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The Journey from Traditional Ops to NoOps

Cloud-Native /

The Journey from Traditional Ops to NoOps In the fast-changing software development landscape, organisations strive to improve their operational processes. Market studies project a 23.95% growth in the global DevOps market, with an estimated value of USD 56.2 Billion by 2030. This blog discusses the shift from traditional ops to NoOps, emphasising automation practices that boost software delivery’s efficiency, scalability, and resiliency. NoOps, short for “no operations,” represents a paradigm shift towards complete automation, eliminating the need for an operations team to manage the environment. This section clarifies the concept of NoOps, debunking misconceptions and emphasising the role of automation, AI/ML, and various technologies in achieving fully automated operations. NoOps represents the pinnacle of the DevOps journey, driving automation to enable developers to focus more on coding. Advancements in cloud services, containerisation, and serverless technologies converge to facilitate increasing levels of automation within the software lifecycle. However, achieving true NoOps environments requires incremental implementation based on organisational maturity. Recognising the significance of stability, reliability, and human expertise is crucial, despite the growing popularity of NoOps. According to a Deloitte survey, 92% of IT executives believe that the human element is crucial for successful automation. Rather than striving for total automation, organisations can take a practical approach by automating specific segments while retaining human involvement in vital areas. This approach acknowledges the value of human skills in monitoring, troubleshooting, and maintenance, serving as a transition towards increased automation and efficiency. Key Steps in the Transition to NoOps: Understanding Traditional Ops: Before embarking on the NoOps journey, it is essential to understand the complexities of traditional operations. Take a deep dive into the practices of manual infrastructure provisioning, deployment, monitoring, and troubleshooting commonly associated with traditional ops. Additionally, explore the limitations and challenges that come with these practices. Embracing the DevOps Culture: To successfully transition to NoOps, it is crucial to adopt the DevOps culture, which places strong emphasis on collaboration, automation, and continuous improvement. This involves exploring the principles and advantages of DevOps, as it sets the foundation for a smooth and effective transition to NoOps. Infrastructure as Code (IaC): The use of declarative configuration files in Infrastructure as Code (IaC) introduces a ground breaking transformation in the management of infrastructure. It is crucial to highlight the advantages of IaC, such as scalability, reproducibility, and version control, and acknowledge its pivotal role in enabling the concept of NoOps. IaC plays a critical role in enabling the NoOps approach, granting organisations the ability to automate the provisioning and management of infrastructure, minimise manual interventions, and attain increased efficiency and agility. Continuous Integration and Continuous Deployment (CI/CD): The automation of software delivery through CI/CD pipelines reduces the need for manual work and guarantees consistent deployments. This highlights the importance of continuous integration, automated testing, and continuous deployment in ensuring smooth transitions to production environments. Containerisation and Orchestration: Containerisation offers a compact and adaptable method for bundling applications, while orchestration platforms such as Kubernetes streamline the process of deploying, scaling, and overseeing them. Take advantage of containerisation and the significance of orchestration in facilitating seamless operations without the need for extensive manual intervention, especially in large-scale environments. Monitoring and Alerting: The presence of strong monitoring and alert systems guarantees the well-being and efficiency of applications and infrastructure. This encompasses the utilisation of tools to capture and analyse metrics, distributed traces, and logs from applications which aid in the proactive detection of problems. Self-Healing Systems: The implementation of methods such as auto-scaling, load balancing, and fault tolerance mechanisms promotes resilience by creating self-healing systems. These mechanisms enable automated handling of failures and resource scaling according to demand. Serverless Architecture: Serverless architecture platforms remove the need for managing and scaling servers, streamlining the deployment process. It examines the advantages of serverless design and how it speeds up development while minimising operational burden. Continuous Learning and Improvement: The continuous learning process of the NoOps journey highlights the significance of keeping abreast of emerging technologies and optimal approaches, while encouraging a culture of experimentation, feedback loops, and knowledge exchange. Conclusion: Transitioning from traditional ops to NoOps involves embracing automation, DevOps practices, and leveraging various technologies. The market trends and statistics highlight the growing adoption of automation practices and the significant market potential. By grasping the constraints of full automation and attaining a harmony between automation and engineering, organizations can improve software delivery, reliability, and scalability. The NoOps journey is an ongoing process of improvement and optimisation, enabling organisations to deliver software faster, more reliably, and at scale.

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How Exploratory Data Analysis (EDA) Can Improve Your Data Understanding Capability

