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Building a Data Pipeline with PySpark and AWS

Analytics Vidhya

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction Apache Spark is a framework used in cluster computing environments. The post Building a Data Pipeline with PySpark and AWS appeared first on Analytics Vidhya.

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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

Flipboard

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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Supercharging Your Data Pipeline with Apache Airflow (Part 2)

Heartbeat

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You might be curious how a simple tool like Apache Airflow can be powerful for managing complex data pipelines.

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What is the Snowflake Data Cloud and How Much Does it Cost?

phData

To simplify this discussion and smooth out assumptions across a longer time period, we typically estimate how many hours a day that a virtual warehouse cluster is required to be on, which is why the following section will state hourly rates. Book a strategy session The post What is the Snowflake Data Cloud and How Much Does it Cost?

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How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

AWS Machine Learning Blog

PII Detected tagged documents are fed into Logikcull’s search index cluster for their users to quickly identify documents that contain PII entities. The request is handled by Logikcull’s application servers hosted on Amazon EC2 and the servers communicates with the search index cluster to find the documents.

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A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

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Unlocking generative AI for enterprises: How SnapLogic powers their low-code Agent Creator using Amazon Bedrock

AWS Machine Learning Blog

Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning. He currently is working on Generative AI for data integration. He is the author of the upcoming book “What’s Your Problem?”

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