Category: Analytics

Amazon Q and generative BI are transforming business operations, and AWS Partners like ZS Associates, Tiger Analytics, and Compass UOL are pioneering use cases leveraging these technologies to build industry-tailored solutions that improve decision making, operations, fraud detection, software development lifecycles, and more. AWS provides resources to help partners develop and deploy such transformative generative AI offerings.

The Amazon Athena Teradata Connector enables Athena to query data in Teradata Vantage using SQL, and comprises two AWS Lambda functions for metadata and record reading. This post describes deploying the connector, creating a Lambda layer for the Teradata JDBC driver, and running queries on Teradata from Athena, including a federated query joining Teradata and S3 data. This provides a scalable, serverless way to analyze data across different data stores without data duplication.

As data volumes grow, visibility into key metrics becomes crucial for optimizing reliability, performance, and cost. Pariveda’s observability solution leverages AWS services to build operational dashboards displaying AWS Glue job details like runtimes, status, and computational load. By unlocking deeper insights, users can pinpoint optimization opportunities, troubleshoot issues faster, and drive greater efficiency across their data pipelines as workloads scale.

Generative AI adoption is rapidly growing to meet the massive data needs of modern machine learning models. Manually labeling data can be time-consuming but AWS has collaborated with Datasaur to offer solutions addressing data labeling challenges using generative AI. Datasaur’s NLP Platform automates annotation tasks and integrates with AWS services, and its LLM Labs evaluates large language models’ performance and cost for labeling.

In today’s industrial landscape, smart manufacturing is pivotal for sustainability and efficiency gains. However, organizations face challenges in gathering and consolidating data from multiple, often outdated sources on the manufacturing floor. LTIMindtree’s AWS-powered Digital Command Center (DCC) solution addresses this by enabling data acquisition, management, real-time KPI monitoring, and analytics-driven insights.

The world of video entertainment is more competitive than ever. The video entertainment landscape is crowded, so content providers must deeply understand viewer behavior to stand out. Synamedia Clarissa, built on AWS, delivers data-driven insights to help businesses boost viewer satisfaction. Clarissa analyzes video consumption and user experiences, providing content and app insights like which videos resonate best and how users discover content.

Learn how to connect the ThoughtSpot analytics platform to an Amazon Aurora MySQL database for real-time operational analytics. This post outlines the steps to create an Aurora MySQL instance, populate it with sample data, create a connection from ThoughtSpot, and build generative AI-powered analytics using natural language search. By integrating ThoughtSpot and Aurora MySQL, businesses can get instant insights from their transactional database.

Cornerstone Consulting Group developed a plugin to simplify migrating on-premises or Amazon EC2 self-managed Apache Kafka clusters to Amazon MSK. The plugin automates MirrorMaker 2 replication, scales connectors, and integrates monitoring and notifications. It supports requirements like zero data loss, minimal code changes, auto-scaling, and performance/security parity. The plugin eases complex setup steps, working seamlessly for a smooth cutover to Amazon MSK after replication completes.

Amazon Athena allows analyzing petabytes of data directly with SQL queries or via analytics tools like ThoughtSpot. This post covers creating an IAM access key, two Amazon S3 buckets (one for data storage, one for query results), creating an Amazon Athena table from sample data using an AWS Glue crawler, and establishing a connection between Athena and ThoughtSpot. With the connection, ThoughtSpot can search and visualize the Athena data using AI capabilities.

Instinet is a SaaS-based solution that Ness Digital Engineering built on AWS for the trading and finance industry. It provides technologically advanced brokerage services and helps institutional investors improve performance by reducing complexity and costs. The solution modernized Instinet’s trade processing and solved challenges with Instinet’s previous on-premises, batch-driven architecture.