# Amazon Redshift Published articles for Amazon Redshift. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Now everyone can put data to work DevFeed: [Now everyone can put data to work]() Original publisher: [Read original article]() Published: 2026-09-10T15:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [data](), [dashboards](), [Authorization](), [Amazon Redshift](), [clickhouse](), [GitHub]() Tags: [agent](), [amazon-redshift](), [analytics](), [chatgpt](), [clickhouse](), [dashboards](), [data](), [github](), [product]() ### AI overview OpenAI introduces a Data agent in ChatGPT Work that connects approved company data sources, answers questions in natural language, and builds interactive dashboards. ### Source excerpt Meet the Data agent in ChatGPT Work. Connect company data, uncover insights, and build interactive dashboards with AI using natural language. ## AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026) DevFeed: [AWS Weekly Roundup: Claude Fable 5.1 on AWS, Amazon Linux 2027 preview, AWS Certified AI Business Strategist, and more (September 7, 2026)]() Original publisher: [Read original article]() Author: Channy Yun (윤석찬) Published: 2026-09-07T14:24:08Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [Claude](), [Amazon Bedrock](), [anthropic](), [Amazon EC2](), [Operating system]() Tags: [amazon-bedrock](), [amazon-ec2](), [amazon-redshift](), [anthropic](), [aws](), [aws-lambda](), [aws-training-and-certification](), [claude](), [linux](), [news](), [security-identity-compliance](), [week-in-review]() ### AI overview AWS's weekly roundup announces Claude Fable 5.1 availability through Amazon Bedrock and Claude Platform on AWS, including retention and safety-review details for Covered Models. It also highlights the Amazon Linux 2027 public preview and EC2 R9g/R9gd memory-optimized instances. ### Source excerpt Last week, Claude Fable 5.1 became available on AWS. According to Anthropic, Claude Fable 5.1 delivers frontier intelligence for ambitious tasks across coding, scientific research, and enterprise workflows. Claude Fable 5.1 is built for long-running, high-stakes work that runs for hours and spans many applications. It can own more of a software project on its [...] ## AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026) DevFeed: [AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)]() Original publisher: [Read original article]() Author: Daniel Abib Published: 2026-08-31T14:45:25Z Content type: news Language: en Sources: [AWS News Blog]() Topics: [data](), [Amazon S3](), [Amazon Redshift](), [Node.js]() Tags: [acquisition](), [amazon-ec2](), [amazon-elastic-container-service](), [amazon-gamelift](), [amazon-redshift](), [amazon-s3](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [analytics](), [announcements](), [aws](), [aws-fargate](), [aws-glue](), [aws-iot-core](), [aws-lambda](), [database](), [duckdb](), [json](), [news](), [node-js](), [open-source](), [parquet](), [python](), [sql](), [week-in-review]() ### AI overview AWS weekly roundup covering the planned acquisition of DuckLabs, the company behind DuckDB, alongside Amazon ECS recovery updates and AWS Lambda preview runtimes for Node.js 26 and Python 3.15. ### Source excerpt The news that interested me the most last week was the DuckLabs acquisition. AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON. DuckDB stays open source under its independent foundation [...] ## Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows DevFeed: [Optimizing Redshift Write Patterns: Tackling Tombstones and Ghost Rows]() Original publisher: [Read original article]() Author: Harichandan Pulagam Published: 2026-08-24T20:18:12Z Content type: article Language: en Sources: [Wealthfront]() Topics: [Amazon Redshift](), [dashboards](), [Data Quality]() Tags: [amazon-redshift](), [batch](), [dashboards](), [data-quality](), [latency](), [load](), [performance](), [redshift](), [space](), [wealthfront-engineering]() ### AI overview This Wealthfront engineering post examines how Redshift tables grew to nearly 10 times the size of their useful data because deleted rows remained on disk as ghost rows. It describes the resulting read and write latency and the write strategies adopted to control table size. ### Source excerpt Amazon Redshift is a core part of our analytics platform, powering dashboards, data quality checks, ad-hoc analytical workloads, and downstream reporting on a shared cluster. Because everything runs on the same cluster, the size and health of our tables directly affects every workload. At Wealthfront, data drives every decision we make, which means any performance... Read more ## Tableflow: Turn Kafka Topics into Iceberg Tables DevFeed: [Tableflow: Turn Kafka Topics into Iceberg Tables]() Original publisher: [Read original article]() Author: Mohtasham Sayeed Mohiuddin Published: 2026-07-10T15:36:14Z Content type: tutorial Language: en