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IaC: The Game Changer for DevOps

Leave a Comment / DevSecOps /

Infrastructure as Code (IaC) is a critical component of contemporary DevOps practices, offering a plethora of advantages to both development and operations. It allows organisations to automate the creation, setup, and administration of infrastructure resources. In essence, IaC solutions provide teams with the capability to oversee and establish their infrastructure using code. After the code is authored, it defines, arranges, or records the configurations of the pertinent infrastructure elements. Subsequently, teams can automate the provisioning procedure, eliminating the necessity for manual configuration via consoles, or command-line interfaces (CLIs). What is IaC? IaC streamlines infrastructure management by using code to automate resource creation, configuration, and removal. It also facilitates testing and validation before deployment. This centralises configuration for consistent settings and standardised provisioning across different deployments and organisations, solving complexity issues. Moreover, IaC lets teams group infrastructure components, assigning ownership and responsibility to specific members. This simplifies complex deployments and promotes full-service ownership, with a comprehensive record accessible to all. IaC instructions can be monitored, committed, and reverted like regular code, enabling teams to adapt to rapid changes in a CI/CD environment. Benefits of IaC IaC brings several advantages for modern DevOps teams: Streamlined and Reliable Deployments: IaC empowers DevOps teams to expedite and ensure the reliability of infrastructure changes, minimising the potential for human errors during deployment. Enhanced Consistency and Compliance: IaC enforces uniform infrastructure configurations across all environments, reducing downtimes and fortifying security by maintaining compliance with standards. Improved Scalability and Agility: IaC simplifies the process of adjusting infrastructure to meet changing demands, allowing for seamless scaling up or down and swift creation of new environments for testing and development. Living Documentation: IaC code serves as dynamic documentation for your infrastructure, offering a transparent and accessible way for anyone to comprehend the infrastructure’s configuration, particularly valuable when onboarding new team members. Cost Efficiency: IaC significantly reduces infrastructure costs by automating manual processes and optimising resource utilisation. This helps in crafting cost-effective infrastructure configurations and instilling resource management best practices. Security Integration: IaC integrates security best practices directly into infrastructure configurations. Security measures are automated and consistently applied, reducing the vulnerability to security breaches. IaC and CI/CD IaC plays a crucial role in the seamless operation of continuous integration and continuous delivery (CI/CD) pipelines. These pipelines automate the processes of creating, testing, and deploying software applications. When IaC is integrated into CI/CD pipelines, it empowers DevOps teams to automate the setup and configuration of infrastructure at each stage of the pipeline, ensuring that applications are consistently deployed in a compliant environment. Within the CI/CD context, Infrastructure as Code (IaC) proves to be an invaluable resource. It allows teams to consolidate and standardise physical infrastructure, virtual resources, and cloud services, enabling them to treat infrastructure as an abstract concept. This, in turn, lets them channel their efforts into the development of new products and services. Most importantly, IaC, as a critical enabling technology for complete service ownership, ensures that the appropriate team member is always prepared to build, manage, operate, and rectify infrastructure issues, thereby guaranteeing efficiency, security, and agility within the realm of DevOps. Use Cases for IaC in Modern DevOps Streamlining Development and Testing Environments: IaC streamlines the process of creating and configuring development and testing environments. This automation accelerates project kick-offs and ensures that testing mirrors production conditions. Efficient Deployment of New Applications to Production: IaC automates the deployment of new applications to production environments. This automation minimises the potential for errors and guarantees consistent deployments, contributing to enhanced reliability. Controlled Management of Infrastructure Changes: IaC empowers teams to manage infrastructure changes in a controlled and repeatable manner. This approach minimises downtime and provides the safety net of rollback procedures in case of unexpected issues. Dynamic Infrastructure Scaling: IaC facilitates dynamic scaling of infrastructure resources to adapt to fluctuations in demand. This flexibility eliminates the risks of over-provisioning and resource wastage, optimising cost-efficiency. These use cases underscore the indispensable role of IaC in modern DevOps, providing a foundation for agile and reliable development and deployment practices. Tips for using IaC in Modern DevOps Here are some technical tips to maximise the benefits of IaC in your DevOps practices: Choose the right IaC tool: Select an IaC tool that aligns with your team’s skillset and the specific needs of your infrastructure. Common IaC tools include Terraform, AWS CloudFormation, Ansible, Puppet, and Chef. Each has its own strengths and use cases. Version control your IaC code: Treat your IaC code just like application code by storing it in a version control system (e.g., Git). This helps you track changes, collaborate with team members, and roll back to previous configurations if needed. Use modular code structures: Break your IaC code into reusable modules and components. This promotes code reusability and maintains a clear, organised structure for your infrastructure definitions. Automate deployments: Integrate IaC into your CI/CD pipeline to automate the provisioning and configuration of infrastructure. This ensures that infrastructure changes are tested and deployed consistently alongside your application code. Implement infrastructure testing: Write tests for your IaC code to ensure that the desired infrastructure state is maintained. Tools like Terratest and InSpec can help you with this. Automated tests help catch issues early in the development process. Separate configuration from code: Keep your infrastructure configuration separate from your IaC code. Store sensitive data like API keys, secrets, and environment-specific variables in a secure secrets management system (e.g., HashiCorp Vault or AWS Secrets Manager). Document your IaC: Create documentation for your IaC code, including how to deploy, configure, and maintain the infrastructure. Proper documentation makes it easier for team members to understand and work with the code. Adopt a “declarative” approach: IaC tools often allow you to define the desired end state of your infrastructure. This “declarative” approach specifies what you want the infrastructure to look like, and the IaC tool figures out how to make it happen. Avoid an “imperative” approach that specifies step-by-step instructions. Use parameterisation and variables: Make use of variables and parameterisation in your IaC code to

