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Navigating Cloud Security

Cloud-Native /

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

Data & AI /

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

Data & AI /

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

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The Importance of Feature Engineering in ML Modelling

Data & AI /

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

Cloud-Native, DevSecOps /

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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The Hidden Costs of Outdated Technology

Cloud-Native /

As the pace of technological advancement continues to soar, numerous enterprises find themselves struggling to keep up with the latest innovations. However, clinging to outdated technology can unleash a cascade of detrimental effects on productivity, employee morale, and the company’s bottom line. While postponing the upgrade of antiquated systems might appear financially prudent, the reality is that it often exacts a higher toll on businesses than the savings it promises. In this article, we will delve into the ways in which reliance on obsolete technology can inflate expenses, compelling businesses to confront the imperative of considering long-term costs. As systems grow older, they demand increasingly laborious and specialised maintenance, coupled with exorbitant fees for updates, patches, and licenses to ensure compatibility with modern counterparts. Astoundingly, studies estimate that a staggering 75% of the average IT budget is allocated solely to maintaining existing systems. Brace yourself as we uncover the hidden costs lurking behind the façade of outdated technology. Security vulnerabilities Security vulnerabilities: Outdated technology often falls behind in terms of the latest security features and patches, leaving it vulnerable to cyber threats. Hackers and malicious actors continuously adapt their tactics, while obsolete systems may lack the necessary safeguards to protect sensitive data and prevent breaches. The consequences of data breaches, compliance violations, and reputational damage can be significant. Unsupported systems are especially prone to security breaches and cyber-attacks, potentially exposing valuable data and intellectual property. In Australia, the average cost of a data breach in 2023 has skyrocketed to $5 Million, marking a substantial 13% increase from previous years. These astonishing statistics underscore the urgent necessity for businesses to prioritise the security of their technology infrastructure. Diminished Efficiency Diminished Efficiency: Outdated technology frequently lacks the latest cutting-edge features and capabilities that are essential for streamlining business processes and maximising productivity. Therefore, these obsolete systems tend to exhibit slower performance, decreased reliability, and an increased propensity for errors and downtime. This predicament forces employees to grapple with inefficient tools, resulting in the squandering of valuable time and resources. In fact, studies have revealed that maintaining outdated systems can lead to a staggering 30% decrease in productivity. This inefficiency incurs significant costs, both in terms of operational expenses and lost opportunities. The combination of sluggishness, unreliability, and a heightened vulnerability to errors or downtime culminates in a noticeable decline in overall efficiency. It is evident that clinging to obsolete systems not only hinders progress but also presents a substantial financial burden for enterprises seeking sustained success. Compatibility issues Compatibility issues: Outdated technology often faces compatibility issues when integrating with newer systems or software. For example, an older CRM system may struggle to sync data with a modern marketing automation platform, hindering information flow across departments. These issues impede data sharing, communication, and collaboration within the organisation. Workarounds and manual processes become necessary, consuming time, and increasing the risk of errors. Incompatibility with external systems or partners can result in missed opportunities and higher operational costs. Addressing these challenges is crucial to avoid inefficiencies, missed opportunities, and unnecessary expenses. Missed Innovation and Competitive Advantage Missed Innovation and Competitive Advantage: Enterprises that rely on outdated technology face challenges in keeping pace with competitors who embrace new and innovative solutions. Adopting modern technology can empower businesses to automate processes, optimise data gathering and analysis, elevate customer experiences, and stay ahead of industry trends. By neglecting to upgrade, businesses risk missing out on opportunities for growth, efficiency, and gaining a competitive edge. Embracing newer technology not only positions businesses for growth but also offers enhanced security features. Additionally, there can be tax benefits associated with operating costs. Unlike capital expenses, Software as a Service (SaaS) or Platform as a Service (PaaS) can be classified as operating costs, allowing for a 100% write-off instead of a smaller portion. Employee Dissatisfaction and Turnover Employee Dissatisfaction and Turnover: Outdated technology can have a detrimental impact on employee morale and job satisfaction. The frustration caused by slow and inefficient tools can significantly reduce productivity and breed discontent among employees. Over time, this dissatisfaction can contribute to higher turnover rates as employees actively seek technologically advanced workplaces that enable them to perform their duties more effectively. The challenges of dealing with sluggish programs and constant issues can generate frustration and stress for both leadership and general employees. It becomes challenging to excel in one’s role when the software fails to keep pace. Consequently, employee morale suffers, leading to an unfortunate increase in turnover. In conclusion, the hidden costs of outdated technology can have detrimental effects on businesses, including decreased productivity, security risks, missed opportunities, and employee dissatisfaction. To overcome these challenges, it is crucial for enterprises to prioritise investments in modern technology solutions. By embracing innovative systems and staying ahead of technological advancements, businesses can enhance productivity, improve security, capitalise on new opportunities, and foster a positive work environment. Investing in updated technology is an investment in the long-term success and sustainability of the business, ultimately leading to greater efficiency, profitability, and competitive advantage. Get in touch with our application modernisation experts at TL Consulting to fast forward your legacy.