Data & AI /

How Exploratory Data Analysis (EDA) Can Improve Your Data Understanding Capability Can EDA help to make my phone upgrade decision more precise? You may have heard the term Exploratory Data Analysis (or EDA for short) and wondered what EDA is all about. Recently, one of the Sales team members at TL Consulting Group were thinking of buying a new phone but they were overwhelmed by the many options and they needed to make a decision suited best to their work needs, i.e. Wait for the new iPhone or make an upgrade on the current Android phone. There can be no disagreement on the fact that doing so left them perplexed and with a number of questions that needed to be addressed before making a choice. What was the specification of the new phone and how was that phone better than their current mobile phone? To help enable curiosity and decision-making, they visited YouTube to view the new iPhone trailer and also learned more about the new iPhone via user ratings and reviews from YouTube and other websites. Then they came and asked us how we would approach it from a Data Analytics perspective in theory. And our response was, whatever investigating measures they had already taken before making the decision, this is nothing more but what ML Engineers/data analysts in their lingo call ‘Exploratory Data Analysis’. What is Exploratory Data Analysis? In an automated data pipeline, exploratory data analysis (EDA) entails using data visualisation and statistical tools to acquire insights and knowledge from the data as it travels through the pipeline. At each level of the pipeline, the goal is to find patterns, trends, anomalies, and potential concerns in the data. Exploratory Data Analysis Lifecycle To interpret the diagram and the iPhone scenario in mind, you can think of all brand-new iPhones as a “population” and to make its review, the reviewers will take some iPhones from the market which you can say is a “sample”. The reviewers will then experiment with that phone and will apply different mathematical calculations to define the “probability” if that phone is worth buying or not. It will also help to define all the good and bad properties of the new iPhone which is called “Inference “. Finally, all these outcomes will help potential customers to make their decision with confidence. Benefits of Exploratory Data Analysis The main idea of exploratory data analysis is “Garbage in, Perform Exploratory Data Analysis, possibly Garbage out.” By conducting EDA, it is possible to turn an almost usable dataset into a completely usable dataset. It includes: Key Steps of EDA The key steps involved in conducting EDA on an automated data pipeline are: Types of Exploratory Data Analysis EDA builds a robust understanding of the data, and issues associated with either the info or process. It’s a scientific approach to getting the story of the data. There are four main types of exploratory data analysis which are listed below: 1. Univariate Non-Graphical Let’s say you decide to purchase a new iPhone solely based on its battery size, disregarding all other considerations. You can use univariate non-graphical analysis which is the most basic type of data analysis because we only utilize one variable to gather the data. Knowing the underlying sample distribution and data and drawing conclusions about the population are the usual objectives of univariate non-graphical EDA. Additionally included in the analysis is outlier detection. The traits of population dispersal include: Spread: Spread serves as a gauge for how far away from the Centre we should search for the information values. Two relevant measurements of spread are the variance and the quality deviation. Because the variance is the root of the variance, it is defined as the mean of the squares of the individual deviations. Central tendency: Typical or middle values are related to the central tendency or position of the distribution. Statistics with names like mean, median, and sometimes mode are valuable indicators of central tendency; the mean is the most prevalent. The median may be preferred in cases of skewed distribution or when there is worry about outliers. Skewness and kurtosis: The distribution’s skewness and kurtosis are two more useful univariate characteristics. When compared to a normal distribution, kurtosis and skewness are two different measures of peakedness. 2. Multivariate Non-Graphical Think about a situation where you want to purchase a new iPhone solely based on the battery capacity and phone size. In either cross-tabulation or statistics, multivariate non-graphical EDA techniques are frequently used to illustrate the relationship between two or more variables. An expansion of tabulation known as cross-tabulation is very helpful for categorical data. By creating a two-way table with column headings that correspond to the amount of one variable and row headings that correspond to the amount of the opposing two variables, a cross-tabulation is preferred for two variables. All subjects that share an analogous pair of levels are then included in the counts. For each categorical variable and one quantitative variable, we create statistics for quantitative variables separately for every level of the specific variable then compare the statistics across the amount of categorical variable. It is possible that comparing medians is a robust version of one-way ANOVA, whereas comparing means is a quick version of ANOVA. 3. Univariate Graphical Different Univariate Graphics Imagine that you only want to know the latest iPhone’s speed based on its CPU benchmark results before you decide to purchase it. Since graphical approaches demand some level of subjective interpretation in addition to being quantitative and objective, they are utilized more frequently than non-graphical methods because they can provide a comprehensive picture of the facts. Some common sorts of univariate graphics are: Boxplots: Boxplots are excellent for displaying data on central tendency, showing reliable measures of location and spread, as well as information on symmetry and outliers, but they can be deceptive when it comes to multimodality. The type of side-by-side boxplots is among the simplest applications for boxplots. Histogram: A histogram, which can be a barplot

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Top Cloud Plays in 2023: Unlocking Innovation and Agility

