Object, Block, and File Store on the Cloud
Storing, retrieving, and sharing American Express data in a simple, reliable, and scalable way—using AWS, Azure, and GCP.
Designing systems for performance, dependability, security, usability, modifiability, reusability, supportability, deployability, and testability.
Storing, retrieving, and sharing American Express data in a simple, reliable, and scalable way—using AWS, Azure, and GCP.
Triggering serverless apps and services—using AWS Lambda, Azure Functions, and GCP Cloud Functions.
Deploying web apps and services—using AWS Elastic Beanstalk, Azure Cloud Services, and Google App Engine.
Deploying containers—using Amazon Elastic Kubernetes Service, Azure Kubernetes Service, and Google Kubernetes Engine.
Spinning up virtual machines—using Amazon Elastic Compute Cloud, Azure Virtual Machine, and Google Compute Engine.
Understanding the various ways cloud services are owned and managed—on AWS, Azure, and GCP.
Comparing these three popular programming paradigms in Julia, Python, and R.
Managing DAG workflows—using Airflow on AWS, Azure, and GCP.
Splitting up data processing and computation of flight data over clusters of nodes—using Spark for Julia, Python, and R.
—using Apache Kafka on AWS, Azure, and GCP.
Managing time series IoT sensor data—using Amazon Timestream, Azure CosmosDB, and GCP Cloud Bigtable/BigQuery.
Managing graph relationships of movies and actors—using Neo4j, Amazon Neptune, Azure CosmosDB, and GCP JanusGraph/Cloud Bigtable.
Searching and analyzing the work of William Shakespeare—using the Elastic Stack on AWS, Azure, and GCP.
Managing key-value dictionary data—using AWS DynamoDB, Azure Cosmos DB, and GCP Cloud Bigtable
Managing wide-column, 2-D key-value data—using Amazon Redshift, Azure Cosmos DB, and GCP Cloud Bigtable.
Managing relational, OLTP grocery data—using Amazon RDS, Azure SQL Database, and GCP Cloud SQL.
Retrieving and parsing sportsbook data from ESPN—using Beautiful Soup, Selenium, and Scrapy for Python.
My nearly 30-year fascination with computer programming humbly started at a New York City public high school.
Minimizing redundancy in WNBA data while preserving important information—using Julia, Python, and R.
Specifying needs not articulated by users, but are essential for product-market fit.
Managing cached data—using Redis, Amazon ElastiCache, Azure Cache for Redis, and GCP Memorystore.
Managing document-oriented data from the Nobel Prize—using Amazon DocumentDB, Azure Cosmos DB, and GCP Firestore.
Comparing the entity-relationship, relational, semi-structured, and object-based data models.
Cleaning the DC Comics dataset from common messiness issues—using Julia, Python, and R.
Understanding the NHANES data types and data structures—one of the first steps of any data analysis—using Julia, Python, and R.
Coding in Python that is low latency (minimum completion time) and low overhead (minimum resource consumption).
Declaring/invoking functions/procedures, executing triggers, and performing other advanced SQL queries—in MariaDB.
Performing join expressions, view definitions, transactions, integrity constraints, and other intermediate SQL queries—in PostgreSQL.
Creating, reading, updating, and deleting relational data—in MySQL.
Performing queries on the Lahman baseball database—in SQLite.
Executing a set of statements—in R, Python, and Julia—for a specified number of times or while a condition is true.
Comparing how courses of action (based on certain conditions) are tested and taken—in R, Python, and Julia.