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Top Cloud Plays in 2023: Unlocking Innovation and Agility

Cloud-Native, Data & AI, DevSecOps /

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

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Top 5 Data Engineering Techniques in 2023

Data & AI /

Top 5 Data Engineering Techniques in 2023 Data engineering plays a pivotal role in unlocking the true value of data. From collecting and organising vast amounts of information to building robust data pipelines, it is a complex and vital capability that is becoming more prevalent in today’s complex technology world. There are various intricacies in data engineering, while exploring its challenges, techniques, and the crucial role it plays in enabling data-driven decision making. In this blog post, we explore the top 5 trending data engineering techniques that are expected to make a significant impact in 2023. TL Consulting see Data engineering as an essential discipline that plays a critical role in maximising the value of key data assets. In recent years, several trends and technologies have emerged, shaping the field of data engineering, and offering new opportunities for businesses to harness the power of their data. These techniques enable better and more efficient management of data, unlocking valuable insights and helping enable innovation in a more targeted manner. Since Data engineering is a rapidly evolving domain, there is a continuous need to introduce new data engineering techniques and technologies to handle the increasing volume, variety, and velocity of data. Data Engineering Techniques DataOps One such trend is DataOps, an approach that focuses on streamlining and automating data engineering processes leveraging agile software engineering and DevOps. By implementing DataOps principles, organisations can achieve collaboration, agility, and continuous integration and delivery in their data operations. This approach enables faster data processing and analysis by automating data pipelines, version controlling data artefacts, and ensuring the reproducibility of data processes aligning to DevOps and CICD practices. DataOps improves quality, reduces time-to-insights, and enhances collaboration across data teams while promoting a culture of continuous improvement. DataMesh Another significant trend is Data Mesh, which addresses the challenges of scaling data engineering in large enterprises. DataMesh emphasises domain-oriented ownership of data and treats data as a product. By adopting DataMesh, organisations can establish cross-functional data teams, where each team is responsible for a specific domain and the associated data products. This approach promotes “self-service” data access through a data platform capability, empowering domain experts to manage and govern their data. Furthermore, as the data mesh gains adoption and evolves, with each team that shares their data as products, enabling data-driven innovation. Data Mesh enables scalability, agility, and improved data quality by distributing data engineering responsibilities across the organisation. Data Streaming Real-time data processing has also gained prominence with the advent of data streaming technologies. Data streaming allows organisations to process and analyse data as it arrives, enabling immediate insights and the ability to respond quickly to dynamic business conditions. Platforms like Apache Kafka, Apache Flink, Azure Stream Analytics and Amazon Kinesis provide scalable and fault-tolerant streaming capabilities. Data engineers leverage these technologies to build real-time data pipelines, facilitating real-time analytics, event-driven applications, and monitoring systems to further. This type of capability can lead to optimised real-time stream processing and can gain valuable insights into understanding of customer behaviours and trends. These insights can help you make timely and informed decisions to drive your business growth. Machine Learning The intersection of data engineering and machine learning engineering has become increasingly important. Machine learning engineering focuses on the deployment and operationalisation of machine learning models at scale. Data engineers collaborate with data scientists to develop scalable pipelines that automate the training, evaluation, and deployment of machine learning models. Technologies like TensorFlow Extended (TFX), Kubeflow, and MLflow are utilised to operationalise and manage machine learning workflows effectively. Real-time data streaming offers numerous benefits and empowers you to make informed business decisions. Data Catalogs Lastly, from our experience, Data Catalogs and metadata management solutions have become crucial for managing and discovering data assets. As data volumes grow, organising and governing data effectively becomes challenging. Data cataloguing enables users to search and discover relevant datasets and helps create a single source of knowledge for understanding business data. Metadata management solutions facilitate data lineage tracking, data quality monitoring, and data governance, ensuring data assets are well-managed and trusted. Data cataloguing accelerates analysis by minimising the time and effort that analysts spend finding and preparing data. These trends and technology advancements are reshaping the data engineering landscape, providing organisations with opportunities to optimise their data assets, accelerate insights, and make data-driven decisions with confidence. By embracing these developments, understanding your data assets and associated value, can lead to smarter informed business decisions. By embracing these trending techniques, organisations can transform their data engineering capabilities to enable some of the following benefits: Accelerated data-driven decision-making. Enhanced customer insights, transparency and understanding of customer behaviours. Improved agility and responsiveness to market trends. Increased operational efficiency and cost savings. Mitigated risks through robust data governance and security measures. Data engineering is vital for optimising organisational data assets since these are an important cornerstone of any business. It ensures data quality, integration, and accessibility, enabling effective data analysis and decision-making. By transforming raw data into valuable insights, data engineering empowers organisations to maximize the value of their data assets and gain a competitive edge in the digital landscape. TL Consulting specialises in data engineering techniques and solutions that drive transformative value for businesses enabling the above benefits. We leverage our expertise to design and implement robust data pipelines, optimize data storage and processing, and enable advanced analytics. Partner with us to unlock the full potential of your data and make data-driven decisions with confidence. Visit TL Consulting’s data services 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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The State of Observability 2023

Cloud-Native, DevSecOps /

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

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