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Aligning the Correct Data Analytics Model to your Business

Data & AI /

Aligning the correct data analytics model to your business needs can lead to a significant return on investment, increased business growth, and better alignment to your business strategy. In addition to financial returns, analytics, and AI can be used to fine-tune business processes and day-to-day operations. In order to leverage the power of data analytics correctly, it’s important for organisations to standardise the way they identify the business questions that need to be answered. Today, many organisations are moving at a rapid pace which sometimes requires timely business decisions to be made. These decisions are sometimes based on the intuition and experience of the business decision-makers, by their current understanding of the business landscape. For data analytics to play a successful role in shaping these decisions, the data presented to the business should add weight and enrichment to ensure the decisions to be made are backed by facts. For this to occur successfully, data analysts need to work cohesively with the business to ensure there is a strong alignment to the business strategy and ensure the right questions are being asked. Taking a holistic approach would help the organisation establish the right process to identify the underlying business problems and then take the appropriate actions, using a data-driven decision-making approach.   4 Major Questions to ask your business A good data analytics model should be aligned to answering a set of business questions to fulfill business requirements. In addition, it’s important for data analysts and data scientists to understand what metrics and KPIs the business needs to measure. What was the cause of the problem? (Reports) Why did it happen? (Diagnosis) What will happen in the future? (Predictions) What is the best way forward? (Recommendations)   What is Data Analytics? Data analytics is the process of utilising quantitative methods to derive actionable insights from data to make informed decisions. There are 4 primary methods of data analysis: Analytics Models deployed in various industries   Type of Analytics: Descriptive   Industry: Education Many LMS platforms and learning systems offer descriptive analytical reporting capabilities with the aim of helping businesses and institutions measure learner performance to ensure that training goals and targets are met. Descriptive Analytics was used to track course enrollments, and course compliance rates, record which learning resources are accessed, collate course survey results, and identify the length of time that learners took to complete a course among other activities Type of Analytics: Diagnostic   Industry: Retail A retail store that sells eco-friendly products noticed a recent surge in revenue from one state. During discovery, the company learned that the surge was driven by a leap in sales of a single product. Research revealed the causal relationship: the state’s governor had signed a law making plastic shopping bags illegal, causing sales of reusable bags to soar. Type of Analytics: Predictive   Industry: E-commerce E-commerce websites are predicting customer preferences and recommend products to customers based on past purchases and search history using state-of-the-art artificial intelligence algorithms. Type of Analytics: Prescriptive   Industry: Insurance Insurance companies want to observe clients who want fast and reliable customer service online. Based on the pricing and premium information for clients, they are prescribing the right pricing and premium information using AI models. However, there are considerations regarding privacy-enhancing technologies (PETS) that allow the AI models to train on homomorphic encrypted data by taking data privacy into account. Businesses can easily adopt a data analytics model to enhance the way they do business. Here is an example of a data analytics lean canvas model encompassing an end-to-end solution shared below:   Conclusion In conclusion, for organisations to extract meaningful insights from their data to make the right decisions about their business, the correct data analytics model eliminates guesswork and manual tasks, be it choosing the right content or developing the right products to your customer needs. TL Consulting provides advisory and transformation services in the data analytics & engineering domain and can help your business design and implement the correct-fit data analytics model aligned to your business needs and transformation goals. Read through our data engineering and data platforms page to learn more about our service capabilities and send us an enquiry if you’d like to learn more about how our dedicated consultants can help you.

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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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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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Demand for Kubernetes and Data Management

