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A New Milestone in Data MasteryWe are absolutely thrilled to announce a major milestone at axcess.io: We have officially been recognized as an AWS Data & Analytics Competency Partner!This designation from Amazon Web Services (AWS) is a testament to the hard work and deep expertise of our team. It validates our proven ability to help customers manage, govern, and analyze their data at scale, driving tangible business outcomes using AWS services.But what does this mean for our current and future clients? In short, it means greater trust, deeper expertise, and faster time-to-value for your most complex data initiatives. What is the AWS Data & Analytics Competency?The AWS Competency Program is designed to identify and vet AWS Partners who have demonstrated technical proficiency and proven customer success in specialized solution areas.The Data & Analytics Competency specifically recognizes partners who have built solutions and demonstrated deep expertise in one or more of the following key areas on AWS:Data Lakes and Data Warehouses: Building scalable, secure, and cost-effective central data repositories.Big Data and Streaming: Handling massive volumes of data and real-time data ingestion.Data Governance and Quality: Ensuring data is reliable, compliant, and trustworthy.Data Visualization and Business Intelligence (BI): Turning complex data into actionable insights for decision-makers.To achieve this status, axcess.io underwent a rigorous validation process, demonstrating technical proficiency, successful project execution, and specialized knowledge using AWS tools like Amazon Redshift, Amazon EMR, AWS Glue, and Amazon S3. The Value Proposition: Why Partner with an AWS Competency Holder?When you partner with an AWS Data & Analytics Competency Partner like axcess.io, you gain immediate confidence that you are working with a company that:A. Proven Expertise, Certified by AWSThe Competency is not just a badge; it is an AWS-verified affirmation of our capability. It eliminates the guesswork, ensuring that our technical approach to your data challenges aligns with AWS best practices and modern architectural patterns.B. Accelerated Time-to-ValueOur specialized experience means we don't have to start from scratch. We leverage repeatable, optimized frameworks for Data Lake modernization, Data Migration, and BI implementations, allowing you to access insights and realize ROI faster.C. Innovative and Cost-Optimised SolutionsData solutions must be designed for scale and efficiency. We are experts at architecting solutions that use the right AWS services at the right time, minimizing operational costs while maximizing performance and security. Our Data & Analytics Focus Areas on AWSAt axcess.io, we are leveraging our newly validated competency to focus on helping clients with:Building Modern Data Platforms: Architecting and implementing cloud-native Data Lakes and Data Warehouses.Data Migration and Modernization: Seamlessly moving legacy data platforms to AWS cloud services.Advanced Analytics and ML: Integrating sophisticated AI/ML services (like Amazon SageMaker) to transform raw data into predictive assets.Data Governance and Compliance: Implementing robust security and compliance standards (e.g., GDPR, HIPAA) within your AWS data environments.

The Shift Towards Assisted CodingThe world of software development is rapidly evolving, with developers increasingly relying on AI-powered tools to streamline workflows, reduce manual effort, and accelerate project delivery. Assisted coding tools are becoming indispensable in modern software engineering, enabling teams to tackle complex challenges with greater efficiency and precision.In this era of digital transformation, legacy systems—particularly those built on older frameworks like ASP.NET—pose significant challenges. Modernizing these systems requires not only technical expertise but also innovative tools that can simplify the process. Enter Amazon Q, AWS’s Generative AI-powered conversational assistant, which is designed not only to build cloud native AWS applications but is also proving to be a game-changer in the modernization of legacy Windows workloads.Strengths of Amazon Q in ModernizationAmazon Q stands out as a powerful tool for modernizing legacy systems, thanks to its ability to provide actionable insights, automate repetitive tasks, and guide developers through complex technical challenges. Its strengths lie in:Intelligent Recommendations: Offering tailored suggestions for containerization, dependency management, and deployment strategies.Seamless Integration: Working effortlessly with AWS services like EKS, making it an ideal choice for cloud migrations.Contextual Understanding: Interpreting legacy codebases and providing solutions that align with the existing architecture.These capabilities make Amazon Q an invaluable partner for teams looking to modernize legacy workloads without compromising on stability or performance.Case Study: Modernizing Legacy ASP.NET Applications with Amazon QIn a recent project, we partnered with a leading financial services provider to modernize their legacy ASP.NET applications by containerizing and migrating them to AWS Elastic Kubernetes Service (EKS). Amazon Q played a pivotal role in overcoming the challenges associated with this transformation. Here are three key highlights of how Amazon Q contributed to the project’s success:Simplifying Containerization of Legacy ASP.NET ApplicationsLegacy ASP.NET applications, built on the .NET Framework, are not inherently designed for containerization. Amazon Q provided step-by-step guidance on how to containerize these applications effectively. As a first but vital step, it recommended using a Windows container image as the base image and ensured compatibility with Internet Information Services (IIS), a critical component of the legacy applications. By automating much of the containerization process, Amazon Q significantly reduced the time and effort required to prepare the applications for migration. Resolving Complex Dependency ChallengesOne of the major hurdles in the project was managing dependencies like the Oracle Client, which was essential for the application’s functionality. Amazon Q provided detailed instructions on: -Installing the Oracle Client within the container.