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Deliver Faster Data Value with DataOps

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Deliver Faster Data Value with DataOps The world of data analytics is rapidly accelerating. To stay competitive and agile, organisations need to continually adapt and invest strategically in their data culture, processes, and data platforms to ensure there is strategic alignment to the needs of their business, while enabling better agility, improved time-to-insight & higher quality data delivered to end-users. By leveraging DataOps practices, organisations can deliver faster data value in a cost-effective manner, enabling businesses to adapt and uncover insights with agility. DataOps is a lifecycle practice and collection of workflows, standards, and architecture patterns that drive agility and innovation to orchestrate data movement from data producers to data consumers, enabling the output of high-quality data with improved security. The Key Objectives of DataOps The primary objectives of DataOps (Data Operations) are to streamline and improve the overall management and delivery of data within an organisation. There are many benefits that can be reaped from leveraging DataOps practices which are summarised below: The building blocks of DataOps practices To reap the full benefits of DataOps practices requires strategic planning & investment into the organisation’s data culture. The following are a few building blocks and steps that can be taken to fully embrace DataOps practices: Conclusion: DataOps aims to enhance the overall effectiveness, efficiency, and value of data operations within an organisation, ultimately driving better business outcomes and data-driven decision-making. As the market of data analytics is rapidly accelerating, the adoption of DataOps practices is continuing to gain momentum. Organisations that wholeheartedly embrace DataOps practices and invest in driving and fostering a data-driven culture will be ideally positioned to deliver faster data value to identify opportunities and challenges and make faster decisions with confidence.

