The OpenSearch Vector Engine has been enhanced to deliver vector search at a fraction of the cost on OpenSearch 2.17+ domains. With the new capability to configure k-NN (vector) indexes to operate in disk mode, this feature is designed for environments with limited memory. This offers a cost-effective and precise vector search solution that provides responses in the low hundreds of milliseconds. Disk mode serves as a budget-friendly alternative to memory mode when ultra-low latency is not a priority. In this article, we will explore the advantages of this feature, the mechanics behind it, success stories from users, and guidance on how to get started. For further insights, be sure to check out this blog post.
Additionally, we will discuss how to access Apache Iceberg tables in Amazon S3 through Databricks utilizing the AWS Glue Iceberg REST Catalog within Amazon SageMaker Lakehouse. The collaboration between Databricks on AWS general-purpose compute and AWS Glue Iceberg REST Catalog streamlines metadata access while employing Lake Formation for data retrieval. For simplicity in this setup, both the Glue Iceberg REST Catalog and the Databricks cluster are hosted within the same AWS account.
Moreover, we will demonstrate how to generate vector embeddings for your data by using AWS Lambda as a processor for Amazon OpenSearch Ingestion. This innovative approach leverages the OpenSearch Ingestion’s Lambda processor to dynamically create embeddings and ingest them into an OpenSearch Serverless vector collection.
Furthermore, we will tackle the common challenges linked to manual configuration management of MSK topics by providing a robust Terraform-based solution that accommodates both provisioned and serverless MSK clusters.
In a practical application, we highlight how EUROGATE has established a data mesh architecture utilizing Amazon DataZone, making data accessible to various business units and accelerating innovation. We will examine two specific use cases that demonstrate this in action for business intelligence (BI) and data science applications through AWS services such as Amazon Redshift and Amazon SageMaker.
Juicebox, an AI-driven talent sourcing search engine, harnesses the power of Amazon OpenSearch Service’s vector database to refine its talent search capabilities. By combining traditional full-text search methods with advanced semantic search capabilities, Juicebox can sift through a dataset of over 800 million profiles to find the best candidates.
Additionally, this post covers batch data ingestion into Amazon OpenSearch Service via AWS Glue, showcasing how to use Spark on AWS Glue for seamless data ingestion into OpenSearch Service. We will provide practical examples, best practices, and methods for batch ingestion that help in building efficient and scalable data pipelines on AWS.
Lastly, you can build a high-performance quant research platform with Apache Iceberg, as discussed in a previous post. This article will delve into data management implementation options, focusing on accessing data directly in Amazon S3, utilizing popular data formats like Parquet, and exploring open table formats like Iceberg, backed by real-world historical data.
For more expert insights, check out this resource, which offers valuable information on this topic. Additionally, for those interested in understanding the workplace culture, this link is an excellent resource.
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