Category: Analytics

A data mesh architecture is a relatively new approach to managing data in large organizations, aimed at improving scalability, agility, and autonomy of data teams. There’s a need for an architecture that removes complexity and friction of provisioning and managing the lifecycle of data. This post outlines an approach to implement a data mesh with Snowflake as the data platform and with many AWS services like to support all pillars of the data mesh architecture.

As technology advances and business requirements change, organizations may find themselves needing to migrate away from legacy data processing systems like HBase, Solr, and HBase Indexer. Explore the advantages of migrating from HBase, Solr, and HBase indexer to a modern data ecosystem based on AWS, and dive deep on the discuss architecture, design, and pathways for implementation. This post offers insights and guidance from Rackspace for those looking to embark on this intricate migration journey.

Data architectures and strategies continue to respond to the need for discoverability and consumer desire to directly connect with producers. Data mesh is one such approach and provides a methodology for how organizations can organize around data domains by delivering data as a product. Learn how Palantir Foundry runs on AWS to help customers deliver and transform their data architectures through such an approach while leveraging and building on existing investments.

It can be challenging for companies to consolidate data from multiple sources into one system, which is why many use AWS Glue to build ETL workflows to load their data into data lakes. Learn how AWS has collaborated with Protegrity to enable organizations with strict security requirements to protect their data while being able to obtain the powerful insights. We’ll demonstrate how data tokenization for data in transit is performed using Protegrity’s Cloud API and AWS Glue.

To fully engage with customers and produce meaningful experiences, creating a 360-degree view of them is essential. The information needed to build this view requires internal and external sources of data. Learn about the concept of data clean rooms and how AWS Clean Rooms helps customers and their partners more easily and securely collaborate and analyze their collective datasets—without sharing or copying one another’s underlying data.

Manufacturing customers face the challenge of deriving insights from their shopfloor to enhance business decisions and production processes. Learn how Syntax’s Synsights platform powered by AWS enables integration of IoT data with manufacturing execution systems and enterprise resource planning systems like SAP. Syntax Synsights offers a pre-built solution that can be tailored to specific customer requirements, thus saving time, effort, and money while boosting ROI.

Many organizations have stitched together a set of siloed functional and technical supply chain systems to solve their business problems, but some lack traceability and transparency across the end-to-end value chain. Learn how Accenture’s SynOps for Supply Chain on AWS addresses these challenges by offering a fully integrated solution which brings traceability and transparency to the decision-making process.

Traditional cloud monitoring, observability, and security approaches often require manual processes for data analytics. The Dynatrace Grail data lakehouse platform provides instant, cost-efficient, AI-powered analytics and automation of unified observability, security, and business data at any scale. Learn how this enables organizations to store, process, and analyze the enormous volume and variety of data from modern cloud ecosystems while retaining its context and without structuring or rehydrating it.

Managing cloud costs has become a top, strategic priority for most IT executives. When considering how to manage cloud cost, there are several primary paths that can be taken to lower total cost of ownership (TCO), both technical and non-technical. In this post, experts from SMX and AWS discuss how leveraging data-driven analysis and tools can improve decisions that impact short- and long-term spend.

Data mesh is a decentralized approach to data management which strives to develop the data platform from a technology-led and project-centric model into a paradigm about federated business-led and product-centric data, by design. Learn how AWS and Accenture are helping customers rapidly set up data mesh architecture on AWS leveraging the newly-announced Velocity platform. Explore how Velocity’s Data Mesh Fabric component can minimize time and effort to set up data mesh architecture on AWS.