the solutions of the future.

    More broadly, highly automated and self-healing networks will drive higher levels of efficiency and customer satisfaction. This includes faster testing with less resources; more efficient utilization of network resources and cloud infrastructure; faster time-to-market with more software releases per year with similar capacity as well as an increased quality for faster and more agile monetization of the CSPs capabilities.

  • Why DataOps is Critical for Digital Transformation

    Why DataOps is Critical for Digital Transformation

    Data lies at the heart of all digital transformation. In fact, today, as organizations look to further accelerate their ongoing digital transformation initiatives, companies are looking to extract even more value from their data, to better drive corporate decision-making, help grow the business, improve customer satisfaction and gain a competitive advantage. 

    Inspired by DevOps, DataOps, which Gartner defines as “a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization”, has grown in popularity in the past decade as organizations seek to accelerate the delivery of business value from their data. But taken even one step further, enhanced data operations (i.e. more than DataOps) can help organizations maximize the business value of the data they own and also the infrastructure in which the data resides to increase data quality, reliability and availability in a hybrid cloud environment. This enables organizations to further improve business resilience and digitalization through collaborative, data-centric operations in an increasingly complex, interconnected world. Particularly as hybrid and multi-cloud environments proliferate and it becomes harder for teams to understand, analyze and streamline data usage.  

    DataOps Enables Digitalization

    By 2023, IDC predicts that 52% of worldwide GDP will come from products and services delivered by digitally-transformed enterprises—and according to Gartner, 91% of enterprises are already involved in some sort of digital initiative. This means that organizations already engaged in digitization are paving the way for a new future of business—commerce, production and innovation. But digitization is only half the battle—the other half (i.e., digitalization) comes from investing in understanding those digitally-driven insights and using those learnings to fuel business growth. 

    In the face of stifling market competition and evolving customer demands, the key differentiator in market leadership today is data and operationalizing that data. Hence, the difference between the market leaders and followers boils down to this: Data operations. Data operations not only plays a vital role in empowering ongoing digital transformation initiatives (think hybrid cloud operations, DevOps optimization and application modernization; data is at the core of all of these efforts) but also serves as a foundation for faster, better-informed innovation—fueling organizational growth and propelling businesses ahead of their competition.

    Who is Driving Digitalization?

    To gauge where your organization stands on your digitalization or digital transformation roadmap, you must first identify who within your organization is driving it and who’s involved. The latter is an easy enough guess—because, essentially, it’s everyone. 

    Although digital transformation is defined as the transformation of the digital operations of an organization, it reaches far beyond IT to all major functional departments and requires a cultural change to reach its fullest potential. That includes everyone from the chief information officer (CIO) and chief data officer (CDO)—collaborating with key stakeholders to map out and define digital transformation strategies and efforts and communicating goals and objectives—to the technical and professional teams—working closely with business units to design and operationalize new architectures and technologies to advance digital transformation initiatives. At the core of all of these modernization efforts is data; the metric for business success in the digital age. As such, integrating DataOps into your digital transformation efforts from the start is mission-critical. 

    As the market becomes increasingly competitive and business leaders look for new ways to grow and expand their business, data will remain a key catalyst in achieving those lofty digital transformation goals. By gaining intelligence from their data, companies can realize the ultimate goal of data empowerment—where all employees have simplified, secure levels of access to the high-quality data they need to do their jobs—enhancing operational efficiency, improving business outcomes and fueling business transformation at scale. 

  • The Power and Potential of Stored Procedures

    The Power and Potential of Stored Procedures

    One way of saving time when writing code is with stored procedures; prepared SQL code that you can save so the code can be reused over and over again. Stored procedures are a powerful vehicle for speeding up your code and giving you gains in performance. 

    Stored procedures have many benefits—they can be easily modified, are reusable and they possess the ability to automate a task that requires multiple SQL statements. Another advantage of stored procedures is the ability to access or modify data in a database.

    Before incorporating stored procedures, it’s important to know exactly what a stored procedure is, how it differentiates itself, how to create a stored procedure and how to use them in embedded analytics.

    Performing Tasks With Stored Procedures

    A stored procedure is defined as a set of SQL statements and other PL/SQL constructs that are stored in a relational database management system as a group that you can save and reuse. A stored procedure can be comprised of multiple SQL statements like SELECT, INSERT, UPDATE or DELETE. These statements run as a unit and are used to solve a specific problem or perform a set of related tasks.