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Navigating the Future of Software Development

Cloud-Native, DevSecOps /

Navigating the Future of Software Development The world of software development is rapidly changing. To stay competitive, organisations need to not only keep up with the changes but also strategically adopt methods that improve agility, security, and dependability. The emergence of cloud computing, microservices, and containers has given rise to an innovative approach to creating and deploying software in a cloud-native way. Cloud-native applications are designed to be scalable, resilient, and secure, and they are often delivered through DevOps or DevSecOps methodologies. The markets for cloud-native development, platform engineering, and DevSecOps are all witnessing substantial growth, fuelled by the growing demand for streamlined software development practices and heightened security protocols. This article will explore how the intersection of cloud-native development, platform engineering, and DevSecOps is reshaping the landscape of software development.  Cloud Native Development: Building for the Future Cloud-native development represents a significant transformation in the approach to designing and deploying software. It revolves around crafting applications specifically tailored for cloud environments. These applications are usually constructed from microservices, which are compact, self-contained units collaborating to provide the application’s features. This architectural approach endows cloud-native applications with superior scalability and resilience when compared to conventional monolithic applications.  Key Benefits of Cloud Native Development:  Platform Engineering: The Glue that Holds It Together  Platform engineering is the bridge between development and operations. It is about providing the tools and infrastructure that developers need to build, test, and deploy their applications seamlessly. Think of it as an internal developer platform, offering a standardised environment for building and running software.  Why Platform Engineering Matters:  DevSecOps: Weaving Security into the Fabric  DevSecOps extends the DevOps philosophy by emphasising the integration of security into every phase of the software development lifecycle. It shifts security from being an afterthought to an initiative-taking and continuous process.  The Importance of DevSecOps:  Embarking on the Cloud Native, Platform Engineering, and DevSecOps Odyssey  While there exist various avenues for implementing cloud-native, platform engineering, and DevSecOps practices, the optimal approach hinges on an organisation’s unique requirements. Nevertheless, some overarching steps that organisations can consider include:  In summation, cloud-native development, platform engineering, and DevSecOps are not mere buzzwords; they are strategic mandates for organisations aiming to flourish in the digital era. These practices pave the way for heightened agility, cost-effectiveness, security, and reliability in software development.  Conclusion: As market intelligence attests, the adoption of these practices is not decelerating; it is gaining momentum. Organisations that wholeheartedly embrace cloud-native development, invest in platform engineering, and prioritise DevSecOps will be ideally positioned to navigate the challenges and seize the opportunities of tomorrow. The moment to embark on this transformative journey is now, ensuring that your software development processes are not just future-ready but also primed to deliver value at an unprecedented velocity and with unwavering security. 

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Navigating Cloud Security

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The cloud computing landscape has undergone a remarkable evolution, revolutionising the way businesses operate and innovate. However, this digital transformation has also brought about an escalation in cyber threats targeting cloud environments. The 2023 Global Cloud Threat Report, a comprehensive analysis by Sysdig, provides invaluable insights into the evolving threat landscape within the cloud ecosystem. In this blog post, we will explore the key findings from the report, combine them with strategic recommendations, and provide a comprehensive approach to fortifying your cloud security defences. Automated Reconnaissance: The Prelude to Cloud Attacks The rapid pace of cloud attacks is underscored by the concept of automated reconnaissance. This technique empowers attackers to act swiftly upon identifying vulnerabilities within target systems. As the report suggests, reconnaissance alerts are the initial indicators of potential security breaches, necessitating proactive measures to address emerging threats before they escalate into full-fledged attacks. A Race Against Time: Cloud Attacks in Minutes The agility of cloud attackers is highlighted by the staggering statistic that adversaries can stage an attack within a mere 10 minutes. In contrast to traditional on-premises attacks, cloud adversaries exploit the inherent programmability of cloud environments to expedite their assault. This demands a shift in security strategy, emphasising the importance of real-time threat detection and rapid incident response. A Wake-Up Call for Supply Chain Security The report casts a spotlight on the fallacy of relying solely on static analysis for supply chain security. It reveals that 10% of advanced supply chain threats remain undetectable by traditional preventive tools. Evasive techniques enable malicious code to evade scrutiny until deployment. To counter this, the report advocates for runtime cloud threat detection, enabling the identification of malicious code during execution. Infiltration Amidst Cloud Complexity Cloud-native environments offer a complexity that attackers exploit to their advantage. Source obfuscation and advanced techniques render traditional Indicators of Compromise (IoC)-based defences ineffective. The report underscores the urgency for organisations to embrace advanced cloud threat detection, equipped with runtime analysis capabilities, to confront the evolving tactics of adversaries Targeting the Cloud Sweet Spot: Telcos and FinTech The report unveils a disconcerting trend: 65% of cloud attacks target the telecommunications and financial technology (FinTech) sectors. This is attributed to the value of data these sectors harbour, coupled with the potential for lucrative gains. Cloud adversaries often capitalise on sector-specific vulnerabilities, accentuating the need for sector-focused security strategies. A Comprehensive Cloud Security Strategy: Guiding Recommendations Azure App Service provides a platform for building and hosting web apps and APIs without managing the infrastructure. It offers auto-scaling and supports multiple programming languages and frameworks. Conclusion: The 2023 Global Cloud Threat Report acts as an alarm, prompting organisations to strengthen their cloud security strategies considering the evolving threat environment. With cloud automation, rapid attacks, sector-focused targeting, and the imperative for all-encompassing threat detection, a comprehensive approach is essential. By embracing the suggested tactics, businesses can skilfully manoeuvre the complex cloud threat arena, safeguarding their digital resources and confidently embracing the cloud’s potential for transformation.

