Tag: transactions

Feed: AWS Big Data Blog. Data is ubiquitous in businesses today, and the volume and speed of incoming data are constantly increasing. To derive insights from data, it’s essential to deliver it to a data lake or a data store and analyze it. Real-time or near-real-time data delivery can be cost prohibitive, therefore an efficient architecture is key for processing, and becomes more essential with growing data volume and velocity. In this post, we show you how to build a scalable producer and consumer application for Amazon Kinesis Data Streams running on AWS Fargate. Kinesis Data Streams is a fully ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Dave McCann. By Dave McCann, VP of Migration, Marketplace, and Control Services at AWS At Amazon Web Services (AWS), we see customers increasingly turn to AWS Marketplace to modernize the software they use, and to transform their software supply chain. AWS Marketplace is a digital catalog with more than 7,000 software listings from over 1,700 independent software vendors (ISVs) that makes it easy to find, test, buy, and deploy software that runs on AWS. If you’re an ISV, we are dedicated to helping you simplify software procurement. We also want to help you ... Read More

Feed: Teradata Blog. Teradata has been the best performing and leading relational data warehouse consistently for over 40 years, and the credit goes to its massively parallel architecture (MPP). Teradata is a Relational Database Management System (RDBMS) catering to data warehousing needs and offers high speed, high efficiency, and multi-user access by utilizing the concept of ‘Parallelism.’ Teradata is based on Massively Parallel Processing (MPP) Architecture. An MPP architecture follows a “Divide and Conquer” approach which means that to execute a task, Teradata’s processor divides the task evenly across the entire system.   AMP (Access Module Processor) is virtual processor which is responsible for parallelism in Teradata and each ... Read More

Feed: Planet PostgreSQL. Big data has an unfortunate tendency to get messy. A few years in, a growing database that use to be small, lean, and well-designed, has better odds than not of becoming something large, bloated, and with best practices tossed aside and now considered unsalvageable.There’s a few common reasons that this happens, some better than others: Technological limitation: The underlying tech doesn’t support the scale. Say transactions or referential integrity across partitions in a sharded system. Stability: Certain operations come to be considered too risky. e.g. Batch update operations that have unpredictable performance properties. Cost/effort: Doing things the ... Read More

Feed: Recent Announcements. Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora. It automatically starts up, shuts down, and scales capacity up or down based on your application's needs. Amazon Aurora Serverless v2 provides the ability to scale database workloads to hundreds of thousands of transactions in a fraction of a second. Instead of doubling capacity every time a workload needs to scale, it adjusts capacity in fine-grained increments to provide just the right amount of database resources for an application’s needs. There is no database capacity for you to manage, you pay only for the capacity your application ... Read More

Feed: Microsoft Dynamics 365 Blog. Author: Murali Krishna. Even in challenging times, the holiday season's irresistible deals attract both customers and fraudsters. A differentiated fraud prevention strategy is essential to keep a merchant's fraud losses minimized while letting legitimate customers continue to have a smooth shopping experience.Consumers often change buying and engagement patterns with merchants during the holiday season like shipping to a new address when buying a gift or adding a new card on file that may give them higher rewards or a higher spending limit.These new consumer behaviors pose a challenge for fraud prevention because it makes it ... Read More

Feed: Cambridge Intelligence. Author: Dan Williams. We’re excited to announce the release of KronoGraph version 1.0: the first developer toolkit for scalable, interactive, browser-based timeline visualizations.Over 100 JavaScript and React developers in some of the world’s leading security and anti-fraud organizations took part in our hugely successful KronoGraph early access program. With the release of version 1.0, timeline visualization applications built using KronoGraph can now be shipped to customers.No matter what kind of data you’re working with – cell phone records, financial transactions, IT network traffic – there’s always a time dimension. Investigators in a range of use cases need ... Read More

Feed: Planet PostgreSQL. 28 Nov 2020 0. How to read me? I have to admit, my research blog posts are getting longer and longer. From one side I find it genuinely encouraging, because if one gets so much information just by scratching the topic, imagine what’s hidden beneath the surface! One university professor once said “what could be interesting in databases?”, and it turns out freaking a lot! On the other side it certainly poses problems for potential readers. To overcome them I would suggest an interesting approach: print this blog post out, or open it on your tablet/e-reader, where ... Read More

Feed: Redis Labs. Author: Steve Naventi. Unified real-time NoSQL data platform positioned furthest to the right on the Completeness of Vision in the Challenger quadrant Mountain View, November 30, 2020—Redis Labs, the home of Redis, today announced the company has been named a Challenger in the 2020 Magic Quadrant for Cloud Database Management Systems1. Gartner evaluated Redis Labs for its product Redis Enterprise Cloud among managed cloud database services from 15 additional vendors, with Redis Labs being positioned furthest to the right on the Completeness of Vision in the Challenger quadrant axis. “We are proud to be positioned as a ... Read More

Feed: AWS Architecture Blog. As businesses race to digitally transform, the challenge is to cope with the amount of data, and the value of that data diminishes over time. The challenge is to analyze, learn, and infer from real-time data to predict future states, as well as to detect anomalies and get accurate results. In this blog post, we’ll explain the architecture for a solution that can achieve real-time inference on streaming data. We’ll also cover the integration of Amazon Kinesis Data Analytics (KDA) with Apache Flink to asynchronously invoke any underlying services (or databases). Managed real-time in-stream data inference ... Read More