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RocksDB 101: Optimizing stateful streaming in Apache Spark with Amazon EMR and AWS Glue

AWS Big Data

Organizations face mounting pressure to process massive data streams instantaneously—from detecting fraudulent transactions and delivering personalized customer experiences to optimizing complex supply chains and responding to market dynamics milliseconds ahead of competitors.

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From data lakes to insights: dbt adapter for Amazon Athena now supported in dbt Cloud

AWS Big Data

The need for streamlined data transformations As organizations increasingly adopt cloud-based data lakes and warehouses, the demand for efficient data transformation tools has grown. This enables you to extract insights from your data without the complexity of managing infrastructure.

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Building a Trusted AI Data Architecture: The Foundation of Scalable Intelligence

Teradata

Learn more Check out Teradata AI Factory close Home Resources Data architecture Article Building a Trusted AI Data Architecture: The Foundation of Scalable Intelligence Discover how AI data architecture shapes data quality and governance for successful AI initiatives. What is AI data architecture?

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. With a unified catalog, enhanced analytics capabilities, and efficient data transformation processes, were laying the groundwork for future growth.

IoT
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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.

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

AWS Big Data

With Amazon AppFlow, you can run data flows at nearly any scale and at the frequency you chooseon a schedule, in response to a business event, or on demand. You can configure data transformation capabilities such as filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps.

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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

This readability becomes valuable when collaborating with domain experts who need to understand and validate your data transformations. Real-world data projects often involve integrating multiple data sources, handling different formats, and dealing with inconsistent data quality.