Sources: [Confluent: Data in motion]() Topics: [Apache Iceberg](), [Kafka](), [schema-evolution](), [Confluent Cloud](), [parquet](), [Amazon S3](), [Streaming](), [AWS Glue](), [Amazon Redshift](), [BigQuery]() Tags: [amazon-redshift](), [amazon-s3](), [apache-iceberg](), [aws-glue](), [bigquery](), [confluent-cloud](), [kafka](), [parquet](), [schema-evolution](), [streaming](), [technologies](), [tutorial]() ### AI overview This tutorial explains how Confluent Cloud Tableflow continuously materializes Apache Kafka topics as Apache Iceberg or Delta Lake tables. It covers automatic schema handling, type conversion, schema evolution, Parquet conversion, catalog publishing, and table maintenance for querying streaming data with analytics engines and warehouses. ### Source excerpt Learn how Confluent Tableflow turns Kafka topics into Iceberg tables for zero-ETL analytics with automatic schema evolution and open catalog access. ## How the 5 major cloud data warehouses really bill you: A unified, engineer-friendly guide DevFeed: [How the 5 major cloud data warehouses really bill you: A unified, engineer-friendly guide]() Original publisher: [Read original article]() Author: Tom Schreiber & Lionel Palacin Published: 2025-12-01T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [data](), [Amazon Redshift](), [cpu]() Tags: [amazon-redshift](), [bigquery](), [clickhouse](), [cloud](), [compute](), [cost](), [cpu](), [data](), [databricks](), [guide](), [performance](), [scale](), [serverless](), [sql](), [storage]() ### AI overview A guide to comparing how five cloud data warehouses meter and bill analytical compute. It explains why published price lists alone do not reveal real query costs and focuses on the execution and scaling models behind those costs. ### Source excerpt This guide explains how the five major cloud data warehouses--Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift--allocate, meter, and bill compute, giving engineers a clear understanding of what the billing units mean and how to compare them. ## What are Apache Iceberg tables? Benefits and challenges | Redpanda DevFeed: [What are Apache Iceberg tables? Benefits and challenges | Redpanda]() Original publisher: [Read original article]() Author: Redpanda Published: 2025-05-21T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Apache Iceberg](), [datasets](), [data-processing](), [Scalability](), [Architecture & Design](), [Streaming](), [Netflix]() Tags: [amazon-redshift](), [analytics](), [apache-flink](), [apache-iceberg](), [apache-iceberg-acid-compliance](), [apache-iceberg-architecture](), [apache-iceberg-challenges](), [apache-iceberg-metadata-management](), [apache-iceberg-table-format](), [apache-iceberg-tables](), [apache-iceberg-vs-data-lakes](), [architecture](), [benefits-of-apache-iceberg](), [data](), [data-lakes-and-apache-iceberg](), [datasets](), [fundamentals](), [managing-large-datasets-with-apache-iceberg](), [netflix](), [object-storage](), [open-source](), [pipelines](), [real-time-analytics-with-apache-iceberg](), [scalability](), [scalability-of-apache-iceberg-tables](), [streaming]() ### AI overview This article explains how Apache Iceberg tables add database-like structure to data lakes for large analytic datasets. It covers schemas, partitioning, metadata catalogs, version history, schema changes, data rewrites, queryability, consistency, scalability, and interoperability across batch and streaming pipelines and analytics engines. ### Source excerpt Apache Iceberg tables introduce a reliable framework for querying large datasets in data lakes. Explore their use cases, benefits, and more. ## Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics DevFeed: [Why Flock Safety turned to ClickHouse for real-time vehicle traffic analytics]() Original publisher: [Read original article]() Author: ClickHouse Published: 2025-04-29T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [real-time](), [data-engineering](), [Amazon Redshift](), [Architecture & Design](), [data]() Tags: [amazon-redshift](), [analytics](), [analytics-pipeline](), [clickhouse](), [dashboards](), [data-architecture](), [data-engineering](), [real-time]() ### AI overview This developer case study explains why Flock Safety moved its vehicle traffic analytics platform from an Amazon Redshift, DBT, Prefect, and QuickSight SPICE pipeline to ClickHouse Cloud. The former architecture refreshed data daily, required up to four hours to process some datasets, and made data unavailable during refreshes. ClickHouse enabled real-time analytics for camera traffic without the previous size constraints or row-level security limitations. ### Source excerpt "With ClickHouse, our customers now have real-time analytics for their camera traffic, and there are no more constraints on size or row-level security." ~ Leon Kozlowski, Data Engineering Manager ## Important Data Systems Problems Understudied by