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The Modern Data Stack with dbt Framework

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In today’s data-driven world, businesses rely on accurate and timely insights to make informed decisions and gain a competitive edge. However, the path from raw data to actionable insights can be challenging, requiring a robust data platform with automated transformation built-in to the pipeline, underpinned by data quality and security best practices. This is where dbt (data build tool) steps in, revolutionising the way data teams build scalable and reliable data pipelines to facilitate seamless deployments across multi-cloud environments. What is a Modern Data Stack? The term modern data stack (MDS) refers to a set of technologies and tools that are commonly used together to enable organisations to collect, store, process, analyse, and visualise data in a modern and scalable fashion across cloud-based data platforms. The following diagram illustrates a sample set of tools & technologies that may exist within a typical modern data stack: The modern data stack has included dbt as a core part of the transformation layer. What is dbt (data build tool)? dbt (i.e. data build tool) is an open-source data transformation & modelling tool to build, test and maintain data infrastructures for organisations. The tool was built with the intention of providing a standardised approach to data transformations using simple SQL queries and is also extendible to developing models using Python. What are the advantages of dbt? It offers several advantages for data engineers, analysts, and data teams. Key advantages include: Overall, dbt offers a powerful and flexible framework for data transformation and modeling, enabling data teams to streamline their workflows, improve code quality, and maintain scalable and reliable data pipelines in their data warehouses across multi-cloud environments. Data Quality Checkpoints Data Quality is an issue that involves a lot of components. There are lots of nuances, organisational bottlenecks, silos, and endless other reasons that make it a very challenging problem. Fortunately, dbt has a feature called dbt-checkpoint that can solve most of the issues. With dbt-checkpoint, data teams are enabled to: Data Profiling with PipeRider Data reliability just got even more reliable with better dbt integration, data assertion recommendations, and reporting enhancements. PipeRider is an open-source data reliability toolkit that connects to existing dbt-based data pipelines and provides data profiling, data quality assertions, convenient HTML reports, and integration with popular data warehouses.  You can now initialise PipeRider inside your dbt project, this brings PipeRider’s profiling, assertions, and reporting features to your dbt models. PipeRider will automatically detect your dbt project settings and treat your dbt models as if they were part of your PipeRider project. This includes – How can TL Consulting help? dbt (Data Build Tool) has revolutionised data transformation and modeling with its code-driven approach, modular SQL-based models, and focus on data quality. It enables data teams to efficiently build scalable pipelines, express complex transformations, and ensure data consistency through built-in testing. By embracing dbt, organisations can unleash the full potential of their data, make informed decisions, and gain a competitive edge in the data-driven landscape. TL Consulting have strong experience implementing dbt as part of the modern data stack. We provide advisory and transformation services in the data analytics & engineering domain and can help your business design and implement production-ready data platforms across multi-cloud environments to align with your business needs and transformation goals.

