Remove Data Lake Remove Data Warehouse Remove Sales
article thumbnail

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

AWS Big Data

Unified access to your data is provided by Amazon SageMaker Lakehouse , a unified, open, and secure data lakehouse built on Apache Iceberg open standards. The final model provides sales teams with the highest-value opportunities, which they can visualize in a business intelligence dashboard and take action on immediately.

article thumbnail

Simplify data ingestion from Amazon S3 to Amazon Redshift using auto-copy

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that makes it simple and cost-effective to analyze your data using standard SQL and your existing business intelligence (BI) tools. Data ingestion is the process of getting data to Amazon Redshift.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Implementing a Pharma Data Mesh using DataOps

DataKitchen

The DataKitchen Platform ingests data into a data lake and runs Recipes to create a data warehouse leveraged by users and self-service data analysts. A sales or marketing team member could propose an idea –– what if we combined data from sources A and B to find potential customers for our new product?

article thumbnail

Write queries faster with Amazon Q generative SQL for Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed, AI-powered cloud data warehouse that delivers the best price-performance for your analytics workloads at any scale. This will take a few minutes to run and will establish a query history for the tpcds data. Amazon Q generative SQL is also personalized to your data domain.

Metadata 102
article thumbnail

Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

Data Lake 120
article thumbnail

Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics

AWS Big Data

Amazon Redshift enables you to efficiently query and retrieve structured and semi-structured data from open format files in Amazon S3 data lake without having to load the data into Amazon Redshift tables. Amazon Redshift extends SQL capabilities to your data lake, enabling you to run analytical queries.

Data Lake 111
article thumbnail

Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

Nonetheless, many of the same customers using DynamoDB would also like to be able to perform aggregations and ad hoc queries against their data to measure important KPIs that are pertinent to their business. Suppose we have a successful ecommerce application handling a high volume of sales transactions in DynamoDB.