Cloud-Native, Data & AI, DevSecOps /

Top Cloud Plays in 2023: Unlocking Innovation and Agility Cloud Computing has been around since the early 2000’s, while the technology landscape continues to evolve rapidly and adoption increased (20% CAGR), offering unprecedented opportunities for innovation and digital transformation. The meaning of digital transformation is also changing with cloud decision makers viewing Digital transformation as more than a “lift and shift”, instead they see vast opportunity within the Cloud ecosystems to help reinforce their long-term success. As businesses increasingly embrace cloud, certain cloud plays have emerged as key drivers of success, underpinned by companies including Microsoft, AWS, Google Cloud and VMWare who have all developed very strong technology ecosystems that have transitioned from a manual and costly Data Centre model. In this blog, we will explore the top cloud plays, from our perspective, that organisations should consider unlocking to reach their full potential in 2023. Multi-Cloud and Hybrid Cloud Strategies Multi-Cloud and Hybrid Cloud Strategies: Multi-cloud and hybrid cloud strategies have gained significant traction in 2023. Organisations are leveraging multiple cloud providers and combining public and private cloud environments to achieve greater flexibility, scalability, and resilience through their investment. Multi-cloud and hybrid cloud approaches allow businesses to choose the best services from different providers while maintaining control over critical data and applications. This strategy helps mitigate vendor lock-in leveraging Kubernetes Container orchestration, including AKS, EKS & GKE and VMWare Tanzu, optimise costs, and tailor cloud deployments to specific business requirements and use cases. Cloud-Native Application Development Cloud-Native Application Development: Cloud-native application development is a transformative cloud play that enables organisations to build and deploy applications, through optimised DevSecOps practices, specifically designed for advanced cloud environments. This model leverages containerization, CICD, microservices architecture, and orchestration platforms again emphasising Kubernetes, a strong Cloud Native foundational play. Cloud-native applications are designed to be highly scalable, resilient, and agile, allowing organisations to rapidly adapt to changing business needs. By embracing cloud-native development, businesses can accelerate time-to-market, improve scalability, and enhance developer productivity embedding strong Developer Experience (DevEx) practices. Serverless Computing Serverless computing: is a game-changer for businesses seeking to build applications without worrying about server management. With serverless computing, developers can focus solely on writing code while the cloud provider handles infrastructure provisioning and scaling. An example of this is Microsoft Azure Serverless Platform or AWS Lambda. This cloud play offers automatic scaling, cost optimisation, and event-driven architectures, allowing businesses to build highly scalable and cost-effective applications. Serverless computing simplifies development efforts, reduces operational overhead, and enables companies to quickly respond to changing application workloads. Cloud Security and Compliance Cloud security and compliance: are critical cloud plays that organisations cannot afford to overlook in 2023 particularly with recent data breaches at Optus and Medicare. Leveraging security as a foundational element of your cloud native journey is crucial for ensuring the protection, integrity, and compliance of your applications and data. Cloud providers offer robust security frameworks, encryption services, identity and access management solutions, and compliance certifications. By leveraging these cloud security products and practices, businesses can enhance their data protection, safeguard customer information, and ensure regulatory compliance. Strong security and compliance measures build trust, mitigate risks, and protect organisations from potential data breaches. Data Analytics and Machine Learning:  Data analytics and machine learning (ML) are powerful cloud plays that drive data-driven decision-making and unlock actionable insights. Cloud providers offer advanced analytics and ML services that enable businesses to leverage their data effectively. By harnessing cloud-based data analytics and ML capabilities, businesses can gain valuable insights, predict trends, automate processes, and enhance customer experiences. These cloud plays empower organisations to extract value from their data, optimize operations, and drive innovation while providing an enhanced customer experience. As the evolution of Cloud Native, Multi-Cloud and Hybrid Cloud Strategies accelerate, strategically adopting the above drivers help enable innovation, agility, and business growth. Importantly Multi-cloud and hybrid cloud strategies provide enhanced security, flexibility, while cloud-native application development empowers rapid application deployment and better developer experience (DevEx), leveraging DevSecOps and Automation practices. These are critical initiatives to consider, if you are looking to advance your technology ecosystem and migrate and/or port workloads for optimum flexibility and Return on Investment (ROI). It is evident the traditional “lift and shift strategy” does not provide this level of value to the consumer. Instead, the above “on-demand cloud plays” may not be realised, with inefficient cloud resource management and unexpected expenses, leading to increased OPEX and TCO. By embracing these top cloud plays, it enables businesses investing in innovation to develop and deploy applications that can scale seamlessly on Cloud, adapting to changing customer demands, reduce TCO/ OPEX, accelerate time-to-market, maintain high availability and security, while future proofing themselves in this competitive digital landscape. For more information about Cloud, Cloud-Native, Data Analytics and more, visit our services page.

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The State of Observability 2023

Cloud-Native, DevSecOps /

The State of Observability 2023: Unlocking the Power of Observability The State of Observability 2023 study, recently released by Splunk, provides insights into the crucial role observability plays in minimising costs related to unforeseen disruptions in digital systems. In the fast-paced and intricate digital landscapes of today, observability has emerged as a beacon of light, illuminating the path towards efficient monitoring and oversight. Gone are the days of relying solely on traditional monitoring methods; observability offers a holistic perspective of complex systems by gathering and analysing data from diverse sources across the entire technology stack. With its comprehensive approach, observability has become an indispensable tool for comprehending the inner workings of digital ecosystems.  While DevOps and cloud-native architectures have become cornerstones of digital transformation, they also introduce a host of intricate observability challenges. The hurdles faced by organisations when implementing observability and security in Kubernetes were brought into focus in this year’s State of Observability survey conducted by Splunk. Respondents acknowledged the difficulties of effectively monitoring Kubernetes itself, which serves as a significant obstacle to achieving complete observability in their environments.  Now, let us explore some of the main findings uncovered in this report.  Main discoveries from this survey Observability leaders outshine beginners: Those who have embraced observability as a core practice outperform their counterparts in various aspects. These leaders experience a staggering 7.9 times higher return on investment (ROI) with observability tools, showing 3.9 times more confidence in meeting requirements, and resolving downtime or service issues four times faster.  The expanding observability ecosystem: The study reveals that the observability landscape has witnessed a recent surge in the adoption of observability tools and capabilities. An impressive 81% of respondents reported using an increasing number of observability tools, with 32% noting a significant rise. However, managing multiple vendors and tools presents a challenge when it comes to achieving a unified view for IT professionals.  Changing expectations around cloud-native apps: While the percentage of respondents expecting a larger portion of internally developed apps to be cloud-native has declined (from 67% to 58%), there has been an increase in those anticipating the same proportion (from 32% to 40%). A small percentage (2%) expects a decrease. This shift highlights the evolving landscape of application development and the growing importance of cloud-native technologies.  The convergence of observability and security monitoring: Organisations are recognising the benefits of merging observability and security monitoring disciplines. By combining these practices, enhanced visibility and faster incident resolution can be achieved, ensuring the overall robustness of digital systems.  Harnessing the power of AI and ML: AI and ML have become integral components of observability practices, with 66% of respondents already incorporating them into their workflows. An additional 26% are in the process of implementing these advanced technologies, leveraging their capabilities to gain deeper insights and drive proactive monitoring.  Centralised teams and talent challenges: Organisations are increasingly consolidating their observability experts into centralised teams equipped with standardised tools (58%), rather than embedding them within application development teams (42%). However, recruiting observability talent remains a significant challenge, with difficulties in hiring ITOps team members (85%), SRE (86%), and DevOps engineers (86%) being highlighted.  Conclusion In conclusion, observability has become an indispensable force in today’s hypercomplex digital environments. By providing complete visibility and context across the full stack, observability empowers organisations to ensure digital health, reliability, resilience, and high performance. Building a centralised observability capability enables proactive monitoring, issue detection and diagnosis, performance optimisation, and enhanced customer experiences. This goes beyond simply adopting tools into a more strategic approach that involves rolling out standardised practices across the full stack in which both platform teams and application teams participate to build and consume. As digital ecosystems continue to evolve, harnessing the power of observability will be key to unlocking the full potential of modern technologies and achieving digital transformation goals.

