Category: Advanced (300)

Customers with data engineers and data scientists are using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) as a central orchestration platform for running data pipelines and machine learning (ML) workloads. To support these pipelines, they often require additional Python packages, such as Apache Airflow Providers. For example, a pipeline may require the Snowflake provider […]

Data streaming enables generative AI to take advantage of real-time data and provide businesses with rapid insights. This post looks at how to integrate generative AI capabilities when implementing a streaming architecture on AWS using managed services such as Managed Service for Apache Flink and Amazon Kinesis Data Streams for processing streaming data and Amazon Bedrock to utilize generative AI capabilities. We include a reference architecture and a step-by-step guide on infrastructure setup and sample code for implementing the solution with the AWS Cloud Development Kit (AWS CDK). You can find the code to try it out yourself on the GitHub repo.

Multimodal search enables both text and image search capabilities, transforming how users access data through search applications. Consider building an online fashion retail store: you can enhance the users’ search experience with a visually appealing application that customers can use to not only search using text but they can also upload an image depicting a […]

In this series, we talk about Swisscom’s journey of automating Amazon Redshift provisioning as part of the Swisscom One Data Platform (ODP) solution using the AWS Cloud Development Kit (AWS CDK), and we provide code snippets and the other useful references. In Part 1, we did a deep dive on provisioning a secure and compliant […]

In this post, we deep dive into provisioning a secure and compliant Redshift cluster using the AWS CDK and discuss the best practices of secret rotation. We also explain how Swisscom used AWS CDK custom resources in automating the creation of dynamic user groups that are relevant for the AWS Identity and Access management (IAM) roles matching different job functions.

In this post, we delve into the key aspects of using Amazon EMR for modern data management, covering topics such as data governance, data mesh deployment, and streamlined data discovery. One of the key challenges in modern big data management is facilitating efficient data sharing and access control across multiple EMR clusters. Organizations have multiple […]

Many organizations use external identity providers (IdPs) such as Okta or Microsoft Azure Active Directory to manage their enterprise user identities. These users interact with and run analytical queries across AWS analytics services. To enable them to use the AWS services, their identities from the external IdP are mapped to AWS Identity and Access Management […]

In this post, we show how to build a Q&A bot with RAG (Retrieval Augmented Generation). RAG uses data sources like Amazon Redshift and Amazon OpenSearch Service to retrieve documents that augment the LLM prompt. For getting data from Amazon Redshift, we use the Anthropic Claude 2.0 on Amazon Bedrock, summarizing the final response based on pre-defined prompt template libraries from LangChain. To get data from Amazon OpenSearch Service, we chunk, and convert the source data chunks to vectors using Amazon Titan Text Embeddings model.

Apache Spark is a powerful big data engine used for large-scale data analytics. Its in-memory computing makes it great for iterative algorithms and interactive queries. You can use Apache Spark to process streaming data from a variety of streaming sources, including Amazon Kinesis Data Streams for use cases like clickstream analysis, fraud detection, and more. Kinesis Data Streams is a serverless streaming data service that makes it straightforward to capture, process, and store data streams at any scale.

With the new open source Amazon Kinesis Data Streams Connector for Spark Structured Streaming, you can use the newer Spark Data Sources API. It also supports enhanced fan-out for dedicated read throughput and faster stream processing. In this post, we deep dive into the internal details of the connector and show you how to use it to consume and produce records from and to Kinesis Data Streams using Amazon EMR.

As data analytics use cases grow, factors of scalability and concurrency become crucial for businesses. Your analytic solution architecture should be able to handle large data volumes at high concurrency and without compromising speed, thereby delivering a scalable high-performance analytics environment. Amazon Redshift Serverless provides a fully managed, petabyte-scale, auto scaling cloud data warehouse to […]