    Stored procedures are among the most frequently used and most popular database subjects. Using stored procedures can help simplify and speed up the execution of SQL queries.

    Stored procedures can reduce network traffic between servers and clients, for example. This is a result of the commands being executed as a single batch of code rather than multiple. Rather than every single line of code being sent for execution individually, only the call to execute the stored procedure is sent to the server.

    The other benefits of using stored procedures include maintainability, performance and efficiency and complex business logic. And maintaining one stored procedure is easier on the server than maintaining copies on multiple machines. Procedure scripts are stored in one location and they can be freely used in other applications, too. In the event of a definition change, only the stored procedure will be affected—not the application that calls it.

    Advantages of Stored Procedures

    Once you compile a stored procedure, it can then be used many times. This can save a lot of time writing, especially if you are doing ad-hoc queries. This also gives you a slight performance edge on the first execution of a stored procedure over the first execution of an ad-hoc query.

    A stored procedure is ultimately a function call on a database. You may include IF statements, LOOPS, security checks and more to ensure the correct data is being sent back to the call of the stored procedure.

    Another advantage of stored procedures is that they are easily modified. Simply change one or more statements in a stored procedure with the ALTER PROCEDURE command and it quickly updates the results sent back to the calling app. Unlike a middle-tier component which includes business logic that would need a potentially costly re-deployment, a quick change to a stored procedure takes seconds.

    Another benefit of using stored procedures is the security it provides to its data source. The security of an application can be enhanced when using stored procedures by restricting unauthorized access. You may grant someone access to a procedure that updates the database table without allowing access to the table itself. You may also use stored procedures to “hide” the underlying table names from the end user of the stored procedure. Since the caller of the stored procedure has the name of the stored procedure and not the name of the tables used in the stored procedure, they cannot directly access the tables with your data.

    Best Practices For Creating a Stored Procedure

    Creating a new stored procedure is fairly straightforward. Here are the three basic steps:

    1. Within the SQL Server Object Explorer, right-click on the Stored Procedures node under Programmability.

    2. Within the New Stored Procedure Template, modify the CREATE PROCEDURE statement to include the tables, parameters and query options to return the desired data from the stored procedure.

    3. Within the Toolbar, click the Execute button to compile and save your stored procedure.

    There are multiple tools you can use to create stored procedures. Using the interactive SQL Server Management Studio is the easiest, as it gives you an easy way to start connecting to many different types of database servers, including Azure and SQL Server Analysis Services.

    Behind the Scenes in Embedded Analytics

    More organizations are using data to predict future outcomes and stay competitive, offering their customers what they want when and where they want.

    Embedded analytics business intelligence software holds the key to unlocking the power of data. Stored procedures can be incorporated into embedded predictive models and continuous computational language code for data streaming scenarios.

    To create a new visualization on a database that supports stored procedures, first review the list of stored procedures that are available. You will treat the stored procedure you select just like any other data source. The only difference is a parameterized stored procedure, which you can use in a Filter by passing variables to the stored procedure.

    Some embedded analytics platforms, for example, offer complete programmatic control over stored procedures and parameters via code. To pass a stored procedure name, as well as any required parameters, use the ChangeVisualizationDataSourceItemAsync function. This is an example of passing a stored procedure named CustOrderHist and passing the CustomerID parameter in the Reveal platform.

    The Power of Stored Procedures

    Stored procedures are a powerful tool to query a database and return results to a calling app. They are secure, offer performance advantages and are easy to maintain and modify. Stored procedures can be a good avenue for simplifying the way you present data and deal with parameters in your application code versus using error-prone and lengthy ad-hoc queries. As you develop applications, you may want to consider incorporating useful stored procedures in your next project or visualization database.

  • Oracle Adds AI Services to Cloud Portfolio

    Oracle Adds AI Services to Cloud Portfolio

    Oracle today unfurled Oracle Cloud Infrastructure (OCI) AI services, a collection of services that make it easier for developers to use application programming interfaces (APIs) to invoke a wide range of services based on algorithms trained by Oracle on their behalf.

    Elad Ziklik, vice president of product management for AI services and data science at Oracle, said the primary goal is to offload the data engineering tasks associated with machine learning operations (MLOps) from organizations that simply want their developers to include AI capabilities within their applications without having to necessarily hire and then retain a dedicated data science team.

    However, organizations also have the option of uploading their own data into OCI AI services to retrain the models provided by Oracle, he noted.

