Tag: Amazon Redshift

Enterprise data warehouses are complex and consist of database objects that need to be modified to reflect the changing needs of business, data analytics, and machine learning teams. In this post, learn about an approach to managing the evolution of enterprise-scale data warehouses based on the experience of Deloitte’s Data and AI global practice teams. The declarative tool developed by Deloitte that can automatically generate DDL statements to align Amazon Redshift’s state to an approved baseline configuration.

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

For a business to effectively prepare for unforeseen circumstances and unpredictability, it requires a healthy and robust data culture inside the organization. Alation provides artificial intelligence-driven data search and discovery, governance, and analytics capabilities to help organizations foster a data culture. Learn how Alation makes data users more effective and productive, and how Alation on AWS can be used to govern data to ensure its proper usage.

To reap the benefits of cloud computing, organizations cannot simply take their existing applications and architectures in their legacy on-premises environment and move them as-is to the cloud. True cloud modernization with MongoDB Atlas on AWS enables you to move data and simultaneously apply the latest innovations in development methodologies, architectural patterns, and technologies to refresh your portfolio of existing applications.

We are excited to highlight 88 AWS Partners that received new designations in October for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

A data-powered culture means overcoming difficult challenges such as using analytics solutions to reduce the complexity of stand-alone data. The 890 by Capgemini solution is a plug-and-play AI and analytics product hosted on AWS. As an activator of data analytics, it informs you so that you can speed up decision-making, flex and grow at scale, increase efficiency, automate processes, and make products and services that will truly connect with your customers.

We are excited to highlight 53 AWS Partners that received new designations in September for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.

Many organizations leverage unstructured data collected from social media feeds, stock streaming, and data clickstream to gain insights about the needs of their customers. The EZ Lake Access (EZLA) solution developed by TCS centralizes and simplifies access management of the Data Lake House by codifying most of the enterprise access controls in the form of a rule engine. This provides increased efficiencies and easy adoption of the Data Lake House.

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.