Remove 2012 Remove Data Warehouse Remove SQL
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CI/CD for Data Pipelines: A Game-Changer with AnalyticsCreator

Data Science Blog

Enter AnalyticsCreator AnalyticsCreator, a powerful tool for data management, brings a new level of efficiency and reliability to the CI/CD process. It offers full BI-Stack Automation, from source to data warehouse through to frontend. It supports a holistic data model, allowing for rapid prototyping of various models.

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Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

In the process of working on their ML tasks, data scientists typically start their workflow by discovering relevant data sources and connecting to them. They then use SQL to explore, analyze, visualize, and integrate data from various sources before using it in their ML training and inference.

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Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning Blog

The workflow includes the following steps: Within the SageMaker Canvas interface, the user composes a SQL query to run against the GCP BigQuery data warehouse. Athena returns the queried data from BigQuery to SageMaker Canvas, where you can use it for ML model training and development purposes within the no-code interface.

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

Flipboard

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. You can use query_string to filter your dataset by SQL and unload it to Amazon S3. If you’re familiar with SageMaker and writing Spark code, option B could be your choice.

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How to use Netezza Performance Server query data in Amazon Simple Storage Service (S3)

IBM Journey to AI blog

This allows data that exists in cloud object storage to be easily combined with existing data warehouse data without data movement. The advantage to NPS clients is that they can store infrequently used data in a cost-effective manner without having to move that data into a physical data warehouse table.

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Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering

AWS Machine Learning Blog

Amazon Redshift is a fully managed, fast, secure, and scalable cloud data warehouse. Organizations often want to use SageMaker Studio to get predictions from data stored in a data warehouse such as Amazon Redshift. This should return the records successfully for further data processing and analysis.

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Why Migrate From Teradata to Snowflake

phData

In short, they are still the model of multiple processors and massive disk storage with data warehouse software on the top layer managing it all. Snowflake was founded in 2012 and is rapidly changing how people think about data warehousing solutions. What is Snowflake? Oh, and let’s not forget those cost savings too!

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