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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.

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Accelerate Offloading to Cloudera Data Warehouse (CDW) with Procedural SQL Support

Cloudera

Did you know Cloudera customers, such as SMG and Geisinger , offloaded their legacy DW environment to Cloudera Data Warehouse (CDW) to take advantage of CDW’s modern architecture and best-in-class performance? The Data Warehouse on Cloudera Data Platform provides easy to use self-service and advanced analytics use cases at scale.

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Migrate a petabyte-scale data warehouse from Actian Vectorwise to Amazon Redshift

AWS Big Data

Amazon Redshift is a fast, scalable, and fully managed cloud data warehouse that allows you to process and run your complex SQL analytics workloads on structured and semi-structured data. Solution overview Amazon Redshift is an industry-leading cloud data warehouse.

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MLOps and DevOps: Why Data Makes It Different

O'Reilly on Data

We need robust versioning for data, models, code, and preferably even the internal state of applications—think Git on steroids to answer inevitable questions: What changed? The applications must be integrated to the surrounding business systems so ideas can be tested and validated in the real world in a controlled manner.

IT 360
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Incremental refresh for Amazon Redshift materialized views on data lake tables

AWS Big Data

Amazon Redshift is a fast, fully managed cloud data warehouse that makes it cost-effective to analyze your data using standard SQL and business intelligence tools. However, if you want to test the examples using sample data, download the sample data. The sample files are ‘|’ delimited text files.

Data Lake 105
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5 things on our data and AI radar for 2021

O'Reilly on Data

The data that powers ML applications is as important as code, making version control difficult; outputs are probabilistic rather than deterministic, making testing difficult; training a model is processor intensive and time consuming, making rapid build/deploy cycles difficult. A Wave of Cloud-Native, Distributed Data Frameworks.

Data Lake 313
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Modernizing the Data Warehouse: Challenges and Benefits

BI-Survey

Many companies are therefore forced to put these concepts to the test. But what are the right measures to make the data warehouse and BI fit for the future? Can the basic nature of the data be proactively improved? The data landscape and the data integration tasks to be solved are often too complex.