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Streaming Big Data Files from Cloud Storage

Towards Data Science

In this post we consider the case in which our data application requires access to one or more large files that reside in cloud object storage. This continues a series of posts on the topic of efficient ingestion of data from the cloud (e.g., here , here , and here ). CPU cores and TCP connections).

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Netflix Cloud Packaging in the Terabyte Era

Netflix Tech

As an example, cloud-based post-production editing and collaboration pipelines demand a complex set of functionalities, including the generation and hosting of high quality proxy content. It is worth pointing out that cloud processing is always subject to variable network conditions.

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A Definitive Guide to Using BigQuery Efficiently

Towards Data Science

BigQuery basics and understanding costs ∘ Storage ∘ Compute · ? Like a dragon guarding its treasure, each byte stored and each query executed demands its share of gold coins. Join as we journey through the depths of cost optimization, where every byte is a precious coin. Photo by Konstantin Evdokimov on Unsplash ?

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Processing medical images at scale on the cloud

Tweag

Thankfully, cloud-based infrastructure is now an established solution which can help do this in a cost-effective way. As a simple solution, files can be stored on cloud storage services, such as Azure Blob Storage or AWS S3, which can scale more easily than on-premises infrastructure. But as it turns out, we can’t use it.

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Google BigQuery: A Game-Changing Data Warehousing Solution

ProjectPro

With the global cloud data warehousing market likely to be worth $10.42 billion by 2026, cloud data warehousing is now more critical than ever. Cloud data warehouses offer significant benefits to organizations, including faster real-time insights, higher scalability, and lower overhead expenses. What is Google BigQuery Used for?

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Byte Down: Making Netflix’s Data Infrastructure Cost-Effective

Netflix Tech

By Torio Risianto, Bhargavi Reddy, Tanvi Sahni, Andrew Park Continue reading on Netflix TechBlog ».

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Modern Data Engineering: Free Spark to Snowpark Migration Accelerator for Faster, Cheaper Pipelines in Snowflake

Snowflake

Ingestion Pipelines : Handling data from cloud storage and dealing with different formats can be efficiently managed with the accelerator. Batch Processing Pipelines : Large volumes of data can be processed on schedule using the tool. This is ideal for tasks such as data aggregation, reporting or batch predictions.