    Ultimate, the goal is to make OCI AI services a simple extension of DevOps workflows by making it simpler to employ AI models; these models become just another software artifact that can be stored and updated in a Git repository alongside all the other artifacts managed by a DevOps team, said Ziklik. In effect, Oracle wants to make AI ‘boring’ in the sense that these capabilities can now be readily invoked by any development team, he added.

    The OCA AI services include OCI Language, a text analysis capability that enables developers to add sentiment analysis, key-phrase extraction, text classification, named entity recognition and other similar capabilities to their applications, and OCI Speech, which converts file-based audio data containing human speech into text transcriptions.

    Oracle has also included OCI Vision for image recognition and document analysis tasks, OCI Anomaly Detection to create models that flag irregularities in processes, OCI Forecasting for tracking business metrics over time and OCI Data Labeling for creating datasets to train AI models.

    These services are designed to address the most critical AI requirements of the average enterprise rather than attempting to outperform on a set of AI benchmarks that don’t reflect the requirements of business development teams, said Ziklik.

    In addition, Ziklik noted Oracle has also made it easier to address AI compliance requirements that will eventually require organizations to document precisely how their AI models work.

    At some point soon, just about every application will be invoking AI capabilities to one degree or another. That shift creates a major workflow challenge for DevOps teams. AI models are typically created by data science teams that typically don’t successfully deploy an AI model in a production environment more than a handful of times a year. Those AI models, however, need to be integrated with applications that, in many cases, are being continuously updated via DevOps workflows.

    Oracle said that goal becomes easier to achieve using models it creates versus allowing data science teams to build out a set of custom MLOps workflows on platforms that then need to be maintained by IT professionals. Instead of having to set up, for example, a feature store to track and manage AI models, the models created using OCI AI can be stored in an existing Git repository.

    Naturally, each organization will need to determine the degree to which they will converge MLOps and DevOps best practices. Oracle, however, is betting that the simplest way to achieve that goal is via AI models surfaced via a cloud service that developers can readily invoke whenever required.

  • Organized Architecture

    Organized Architecture

    I’ve mentioned IT pendulums plenty of times before, but today I’m going to use them to explore a different angle on IT: The age of containers and microservices. It’s time to ask yourself if the current architectural design of the overall product offering of IT is the best one you can afford to offer.

    Agile and the whole “developer is king” movement brought us a whole lot of independent app teams running off and doing their own thing. While the core of that movement was short-lived, we got a lot of good and bad out of it. The good included breaking the traditional “We change at glacial rates” process that some IT shops had—because a team absolutely wants the tools that will make their job easier, not the ones pre-approved by committee or whatever process a given org used. And the bad included the very things those of us who’ve seen this type of thing play out before warned about. Suddenly, we had critical apps and infrastructure relying on tools that one dev used in his hobby development. Sometimes that was good, and for sure it happened before Agile came along, but now we had an avalanche of it. Stuff that hadn’t been through security; often code/libraries/apps that no one outside the team (or the individual) knew was running in the organization. Some of that stuff is still happening.

    And then we hit true uptake of microservices. That drove us to break things up in non-traditional ways. A lot of good came of that process, but a lot of complexity did, too. Complexity shared across applications with a huge spike in API calls and operations with a spike in deployed processes and routing. Perhaps the worst thing to come out of the combination of the two is “Who cares why it failed? Just spin up another!” That movement is mostly dead (thank goodness), but it only died because of the number of high-profile “Oops” issues that it caused. It allowed attackers to go unnoticed; it caused paying customers to spread security details across the web; it caused users to suffer crashes that would have been fixed in any sane development environment … and some of those are still out there. Cases where there’s a script to restart an app or service every X hours because it crashes randomly. Not acceptable. Make all the excuses you like, but if you don’t know what’s causing the crash, you’re wrong to leave the app or service running.

    And we introduced a huge amount of complexity. Now we have double the infrastructure—one physical, one virtual (container or cloud). We have applications spread across a lot more systems (be they instances or physical systems) and often across platforms; even across data centers. We live in a very cool time, with some sweet stuff going on. But complexity is a very real problem we need to solve. One app/app portfolio may have limited complexity, but we still allow teams too much leeway in defining the environment the application lives in. We need to offer a happy medium—a world where there are options, but those options are defined, manageable, and secure.

