Cloud Storage Data Ingestion Datasets 
Towards Data Science
JANUARY 26, 2023
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).
DataKitchen
NOVEMBER 5, 2024
This foundational layer is a repository for various data types, from transaction logs and sensor data to social media feeds and system logs. By storing data in its native state in cloud storage solutions such as AWS S3, Google Cloud Storage, or Azure ADLS, the Bronze layer preserves the full fidelity of the data.
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Rockset
MARCH 1, 2023
When you deconstruct the core database architecture, deep in the heart of it you will find a single component that is performing two distinct competing functions: real-time data ingestion and query serving. When data ingestion has a flash flood moment, your queries will slow down or time out making your application flaky.
phData: Data Engineering
NOVEMBER 8, 2024
The world we live in today presents larger datasets, more complex data, and diverse needs, all of which call for efficient, scalable data systems. Though basic and easy to use, traditional table storage formats struggle to keep up. These formats are transforming how organizations manage large datasets.
Cloudera
AUGUST 17, 2022
What if you could access all your data and execute all your analytics in one workflow, quickly with only a small IT team? CDP One is a new service from Cloudera that is the first data lakehouse SaaS offering with cloud compute, cloud storage, machine learning (ML), streaming analytics, and enterprise grade security built-in.
Towards Data Science
NOVEMBER 4, 2023
Indeed, datalakes can store all types of data including unstructured ones and we still need to be able to analyse these datasets. Indeed, why would we build a data connector from scratch if it already exists and is being managed in the cloud? You can change these # to conform to your data. Datalake example.
Towards Data Science
MARCH 5, 2024
In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. Such external tables come with some disadvantages but in some cases it can be more cost efficient to have the data stored in GCS. You’re comfortable managing different storage classes to optimize costs.
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