Category: AWS Glue

Amazon Redshift is a massively parallel processing (MPP), fully managed petabyte-scale data warehouse that makes it simple and cost-effective to analyze all your data using existing business intelligence tools. When businesses are modernizing their data warehousing solutions to Amazon Redshift, implementing additional data protection mechanisms for sensitive data, such as personally identifiable information (PII) or […]

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. To illustrate an example, in a typical sales domain, customer, time or product are dimensions and sales transactions is a fact. Attributes within the dimension can change over time—a customer can change […]

Data lakes have come a long way, and there’s been tremendous innovation in this space. Today’s modern data lakes are cloud native, work with multiple data types, and make this data easily available to diverse stakeholders across the business. As time has gone by, data lakes have grown significantly and have evolved to data meshes […]

In the first post of this series, we described how AWS Glue for Apache Spark works with Apache Hudi, Linux Foundation Delta Lake, and Apache Iceberg datasets tables using the native support of those data lake formats. This native support simplifies reading and writing your data for these data lake frameworks so you can more […]

This is a guest post co-written with Gowtham Dandu from Infomedia. Infomedia Ltd (ASX:IFM) is a leading global provider of DaaS and SaaS solutions that empowers the data-driven automotive ecosystem. Infomedia’s solutions help OEMs, NSCs, dealerships and 3rd party partners manage the vehicle and customer lifecycle. They are used by over 250,000 industry professionals, across […]

Thousands of customers rely on Amazon Redshift to build data warehouses to accelerate time to insights with fast, simple, and secure analytics at scale and analyze data from terabytes to petabytes by running complex analytical queries. Organizations create data marts, which are subsets of the data warehouse and usually oriented for gaining analytical insights specific […]

The value of data is time sensitive. Real-time processing makes data-driven decisions accurate and actionable in seconds or minutes instead of hours or days. Change data capture (CDC) refers to the process of identifying and capturing changes made to data in a database and then delivering those changes in real time to a downstream system. […]

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. It adds tables to compute engines including Spark, Trino, PrestoDB, Flink, and Hive using a high-performance table format that works just like a SQL table. Iceberg has […]

Finding similar columns in a data lake has important applications in data cleaning and annotation, schema matching, data discovery, and analytics across multiple data sources. The inability to accurately find and analyze data from disparate sources represents a potential efficiency killer for everyone from data scientists, medical researchers, academics, to financial and government analysts. Conventional […]

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. But regulations such as the General Data Protection Regulation (GDPR) have created obligations for data operators who must be able to erase or […]