Data Modeling for Developers
An Introduction to Data Modeling and Why it Matters for Development Teams Data modeling is a critical yet often underrated skill in technology development and within development teams. This article aims to teach you the basics of it and why it is important. This article will introduce you to the concept of a data model, […]
Mounting EFS Volume to Batch Jobs in AWS
Introduction In the realm of distributed computing and batch processing, operational challenges frequently arise that necessitate innovative solutions. A particular challenge we encountered involved a scenario where multiple jobs within our AWS environment were generating tens of thousands of files and storing them in an Amazon S3 bucket. Subsequently, a specific job was tasked with […]
Green with Envy: Improving Python Performance with a Sprinkling of Feature Envy
Python Performance: Issue 2 – Feature Envy Previous Issue Recap In the previous issue we discussed the differences between the “Clean Code” version of calculating the cumulative area of a collection of shapes and “the old fashioned way”. Robert Martin, aka “Uncle Bob”, advocates for a “clean” polymorphic approach to the problem, where each shape […]
Clean Code’s Hidden Impact: Unraveling the Python Performance Paradox
Python Performance: Issue 1 – The Polymorphism Rule Welcome to Python Performance Welcome to the Python Performance blog series. In this series, I will be exploring various performance topics in Python, with the aim to create a list of heuristics to help developers write more performant Python code before they ever start thinking about reaching […]
Revolutionizing Data Management in AWS: The Case for Apache Iceberg Over Traditional Table Formats
Introduction In the digital era, where data is king, the choice of table format for data storage and processing is crucial. Common file formats like CSV, Avro, and Parquet have long been the go-to solutions in various data handling scenarios. However, with the evolving needs of big data and cloud computing, newer and more efficient […]
Serverless, Fan-out Architecture Using SNS, SQS, and Lambda
Case Study: AWS re:invent 2023 featured a lab session on building out serverless architecture which utilized SNS, SQS, and Lambda. I found this lab particularly helpful because it helped me design a solution for a problem where data was being throttled through a single chokepoint. In this case, a large batch of data was being […]
Data Engineering Methodology: From requirements to hand-off
Introduction Joining or starting data projects in large enterprise environments with many stakeholders can be stressful, not to mention a technical implementation nightmare. When the primary stakeholders can’t (or won’t) give the project team clear requirements, the onus falls to the technical implementation team to create order from the chaos and organize the delivery team […]
Remote Development in Sagemaker Studio with VS Code
Disclaimer about Changes to Sagemaker Studio As of Nov. 30 2023, there have been major changes to Sagemaker Studio. Existing customers of Sagemaker Studio will get the default experience now called Sagemaker Studio Classic — this is the Studio experience this article was written for. New Sagemaker Studio customers (and existing customers that choose to […]
The Data Journey
Many organizations share similar challenges with growing their operational capabilities with data. I have given several talks on data lake design and avoiding the “swampiness” of your data lake, invariably there are various pockets of mess or a “junk drawer” where people hide little bits of critical information. A complex data environment with myriad source […]
Boost AI Fairness and Explainability with Amazon SageMaker Clarify
From hiring decisions to loan approvals and even healthcare recommendations, machine learning (ML) impacts our lives daily. Fairness and explainability are crucial in this context. Fairness means data is balanced, and model predictions are fair across groups. Checking for fairness ensures that negative outcomes are fair across all groups, such as age or gender. Explainability […]