Remove Dashboards Remove Data Warehouse Remove Modeling
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Recap of Amazon Redshift key product announcements in 2024

AWS Big Data

Amazon Redshift , launched in 2013, has undergone significant evolution since its inception, allowing customers to expand the horizons of data warehousing and SQL analytics. Industry-leading price-performance Amazon Redshift offers up to three times better price-performance than alternative cloud data warehouses.

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Domo Addresses Data Products and Agentic AI

David Menninger's Analyst Perspectives

Additionally, as I recently explained , the companys platform addresses a broad range of capabilities that includes data governance and security, data integration and application development, as well as the automation and incorporation of artificial intelligence (AI) and machine learning (ML) models into BI and analytics.

Metrics 130
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Unlocking near real-time analytics with petabytes of transaction data using Amazon Aurora Zero-ETL integration with Amazon Redshift and dbt Cloud

AWS Big Data

While customers can perform some basic analysis within their operational or transactional databases, many still need to build custom data pipelines that use batch or streaming jobs to extract, transform, and load (ETL) data into their data warehouse for more comprehensive analysis. Create dbt models in dbt Cloud.

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Empower financial analytics by creating structured knowledge bases using Amazon Bedrock and Amazon Redshift

AWS Big Data

Now with Amazon Bedrock Knowledge Bases integration with structured data, you can use simple, natural language prompts to query complex financial datasets. Developers can rapidly implement sophisticated data querying features without complex codingjust connect to the API endpoints and let users explore financial data using plain English.

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Accelerate analytics and AI innovation with the next generation of Amazon SageMaker

AWS Big Data

From within the unified studio, you can discover data and AI assets from across your organization, then work together in projects to securely build and share analytics and AI artifacts, including data, models, and generative AI applications.

Analytics 110
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Looker Simplifies Business Intelligence in the Cloud

David Menninger's Analyst Perspectives

Organizations face various challenges with analytics and business intelligence processes, including data curation and modeling across disparate sources and data warehouses, maintaining data quality and ensuring security and governance.

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Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science

O'Reilly on Data

We’ll share why in a moment, but first, we want to look at a historical perspective with what happened to data warehouses and data engineering platforms. Lessons Learned from Data Warehouse and Data Engineering Platforms. Data Science and Machine Learning Require Flexibility. Consider user interfaces.