For decades, enterprises used online analytical processing (OLAP) workloads to answer complex questions about their business by filtering and aggregating their data. These complex queries were compute and memory-intensive. This required teams to build and maintain complex extract, transform, and load (ETL) pipelines to model and organize data, oftentimes with commercial-grade analytics tools.
Organizations today want to make data-driven decisions. The data could lie in multiple source systems, such as line of business applications, log files, connected devices, social media, and many more. As organizations adopt software as a service (SaaS) applications, data becomes increasingly fragmented and trapped in different “data islands.” To make decision-making easier, organizations are building data lakes, which is a centralized repository that allows you to store all your structured and unstructured data at any scale.
In this blog, you learn how Amazon Neptune as a graph database, combined with Amazon Elasticsearch Service (Amazon ES) for full text indexing helps you shorten risk analysis processes from weeks to minutes. We give a walk-through of the steps involved in creating this knowledge management solution, which includes natural language processing components.
Amazon Elasticsearch Service (Amazon ES) is a fully managed service that you can use to deploy, secure, and run Elasticsearch cost-effectively at scale. The service provides support for open-source Elasticsearch APIs, managed Kibana , and integration with Logstash and other AWS services.
A multitenant Kubernetes cluster is shared by multiple users and workloads that are commonly referred to as “tenants.” This definition includes Kubernetes clusters that are shared by different teams or divisions within an organization, as well as clusters that are shared by per-customer instances of a software-as-a-service (SaaS) application.
A popular approach in digital transformation scenarios is to see whether existing applications and middleware tiers can run as-is in a hybrid setup where Microsoft Azure serves as the scalable, distributed data platform. This example describes a data-first approach to middleware integration that enables IBM message queues (MQs) running on mainframe or midrange systems to work with Azure services so you can find the best data platform for your workload.
Mr. Cooper Group is an industry-leading mortgage services provider serving customers through servicing, originations, and digital real estate solutions. Using Google Cloud AI and ML solutions, they created a highly reliable, cloud native document analysis and processing platform to process lending documents and unlocked new levels of accuracy and operational efficiency that help them to scale and control the cost at the same time.
Transitioning to DevOps for faster workflows can be done in several key steps. Of course, DevOps implementations such as continuous integration, delivery, and deployment save businesses time while reducing costs. Additionally, long-term DevOps adoptions empowers companies to maximize resource utilization, integrate new developers into existing workflows, and construct a reliable infrastructure.
While use cases for IoT abound and deployments are growing rapidly, the technology landscape can still overwhelm newcomers. This article gives an overview of wireless connectivity protocols and their respective strengths, weaknesses, and recommended fields of application.
Editor’s note : Today we hear from ForgeRock , a multinational identity and access management software company with more than 1,100 enterprise customers, including a major public broadcaster. In total, customers use the ForgeRock Identity Platform to authenticate and log in over 45 million users daily, helping them manage identity, governance, and access management across all platforms, including on-premises and multicloud environments.
While creating data lakes on the cloud, the data catalog is crucial to centralize metadata and make the data visible, searchable, and queryable for users. With the recent exponential growth of data volume, it becomes much more important to optimize data layout and maintain the metadata on cloud storage to keep the value of data lakes
Maintaining customer data privacy, protection against intellectual property loss, and compliance with data protection laws are essential objectives of today’s organizations. To protect data against security threats, vulnerabilities within the organization, malicious software, or cyber criminality, organizations are increasingly encrypting their data.
This is a guest blog post co-written by Rajagopal Mahendran, Development Manager at the Optus IT Innovation Team. Optus is part of The Singtel group, which operates in one of the world’s fastest growing and most dynamic regions, with a presence in 21 countries.
Good artists copy, great artists steal, and smart software developers use other people’s machine learning models. If you’ve trained ML models before, you know that one of the most time-consuming and cumbersome parts of the process is collecting and curating data to train those models.