Cloud-Native, DevSecOps /

Transforming the Way We Manage Data Data is the backbone of today’s digital economy. With the ever-increasing volume of data being generated every day, the need for efficient, scalable, and robust data management solutions is more pressing than ever. Enter Kubernetes, the revolutionary open-source platform that’s changing the game of data management. Market research suggests that the demand for Kubernetes in data management is growing at a rapid pace, with a projected compound annual growth rate of over 30% by 2023. There is an increase in demand for Kubernetes. With its ability to automate deployment, scaling and management of containerized applications, is providing organisations with a new way to approach data management. By leveraging its container orchestration capabilities, Kubernetes is making it possible to handle complex data management tasks with ease and efficiency. Stateful applications, such as databases and data pipelines, are the backbone of any data management strategy. Traditionally, managing these applications has been a complex and time-consuming task. But with Kubernetes, stateful applications can be managed with ease, thanks to its Persistent Volumes and Persistent Volume Claims. Data pipelines, the critical component of data management, are transforming the way organizations process, transform and store data. Kubernetes makes it possible to run data pipelines as containers, simplifying their deployment, scaling, and management. With Kubernetes in-built jobs support, these workflows can run as a scheduled or triggered jobs that are orchestrated by the Kubernetes engine. This enables organizations to ensure the reliability and efficiency of their data pipelines, even as the volume of data grows. Scalability is a major challenge in data management, but with Kubernetes, it is by design. Its ability to horizontally scale the number of nodes in a cluster makes it possible to easily handle the growing volume of data. This ensures that data management solutions remain robust and scalable, even as data volumes increase. Resilience in another key requirement in data management. Traditionally, a single point of failure can bring down the entire system. But with Kubernetes, failures are handled gracefully, with failed containers automatically rescheduled on healthy nodes. This provides peace of mind, knowing that data management solutions remain available even in the event of failures. Kubernetes also offers zero downtime deployment in the form of rolling updates. This also applies to databases where the administrator can upgrade the database version without any impact to the service by rolling the update to one workload at a time until all replicas are upgraded. To complement the resilience features, operations such as memory or CPU upgrades which, in the past, were considered destructive changes that required planning and careful change and release management. Today, since Kubernetes relies on declarative management of its objects, this change is just a single line of code. This change can be deployed similar to any code change that progresses to the different environments using CI/CD pipelines. Conclusion In conclusion, Kubernetes is transforming data management. Gone are the days of regarding Kubernetes as a platform suitable only for stateless workloads leaving databases running on traditional VMs. Many initiatives took place to adapt stateful workloads to run efficiently and reliably in Kubernetes from releasing the StatefulSets API and Storage CSI, to building Kubernetes operators that will ensure databases can run securely in the cluster with massive resilience and scalability. With these operators being released for common database systems such as Postgres and mySQL to name a few, daunting database operations such as automatic backups, rolling updates, high availability and failover are simplified and taken care of in the background transparent to the end user. Today, with more database vendors either releasing or endorsing Kubernetes operators for their database systems, and enterprises running databases in Kubernetes production environments successfully, there is no reason to think that it lacks the necessary features to run production enterprise database systems. The future of data management is looking bright, and we excitedly await what lies ahead thanks to the Kubernetes community’s constant drive for innovation and the expansion of the possibilities. To learn more about Kubernetes and our service offering here.

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Building a Robust Data Governance Framework in 2023

Cloud-Native, Data & AI /

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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What is Cloud Transformation? 