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Embracing Serverless Architecture for Modern Applications on Azure

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In the ever-evolving realm of application development, serverless architecture has emerged as a transformative paradigm, and Azure, Microsoft’s comprehensive cloud platform, offers an ecosystem primed for constructing and deploying serverless applications that exhibit unparalleled scalability, efficiency, and cost-effectiveness. In this insightful exploration, we will unravel the world of serverless architecture and illuminate the manifold advantages it bestows when seamlessly integrated into the Azure environment. Understanding Serverless Architecture The term “serverless” might be misleading, as it doesn’t negate the presence of servers; rather, it redefines the relationship developers share with server management. A serverless model empowers developers to concentrate exclusively on crafting code and outlining triggers, while the cloud provider undertakes the orchestration of infrastructure management, scaling, and resource allocation. This not only streamlines development but also nurtures an environment conducive to ingenuity and user-centric functionality. Azure Serverless Offerings Azure’s repertoire boasts an array of services tailored for implementing serverless architecture, among which are: Azure Functions Azure Functions is a serverless compute service that enables you to run event-triggered code without provisioning or managing servers. It supports various event sources, such as HTTP requests, timers, queues, and more. You only pay for the execution time of your functions. Azure Logic Apps Azure Logic Apps is a platform for automating workflows and integrating various services and systems. While not purely serverless (as you pay for execution and connector usage), Logic Apps provide a visual way to create and manage event-driven workflows. Azure Event Grid Azure Event Grid is an event routing service that simplifies the creation of reactive applications by routing events from various sources (such as Azure services or custom topics) to event handlers, including Azure Functions and Logic Apps. Azure API Management While not fully serverless, Azure API Management lets you expose, manage, and secure APIs. It can be integrated with serverless functions to provide API gateways and management features. Azure App Service Azure App Service provides a platform for building and hosting web apps and APIs without managing the infrastructure. It offers auto-scaling and supports multiple programming languages and frameworks. Benefits of Serverless Architecture on Azure Conclusion: Azure’s serverless architecture offers unlimited possibilities for modernized application development, marked by efficiency, scalability, and responsiveness while liberating developers from infrastructure management intricacies. Azure’s serverless computing will definitely unlock the potential of your cloud-native applications. The future of innovation beckons, and it is resolutely serverless.

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The Journey from Traditional Ops to NoOps

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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

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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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The Importance of Feature Engineering in ML Modelling

