Category: Analytics

Over the past few years, midsize and large enterprises have adopted vertical integration as part of their strategy to optimize operations and profitability. Vertical integration consists of separating different stages of the production line from other related departments, such as marketing and logistics. Enterprises implement such strategy to gain full control of their value chain: from the […]

Enterprises with centralized IT organizations and multiple lines of businesses frequently use showback or chargeback mechanisms to hold their departments accountable for their technology usage and costs. Chargeback involves actually billing a department for the cost of their division’s usage. Showback focuses on visibility to make the department more cost conscientious and encourage operational efficiency. […]

This post was updated November 18, 2021.  Data scientists continuously look for ways to accelerate time to value for analytics projects.  RStudio Server is a popular Integrated Development Environment (IDE) for R, which is used to render analytics visualizations for faster decision making. These visualizations are traditionally hosted on legacy unix servers along with Shiny […]

Many organizations are moving their data from silos and aggregating it in one location. Collecting this data in a data lake enables you to perform analytics and machine learning on that data. You can store your data in purpose-built data stores, like a data warehouse, to get quick results for complex queries on structured data. […]

Hospitals and clinical trial sites manage sensitive patient data. They are often required to grant remote access to custom Windows-based applications for patient record review and medical image analysis. This typically requires providing physicians and staff with remote access to on-premises workstations over VPN, with some flavor of remote desktop software. This can be both […]

This post was co-written with Marcus Rosen, a Principal  – Machine Learning Operations with Rio Tinto, a global mining company.  Data pre-processing is an important step in setting up Machine Learning (ML) projects for success. Many AWS customers use Apache Spark on AWS Glue or Amazon EMR to run data pre-processing scripts while using Amazon SageMaker […]

With the introduction of cloud technology and by extension the rapid emergence of Internet of Things (IoT), the barrier to entry for creating smart building solutions has never been lower. These solutions offer commercial real estate customers potential cost savings and the ability to enhance their tenants’ experience. You can differentiate your business from competitors […]

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. In this […]

September 8, 2021: Amazon Elasticsearch Service has been renamed to Amazon OpenSearch Service. See details. This blog post is co-authored with Charles Crouspeyre and Angad Srivastava. Charles is Director at Accenture Applied Intelligence and ASEAN AI SME (Subject Matter Expert) and Angad is Data and Analytics Consultant at AWS and NLP (Natural Language Processing) expert. […]

Serverless technologies reduce the work needed to set up and maintain computing resources, provide built-in scalability, and optimize agility, performance, cost, and security. The pay-as-you-go model is particularly liberating for developers. You can fail fast, experiment more, and do it fairly cheaply. However, serverless brings its own challenges. In this blog, we’ll examine how to […]