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Key Considerations When Selecting a Data Visualisation Tool

Data & AI /

Data visualisation is the visual representation of datasets that allows individuals, teams and organisations to better understand and interpret complex information both quickly and more accurately. Besides considering the cost of the tool itself, there are other key considerations when selecting a data visualisation tool to implement within your business. These include: Identifying who are the end-users that will be consuming the data visualisation What level of interactivity, flexibility and availability of the data visualisation tool is required from these users?   What type of visualisations are needed to fit the business/problem statement and what type of analytics will drive this?  Who will be responsible for maintaining and updating the dashboards and reports within the visualisation tool? What is the size of the datasets and how complex are the workloads to be ingested into the tool? Is there an existing data pipeline setup or does this need to be engineered? Are there any requirements to perform pre-processing or transformation on the data before it is ingested into the data visualisation tool? The primary objective of data visualisation is to help individuals, teams and companies explore, monitor and explain large amounts of data by organizing and allowing for more efficient analysis and decision-making by enabling users to quickly identify patterns, correlations, and outliers in their data.  Data visualisation is an important process for data analysis and other interested parties as it can provide insights and uncover hidden patterns in data that may not be immediately apparent through either tabular or textual representations. With data visualisation, data analysts and other interested parties such as business SMEs can explore large datasets, identify trends from these datasets, and communicate findings with stakeholders more effectively.      There are many types of data visualisations that can be used depending on the type of data being analysed along with the purpose of the analysis. Common types of visualisations include graphs, bar charts, line scatter plots, heat maps, tree maps, and network diagrams.  For data visualisation to be effective, it requires careful consideration of the data being presented, the intended audience, and the purpose of the analysis. The visualisation that is being presented should be clear, concise, and visually appealing, with labels, titles, and colours used to highlight important points and make the information more accessible to the audience. The data visualisation needs to an effective storytelling mechanism for all end-users to understand easily. Another consideration is the choice of colours used, as the wrong colours can impact the consumers of the data visualisation and can impact visually impaired people (i.e., colour blindness, Darker vs Brighter contrasts as examples)  In recent years, data visualisation has become increasingly important as data within organisations continues to grow in complexity. With the advent of big data and machine learning technologies, data visualisation is playing a critical role in helping organisations make sense of their data, and become more data-driven with increased ‘time to insight’, as organisations facilitate better and faster decision-making.    Data Visualisation Tools & Programming Languages  At TL Consulting, our skilled and experienced data consultants use a broad range and variety of data visualisation tools to help create effective visualisations of our customer’s data. The most common are listed below:   Power BI is a business intelligence tool from Microsoft that allows users to create interactive reports and dashboards using data from a variety of sources. It includes features for data modelling, visualisation, and collaboration.  Excel: Excel is a Microsoft spreadsheet application and from a data visualisation perspective includes the capability to represent numerical data in a visual format.  Tableau: Tableau is a powerful data visualisation tool that allows users to create interactive dashboards, charts, and graphs using drag-and-drop functionality. It supports a wide range of data sources and has a user-friendly interface.  QlikView: QlikView is a first-generation business intelligence tool that allows users to create interactive visualisations and dashboards using data from a variety of sources. QlikView includes features for data modelling, exploration, and collaboration.  Looker:  Looker is a cloud-based Business Intelligence (BI) tool that helps you explore, share, and visualise data that drive better business decisions. Looker is now a part of the Google Cloud Platform. It allows anyone in your business to analyse and find insights into your datasets quickly.  Qlik Sense: Qlik Sense is the next-generation platform for modern, self-service-oriented analytics. Qlik Sense supports from self-service visualisation and exploration to guided analytics apps and dashboards, conversational analytics, custom and embedded analytics, mobile analytics, reporting, and data alerting.      In conjunction with the data visualisation tools listed above, there are a variety of programming languages using their various libraries that TL Consulting use in delivering outcomes to our customers that support not just Data Visualisation but also Data Analytics.  Python is a popular programming language that can be used for data analysis and visualisation. This can be done via tools such as Jupyter, Apache Zeppelin, Google Colab and Anaconda to name a few. Python includes libraries such as Matplotlib, Seaborn, Bokeh and Plotly for creating visualisations.  R is a programming language used for statistical analysis and data visualisation. It includes a variety of packages and libraries for creating charts, graphs, and other visualisations.  Scala is a strong statically typed high-level general-purpose programming language that supports both object-oriented programming and functional programming. Scala has several data visualisation libraries such as breeze-viz, Vegas, Doodle and Plotly Scala.  Go or Golang is a statically typed, compiled high-level programming language designed at Google. Golang has several data visualisation libraries that facilitate the creation of charts such as pie charts, heatmaps, scatterplots and boxplots.  JavaScript is a popular programming language that is a core client-side language of the w3.  It has rich data visualisation libraries such Chart JS, D3, FusionCharts suite, Pixi etc.      Conclusion In conclusion, there are several data visualisation tools and techniques available in the market. For organisations to extract meaningful insights from their data in a time-efficient manner, it’s important to consider these factors before selecting and implementing a new data visualisation tool for your business. TL