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When building Machine Learning (ML) models, we often encounter unorganised and chaotic data. In order to transform this data into explainable features, we rely on the process of feature engineering. Feature engineering plays a crucial role in the Cross Industry Standard Process for Data Mining (CRISP-DM). It is an integral part of the Data Preparation Step, responsible for organising the data effectively before it is ready for modeling. The diagram below illustrates the significance of feature engineering (FE) in the data mining process. CRISP-DM Process Model What is Feature Engineering? Feature Engineering (FE) is the process of extracting and organising important information from raw data in such a way that it fits the machine learning (ML) model. Feature Engineering Process(FE) Source: https://www.omnisci.com/technical-glossary/feature-engineering) Why is Feature Engineering Important? Feature Engineering (FE) has many benefits to offer in the CRISP-DM process. They include: Provides more flexibility and less complexity in models Faster data processing Understanding of models becomes easier Better understanding of the problem and questions to be answered Feature Engineering Techniques for Machine Learning (ML) Below is a list of feature engineering techniques and we will summarise each: Imputation: Handling Outliers Log Transformation One-Hot Encoding Scaling 1. Imputation Missing values is one of the most typical problems when it comes to data preparation. Human errors and dataflow interruptions are some of the major contributors to this problem. Moreover, missing values can detrimentally impact the performance of the ML models. An example of an imputation of NA values with Zero Imputation is frequently employed in healthcare research, such as when dealing with patient records that may have missing values for certain medical measurements. By imputing the missing data using methods like mean imputation or regression imputation, researchers can ensure that a complete dataset is available for analysis, allowing for more accurate assessments and predictions. 2. Handling Outliers Handling Outliers within datasets is an important technique with the purpose of creating an accurate representation of the data. This step must be completed prior to the model training step. There are various methods of handling outliers that include removal, replacing values, capping, and discretization. These methods will be discussed in detail in future blogs. An example of outliers Handling outliers is essential in financial analysis, for instance, when examining stock market data. By detecting and appropriately treating outliers using techniques like Winsorization or trimming, analysts can ensure that extreme values do not unduly influence statistical measures, leading to more robust and reliable insights and decision-making. 3. Log Transformation Log Transformation is one of the most prevalent methods used by data professionals. The technique transforms a skewed distribution of data into normally distributed or slightly skewed data. Therefore, making the data approximate for normal applications is required for different kinds of data analysis. Examples of Log Transformed Data Log transformation is commonly applied in skewed data distributions, such as when dealing with income or population data. By taking the logarithm of the values, the skewed distribution can be transformed into a more symmetric shape, facilitating more accurate modeling, analysis, and interpretation of the data. 4. One Hot Encoding One-Hot Encoding is a technique of preprocessing categorical variables into ML models. The encoding transforms a category variable into a binary feature for each category. It typically assigns a value of ‘1’ to the binary feature it corresponds to and all other binary features are set to ‘0’. An example of One Hot Encoding One-hot encoding is widely used in categorical data processing, such as in natural language processing tasks like sentiment analysis. By converting categorical variables into binary vectors, each representing a unique category, one-hot encoding enables machine learning algorithms to effectively interpret and utilize categorical data, facilitating accurate classification and prediction tasks. 5. Scaling Feature scaling is one of the hardest problems in data science to get right. However, it is not a mandatory step for all machine learning models. It is only applicable to distance-based machine learning models. The training model process requires data with a known set of features that need to be scaled up or down where it is deemed appropriate. The outcome of the scaling operation transforms continuous data to be similar in terms of range. The most popular techniques for scaling are Normalization and Standardisation, which will be discussed in detail in future blogs. Examples of Scaling Scaling is often used in image processing, such as when resizing images for a computer vision task. Scaling the images to a consistent size, regardless of their original dimensions ensures that the images can be properly processed and analysed, allowing for fair comparisons and accurate feature extraction in tasks like object recognition or image classification. Feature Engineering Tools There are a set of feature engineering tools that are popular in the market in terms of the capabilities it provides. We have listed a few of our recommendations: FeatureTools AutoFeat TsFresh OneBM ExploreKit Conclusion In summary, Feature Engineering is a crucial step in the CRISP-DM process before we even think about training our machine learning models. One of the core advantages include the training time of models is reduced significantly. As a result, it allows for a drastic reduction of cost in terms of utilisation of expensive computing resources. In this article, we learned a number of feature engineering techniques and tools that are used in the industry. Here at TL Consulting, our data consultants are experts at using feature engineering techniques to build highly accurate machine learning models, enabling us to deliver high-quality outcomes to support our customer’s data analytics needs. TL Consulting provides advisory and transformation services in the data analytics & engineering domain and has helped many organisations achieve their digital transformation goals. Visit TL Consulting’s data-engineering 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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Measuring Success Metrics that Matter

