Tag: Amazon S3

Next Caller uses machine learning on AWS to drive data analysis and the processing pipeline. Amazon SageMaker helps Next Caller understand call pathways through the telephone network, rendering analysis in approximately 125 milliseconds with the VeriCall analysis engine. VeriCall verifies that a phone call is coming from the physical device that owns the phone number, and flags spoofed calls and other suspicious interactions in real-time.

Small businesses need support from bankers that understand their business. Lenders, overwhelmed with demand, need help cutting through all of the noise to focus on the most at-risk parts of their portfolio and prioritize engaging those customers. Together, banks, credit unions, and fintechs such as OakNorth can deploy capital more effectively and save the jobs and small and medium-sized businesses that power our economies. OakNorth is s a next-generation credit analysis and monitoring platform powered by AWS.

Data scientists creating enterprise machine learning models to process large volumes of data spend a significant portion of their time managing the infrastructure required to process the data, rather than exploring the data and building ML models. You can reduce this overhead by running Qubole data processing tools and Amazon SageMaker. An open data lake platform, Qubole automates the administration and management of your resources on AWS.

Customers want to use Teradata Vantage to analyze the data they have stored in Amazon S3, but the AWS service that prepares and loads data stored in S3 for analytics, AWS Glue, does not natively support Teradata Vantage. To use AWS Glue to prep and load data for analysis by Teradata Vantage, you need to rely on AWS Glue custom database connectors. Follow step-by-step instructions and learn how to set up Vantage and AWS Glue to perform Teradata-level analytics on the data you have stored in Amazon S3.

Data is the lifeblood of a digital business and a key competitive advantage for many companies holding large amounts of data in multiple cloud regions. Imperva protects web applications and data assets, and in this post we examine how you can use SQL to analyze big data directly, or to pre-process the data for further analysis by machine learning. You’ll also learn about the benefits and limitations of using SQL, and see examples of clustering and data extraction.

The amount and variety of existing and newly-generated data in today’s connected world is unparalleled. As this growth continues, so does the opportunity for organizations to extract real value from their data. Teradata Vantage is a modern analytics platform that combines open source and commercial analytic technologies. It can drive autonomous decision-making by helping you to operationalize insights, solve complex business problems, and enable descriptive, predictive, and prescriptive analytics.

Learn how Mactores helped Seagate Technology to use Apache Hive on Apache Spark for queries larger than 10TB, combined with the use of transient Amazon EMR clusters leveraging Amazon EC2 Spot Instances. It was imperative for Seagate to have systems in place to ensure the cost of collecting, storing, and processing data did not exceed their ROI. Moving to Hive on Spark enabled Seagate to continue processing petabytes of data at scale with significantly lower TCO.

Terraform and AWS CloudFormation allow you to express infrastructure resources as code and manage them programmatically. Each has its advantages, but some enterprises already have expertise in Terraform and prefer using it to manage their AWS resources. To accommodate that preference, CloudFormation allows you to use non-AWS resources to manage AWS infrastructure. Learn the steps to create a CloudFormation registry resource type for Terraform and deploy it as an AWS Service Catalog product.

In spite of the rich set of machine learning tools AWS provides, coordinating and monitoring workflows across an ML pipeline remains a complex task. Control-M by BMC Software that simplifies complex application, data, and file transfer workflows, whether on-premises, on the AWS Cloud, or across a hybrid cloud model. Walk through the architecture of a predictive maintenance system we developed to simplify the complex orchestration steps in a machine learning pipeline used to reduce downtime and costs for a trucking company.

In order to deliver serverless applications, customers often turn to DevOps principles to efficiently build, deploy, operate, and iterate on features and changes. CI/CD is one of the major components of DevOps that helps deliver code faster and more reliably to production. GitLab’s continuous integration offering provides a rich set of features for automating how new code is incorporated into your software and how new versions of your software get built and deployed.