Tag: HAQM S3

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 HAQM 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 HAQM 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 HAQM 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 HAQM EMR clusters leveraging HAQM 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 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.

Seagate asked Mactores Cognition to evaluate and deliver an alternative data platform to process petabytes of data with consistent performance. It needed to lower query processing time and total cost of ownership, and provide the scalability required to support about 2,000 daily users. Learn about the the three migration options Mactores tested and the architecture of the solution Seagate selected. This effort improved the overall efficiency of Seagate’s HAQM EMR cluster and business operations.

Financial institutions possess and process data that are very sensitive and have immense business value. In recent years, regulations like open banking and data residency law have forced organizations to be even more adaptive to frequent challenges to systems storing and processing the data. Explore how Capgemini developed an application to address this customer challenge and learn how the approach helped worldwide credit card provider comply with PCI DSS security standards.

HAQM SageMaker provides all the components needed for machine learning in a single toolset. This allows ML models to get to production faster with much less effort and at lower cost. Learn about the data modeling process used by BizCloud Experts and the results they achieved for Neiman Marcus. HAQM SageMaker was employed to help develop and train ML algorithms for recommendation, personalization, and forecasting models that Neiman Marcus uses for data analysis and customer insights.