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Kubernetes container design patterns

Cloud-Native, DevSecOps /

Kubernetes container design patterns Kubernetes is a robust container orchestration tool, but deploying and managing containerised applications can be complex. Fortunately, Kubernetes container design patterns can help simplify the process by segregating concerns, enhancing scalability and resilience, and streamlining management. In this blog post, we will delve into five popular Kubernetes container design patterns, showcasing real-world examples of how they can be employed to create powerful and effective containerised applications. Additionally, we’ll provide valuable insights and tool recommendations to help you implement these patterns with ease. Sidecar Pattern: The first design pattern we’ll discuss is the sidecar pattern. The sidecar pattern involves deploying a secondary container alongside the primary application container to provide additional functionality. For example, you can deploy a logging sidecar container to collect and store logs generated by the application container. This improves the scalability and resiliency of your application and simplifies its management. Similarly, you can deploy a monitoring sidecar container to collect metrics and monitor the health of the application container. The sidecar pattern is a popular design pattern for Kubernetes, with many open-source tools available to simplify implementation. For example, Istio is a popular service mesh that provides sidecar proxies to handle traffic routing, load balancing, and other networking concerns. Ambassador Pattern: The ambassador pattern is another popular Kubernetes container design pattern. This pattern involves using a proxy container to decouple the application container from its external dependencies. For example, you can use an API gateway as an ambassador container to handle authentication, rate limiting, and other API-related concerns. This simplifies the management of your application and improves its scalability and reliability. Similarly, you can use a caching sidecar container to cache responses from external APIs and reduce latency and improve performance. This ensures that the application is properly configured and ready to run when the primary container runs. The ambassador pattern is commonly used for API management in Kubernetes. Tools like Nginx,Kong and Traefik provide API gateways that can be deployed as ambassador containers to handle authentication, rate limiting, and other API-related concerns. Adapter Pattern: The adapter pattern is another powerful Kubernetes container design pattern. This pattern involves using a container to modify an existing application to make it compatible with Kubernetes. For example, you can use an adapter container to add health checks, liveness probes, or readiness checks to an application that was not originally designed to run in a containerised environment. This can help ensure the availability and reliability of your application when running in Kubernetes. Similarly, you can use an adapter container to modify an application to work with Kubernetes secrets, environment variables, or other Kubernetes-specific features. The adapter pattern is often used to migrate legacy applications to Kubernetes. Tools like Kubernetes inlets and kompose provide an easy way to convert Docker Compose files to Kubernetes YAML and make the migration process smoother Sidecar injector Pattern: The sidecar injector pattern is another useful Kubernetes container design pattern. This pattern involves dynamically injecting a sidecar container into a primary application container at runtime. For example, you can inject a container that performs security checks and monitoring functions into an existing application container. This can help improve the security and reliability of your application without having to modify the application container’s code or configuration. Similarly, you can inject a sidecar container that provides additional functionality such as authentication, rate limiting, or caching. The Sidecar Injector pattern is a dynamic method of injecting sidecar containers into Kubernetes applications during runtime. By utilizing the Kubernetes admission controller webhook, the injection process can be automated to guarantee that the sidecar container is always present when the primary container initiates. An excellent instance of the Sidecar Injector pattern is the HashiCorp Vault Injector, which enables the injection of secrets into pods. Init container pattern: Finally, the init container pattern is a valuable Kubernetes container design pattern. This pattern involves using a separate container to perform initialization tasks before the primary application container starts. For example, you can use an init container to perform database migrations, configuration file generation, or application setup. This ensures that the application is properly configured and ready to run when the primary container. In conclusion, Kubernetes container design patterns are essential for building robust and efficient containerised applications. By using these patterns, you can simplify the deployment, management, and scaling of your applications. The patterns we discussed in this blog are just a few examples of the many design patterns available for Kubernetes, and they can help you build powerful and reliable containerised applications that meet the demands of modern cloud computing. Whether you’re a seasoned Kubernetes user or just starting out, these container design patterns are sure to help you streamline your containerised applications and take your development to the next level.

