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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

KDnuggets

But lets be honest: creating a reliable, scalable, and maintainable data pipeline is not an easy task. Whether its integrating multiple data sources, managing data transfers, or simply ensuring timely reporting, each component presents its own challenges. It may also be sent directly to dashboards, APIs, or ML models.

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Data Quality Testing: A Shared Resource for Modern Data Teams

DataKitchen

According to Gartner’s breakdown of analytics and data roles , data teams now span far beyond traditional data engineering and business intelligence (BI) analysts. We have ingestion engineers, analytic engineers, stewards, governors, modelers, owners, scientists, product managers, compliance officers, and executives.

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Recap of Amazon Redshift key product announcements in 2024

AWS Big Data

Amazon Redshift , launched in 2013, has undergone significant evolution since its inception, allowing customers to expand the horizons of data warehousing and SQL analytics. Industry-leading price-performance Amazon Redshift offers up to three times better price-performance than alternative cloud data warehouses.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Their terminal operations rely heavily on seamless data flows and the management of vast volumes of data. With the addition of these technologies alongside existing systems like terminal operating systems (TOS) and SAP, the number of data producers has grown substantially. This led to a complex and slow computations.

IoT
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Ingest data from Google Analytics 4 and Google Sheets to Amazon Redshift using Amazon AppFlow

AWS Big Data

In this post, we show you how to establish the data ingestion pipeline between Google Analytics 4, Google Sheets, and an Amazon Redshift Serverless workgroup. It also helps you securely access your data in operational databases, data lakes, or third-party datasets with minimal movement or copying of data.

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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. To enable the feature, complete the following steps: On the Amazon Redshift console, open the Redshift Serverless dashboard. Choose Query data.

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Accelerate your data workflows with Amazon Redshift Data API persistent sessions

AWS Big Data

Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that you can use to analyze your data at scale. Reusing database sessions to simplify the connection management logic in your API implementation, reducing the complexity of the code and making it more straightforward to maintain and scale.