    So we’re likely to see a resurgence in the importance of enterprise architecture teams again, with their focus being on organizing the mess we’ve made. Indeed, I know this is underway in a couple of Fortune 500 companies I stay in touch with. And I know of one organization that isn’t doing this and has all their UI functionality running through a single container … for multiple applications. That is no better architecture than each using a completely different UI toolset. So, I expect they’ll want coordinated solutions also. Just so something like that can’t happen again.

    You all are doing it, day in and day out. As I often say, just don’t let this bit drag you down. Confront it head-on, approach standardization with “Yes, where it is good; no, where it is not” as your credo. Security is the big one, but maintainability is another. Even with tools like Ansible at their disposal, managing 50 different environments is a bit much for ops if it could be done with five. And keep rocking it. Through a pandemic, meeting increased demand, giving users more and more. Thanks once again, from all of those users who don’t know to thank you.

     

  • How AI is Changing DevOps for the Better

    How AI is Changing DevOps for the Better

    Managing a DevOps environment involves a high degree of complexity. The rapid proliferation of data has made it difficult for DevOps teams to effectively onboard, ingest and unlock data and implement information to make good business decisions and deliver delightful experiences. Imagine a team poring over troves of information to identify critical factors that triggered an event—they would end up investing long hours to interpret and analyze the information.

    The future of DevOps will be AI-driven. It’s fair to say that it’s challenging for human intelligence to harness large volumes of highly complex data. Consequently, AI-powered solutions will become the number one choice for integrating and analyzing data and transforming how teams develop, deliver, deploy and manage applications.

    By 2023, 40% of DevOps teams will leverage application and infrastructure monitoring solutions with built-in AI capabilities, Gartner noted. Currently, in times of disruption, Gartner predicts the AIOps market will grow at breakneck speed, between $300 million and $500 million per year.

    Let us explore how AI and DevOps are intertwined before we explore how AI is transforming DevOps.

    DevOps and AI are Intertwined

    AI and DevOps are intertwined. Integrating AI in a DevOps environment boosts the overall performance and efficiency of an organization.

    AI and ML-powered solutions enable DevOps teams to onboard, map and integrate large volumes of customer or partner data with accuracy and speed. That is to say, business users find it easier to drive processes like data onboarding, data mapping and data integration, thus improving ease of doing business and delivering improved customer experience (CX).

    Organizations that rely on AI-powered solutions no longer need to devote long hours to custom coding and EDI mapping to analyze large volumes of data. The time saved can be used to focus on more important, innovation-driven tasks.

    AI also boosts automation, allows users to address and resolve issues in both data and applications and facilitates better cooperation among teams.

    4 Ways AI is Transforming DevOps

    The efficacy of DevOps correlates with an upward shift in AI. It can empower even non-technical business users to create connections and drive transactions at the speed of business. As a result, it brings non-technical business users to the forefront of business, thus freeing IT and developer teams to focus on more important tasks. Business users can leverage machine learning algorithms to collect data from different parts of DevOps systems and then map and integrate it in minutes through predictions.

    Here are four ways AI is transforming DevOps:

    1. Improved data access: The lack of free access to complex data streams is a challenge for DevOps teams. AI enables business users to access all the siloed data and harness its true potential for better insights delivery and decision-making. AI-powered tools can gather data from various sources for consistent and repeatable analysis.
    1. Faster data integration: AI and ML-enabled technologies enable non-technical business users to execute data mapping and data integration in minutes instead of months. As non-technical users drive operations, IT becomes free to take up governance and instead focus on more high-value tasks. Thus, not only can AI help business users integrate data faster to deliver experiences and value but also enables IT teams to drive innovation and growth.
    1. Higher implementation efficiency: With the support of AI, businesses can perform more tasks with less human intervention and minimal coding. This reduces the burden laid on IT or developer teams, enabling them to concentrate more on innovation and creativity.
    1. Better security: As security is essential to facilitate a successful software implementation, DevSecOps is a fundamental aspect of software development. Organizations must bolster their security systems to guard against an increase in threats. AI can play a vital role here. It can enhance DevSecOps and boost security by recording threats and executing ML-based anomaly detection through a central logging architecture. By combining AI and DevOps, business users can maximize performance and prevent breaches and thefts.

    Accelerating development cycles while making sure data is interpreted and analyzed properly is a challenge faced by DevOps teams today. Artificial intelligence-powered solutions are helping organizations transform every stage of DevOps building cycles without burdening IT teams or adding costs. Ergo, AI can enable users to transform the DevOps environment, deliver delightful experiences and grow revenue.