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Maximising Kubernetes ROI

Cloud-Native, DevSecOps /

Maximising ROI and Minimising OPEX with Kubernetes At TL Consulting, we offer specialised services in managing Kubernetes instances, including AKS, EKS, and GKE, as well as bare metal setups and VMWare Tanzu on private cloud. Our Kubernetes consulting services are tailored to help businesses optimise their IT costs and improve their ROI, enabling them to leverage the full potential of Kubernetes. We streamline operations, optimise resource utilisation, and reduce infrastructure expenses, ensuring that our clients get the most out of their Kubernetes deployments. Thus ensuring that your teams are maximising Kubernetes ROI while minimising IT costs. With our expertise, we can work with organisations to assess their current infrastructure and identify areas where Kubernetes can be implemented to achieve better ROI. Our services cover advisory, design and architecture, engineering, and operations. We guide organisations on containerisation, scalability, and automation best practices to optimise their use of Kubernetes. We provide customised Kubernetes solutions and ensure seamless implementation, management, and maintenance. With our help, businesses can streamline operations, enhance resource utilisation, and reduce infrastructure costs. We do not just provide one-off Kubernetes solutions. We’re committed to ongoing management and support, staying up to date with the latest innovations and best practices in Kubernetes. By collaborating with us, organisations can stay ahead of the curve and continue to optimise their IT costs and improve their ROI over time. Our partnership ensures that businesses can adapt and thrive in an ever-changing technological landscape, confidently leveraging Kubernetes’ full potential. Additionally, we offer a cloud-agnostic approach to Kubernetes, enabling businesses to choose the cloud platform that best fits their requirements. Our team provides guidance on cloud platform selection, deployment, and optimisation to ensure that clients can maximise their investments in the cloud. We specialise in multi-cloud approaches, making it seamless for organisations to manage Kubernetes across various cloud providers.

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What can we expect for Kubernetes in 2023?

Cloud-Native, DevSecOps /

What can we expect for Kubernetes in 2023? As Kubernetes approaches the eighth anniversary of its first version launch, we look into the areas of significant change. So what does the Kubernetes ecosystem look like and What can we expect for Kubernetes in 2023? In short, is huge and continues to grow. As more businesses, teams, and people use it as a platform for innovation, more new applications will be created and old ones will be scaled more quickly than ever before, fuelling its continual development. The State of Kubernetes 2022 study from VMware Tanzu and the most recent Annual Cloud Native Computing Foundation (CNCF) Survey both indicate that Kubernetes is widely adopted and continues to grow in popularity as a platform for container orchestration. These studies suggest that Kubernetes has become a de facto standard in the industry and its adoption will likely continue to increase in the coming years. Anticipated Shift towards Kubernetes on multi cloud As we move forward into 2023, it’s becoming increasingly common for businesses to utilize multiple cloud providers for their Kubernetes deployments. This trend, known as multi-cloud/hybrid deployments, often involves the use of container orchestration and federated development and deployment strategies. While there are already tools available for deploying and managing containers across a variety of cloud providers and on-premises platforms, we can expect to see even more advancements in this area. Specifically, there will likely be an increase in technology that makes it easier to create and deploy multi-cloud systems using native cloud services that work seamlessly across different providers. Multi-cloud adoption allows businesses to take advantage of the strengths of different cloud providers, such as leveraging the best database solutions from one provider and the best serverless offerings from another. This approach can also increase flexibility, reduce vendor lock-in, and provide redundancy and disaster recovery options. Additionally, it can allow for cost optimization by taking advantage of different pricing models and promotions offered by different providers. Continual Evolution of DevOps and Platform Teams: To survive in this digital age, businesses need to have a diverse set of skills and knowledge areas within their workforce. Close collaboration between different departments and disciplines is essential for leveraging new technologies like Kubernetes and other cloud platforms. However, these technologies can be difficult to learn and maintain, and teams may struggle to gain in-depth understanding of them. Businesses should focus on automation and acceleration, but also invest in training and development programs to help their teams acquire the necessary skills to effectively use these technologies. Companies of all sizes should think about where they want to develop their Kubernetes knowledge base. Many businesses choose a platform team to develop and implement this knowledge. Multiple DevOps teams can be supported by a single platform team. This separation allows DevOps teams to continue concentrating on creating and running business applications while the platform team looks after a solid and dependable underpinning platform. Improved Stateful Application Management: Containers were originally intended to be a means of operating stateless applications. However, the value of running stateful workloads in containers has been recognised by the community over the last few years, and the newer versions of Kubernetes have added the required functionalities. Now there are better ways to deploy stateful applications, but the outcome is far from ideal and inconsistent. By including a controller in the cluster, K8s operators can resolve this difficulty. Reconciliation loops are controller loops that monitor differences between the current and intended states and adjust return the current state to the desired state. Maturity in Policy-as-Code for Kubernetes The goal has been to give teams more autonomy when delivering applications to Kubernetes for several years. In many businesses today, creating pipelines that can quickly send out apps is standard procedure. Although having autonomy is a great advantage, maintaining some manual control still requires finding the proper balance. The transition to everything as a code has opened a plethora of opportunities. Following accepted engineering principles will make it simple to validate and review policies defined as-code. As a result, the importance of policy frameworks will increase. Within the CNCF, Open Policy Agent (OPA) is the most common policy framework. Practices like this will advance concurrently with the adoption of Kubernetes and autonomous teams to enable continual growth while preserving or even gaining more control. Adoption enables you to control how Kubernetes is used by a wide range of teams. Enhanced Observability and Troubleshooting capabilities: Troubleshooting applications running on a Kubernetes cluster at scale can be challenging due to the complexity of Kubernetes and the relationships between different elements. Providing teams with effective troubleshooting solutions can give an organization a competitive advantage. The Four elements (Events, Logs, Traces, Metrics) are important in understanding the performance and behaviour of a system. They provide different perspectives and details on system activity, and when combined, give a more complete picture of the issue. Solutions that integrate these four elements can aid in faster troubleshooting and problem resolution and can also help in identifying and preventing future issues. Vendors and open-source frameworks will continue to drive this trend. Focus on supply chain security: Software supply chain security has been in laser sights for a while now, as most software rooted from other software. The necessity of ensuring Kubernetes’ strength has increased along with its importance as it becomes more widely adopted, it is important to ensure its security as it is a critical component of the software supply chain. This includes securing the infrastructure on which it runs, as well as securing the containerized applications that are deployed on it. The “4C’s of cloud native security” model is a good place to start thinking about the security of the different layers of a cloud native application: Cloud, Clusters, Containers, and Code. Each layer of the Cloud Native security model builds upon the next outermost layer, and they are equally important when considering security practices and tools. This can be done through a variety of methods, such as using secure configurations, implementing network