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Measuring DevSecOps Success: Metrics that Matter In today’s fast-paced digital world, security threats are constantly evolving, and organisations are struggling to keep up with the pace of change. According to a recent Cost of a Data Breach Report by IBM, the average total cost of a data breach reached a record high of $4.35 million, with the average time to identify and contain a data breach taking 287 days. To mitigate these risks, enterprises are turning to DevSecOps, an approach that integrates security into the software development process. However, just adopting DevSecOps is not enough. Organisations must continually evaluate the effectiveness of their DevSecOps practices to ensure that they are adequately protecting their systems and data. As more businesses embrace DevSecOps, measuring DevSecOps success has become a critical component of security strategy. DevSecOps KPIs enable you to monitor and assess the advancement and effectiveness of DevSecOps practices within your software development pipeline, offering comprehensive insights into the determinants that impact success. These critical indicators facilitate the evaluation and measurement of collaborative workflows by development, security, and operations teams. By utilising these metrics, you can monitor the progress of your business objectives, such as expedited software-delivery lifecycles, enhanced security, and improved quality. Moreover, these key metrics furnish vital data for transparency and control throughout the development pipeline, facilitating the streamlining of development and enhancement of software security and infrastructure. Additionally, you can identify software defects and track the average time required to rectify those flaws. Number of Security Incidents One critical metric to track is the number of security incidents. Tracking the number of security incidents can help organisations identify the most common types of incidents and assess the frequency of incidents. By doing so, they can prioritise their efforts to address the most common issues and improve their overall security posture. Organisations can track the number of security incidents through various tools such as security incident and event management (SIEM) systems or logging and monitoring tools. By analysing the data from these tools, one can identify patterns and trends in the types of security incidents occurring and use this information to prioritise their security efforts. For instance, if an organisation finds that phishing attacks are the most common type of security incident, they can focus on training employees to be more vigilant against phishing attempts. Time to Remediate Security Issues Another essential metric to track is the time it takes to remediate security issues. This metric can help organisations identify bottlenecks in their security processes and improve their incident response time. By reducing the time, it takes to remediate security issues, organisations can minimise the impact of security incidents and ensure that their products remain secure. This metric can be tracked by setting up a process to monitor security vulnerabilities and track the time it takes to fix them. This process can include automated vulnerability scanning and testing tools, as well as manual code reviews and penetration testing. By tracking the time it takes to remediate security issues, organisations can identify areas where their security processes may be slowing down and work to improve those processes. Code Quality Metrics Code quality is another important aspect of DevSecOps, and tracking code quality metrics can provide valuable insights into the effectiveness of DevSecOps practices. Code quality metrics such as code complexity, maintainability, and test coverage can be tracked using code analysis tools such as SonarQube or CheckMarx. These tools can provide insights into the quality of the code being produced and identify areas where improvements can be made. For example, if a business finds that their code has high complexity, they can work to simplify the code to make it more maintainable and easier to secure. Compliance Metrics Compliance is another essential aspect of security, and measuring compliance metrics can help organisations ensure that they are meeting the necessary regulatory and industry standards. Tracking compliance metrics such as the number of compliance violations and the time to remediate them can help organisations identify compliance gaps and address them. Additionally, to ensure security, monitoring, vulnerability scanning, and vulnerability fixes are regularly conducted on all workstations and servers. Compliance metrics such as the number of compliance violations can be tracked through regular compliance audits and assessments. By monitoring compliance metrics, organisations can identify areas where they may be falling short of regulatory or industry standards and work to address those gaps. User Satisfaction Finally, tracking user satisfaction is an essential metric to ensure that security is not hindering user experience and that security is not compromising the overall quality of the product. Measuring user satisfaction can help organisations ensure that their security practices are not negatively impacting their users’ experience and that they are delivering a high-quality product. User satisfaction can be measured through surveys or feedback mechanisms built into software applications. By gathering feedback from users, businesses can identify areas where security may be impacting the user experience and work to improve those areas. For example, if users are finding security measures such as multi-factor authentication too cumbersome, organisations can look for ways to streamline the process while still maintaining security. In conclusion, measuring DevSecOps success is crucial for organisations that want to ensure that their software products remain secure. By tracking relevant metrics such as the number of security incidents, time to remediate security issues, code quality, compliance, and user satisfaction, organisations can evaluate the effectiveness of their DevSecOps practices continually. Measuring DevSecOps success can help organisations identify areas that need improvement, prioritise security-related tasks, and make informed decisions about resource allocation. To read more on DevSecOps security and compliance, please visit our DevSecOps services page.

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