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What is Cloud Transformation?  What is cloud transformation? In today’s world, cloud is the first option for everyone to run their workloads, unless they have a compelling reason such as compliance or security concerns to deploy it on-premises. Most of the organisations who manages their workloads on their own data centres, are looking for an opportunity to move to the cloud for numerous benefits which most of the cloud services providers offer. As per the recent survey by Forbes and Gartner recently increased prior forecasts of worldwide end-user spending on public cloud services to anticipate a 23.1% jump this year, followed by a more than 16% increase in 2022 — up from $270 billion in 2020 to just under $400 billion.  While the acceleration of cloud transformations continuous, most businesses data still reside on on-premises. Consequently, hybrid solutions that were once downplayed by virtualisation have emerged as not only practical but likely a preferred approach. We’ve moved past the “cloud-first” era to a time when clouds are becoming omnipresent.   There are numerous benefits in using cloud services. Some of key benefits are discussed below;  Pay per use: Switching from the on-premises IT infrastructure to remote cloud infrastructure provided by a third-party cloud provider allows businesses to make potentially significant cost savings in their IT expenditure.  Disaster Recovery: Cloud computing ensures that disaster recovery is much easier than it might otherwise be. This is because critical data is stored off-site in third-party data centres, thereby making it easier to retrieve in the event of unscheduled downtime.  Scalable: As your business grows, so is your infrastructure needs. Alternatively, it may be that you’ve had to scale down your operation, and with it your IT compute and storage needs. Cloud computing provides easy scalability, allowing you to scale up and scale down as your circumstances change.   Less maintenance: By adopting cloud, businesses can free up the resources (including both financial and human resources) for deployment in other areas. This allows them to have more focus on customer base, rather than managing and maintaining their own IT resources.  Security: Data security has been one of the key aspects to be considered when migrating into cloud. cloud providers go to great lengths to ensure that data is kept secure. They are tasked with protecting data from threats and unauthorized access, and this is something they do very effectively using robust encryption.  Because of these obvious reasons and much more benefits, many businesses are starting their journey to move or transform their applications or workloads to the cloud and this process of migrating or transforming the applications or workload is called as “Cloud Transformation”  What is Cloud Transformation? Cloud transformation is simply the process of migrating or transforming your work to the cloud, including migration of apps, software programs, desktops, data, or an entire infrastructure in alignment with the business objectives of the organization  The first step in performing the transformation is to do a comprehensive assessment if the cloud computing is suitable for our organisation from a long-term business strategy. Cloud transformation is popular because, among many other benefits, it increases the efficiency of sharing and storing data, accelerated time-to-market, enhanced organizational flexibility and scalability, and centralize their network security. Overall, it hugely changes the way of operating a business.  How to Approach Cloud Transformation? As state above cloud transformation is the enablement of a complete business transformation. To achieve this, organizations focus on cloud strategy, migration, management and optimization, data and analytics, and cloud security to become more competitive and resilient.  There are various ways the transformation to the cloud can be done but you may need to choose the option that better suits your organisation and its goals. A few options listed below will help you to consider the right options for the transformation approach.   Understanding the Organisation long term goals and environment   Security and regulatory considerations  Building a cloud transformation strategy and roadmap  Choosing the right cloud and approach   Defining a Robust Governance model  Layers of Cloud transformation  All or any of the below component layers are to be changed as a part of transformation when migrating to the cloud.  Application layer  It is the core layer where your application is hosted to run. It is also known as compute layer to run application code which performs business operations. Along with application code base, it also contains dependencies and software packages which are required to run your application.  Data layer  It consists of data which are processed by the application layer. This is the layer which maintains the state of your application. Storage (Files, Databases, stage management tools) is the key components of this layer.   Network layer  It consists of network components like LAN, router, load balancers, firewalls, and VPN etc. It is responsible for providing the segregation between different components and ensure restriction is applied between them as needed.  Security layer  Though it is mentioned as a separate layer, it will be part of each other layer mentioned above. For e.g., when migrating application layer, we will not be just migrating it but will be considering proper security in place by having security rules (firewall rules) in place and only the required traffic is allowed from and to the application. It applies for data and network layer as well.  Types of Cloud transformation  Distinct types of cloud transformation are listed and discussed below,  Lift & shift (or) Re-hosting  Re-platform  Re-factor (or) Re-architect  Develop in cloud  Lift & Shift (or) Re-hosting  This approach is nothing but lifting the application from on-prem and deployed to the cloud as-is. This is one of the quickest ways to transform the application from on-premises to the cloud but will not utilize the benefits of cloud-native features. The applications which do not have dependencies with on-premises and have less business impact are the ideal candidates for this approach. It is a way to start your cloud journey with smaller applications and then progress to a bigger one.  Application layer – No change  Data layer – No

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Application Security in Kubernetes

“Shift Left” Application Security in Kubernetes with Open Policy Agent (OPA) and Tanzu Mission Control (TMC)