-Handling scenarios where both 32-bit and 64-bit Oracle Client versions were required.-Configuring IIS to accommodate these dependencies seamlessly.This level of granular guidance ensured that the applications ran smoothly in the new containerized environment. Streamlining Build and Deployment PipelinesAmazon Q also played a crucial role in optimizing the build and deployment process. It helped the team determine whether the applications could be built using a standard pipeline or required additional steps. For applications lacking .csprojg and .sln files, Amazon Q suggested building them using Visual Studio, while the Dockerfile handled the publishing and deployment processes. This approach minimized manual intervention and ensured a smooth transition to AWS EKS.Final ThoughtsAmazon Q and Q Transform (Currently in Preview) has emerged as a go-to tool for modernizing legacy Windows workloads, as demonstrated by its pivotal role in the successful migration of legacy ASP.NET applications to AWS EKS. By simplifying containerization, resolving complex dependencies, and streamlining build pipelines, Amazon Q enabled us to deliver a robust and scalable solution.As organizations continue to embrace cloud technologies, tools like Amazon Q will play an increasingly critical role in bridging the gap between legacy systems and modern infrastructure.Key Takeaways:Amazon Q is a powerful ally for modernizing legacy Windows workloads.Its intelligent recommendations and contextual understanding simplify complex challenges.By leveraging Amazon Q, teams can accelerate modernization efforts while maintaining stability and performance.

In today’s fast-paced business world, organizations need instant access to insights to stay competitive. Yet, turning raw data into actionable intelligence remains slow, manual, and error prone. Reports often take days or weeks, while analysts spend valuable time preparing datasets instead of driving strategic analysis. At Axcess.io, we developed AutoQ — a serverless, AI-powered automation framework for Amazon QuickSight Q — to eliminate these challenges and redefine business intelligence at scale. The Analytics BottleneckTraditional BI workflows rely heavily on repetitive, manual tasks. Here’s a typical process: 1. Identify the correct data source. 2. Build and configure a dataset manually. 3. Wait for ingestion and load times. 4. Rename columns and add descriptions for clarity (often skipped). 5. Create a Q Topic to allow natural language queries. 6. Configure access and permissions. 7. Repeat the entire cycle for every new dataset or request. This manual treadmill creates major challenges: • Slow time-to-insight, delaying critical decisions. • High risk of errors due to repetitive manual steps. • Inability to scale as data and user demands grow. The result is inefficiency, high costs, and frustration for both analysts and business users. Introducing AutoQ: The Automated BI FactoryAutoQ transforms BI with an automated, event-driven pipeline on AWS. Think of it as an assembly line for data — where raw Athena tables are automatically discovered, enriched, and turned into queryable topics within QuickSight Q.Key capabilities include: • Intelligent Discovery – Automatically identifies and catalogs Athena tables. • AI-Powered Metadata – Amazon Bedrock (Claude 3 Haiku) enriches columns with business-friendly names, descriptions, and synonyms. • Instant Topic Creation – Datasets and Q Topics are built programmatically in QuickSight. • Scalable Automation – Handles thousands of tables with the same ease as a handful. With AutoQ, reports that used to take days are ready in minutes. How AutoQ WorksAutoQ integrates AWS services, AI, and automation into one seamless process: 1. A configuration file is updated with dataset or topic details. 2. Lambda functions are triggered to manage schema discovery and dataset setup. 3. Glue Catalog and Athena provide schema details. 4. Data is ingested into QuickSight SPICE for performance. 5. Amazon Bedrock enriches columns with descriptive names and synonyms. 6. Enriched datasets are packaged into Q Topics, ready for natural language queries. 7. Updates happen automatically — refreshing schemas, metadata, and permissions without manual work. From a single trigger, AutoQ executes a cascade of events that results in fully enriched, ready-to-use datasets. The Business ImpactAdopting AutoQ delivers tangible and measurable benefits: • 95% reduction in manual BI setup effort. • Time-to-insight reduced from days to minutes. • Improved trust in data through consistent AI-generated metadata. • Lower total cost of ownership with AWS serverless architecture. • Higher productivity — analysts focus on insights, not repetitive setup. For example, a raw Athena table with cryptic, technical column names is automatically transformed into a business-friendly QuickSight Q Topic. Users see clear names, detailed descriptions, and synonyms that make natural language queries intuitive. This means executives and business users can simply ask questions in plain English — and get reliable answers instantly. Why AutoQ MattersBI automation is more than a time-saver — it’s a competitive advantage. AutoQ ensures that: • Business Users are empowered to self-serve insights without waiting on IT. • Data Teams spend time on strategic analysis, not repetitive setup. • Executives make faster, smarter decisions based on trusted data. By bridging the gap between raw data and ready-to-use insights, AutoQ helps organizations unlock the full value of their data, at scale.