Category: Learning Levels

A multi-account architecture on AWS is essential for enhancing security, compliance, and resource management by isolating workloads, enabling granular cost allocation, and facilitating collaboration across distinct environments. It also mitigates risks, improves scalability, and allows for advanced networking configurations. In a streaming architecture, you may have event producers, stream storage, and event consumers in a […]

This post explains the internals of hybrid search and how to build a hybrid search solution using OpenSearch Service. We experiment with sample queries to explore and compare lexical, semantic, and hybrid search. All the code used in this post is publicly available in the GitHub repository.

As businesses expand, the demand for IP addresses within the corporate network often exceeds the supply. An organization’s network is often designed with some anticipation of future requirements, but as enterprises evolve, their information technology (IT) needs surpass the previously designed network. Companies may find themselves challenged to manage the limited pool of IP addresses. […]

Apache Flink is an open source distributed processing engine, offering powerful programming interfaces for both stream and batch processing, with first-class support for stateful processing and event time semantics. Apache Flink supports multiple programming languages, Java, Python, Scala, SQL, and multiple APIs with different level of abstraction, which can be used interchangeably in the same […]

It always pays to know more about your customers, and AWS Data Exchange makes it straightforward to use publicly available census data to enrich your customer dataset. The United States Census Bureau conducts the US census every 10 years and gathers household survey data. This data is anonymized, aggregated, and made available for public use. […]

Many organizations operate data lakes spanning multiple cloud data stores. This could be for various reasons, such as business expansions, mergers, or specific cloud provider preferences for different business units. In these cases, you may want an integrated query layer to seamlessly run analytical queries across these diverse cloud stores and streamline your data analytics […]

For building any generative AI application, enriching the large language models (LLMs) with new data is imperative. This is where the Retrieval Augmented Generation (RAG) technique comes in. RAG is a machine learning (ML) architecture that uses external documents (like Wikipedia) to augment its knowledge and achieve state-of-the-art results on knowledge-intensive tasks. For ingesting these […]

In this post, we provide benchmark results of running increasingly complex data quality rulesets over a predefined test dataset. As part of the results, we show how AWS Glue Data Quality provides information about the runtime of extract, transform, and load (ETL) jobs, the resources measured in terms of data processing units (DPUs), and how you can track the cost of running AWS Glue Data Quality for ETL pipelines by defining custom cost reporting in AWS Cost Explorer.

This is a guest post co-written with Brandon Abear, Dinesh Sharma, John Bush, and Ozcan IIikhan from GoDaddy. GoDaddy empowers everyday entrepreneurs by providing all the help and tools to succeed online. With more than 20 million customers worldwide, GoDaddy is the place people come to name their ideas, build a professional website, attract customers, […]

When running Apache Flink applications on Amazon Managed Service for Apache Flink, you have the unique benefit of taking advantage of its serverless nature. This means that cost-optimization exercises can happen at any time—they no longer need to happen in the planning phase. With Managed Service for Apache Flink, you can add and remove compute […]