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“Shift Left” Application Security in Kubernetes with Open Policy Agent (OPA) and Tanzu Mission Control (TMC) To secure a Kubernetes environment, we must adopt the “shift left” security approach right from the initial phases of the development, rather than wait for the deployment to complete and focus on the security at later stages of the build. Kubernetes security is constantly evolving with new features to strengthen both the application and cluster security. Kubernetes offers several mechanisms to administer security within the cluster. Some of these include enforcing resource limits, API security, standardizing containers, auditing and so on. Here we will discuss one of such mechanism, which helps to implement the shift left security in a Kubernetes cluster. What is OPA? Open Policy Agent (OPA) is an open-source policy engine that provides a way of manifesting the policies declaratively as code, which helps to ease out some of the decision-making processes with the Kubernetes cluster end users, such as developers, operations teams without impacting the agility of the development. OPA uses a policy language called Rego, which allows you to write policies as code for various services like Kubernetes, CI/CD, Chef, and Terraform using the same language. OPA enforces the separation of concern by decoupling the decision-making from the core business logic of the applications. OPA Workflow: OPA provides centralized policy management and generates policy decisions by evaluating the input data against policies (written in Rego) and data (in JSON) through RESTful APIs. Here we have some of the example policies we can enforce using OPA: Which users can access which resources? Which subnets egress traffic is allowed to? Include node and pod (anti-), affinity selectors, on Deployments Which clusters a workload must be deployed to? Ensure all the images come from a trusted registry Which OS capabilities a container can execute with. Implementing Kubernetes Admission Controllers to validate API requests. Allowing or denying Terraform changes based on compliance or safety rules. Enforcing certain deployment policies (such as resource limits, meta data types of resources) Creating Custom Policies using OPA in Tanzu Mission Control (TMC) VMware Tanzu Mission Control is a centralized hub for simplified, multi-cloud, multi-cluster Kubernetes management. Tanzu Mission Control aims to help with the following list of Kubernetes operations: Managing clusters on both public, private cloud and edge Cluster lifecycle management on supported providers Manage security across multiple clusters Centralized policy management Access management Cluster conformance VMware Tanzu Mission Control provides centralized policy management for specific policies that you can use to govern your fleet of Kubernetes clusters, The polices include access controls, image registry policies, and resource limit policies. While these cover the baseline polices, it also offers an ability to create custom policies using Open Policy Agent (OPA). Custom policies are somewhat open-ended and provide the opportunity to address aspects of cluster management that specifically suit the needs of your organization. As described above OPA implement specialized policies that enforce and govern your Kubernetes clusters. Closing thoughts: Enterprises use the OPA to enforce, govern, audit, and remediate policies across all IT environments. You can use OPA to centralize operational, security, and compliance aspects of Kubernetes, in the context of cloud-native deployments, (CI/CD) pipelines, auditing and data protection. Thus, OPA enables DevOps teams to shift control over application authorization further left to advance the adoption of best DevSecOps practices. TL Consulting TLConsulting brings its consulting and engineering personnel to application modernisation adoption and implementation by providing range of services – as If you need assistance with your Containers/Kubernetes adoption, please contact us at our kubernetes consulting services  page.

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Secrets management in Kubernetes using Sealed Secrets

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Secrets management in Kubernetes using Sealed Secrets: Kubernetes has gained its popularity due to its core nature of running an immutable infrastructure, where the pods, containers can be destroyed, and replaced automatically. This helps to ease out the deployment friction as you declaratively describe the resources in a manifest file.  Kubernetes manifest files can be stored in a source code repository like GitHub and the Kubernetes operations can be managed easily using the GitOps methodology. However, one of the biggest challenges in Kubernetes is the secure storage and rotation of credentials / secrets such us passwords, keys, and certificates. While Kubernetes offers basic secrets management capabilities, it doesn’t help secure secrets needed both inside and outside of Kubernetes. Here we discuss one of the ways to address this issue using “sealed secret”: Sealed Secrets: When looking at optimising the infrastructure costs, enterprises consider various cost-management best practices, but Kubernetes require a specialised a Sealed Secrets is a Kubernetes object, which helps to store the encrypted Kubernetes secrets in a version control.It consists 2 main components. Sealed Secret Controller (At Server Side) Kubeseal Utility (At Client Side) First step is to use sealed secrets is, install the sealed secret controller in the target cluster using the sealed-secret-controller helm chart. helm repo add sealed-secrets https://bitnami-labs.github.io/sealed-secrets helm repo update helm install sealed-secrets-controller –namespace kube-system –version 2.13 sealed-secrets/sealed-secrets Install the kubeseal client in our machine wget https://github.com/bitnami-labs/sealed-secrets/releases/download/v0.17.3/kubeseal-linux-amd64 -O /usr/local/bin/kubeseal brew install kubeseal or yum install kubeseal Create and encrypt the secrets using the kubeseal kubectl create secret generic db-password -n test –from-file=dbpassword.txt –dry-run=client -o yaml | kubeseal -o yaml > db-password.yaml The output of the above command is apiVersion: bitnami.com/v1alpha1 kind: SealedSecret metadata:   creationTimestamp: null   name: secret-sql-password   namespace: test spec:   encryptedData:     DB_PASSWORD: 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   template:     data: null     metadata:       creationTimestamp: null       name: db-password       namespace: test In the above manifest file, we can see that our database password is encrypted. Only the sealed-secret-controller within the cluster can decrypt the value. Hence these can be safely stored in a version control. TL Consulting TLConsulting brings its consulting and engineering personnel to application modernisation adoption and implementation by providing range of services – as If you need assistance with your Containers/Kubernetes adoption, please contact us at our kubernetes consulting services  page.

