Explore how big data and cloud computing revolutionise analytics, offering scalability and agility to drive growth and innovation across industries.
Unleashing the Power of Big Data in the Cloud Read More »
Cloud-Native, Data & AI
Microservices architecture has become the go-to choice for modern companies, offering unparalleled flexibility and scalability, especially in cloud-native environments. However, this architectural shift introduces new and unique security challenges, with authorisation standing out as a paramount concern.
Building a Secure & Scalable Microservices Authorisation Architecture with Kubernetes Read More »
Cloud-Native
For organisations building Data Lakehouse platforms, an important consideration is defining a structured approach to designing data ingestion patterns, encompassing best practices for each data workload that is ingested into the Data Lakehouse environment. This is crucial for organisations looking to scale with big data analytics and enable more data consumers to perform efficient decision-making, with access to enriched data in real-time. In this article, we explore some of the best practices, key considerations and common pitfalls to avoid when defining the data ingestion patterns into the Data Lakehouse platform. The Data Lakehouse Paradigm The Data Lakehouse is a modern architecture that merges the expansive storage of a Data Lake with the structured data management of a Data Warehouse. The Data Lakehouse is the latest paradigm in Data Platform Architecture, combining the capabilities and benefits of the Data Warehouse and Data Lake into a flexible, comprehensive, and unified platform to serve many use cases including: Defining the data ingestion design patterns for the Data Lakehouse requires defining a structured approach to collect and manage data workloads in the lakehouse while ensuring there are robust data quality and security controls in place as part of the data ingestion. Key Considerations for Data Ingestion Patterns: Common Pitfalls to Avoid Conclusion In summary, the Data Lakehouse is a pathway to unlocking the full potential of your data, fostering innovation, and driving business growth. With the right components and strategic approach, your organisation can leverage Data Lakehouses to stay ahead of the curve, while maintaining a unified, cost-effective data platform deployed on your Cloud environment. Designing correct data ingestion patterns will enable the Data Lakehouse platform to run efficient and scalable data pipelines to serve big data analytics use cases. TL Consulting are a solutions partner with Microsoft in the Data & AI domain. We offer specialised and cost-effective data analytics & engineering services tailored to our customer’s needs to extract maximum business value. Our certified cloud platform & data engineering team are tool-agnostic and have high proficiency working with traditional and cloud-based data platforms. Refer to our service capabilities to find out more.
Key Considerations for Data Ingestion into the Data Lakehouse Read More »
Data & AI
Accelerate State of DevOps Report (2023) by DORA In the recently released Accelerate State of DevOps Report 2023 by the DevOps Research and Assessment (DORA) team, the spotlight is on the pivotal role of user-centricity in attaining organisational excellence. This year’s comprehensive survey, spanning more than 36,000 professionals from over 1,000 organisations, uncovers a direct correlation between prioritising a user-centric approach and achieving heightened performance. Teams that prioritise understanding and aligning with user needs showcase an impressive 40% boost in organisational performance. This enhanced performance results from various factors, including: Beyond user-centricity, the report underscores several other pivotal practices that set high-performing teams apart: The DORA research team provides actionable recommendations for organisations aiming to elevate their performance: By incorporating these recommendations, organisations can empower their teams to deliver high-quality software swiftly and reliably, ultimately achieving superior organisational performance. The link to download the report can be found here: https://cloud.google.com/devops/state-of-devops
Prioritising User-Centricity to Attain Organisational Excellence Read More »
DevSecOps
As organisations continue to collect more diverse data, it is important to consider a strategic & viable approach to unify and streamline big data analytics workloads, ensuring it is optimised to drive data-driven decisions and enable teams to continue innovating and create a competitive edge. Traditionally, data warehousing has supported the need for ingesting and storing structured data, and the data lake as a separate platform for storing semi-structured/unstructured data. The data lakehouse combines the benefits and capabilities between both and bridges the gap by breaking silos created by the traditional/modern data warehouse, enabling a flexible and modern data platform to serve big data analytics, machine learning & AI workloads in a uniform manner. What is a Data Lakehouse? A data lakehouse is a modern architecture that merges the expansive storage of a data lake with the structured data management of a data warehouse. Data lakehouse platforms offer a comprehensive & flexible solution for big data analytics including Data Engineering and real-time streaming, Data Science, and Machine Learning along with Data Analytics and AI. Key Benefits of Implementing a Data Lakehouse: There are many benefits that can be derived from implementing a data lakehouse correctly: Azure Data Lakehouse Architecture: The following are some of the key services/components that constitute a typical Data Lakehouse platform hosted on Microsoft Azure: Key Considerations when transitioning to a Data Lakehouse: The following are key considerations that need to be factored in when transitioning or migrating from traditional data warehouses/data lakes to the Data Lakehouse: Implementing a Data Lakehouse: Quick Wins for Success The following are small, actionable steps that organisations can take when considering to implement a Data Lakehouse platform: Conclusion In summary, the data lakehouse is a pathway to unlocking the full potential of your data, fostering innovation, and driving business growth. With the right components and strategic approach, your organisation can leverage Data Lakehouses to stay ahead of the curve, while maintaining a unified, cost-effective data platform deployed on your Cloud environment. TL Consulting are a solutions partner with Microsoft in the Data & AI domain. We offer specialised and cost-effective data analytics & engineering services tailored to our customer’s needs to extract maximum business value. Our certified cloud platform & data engineering team are tool-agnostic and have high proficiency working with traditional and cloud-based data platforms and open-source tools. Refer to our service capabilities to find out more.
Harnessing the Power of the Data Lakehouse Read More »
Cloud-Native, Data & AI
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
IaC: The Game Changer for DevOps Read More »
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.
Navigating the Future of Software Development Read More »
Cloud-Native, DevSecOps
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.
Navigating Cloud Security Read More »
Cloud-Native
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.
The Modern Data Stack with dbt Framework Read More »
Data & AI
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.
Embracing Serverless Architecture for Modern Applications on Azure Read More »
Cloud-Native