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Progressive Delivery with Kubernetes:

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Progressive Delivery (the GitOps way) with Kubernetes: One of the biggest challenges organisations faces, especially when running microservices, is managing application deployments. Having a proper deployment strategy is necessary. For instance, in a production environment, it is always a change management process requirement to ensure that the downtime impact on the end-user is minimised and maintenance windows need to be planned to cater for any changes that will cause an outage. It is also mandated that in case of any issues when deploying the change, a rollback plan must be ready for execution to recover from any failures. These challenges amplify with the increase of the number of microservices and makes it more difficult to assess the result of the deployment and execute the rollback if required. Enter progressive delivery. Thankfully, cloud native architectures using Kubernetes running microservices addresses this problem by offering increased flexibility, allowing teams to publish more useful updates more frequently and progressively. The use of release techniques like Canary, Blue-Green, and Feature flagging as part of progressive delivery enables teams to maximise an enterprise’s software delivery. It is predicated on the notion that consumers desire to test features prior to completion to enhance the user experience. In Kubernetes, there are different ways to release an application. It is necessary to choose the right strategy to make the infrastructure reliable and more resilient during an application deployment or update. The out of the box Kubernetes Deployment Object supports the Rolling Update strategy which comes as a standard and provides a basic set of safety guarantees (aka. readiness probes) during an update. When deploying into a development/staging environment, standard Kubernetes deployment strategies such as a recreate or rolling deployment might be a good option. However, the rolling update strategy faces may limitations such as controlling the speed and flow of the rollout. in large scale high-volume production environments, a rolling update is often considered too risky of an update procedure since it provides no control over the blast radius, may rollout too aggressively, and provides no automated rollback upon failures. In production environments, more advanced deployment strategies are much needed to satisfy the business requirements. These advanced strategies are called “Progressive Deployments”. An example of these deployment strategies is the Blue/Green deployment which allows for a quick transition between the old version and the new version by deploying them side by side and then switching to the new version when testing has been successful. This testing of the platform needs to be thorough to avoid having to rollback frequently. If unsure of the platform’s stability or the potential effects of releasing a new software version, a canary deployment offers a smaller scale next version of the release running side by side the current version in production. By doing so, the new release is rolled-out to a small subset of users to test the application and provide their feedback. Once the change is accepted, it is rolled out to the rest of the users. Benefits of Progressive Delivery: Progressive delivery lowers the risk of releasing new features, as well as identifying and resolving possible issues with those additions. It also offers early feedback on any version of your application. Before a feature is fully deployed, the developer can test out various changes on the product to see how the application behaves. The idea is that the developer can alter the release strategy if the modifications are unfavourable to prevent end users from experiencing any glitches. Secondly, improved release frequency results from sequential delivery. While the primary goal of progressive delivery is to provide end users with safer, more dependable releases, you as the DevOps team will benefit from being able to deploy new versions in smaller parts and hence release more frequently. You can work on each feature separately and release it in tiny sprints. The time to market is shortened, and any DevOps team can now deploy better software more quickly. Finally, and this is something that is frequently ignored, progressive delivery leads to improved segregation of duties between the development and operations teams. This segregation of duties works better with progressive delivery since developers concentrate on creating new features while operations concentrate on rolling out the new features gradually in a strategy that suits the operational needs of the platform. Progressive delivery is best achieved with GitOps: This demand for progressive delivery in a cloud native manner can be achieved with GitOps. The objective behind GitOps is to define and declare everything in Git including operational tasks. Git is already used by developers to generate and collaborate on code. GitOps simply expands this concept to include the creation and setup of infrastructure as well. Git becomes the control plane for operations and deployments because everything is declared as code in Git. GitOps is being enabled by open-source tooling such as ArgoCD, Flux and Flagger, which automatically checks Git repositories for any new changes, and if it detects a change, it automatically deploys it to production. With progressive delivery, these automated deployments need to be done in phases and to multiple target Kubernetes clusters. These tools offer full control of the software delivery pipeline, rollback strategies, test executions, feature releases, and scaling of infrastructure resources. In conclusion, there are various methods for deploying an application to cater for applications with varying complexities, teams with different demands, and environments with different operational requirements and compliance levels. Selecting the right strategy or strategies and having full control over these strategies in code when combined by the right tools is an extremely powerful feature of cloud native platforms that greatly simplifies change management, release management, and operations of the applications. It completely disrupts the way operations teams traditionally thought of these processes as rigid and extremely sensitive with a dramatic business impact in case anything went wrong, into simplified processes and tasks that can be executed every day in the background without having an impact on the end-user.