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How to Optimise Kubernetes Costs?

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How to Optimise Kubernetes Costs? The increasing popularity of cloud-native applications has brought technologies like microservices and containers to the frontline. Kubernetes is the most preferred container orchestration platform by most enterprises for automating the deployment, scaling, and management of containers. Most of the Kubernetes implementations thrive to focus on technical aspects and are least bothered by the costs involved with their benefits. In a recent survey from the Cloud Native Computing Foundation (CNCF), 68% of participants reported that their Kubernetes costs increased in the past year, with bills surging more than 20% year-on-year for most organisations. So, how to optimise Kubernetes costs? How much has your Kubernetes-related spend grown in the last 12 months?   Source:  FinOps Foundation survey When looking at optimising the infrastructure costs, enterprises consider various cost-management best practices, but Kubernetes require a specialised approach. Here we will discuss some of the key aspects to reduce overall Kubernetes costs. Size of the infrastructure as per the need: First and foremost, reducing the consumption costs is to have the correct infrastructure size in terms of pods and nodes. While it is always advisable to overprovision to cater to the unusual spikes, leaving the applications to use unlimited resources can lead to unexpected repercussions. For instance, a stateful database container consumes all the available memory in the node due to an application fault; this leads other pods to wait indefinitely for the resources. This can be prevented by setting up Quotas at Pod and namespace levels. Additionally, it is good to enforce the resource request limits at a container level. Other enforcement is to limit the number of pods running on a node, as running many pods can lead to inefficient resource utilisation. Due to this issue, most cloud providers have set hard limits on their managed instances if Kubernetes. Choosing the right tools: A fundamental way of managing any cloud or infrastructure costs is by monitoring utilisation and costs involved for the resources over a period. It allows users to get better insights into storage, memory, computing, network traffic utilisation, etc, and how the costs associated are distributed between them. Irrespective of managed instances or bare-metal clusters, today, almost all the clusters support one or other tools for monitoring to get the basic information. Suppose we are looking at an enterprise with many clusters. In that case, it is always advisable to have a propriety APIM tooling like Dynatrace, New Relic, App D, Splunk, and Prometheus and so have a proper drill-down of the resources and utilisation. It enables SREs and Kubernetes admins to gain a more comprehensive view of the environment and optimise the costs. Use the monitoring insights to analyse and create actions. And start implementing more concrete actions for better utilisation and cost optimisation.  Adopting the Best Practices Across the Delivery Pipeline: DevOps is a proven practice which helps to reduce the barriers between the Development teams and Operations. It allowed users to create robust and flexible deployments through pipelines. One of the possibility of reducing the time and effort to deploy containers to the Kubernetes cluster is to automate the build and deployment pipelines using CI/CD tooling. Also, practices like GitOps are tailor-made to facilitate continuous delivery when manifests are used and version-controlled in a source code repository, greatly reducing the deployment workloads of the team. An Initial investment will be needed to set up a continuous integration to build, test, and publish containers and continuous delivery to deploy these containers on the cluster. Tools like Harness Argo CD will significantly reduce the manual errors that can cause disruptions in the application, leading to less troubleshooting. This reduced workload will allow teams to focus on more valuable tasks such as functionality development, bug fixes, and improving the security posture of the environment. Conclusion: Kubernetes deployments and operations can be very costly if implemented and managed inefficiently. Most enterprises incorporate Kubernetes without any proper practices, tooling, and personal experience in the organisation. However, without proper guidance, it is often will become unoptimised and businesses don’t think about expenses forefront and will be a heavy operational burden in the long run. Considering the above-mentioned practices could save a lot of unnecessary Kubernetes costs and encourage the implementation of best practices from the beginning. TL Consulting TLConsulting brings its consulting and engineering personnel to application modernisation adoption and implementation by providing range of services – as If you need assistance with your Containers/Kubernetes adoption, please contact us at our kubernetes consulting services  page.

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