Tag: AWS Glue Data Catalog

A SaaS data platform may run in the account of an ISV or a dedicated account provided by the customer. Learn about the main AWS services SaaS data platforms can integrate with to provide customers with a seamless experience and take advantage of AWS services in order to accelerate their drive to meeting their business goals. Explore how those integrations can be built and examples of AWS ISV Partners who have successfully developed these integrations.

Data mesh is emerging as a paradigm for generating data-driven value and is gaining real-world adoption within industries like financial services and automotive. Learn about the user journeys of two types of data consumers in a mesh platform: business intelligence and data scientists. Explore how BI and AI/ML overlap within a set of data domains, and how a platform architecture further enables the desired experiences within a data mesh.

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.

Amazon Athena is a serverless interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Data stored in S3 can span gigabytes to petabytes, however, and querying such massive data poses unique challenges. Follow along as experts from AWS and Innovative Solutions present a use case-based approach to help evaluate these challenges and propose solutions to improve Amazon Athena query performance.

There has been a lot of buzz about a new data architecture design pattern called a Lake House. A Lake House approach integrates a data lake with the data warehouse and all of the purpose-built stores so customers no longer have to take a one-size-fits-all approach and are able to select the storage that best suits their needs. Learn how to couple Amazon Redshift with a semantic layer from AtScale to deliver fast, agile, and analysis-ready data to business analysts and data scientists.

Big datasets have traditionally been locked on-premises because of data gravity, making it difficult to leverage cloud-native, serverless, and cutting-edge technologies provided by AWS and its community of partners. Modernizing an on-premises analytics platform takes time, effort, and careful planning. Explore the challenges of migrating large, complex, actively-used structured datasets to AWS and how the combination of WANdisco LiveData Migrator, Amazon S3, and AWS Glue Data Catalog overcome those challenges.

Running virtual machines with databases or datastores on VMware Cloud on AWS lets you use the same management tools and VMs as on your on-premises VMware vSphere environment. You can easily extend these workloads to the cloud and take advantage of AWS on-demand delivery, global footprint, elasticity, and scalability. Learn how VMware Cloud on AWS brings these datasets closer to AWS Analytics Services, making it easier to use services to draw meaningful insights from business data.

Systems are growing more complex, cloud applications are growing in adoption, and cloud data lakes are being increasingly deployed. At the same time, organizations need to implement data cataloging solutions to provide data governance, data analytics, or pure metadata management. Informatica Enterprise Data Catalog (EDC) scans and catalogs an enterprise’s data assets, whether hosted on the cloud or stored on-premises.

Change Data Capture (CDC) is the technique of systematically tracking incremental change in data at the source, and subsequently applying these changes at the target to maintain synchronization. You can implement CDC in diverse scenarios using a variety of tools and technologies. Here, Cognizant uses a hypothetical retailer with a customer loyalty program to demonstrate how CDC can synchronize incremental changes in customer activity with the main body of data already stored about a customer.