Tag: Amazon Redshift

Given the breadth of use cases, data lakes need to be a complete analytical environment with a variety of analytical tools, engines, and languages supporting a variety of workloads. These include traditional analytics, business intelligence, streaming event and Internet of Things (IoT) processing, advanced machine learning, and artificial intelligence processing. Learn how Cazena builds and deploys a production ready data lake in minutes for customers.

To receive the AWS Service Ready designation, AWS Partners must undergo rigorous technical validation and are assessed on the security, performance, and reliability of their AWS solutions. To help AWS Technology Partners better understand this process, we are releasing new versions of the AWS Service Ready Validation Checklists (VCL). These Validation Checklists outline for AWS Technology Partners the customer case study and technical criteria needed to achieve an AWS Service Ready designation.

Many SaaS applications boast of microservices as a means to divide a monolithic architecture into easily manageable solutions. Sometimes these microservices have their own databases. To maintain data consistency across the system, you need to implement a data synchronization system between source and target data persistence layers. Learn how Trantor has developed a solution using Amazon AppFlow to easily synchronize the data between Salesforce and Amazon RDS for PostgreSQL in near real-time.

As a SaaS provider, it’s essential to have a clear picture of how tenants are exercising your system. Being able to model the functional and operational profile of tenants and tenant tiers is critical to evolving the business and technical strategies of a SaaS organization. Learn about the role metrics play within a SaaS-based application, and dive deep into a solution that provides all of the infrastructure that will support the ingestion, aggregation, and analysis of SaaS metric data.

Many organizations have started applying machine learning and artificial intelligence expertise to scale customer communications and accelerate research during the COVID-19 pandemic. Onica has been actively involved in these efforts, leveraging AWS technologies to help decision makers navigate this pandemic. In this post, dive into the technical details of two COVID-19-related solutions Onica has produced and learn about their results and impact.

We are excited to highlight 129 AWS Partners that received new designations in December 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.

When VMware Cloud on AWS is connected to the connected VPC, it always uses the default VPC route table in the VPC. During the deployment phase of VMware Cloud on AWS, the Software Defined Data Center is connected to an AWS (or customer) account for seamless access to native AWS services. In this post, we provide guidance on which AWS account and respective VPC to connect VMware Cloud on AWS to take advantage of native AWS service integrations.

Many companies are using Amazon Redshift to analyze and transform their data. As data continues to grow and become even more important, they are looking for more ways to extract valuable insights. One use case we’re especially excited to support is that of data tokenization and masking. Amazon Redshift has collaborated with Protegrity, an AWS Advanced Technology Partner, to enable organizations with strict security requirements to protect their data while being able to obtain the powerful insights.

Follow along as Agilisium provides an early look at Amazon Redshift’s ra3.4xlarge instance type (RA3). This post details the result of various tests comparing the performance and cost for the RA3 and DS2 instance types. It will help AWS customers make an informed decision on choosing the instance type best suited to their data storage and compute needs. As a result of choosing the appropriate instance, your applications can perform better while also optimizing costs.

Moving data between multiple data stores requires an extract, transform, load (ETL) process using various data analysis approaches. ETL operations form the backbone of any modern enterprise data and analytics platform. AWS provides a broad range of services to deploy enterprise-grade applications in the cloud. This post explores a strategic collaboration between Tech Mahindra and a customer to build and deploy an enterprise data repository on AWS and create ETL workflows using a serverless architecture.