Database Research DevFeed: [Important Data Systems Problems Understudied by Database Research]() Original publisher: [Read original article]() Author: Viktor Leis (noreply@blogger.com) Published: 2024-12-13T07:37:00Z Content type: opinion Language: en Sources: [Database Architects]() Topics: [data](), [systems](), [Database](), [Compression](), [Amazon Redshift]() Tags: [amazon-redshift](), [analysis](), [compression](), [data](), [distributed](), [paper](), [performance](), [research](), [standard](), [storage](), [systems]() ### AI overview This discussion of database research priorities highlights variable-length string processing, database-specific string compression, unrealistic benchmarks, and the need for more representative analytical workloads. It also notes challenges in distributed query processing. ### Source excerpt In November, I had the pleasure of attending the Dutch-Belgian DataBase Day, where I moderated a panel on practical challenges often overlooked in database research. Our distinguished panelists included Allison Lee (founding engineer at Snowflake), Andy Pavlo (professor at CMU), and Hannes Mühleisen (co-creator of DuckDB and researcher at CWI), with attendees contributing to the discussion and sharing their perspectives. In this post, I'll attempt to summarize the discussion in the hope that it inspires young (and young-at-heart) researchers to tackle these challenges. Additionally, I'll link to some paper that can serve as motivation and starting points for research in these areas. One significant yet understudied problem raised by multiple panellists is the handling of variable-length strings. Any analysis of real-world analytical queries reveals that strings are ubiquitous. For instance, Amazon Redshift recently reported that around 50% of all columns are strings. Since strings are typically larger than numeric data, this implies that strings are a substantial majority of real-world data. Dealing with strings presents two major challenges. First, query processing is often slow due to the variable size of strings and the (time and space) overhead of dynamic allocation. Second, surprisingly little research has been dedicated to efficient database-specific string compression. Given the importance of strings on real-world query performance and storage consumption, it is surprising how little research there is on the topic (there are some exceptions). Allison highlighted a related issue: standard benchmarks, like TPC-H, are overly simplistic, which may partly explain why string processing is understudied. TPC-H queries involve little complex string processing and don't use strings as join or aggregation keys. Moreover, TPC-H strings have static upper bounds, allowing them to be treated as fixed-size objects. This sidesteps the real challenges of variable-size strings ## Teleport 6.2 - Redshift, Listing Databases, and K8S in the UI DevFeed: [Teleport 6.2 - Redshift, Listing Databases, and K8S in the UI]() Original publisher: [Read original article]() Author: ben@goteleport.com (Ben Arent) Published: 2021-05-27T00:00:00Z Content type: release Language: en Sources: [Teleport]() Topics: [Amazon Redshift](), [Databases](), [Kubernetes clusters](), [Grafana](), [JSON Web Tokens](), [Jenkins]() Tags: [amazon-redshift](), [databases](), [grafana](), [jenkins](), [jwt](), [kubernetes-clusters](), [redshift](), [release]() ### AI overview Teleport 6.2 is a release with improvements to Application Access, Kubernetes Access, Database Access, Server Access, and Trusted Clusters. It adds Amazon Redshift support, database and Kubernetes cluster listing in the UI, pass-through headers, native JWT authentication for Grafana, and other fixes. ### Source excerpt Teleport 6.2 brings enhancements across the board, including the ability to list Databases and Kubernetes Clusters in Teleport and Amazon Redshift support. ## The Journey of Corpus DevFeed: [The Journey of Corpus]() Original publisher: [Read original article]() Published: 2021-04-29T00:00:00Z Content type: article Language: en Sources: [SoundCloud Backstage Blog]() Topics: [data-governance](), [BigQuery](), [Data Quality](), [Data Management](), [Amazon Redshift](), [datasets](), [data]() Tags: [amazon-redshift](), [bigquery](), [data](), [data-engineering](), [data-governance](), [data-management](), [data-quality](), [datasets]() ### AI overview SoundCloud describes its effort to replace an outdated, poorly documented data warehouse and inconsistent ETL processes with Corpus, a centralized BigQuery project serving as a single source of truth. The Data Corpus team focuses on applying data governance to datasets critical for product and business decisions, including maintaining high data quality throughout the data lifecycle. ### Source excerpt It seems like a simple enough concept: You take data from how your users interact with your product, and you use it to make business and...