Category: Analytics

Data scientists and engineers spend hours writing complex data pipelines to extract, transform, and load (ETL) data from various sources into their data lakes for data integration and creating unified data models to build business insights. The process involves understanding the source and target systems, discovering schemas, mapping source and target, writing and testing ETL […]

Today, consumers navigate thousands of products on e-commerce sites, hundreds of shows on streaming platforms, and countless options in digital marketplaces. This choice overload creates decision fatigue, yet consumers continue to demand more variety and make more purchases online. As a result, personalization has become essential—consumers reward brands that deliver relevant, tailored online experiences. However, […]

Organizations face mounting challenges in managing and operationalizing their ever-growing data assets for machine learning and analytics workflows. When dealing with billions and trillions of objects, teams struggle to find what data they have and how to efficiently find specific datasets. Without proper data discovery and metadata management, teams spend valuable time searching for relevant […]

Zeta Global is a data-driven marketing technology company that uses consumer insights to empower brands in customer acquisition, growth, and retention. At the core of its operations is the Zeta Marketing Platform, an advanced system that applies sophisticated AI and machine learning (ML) capabilities on proprietary data from over 245 million U.S. consumer profiles. This […]

Organizations across industries must regularly verify the integrity of their stored datasets to protect valuable information, satisfy compliance requirements, and preserve trust. Media and entertainment customers validate assets to make sure that content remains intact, financial institutions run integrity checks to meet regulatory obligations, and research institutions confirm the reproducibility of scientific results. These verifications […]

Modern cybersecurity teams are facing unprecedented challenges in data analysis by the scale, complexity, and velocity of data. Cloud environments continuously generate massive amounts information in form of access logs, configuration changes, alerts, and telemetry. Traditional analysis methods of looking at these data points in isolation can’t effectively detect threats such as lateral movement and […]

Data has become the lifeblood of modern financial markets, driving everything from investment decisions to regulatory compliance. Nowhere is this more evident than in electronic trading, where the ability to efficiently store, process, and analyze historical market data can make the difference between success and failure. Market participants are witnessing an unprecedented surge in tick […]

Organizations often struggle to extract maximum value from their data lakes when running generative AI and analytics workloads due to data quality challenges. Although data lakes excel at storing massive amounts of raw, diverse data, they need robust governance and management practices to prevent common quality issues. Without proper data validation, cleansing processes, and ongoing […]

Editor’s note: AWS is not responsible for UCI’s public GitHub repo linked in this post, which has been provided so that interested parties can explore the solution described in this post in more detail. The University of California, Irvine (UCI) is a public land-grant research university with troves of research data stored on servers in […]

When was the last time you found yourself trying to look at rows and rows of data in a spreadsheet struggling to interpret and draw conclusions? Many analysts and engineers experience the same challenge every day. Whether it’s analyzing sales trends, monitoring operational metrics, or understanding customer behavior, the challenge lies not just in interpreting […]