Remove Big Data Remove Data Processing Remove Data Warehouse Remove Optimization
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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. The rise of cloud has allowed data warehouses to provide new capabilities such as cost-effective data storage at petabyte scale, highly scalable compute and storage, pay-as-you-go pricing and fully managed service delivery.

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Accelerate your data warehouse migration to Amazon Redshift – Part 7

AWS Big Data

With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex. You have to migrate terabytes or petabytes of data from your legacy system while not disrupting your production workload.

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. In an optimal environment, we store the credentials in AWS Secrets Manager and retrieve them. This includes the host, port, database name, user name, and password. These SCDs identify how a row in a table changes over time.

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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

Analyzing historical patterns allows you to optimize performance, identify issues proactively, and improve planning. Typically, you have multiple accounts to manage and run resources for your data pipeline. About the Authors Noritaka Sekiyama is a Principal Big Data Architect on the AWS Glue team.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Deciphering The Seldom Discussed Differences Between Data Mining and Data Science

Smart Data Collective

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Unfortunately, despite the growing interest in big data careers, many people don’t know how to pursue them properly. It hosts a data analysis competition. Use Kaggle.

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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.

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