Five Things to Consider When Choosing a Data Catalog

whitePaper | May 26, 2022

Since their invention in 1970 by Edgar Codd, relational databases have served as the default data store for almost every IT organization, large or small. Today, the most iconic and familiar relational databases include IBM DB2, Oracle Database, Microsoft SQL Server, PostgreSQL, and MySQL.

whitePaper | August 25, 2022

The new PowerMax 2500 and 8500 storage systems with PowerMaxOS 10 feature some of the most advanced performance, data protection, data reduction, and data mobility servicing enterprise workloads and applications. The new PowerMax systems come with large DRAM for user data and persistent memory for metadata, end-to-end NVMe, 100 Gbit RDMA fabric, real-time machine learning, 64-bit native file services, seamless cloud mobility, and ability to deliver millions of IOPS at submillisecond latency.

whitePaper | November 18, 2022

Data lakes have enormous potential as a source of business intelligence when included as part of an advanced open data architecture. Organizations use data lakes to efficiently store and process a combination of structured, semi-structured and unstructured data at scale, powering multiple business intelligence and data science initiatives to support a variety of business units across an organization.

whitePaper | October 1, 2021

centering your database and data analytics workload development and deployment on a Kubernetes-based container, you can create a more efficient and speedy data life cycle. Access this white paper to learn how to improve key capabilities for database and data analytics workloads across hybrid cloud environments.

whitePaper | February 22, 2023

There are two reasons financial institutions are learning as much about their customers as possible by gathering—and processing—as much data as they can. Firstly, this information is invaluable for delivering more personalized products and services to increase revenues while making smarter decisions and improving operational efficiency. Better customer understanding can improve things like underwriting accuracy, for example, and many others.

whitePaper | April 27, 2023

Data analytics are used to test controls and validate that business risks are managed. This would generally occur at a point-in-time when an assurance activity is scheduled. Rather than test a number of transactions, the entire population of transactions can be reviewed for greater coverage. Data analytics includes automated tools such as generalised audit software, test data generators, computerised audit programs, specialised audit utilities and computer-assisted audit techniques (CAATs).