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Streaming Big Data Files from Cloud Storage

Towards Data Science

This continues a series of posts on the topic of efficient ingestion of data from the cloud (e.g., Before we get started, let’s be clear…when using cloud storage, it is usually not recommended to work with files that are particularly large. here , here , and here ). CPU cores and TCP connections).

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Google BigQuery: A Game-Changing Data Warehousing Solution

ProjectPro

BigQuery also supports many data sources, including Google Cloud Storage, Google Drive, and Sheets. Borg, Google's large-scale cluster management system, distributes computing resources for the Dremel tasks. Due to this, combining and contrasting the STRING and BYTE types is impossible. What is Google BigQuery Used for?

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Netflix Cloud Packaging in the Terabyte Era

Netflix Tech

After the inspection stage, we leverage the cloud scaling functionality to slice the video into chunks for the encoding to expedite this computationally intensive process (more details in High Quality Video Encoding at Scale ) with parallel chunk encoding in multiple cloud instances.

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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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Databricks Delta Lake: A Scalable Data Lake Solution

ProjectPro

Want to process peta-byte scale data with real-time streaming ingestions rates, build 10 times faster data pipelines with 99.999% reliability, witness 20 x improvement in query performance compared to traditional data lakes, enter the world of Databricks Delta Lake now. This results in a fast and scalable metadata handling system.

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

Snowflake

Designed for processing large data sets, Spark has been a popular solution, yet it is one that can be challenging to manage, especially for users who are new to big data processing or distributed systems. Ingestion Pipelines : Handling data from cloud storage and dealing with different formats can be efficiently managed with the accelerator.

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50 PySpark Interview Questions and Answers For 2025

ProjectPro

Hadoop Datasets: These are created from external data sources like the Hadoop Distributed File System (HDFS) , HBase, or any storage system supported by Hadoop. The following methods should be defined or inherited for a custom profiler- profile- this is identical to the system profile.

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