Remove Data Analytics Remove Data Processing Remove Data Warehouse
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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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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. The system had an integration with legacy backend services that were all hosted on premises.

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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Amaterasu — is a deployment tool for data pipelines.

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Amazon Q data integration adds DataFrame support and in-prompt context-aware job creation

AWS Big Data

You can now generate data integration jobs for various data sources and destinations, including Amazon Simple Storage Service (Amazon S3) data lakes with popular file formats like CSV, JSON, and Parquet, as well as modern table formats such as Apache Hudi , Delta , and Apache Iceberg.

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Take Your SQL Skills To The Next Level With These Popular SQL Books

datapine

Business leaders, developers, data heads, and tech enthusiasts – it’s time to make some room on your business intelligence bookshelf because once again, datapine has new books for you to add. We have already given you our top data visualization books , top business intelligence books , and best data analytics books.

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How Will The Cloud Impact Data Warehousing Technologies?

Smart Data Collective

Dating back to the 1970s, the data warehousing market emerged when computer scientist Bill Inmon first coined the term ‘data warehouse’. Created as on-premise servers, the early data warehouses were built to perform on just a gigabyte scale. The post How Will The Cloud Impact Data Warehousing Technologies?

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

In addition to real-time analytics and visualization, the data needs to be shared for long-term data analytics and machine learning applications. The applications are hosted in dedicated AWS accounts and require a BI dashboard and reporting services based on Tableau.

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