Introduction Data pipelines in AWS orchestrate the movement and transformation of data across various AWS services. The core objective of these pipelines is to enable efficient data processing, analysis, and storage, ensuring that data is available where and when it is needed. Maintaining high data quality throughout this process is critical; it ensures reliability, accuracy, […]
By Lena Qian Introduction Machine learning stands as a pivotal element in contemporary data science, fundamentally altering the landscape of predictive analytics and decision-making across various domains. Despite its widespread adoption, a significant impediment persists in the form of overfitting, wherein machine learning models have high accuracy with training data but fail when presented with […]
Introduction The ability to efficiently process, store, and analyze vast amounts of data in real-time is not just a competitive advantage but a necessity for survival and growth. This transformative potential, however, hinges on the capabilities of the teams at the helm, tasked with navigating the intricacies of cloud data systems to unlock valuable insights […]
Introduction In today’s rapidly evolving technological landscape, cloud data engineering projects are at the forefront of innovation, driving businesses towards unprecedented levels of efficiency, scalability, and data-driven decision-making. Central to the success of these projects is the concept of distributed teams – groups of individuals who work together from various geographical locations, leveraging the power […]
The final installment of our blog series on AWS testing methodologies focuses on integration testing. This crucial phase ensures that all components of your application work together seamlessly in a live environment, simulating real-world usage with production code and test data. Below is an outline designed to guide the creation of a comprehensive and informative […]
A deep dive into functional testing for AWS development Introduction In our exploration of advanced testing techniques for AWS development, we’ve delved into powerful tools like moto for unit testing and pytest.mark.parametrize for enhancing test coverage and efficiency. Building on this foundation, we turn our focus to a pivotal tool that bridges the gap between […]
Leveraging Moto and Pytest Introduction In the world of AWS development, ensuring the reliability, efficiency, and correctness of your cloud-based applications is paramount. As cloud solutions grow increasingly complex, so too does the challenge of effectively testing these systems. Traditional testing methods often fall short in the face of AWS’s vast and intricately interconnected services. […]
Introduction In the rapidly evolving field of data engineering, maintaining high-quality, reliable, and efficient data pipelines is crucial for businesses to make informed decisions and stay competitive. One methodology that has been instrumental in achieving these objectives is Test-Driven Development (TDD). At its core, TDD involves a simple, yet powerful cycle: write a failing test […]
Generally and historically, data engineering, analytics, and science efforts focused on progressing from data to knowledge/wisdom. The emergence of LLMs allows for the decomposition of wisdom/knowledge back down to data. This can enable novel discovery, integrate with information systems, and drive automated processes. GenAI Categories Generation: Use bedrock models to create code, text, or images […]
Introduction In today’s fast-paced development environment, the Agile methodology stands out for its emphasis on delivering functional features to users as early as possible. This approach challenges traditional, lengthy development cycles by advocating for the incremental release of a product’s most essential functionalities. By prioritizing early delivery, Agile aims to provide immediate value to users, […]