Tag: Amazon S3

This post describes a solution created by IBM during the migration of a big data and analytics platform for one of the top 10 banks worldwide. The primary drivers were cost efficiency, business agility, and performance. The “pet to cattle” concept was applied to this solution to transform the legacy high availability disaster recovery solution to a more robust and cost-effective cattle-based solution through the use of AWS-native services.

There are multiple use cases where applications running on AWS may require a Lambda function to process the file stored in Box storage before it’s sent to the next stage for further processing. This post explains how a Lambda function can be invoked on the upload of a file in a Box folder using a webhook. With the help of a Box webhook and Amazon API Gateway, users can invoke a Lambda function on file upload operation and download the file to Amazon S3.

Verisk provides data analytic insights to customers in insurance, energy and specialized markets, and financial services. Learn how a joint team from AWS and Verisk Argus migrated petabytes of SQL Server data from on-premises to Amazon S3 and Amazon S3 Glacier using AWS Snowball Edge and the Amazon EMR custom Spark ingestion framework. Through this migration framework, Verisk Argus is positioned to save millions of dollars by moving to AWS from their data center.

Customer onboarding remains a challenging and time-consuming process for most banks. Both digital and traditional processes are often overly complex, resulting in lower conversion rates and higher cost of acquisition. To overcome these challenges, numerous financial institutions have started customer onboarding online. In this post, walk through the use case of one of the largest financial institutes of Europe for whom Infostretch provided a substantial breakthrough to onboard the customers digitally.

The concept of an Internal Data Marketplace (IDM) is increasingly resonating with data organizations. An IDM is a secure, centralized, simplified, and standardized data shopping experience for data consumers. Explore how the IDM framework includes data governance and data catalogs, role-based access controls, data profiling, and powerful contextual search to easily identify the most relevant data. The end result is a seamless data consumption experience for end users.

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.

Dataiku orchestrates the entire machine learning lifecycle and makes it accessible to data scientists and analysts alike. With deep integration with AWS AI tools, Dataiku enables users to augment their analytics workflow with pretrained NLP and computer vision models. Learn how you can use Amazon Comprehend and Amazon Rekognition plugins on Dataiku Data Science Studio (DSS) to build a simple workflow of NLP and computer vision use cases, respectively.

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.

Organizations the world over rely on the Exadata database platform to operate and manage Oracle databases, simplify data management, and ensure performance-intensive systems are running as expected. While Exadata workloads perform well on premises, customers are looking to achieve the scalability, flexibility, and cost benefits of AWS. Learn how Navisite tests the performance of Exadata workloads once they have been migrated to AWS using Oracle Real Application Testing (RAT).