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Building a Robust Data Governance Framework in 2023

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In today’s data-driven world with accelerating advancements in Artificial Intelligence (AI) and advanced analytics, organisations play an important role in ensuring that the data they collect, store, and analyse is underpinned by a strong data governance framework. Embedding the right data governance framework is the enablement of an organisation’s data strategy which requires dedicated planning and strategic direction from various business & technical stakeholders and should be driven from the “top-down” rather than “bottom-up”. To achieve this, organisations should focus on defining their information and data lifecycle management, data relationships and classification, data privacy, data quality, and data integrity to become more competitive and resilient.  The key fundamental challenge for organisations is to embed data standardisation, data security & compliance horizontally across the enterprise, thereby eliminating silos with their own disparate ways of working. In addition, it’s important for organisations to align their data governance framework with their data lifecycle, business strategy and goals, enabling a more agile approach to accommodate the organisation’s current & future needs.   Data Governance Framework Best Practices As organisations collect more and more data points, it’s important to define the right standards, policies, controls, accountability, and ownership (roles & responsibilities). A data governance framework will ensure the organisation abides by these standards while ensuring data that is collected and stored is secure, with a focus on maintaining data integrity and data quality. Ultimately, the data that is consumed by end-users should enable informative, data-driven decisions to be made. A constant re-evaluation is recommended to ensure the organisation’s data governance program is modernised and caters to the latest advancements in data and technology. Prior to defining a data governance framework, a comprehensive data discovery should be performed across the business landscape to create a unified view. This would aid in establishing data governance across the following areas: Data cataloging of data relationships, data quality, and data lineage Data classification and sourcing Metadata definition (Technical and Enterprise metadata) Data compliance, security, and privacy Data analytics & engineering Data storage & sharing The following diagram is a high-level example of a data governance framework. This model should be aligned with the organisation’s data and information management lifecycle. The framework definition should be evaluated from a People, Processes & Technology/Tooling perspective considering data stewardship, efficiencies, data security & access controls, alongside standardised processes governing the technology and tools that facilitate the production, consumption, and processing of the organisation’s data. The following sections highlight a few key areas which the data governance framework should address: Alignment to the Organisation’s Cloud Strategy When uplifting the data governance program, another important consideration for organisation’s that are building technology solutions on Cloud is to define an integrated data governance architecture across their environments, whether it be hybrid or multi-cloud. Alignment to their cloud strategy can help in the following areas: Improve data quality with better management & tooling available around data cleansing and enrichment Build a holistic, unified view of the organisation’s data through discovery and benchmarking Gain higher visibility into data lineage and track data end-to-end from source to target Build more effective data catalogs to ensure it benefits organisational needs to search and access the right data when needed Proactively review, monitor, and measure the data to ensure data consistency and data integrity is preserved For example, Microsoft offers an Azure Governance service as a management and governance cloud solution that features advanced capabilities to help manage data throughout its entire IT lifecycle and track data flows end-to-end, ensuring the right people have access to reliable, accurate data they need, whenever they need it. Data Privacy & Compliance As organisations continue building insights and implementing advanced analytics to learn more about their customers and create more tailored experiences, protecting sensitive data attributes including Personal Information (PI) should be at the heart of the organisation’s data security & data privacy practices, as part of their data governance framework. With the rise of cyber-attacks & data breaches, organisations should consider implementing data obfuscation techniques to “mask” or “encrypt” their PI source data, especially across non-production environments where the access controls are considered weaker than production environments, and the “internal” threat can be considered just as high as the external cyber threats. Applying data obfuscation techniques would ensure the PI data attributes are de-sensitized prior to their use in development, testing and data analytics. In addition, organisations should ensure data controls & access policies are reviewed more frequently than ever. Understanding who has access to the underlying data sources and platforms will help organisations maintain a good risk posture and should be assessed against their data governance framework, across their environments whether on-premise or on Cloud. Augmented Analytics & Machine Learning Without advanced analytics, data loses a lot of its usability and power. Advanced analytics combines the power of machine learning and artificial intelligence to help teams make data-driven decisions based on in-depth insights. Advanced analytics tools greatly streamline the data analysis process and help to provide a competitive edge, uncovering patterns and insights that manual data analysis may overlook. With the introduction of open-source machine learning models such as Open AI’s ChatGPT, how do organisations ensure the data that is collected, analysed, and presented is highly accurate and high quality? Depending on the data models & training algorithms used, these insights can be deeply flawed and it’s important for organisations to embed the right data governance policies around the use of open-source data models, including the collection, use, and analysis of the data points collected. A few roles that data governance plays in the world of augmented analytics, machine learning, and AI include: Providing guidance on what data is collected and how it’s used to train and validate data models for machine learning models to generate advanced analytics Providing standardization on the data science lifecycle and algorithms applied for generating insights, along with data cleansing & enrichment exercises Defining the best practices and policies when introducing new data models, along with measures to fine-tune and train models to increase data accuracy

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