Amazon Redshift is a data warehouse product
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product2026-07-07
The article discusses the introduction of Multi-Dataset Relationships in Amazon Quick Sight, allowing users to define logical relationships between datasets and perform runtime joins at query time. This approach reduces upfront data preparation, preserves native granularity, and simplifies governance. It also enables independent refresh schedules and row-level security during runtime joins. The article outlines best practices for designing multi-dataset models, including starting with a star schema and managing granularity deliberately.
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product2026-07-10
This article discusses the construction of a semantic layer on AWS using Stardog's Semantic AI Application integrated with Amazon Aurora and Amazon Redshift. It highlights the importance of a semantic layer in enabling AI agents to reason over fragmented enterprise data without the need for ETL processes. The article also covers the architectural components necessary for implementing agentic analytics, including the model layer, meaning layer, and agent runtime layer, while providing a practical example of a customer 360 agent that utilizes this semantic layer.
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tutorial2026-07-15
The article discusses how Salesforce and AWS have collaborated to enable Zero Copy access to Apache Iceberg tables stored in Amazon S3. This integration allows users to query and analyze data across platforms without the need for data replication, thereby enhancing cost efficiency, scalability, and operational agility. It provides a detailed walkthrough for setting up this integration, including prerequisites and configuration steps. The article emphasizes the benefits of real-time data access and streamlined data architecture.
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project2026-06-22
The article discusses Avanse Financial Services' migration from an external analytics application to a cloud-native lakehouse architecture using Amazon SageMaker Unified Studio. This transition addressed several operational challenges, including data synchronization bottlenecks, high licensing costs, and limited auditability. By leveraging AWS services, Avanse improved their analytics capabilities, achieving faster report generation and better governance. The article outlines their migration journey, detailing the phases and outcomes of adopting a unified analytics environment.
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project2026-03-03
The article details Yggdrasil Gaming's migration from Google BigQuery to an AWS-based lakehouse architecture, highlighting the challenges faced due to multi-cloud operational complexity and the need for a scalable analytics foundation. It outlines the phased approach taken to establish a new architecture using AWS services, including Amazon S3, Apache Iceberg, and Amazon Athena, which enabled real-time data ingestion and advanced analytics capabilities. The migration resulted in significant cost savings, improved data freshness, and enhanced governance for analytics workloads. The article serves as a case study for organizations looking to modernize their data architecture.
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project2026-01-30
The article outlines Halodoc's comprehensive approach to data validation within a Lakehouse architecture, emphasizing the importance of data accuracy and reliability. It describes a multi-layered validation strategy that employs AI to enhance data quality checks at various stages of the data pipeline. The validation layers include checks for data consistency, structural correctness, business correctness, and reconciliation, ensuring that data remains trustworthy throughout its journey. The implementation of this strategy has led to reduced data incidents and increased trust among analytics and product teams.
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tech22024-12-12
This article discusses the challenges and solutions for building end-to-end data lineage in enterprise data analytics, particularly for one-time and complex queries. It highlights the use of Amazon Athena, Amazon Redshift, Amazon Neptune, and dbt to create a unified data modeling language across different platforms. The authors explain how to automate the data lineage generation process using AWS services like Lambda and Step Functions, ensuring accuracy and scalability. The article provides insights into the architecture and implementation details necessary for effective data lineage tracking.
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product2025-05-20
Interested to know when an Amazon Bedrock knowledge base for the Redshift database, how to open the access for Apps with nature language.
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project2024-12-05
Twitch has leveraged Views in their Data Lake to enhance data agility, minimize downtime, and streamline development workflows. By utilizing Views as interfaces to underlying data tables, they've enabled seamless schema modifications, such as column renames and VARCHAR resizing, without necessitating data reprocessing. This approach has facilitated rapid responses to data quality issues and supported efficient ETL processes, contributing to a scalable and adaptable data infrastructure.
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product2024-12-12
Build a process to built the complete data lineage information by merging the partial lineage generated by dbt automatically.
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project2024-11-06
Why we load the S3 data into the Redshift again? it already queryable via Redshift Spectrum? I guess it's for the performance? Transform the S3 raw data, build the data models and write back into S3?
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poc2021-12-27