Category: AWS Glue

In a modern data architecture, unified analytics enable you to access the data you need, whether it’s stored in a data lake or a data warehouse. In particular, we have observed an increasing number of customers who combine and integrate their data into an Amazon Redshift data warehouse to analyze huge data at scale and […]

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. Apache Hive has performed pretty well for a long time. But with advancements in infrastructure such as cloud computing and multicore machines with large RAM, Apache Spark started to gain visibility by […]

Inventory management is a critical function for any business that deals with physical products. The primary challenge businesses face with inventory management is balancing the cost of holding inventory with the need to ensure that products are available when customers demand them. The consequences of poor inventory management can be severe. Overstocking can lead to […]

This post was co-written by Ashish Prabhu, Stephen Johnston, and Colin Ingarfield at Morningstar and Don Drake, at AWS. With “Empowering Investor Success” as the core motto, Morningstar aims at providing our investors and advisors with the tools and information they need to make informed investment decisions. In this post, Morningstar’s Data Lake Team Leads […]

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 […]