Category: Learning Levels

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.

As part of an indexing operation, the ingested documents are stored as immutable segments. Each segment is a collection of various data structures, such as inverted index, block K dimensional tree (BKD), term dictionary, or stored fields, and these data structures are responsible for retrieving the document faster during the search operation. Out of these data structures, stored fields, which are largest fields in the segment, are compressed when stored on the disk and based on the compression strategy used, the compression speed and the index storage size will vary. In this post, we discuss the performance of the Zstandard algorithm, which was introduced in OpenSearch v2.9, amongst other available compression algorithms in OpenSearch.

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 […]

We are excited to announce the general availability of Amazon OpenSearch Service zero-ETL integration with Amazon Simple Storage Service (Amazon S3) for domains running 2.13 and above. The integration is new way for customers to query operational logs in Amazon S3 and Amazon S3-based data lakes without needing to switch between tools to analyze operational data. By querying across OpenSearch Service and S3 datasets, you can evaluate multiple data sources to perform forensic analysis of operational and security events. The new integration with OpenSearch Service supports AWS’s zero-ETL vision to reduce the operational complexity of duplicating data or managing multiple analytics tools by enabling you to directly query your operational data, reducing costs and time to action.

Amazon Kinesis Data Streams is used by many customers to capture, process, and store data streams at any scale. This level of unparalleled scale is enabled by dividing each data stream into multiple shards. Each shard in a stream has a 1 Mbps or 1,000 records per second write throughput limit. Whether your data streaming […]

This blog post is co-written with Sid Wray and Jake Koskela from Salesforce, and Adiascar Cisneros from Tableau.  Amazon Redshift is a fast, scalable cloud data warehouse built to serve workloads at any scale. With Amazon Redshift as your data warehouse, you can run complex queries using sophisticated query optimization to quickly deliver results to […]

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 […]

AWS recently announced that Apache Flink is generally available for Amazon EMR on Amazon Elastic Kubernetes Service (EKS). Apache Flink is a scalable, reliable, and efficient data processing framework that handles real-time streaming and batch workloads (but is most commonly used for real-time streaming). Amazon EMR on EKS is a deployment option for Amazon EMR […]

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.