Category: Analytics

AWS partners have asked us to do more to help them build analytics-driven applications, so we are excited to announce the launch of the ‘Powered by Amazon Redshift’ program. This program enables ISVs to build their applications using Amazon Redshift—our fully-managed cloud data warehouse service—and deliver fast, easy, and secure analytics at any scale available within their applications. With Amazon Redshift powering applications, ISVs can drive up to 3X price performance compared to any other cloud data warehouse.

One of the biggest advantages of VMware Cloud on AWS is that it can readily integrate with other AWS services. That gives you countless ways to elevate your workloads. If you’re amassing data in your databases over time and are looking for novel ways to glean fresh insights out of it, using Amazon Redshift and Amazon QuickSight is an easy and accessible way to achieve it. This post describes how to get more out of existing data residing inside your databases running in VMware Cloud on AWS.

Data-centric AI (DCAI) has been described as the discipline of systematically engineering the data used to build an AI system. It prescribes prioritizing improving data quality over tweaking algorithms to improve machine learning models. In this post, explore a DCAI solution built on Snowflake and Amazon SageMaker to serve as a factory for predictive analytics solutions. Learn about Snowflake’s integrations with SageMaker and get hands-on resources to help you put these capabilities into practice.

To improve the fault detection process, it’s crucial to monitor production systems and collect performance data in real-time using smart sensors and IoT devices. This post highlights the critical aspects of Quantiphi’s serverless, fully-managed, and streaming ETL pipeline, along with the benefits of the centralized lakehouse (data lake + data warehouse) solution built on AWS. Learn how the solution helped a U.S.-based manufacturing company make better decisions and improve their customer’s production efficiency.

Superior Integrated Systems (SIS), an integrator of custom-built test equipment and data acquisition systems, launched new services during the pandemic to help their customers satisfy specific product test requirements. Learn how AllCloud supported SIS to build a proof of concept, developing the architecture for the desired data-as-a service platform using AWS Control Tower and AWS Cloud Development Kit (AWS CDK).

While the global Hadoop-as-a-service market size is growing at a CAGR of ~39%, it leaves out many small, mid, and large-scale organizational players due to the inherent pains of migration. This post explores how Virtusa complements Amazon EMR Migration by providing approaches and utilities to streamline and manage large-scale Hadoop-to-Amazon EMR migrations by creating a migration framework of automated modules.

More people are using data today than ever before, but it’s getting harder and harder for everyone to collaborate on the same data. Atlan has pioneered a collaborative workspace helping modern data teams work together better. Atlan is a collaboration and orchestration layer—the glue that brings together your team, the tools you love, and the data you need. Learn how companies use Atlan and AWS to democratize their data, collaborate more effectively, and unify knowledge and context in one place.

Although the business case for digital analytics is well-articulated, many organizations are looking for ways to build stronger cases around transformations by consolidating data generated across the enterprise with customer behavioral data. Learn how Softcrylic developed the Analytics Shift solution which helps businesses bring Adobe Analytics data into Amazon Redshift to drive deeper insights and data integration.

Ahana provides a fully managed and easy-to-use service for running Presto on AWS, and customers like Metropolis use Ahana to query data in their Amazon S3-based data lake for business intelligence, ad-hoc analytics, and reporting. Learn how Metropolis uses AWS Lake Formation and Ahana to build a data lake that allows their analysts and data scientists to develop a simple, hands-free parking experience for their customers.

As data grows at an exponential rate both in volume and velocity, it becomes important for organizations to carve out a strategy to store data in appropriate locations with the correct safeguards to address data access and privacy concerns. Learn how organizations can transform their data landscape into a more controlled, flexible, and secure landscape using LTI’s Canvas Scarlet Data Mesh framework built on AWS to meet the data challenges an organization faces today.