AWS Big Data Blog

Tag: Amazon Redshift

Multi-Warehouse ETL Architecture. Two workloads--a Purchase History ETL job ingesting 10M rows nightly and users running 25 read queries per hour--using a 32 RPU serverless workgroup to read from and write to the database Customer DB. It shows a separate workload--a Web Interactions ETL job ingesting 400M rows/hour--using a separate 128 RPU serverless workgroup to write to the database Customer DB.

Improve your ETL performance using multiple Redshift warehouses for writes (Preview)

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Thousands of customers use Amazon Redshift read data sharing to enable instant, granular, and fast data access across Redshift provisioned clusters and serverless workgroups. This allows you to scale your read workloads to […]

Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

Data loses value over time. We hear from our customers that they’d like to analyze the business transactions in real time. Traditionally, customers used batch-based approaches for data movement from operational systems to analytical systems. Batch load can run once or several times a day. A batch-based approach can introduce latency in data movement and […]

Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

Building a data platform involves various approaches, each with its unique blend of complexities and solutions. A modern data platform entails maintaining data across multiple layers, targeting diverse platform capabilities like high performance, ease of development, cost-effectiveness, and DataOps features such as CI/CD, lineage, and unit testing. In this post, we delve into a case […]

Create, train, and deploy Amazon Redshift ML model integrating features from Amazon SageMaker Feature Store

Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Data analysts and database developers want to use this data to train machine learning (ML) models, which can then be used to generate insights on new data for use cases such as forecasting […]

Manage your workloads better using Amazon Redshift Workload Management

Amazon Redshift workload management (WLM) helps you maximize query throughput and get consistent performance for the most demanding analytics workloads by optimally using the resources of your existing data warehouse. This post provides examples of analytics workloads for an enterprise, and shares common challenges and ways to mitigate those challenges using WLM. We guide you through common WLM patterns and how they can be associated with your data warehouse configurations. We also show how to assign user roles to WLM queues and how to use WLM query insights to optimize configuration.

Accelerate Amazon Redshift secure data use with Satori – Part 1

This post is co-written by Lisa Levy, Content Specialist at Satori. Data democratization enables users to discover and gain access to data faster, improving informed data-driven decisions and using data to generate business impact. It also increases collaboration across teams and organizations, breaking down data silos and enabling cross-functional teams to work together more effectively. […]

Build an ETL process for Amazon Redshift using Amazon S3 Event Notifications and AWS Step Functions

In this post we discuss how we can build and orchestrate in a few steps an ETL process for Amazon Redshift using Amazon S3 Event Notifications for automatic verification of source data upon arrival and notification in specific cases. And we show how to use AWS Step Functions for the orchestration of the data pipeline. It can be considered as a starting point for teams within organizations willing to create and build an event driven data pipeline from data source to data warehouse that will help in tracking each phase and in responding to failures quickly. Alternatively, you can also use Amazon Redshift auto-copy from Amazon S3 to simplify data loading from Amazon S3 into Amazon Redshift.

Dimensional modeling in Amazon Redshift

Amazon Redshift is a fully managed and petabyte-scale cloud data warehouse that is used by tens of thousands of customers to process exabytes of data every day to power their analytics workload. You can structure your data, measure business processes, and get valuable insights quickly can be done by using a dimensional model. Amazon Redshift […]

Centralize near-real-time governance through alerts on Amazon Redshift data warehouses for sensitive queries

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud that delivers powerful and secure insights on all your data with the best price-performance. With Amazon Redshift, you can analyze your data to derive holistic insights about your business and your customers. In many organizations, one or multiple Amazon Redshift data warehouses […]