Google Cloud Storage VS Azure Synapse Analytics
Compare Google Cloud Storage VS Azure Synapse Analytics and see what are their differences
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Google Cloud Storage
Minio
Amazon S3
Azure Blob Storage
DigitalOcean Spaces
IBM Cloud Object Storage
Zadara Storage
Amazon Simple Storage Service (S3)
Azure Synapse Analytics
Databricks Unified Analytics Platform
Apache Zeppelin
Saturn Cloud
Amazon SageMaker
Cloud Dataprep
GeoSpock
Delta Lake
Google Cloud StorageNo Google Cloud Storage videos yet. You could help us improve this page by suggesting one.
Based on our record, Google Cloud Storage seems to be a lot more popular than Azure Synapse Analytics. While we know about 36 links to Google Cloud Storage, we've tracked only 3 mentions of Azure Synapse Analytics. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Cloud Storage: blog storage for static assets and media files. - Source: dev.to / 4 months ago
Preevy includes built-in support for saving profiles on AWS S3 and Google Cloud Storage. You can also store the profile on the local filesystem and copy it manually before running Preevy - we won't show this method here. - Source: dev.to / 5 months ago
Google Cloud Storage{:target="_blank"} is a globally distributed object storage service offered by Google Cloud Platform. They provide trustworthy and scalable databases for storing large amounts of blob data. They also provide a way to optimize cost and performance with different storage classes and pricing options. - Source: dev.to / 10 months ago
Google Cloud Storage - https://cloud.google.com/storage/. - Source: dev.to / 10 months ago
Also, in terms of packing a pre-trained model you will probably want to puts weights, biases etc into S3 or similar object storage (https://cloud.google.com/storage etc) and load it on application start. Source: 11 months ago
A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of... - Source: dev.to / over 1 year ago
You don't run into these kinds of problems with other tools, like the ones I mentioned. I've never tried the Azure ones, but my gut says they may have some scaling issues (synapse analytics looks promising but I have no experience with it). Source: about 2 years ago
Popular managed cloud data warehouse solutions include Azure Synapse Analytics, Azure SQL Database, and Amazon Redshift. - Source: dev.to / about 2 years ago
Minio - Minio is an open-source minimal cloud storage server.
Databricks Unified Analytics Platform - One platform for accelerating data-driven innovation across data engineering, data science & business analytics
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.
Apache Zeppelin - A web-based notebook that enables interactive data analytics.
Azure Blob Storage - Use Azure Blob Storage to store all kinds of files. Azure hot, cool, and archive storage is reliable cloud object storage for unstructured data
Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.