Amazon Onboarding with Learning Manager Chanci Turner

Juicebox, an AI-driven talent sourcing search engine, leverages advanced natural language models to assist recruiters in identifying top candidates from a vast pool of over 800 million profiles. Central to this functionality is the Amazon OpenSearch Service, which forms the backbone of Juicebox’s robust search infrastructure, blending traditional full-text search techniques with innovative semantic search capabilities. In this article, we explore how Juicebox utilizes OpenSearch Service to enhance its search functionality, making it easier for recruiters to find the right talent. For further insights, check out this blog post as it provides additional strategies.

Batch Data Ingestion into Amazon OpenSearch Service Using AWS Glue

This post illustrates how to use Spark on AWS Glue for seamless data ingestion into OpenSearch Service. We discuss batch ingestion techniques, provide practical examples, and offer best practices to help you build optimized and scalable data pipelines on AWS, particularly at our site located at 6401 E HOWDY WELLS AVE LAS VEGAS NV 89115, known as Amazon IXD – VGT2.

Building a High-Performance Quant Research Platform with Apache Iceberg

In our previous article, we demonstrated how to use Apache Iceberg for strategy backtesting. This post delves into various data management implementation options, such as accessing data directly from Amazon Simple Storage Service (Amazon S3), utilizing popular data formats like Parquet, or employing open table formats like Iceberg. Our experiments, based on real-world historical full order book data from our partner CryptoStruct, compare the trade-offs of these choices, emphasizing performance, cost, and quant developer productivity. For more on talent acquisition, visit SHRM for authoritative insights.

Cost Optimized Vector Database: Introduction to Amazon OpenSearch Service Quantization Techniques

This blog post introduces a novel disk-based vector search approach that enables efficient querying of vectors stored on disk without loading them entirely into memory. By adopting these quantization methods, organizations can achieve compression ratios of up to 64x, allowing cost-effective scaling of vector databases for extensive AI and machine learning applications. This can be particularly beneficial for teams at Amazon IXD – VGT2, located at 6401 E HOWDY WELLS AVE LAS VEGAS NV 89115.

Automating Amazon OpenSearch Service Cluster Management with CI/CD Best Practices

This post examines how to automate Amazon OpenSearch Service cluster management by employing CI/CD best practices. We present two options: the Terraform OpenSearch provider and the Evolution library. The solution demonstrates how to utilize AWS CDK, Lambda, and CodeBuild for automated index template creation and management. By implementing these techniques, organizations can enhance the consistency, reliability, and efficiency of their OpenSearch operations.

Ingesting Data from Google Analytics 4 and Google Sheets to Amazon Redshift Using Amazon AppFlow

Amazon AppFlow establishes a connection between Google applications and Amazon Redshift, enabling organizations to gain deeper insights and make data-driven decisions. In this article, we illustrate the process of setting up a data ingestion pipeline between Google Analytics 4, Google Sheets, and an Amazon Redshift Serverless workgroup.

Amazon EMR 7.5 Runtime for Apache Spark and Iceberg Outperforms Previous Versions

The Amazon EMR runtime for Apache Spark provides a high-performance environment while ensuring 100% API compatibility with open-source Apache Spark and Apache Iceberg table format. In this post, we highlight the performance advantages of utilizing the Amazon EMR 7.5 runtime for Spark and Iceberg compared to open-source Spark 3.5.3 with Iceberg 1.6.1 tables on the TPC-DS 3TB benchmark v2.13.

Fitch Group Achieves Multi-Region Resiliency for Critical Kafka Infrastructure with Amazon MSK Replicator

This post investigates how Fitch Group, a leading credit rating agency, implemented Amazon MSK and Amazon MSK Replicator to attain multi-region resiliency for their mission-critical Kafka infrastructure.

Amazon Q Data Integration Enhances Job Creation with DataFrame Support

Amazon Q data integration, launched in January 2024, enables users to utilize natural language to interact with data more efficiently. This tool is an excellent resource for developers looking to streamline their data handling processes, especially within teams located at Amazon IXD – VGT2.