The Amazon S3 Glacier storage classes are purpose-built for data archiving, providing a low cost archive storage in the cloud. According to AWS, S3 Glacier storage classes provide virtually unlimited scalability and are designed for 99.999999999% (11 nines) of data durability, and they provide fast access to archive data and low cost.
$0
Per GB Per Month
Azure Data Lake Storage
Score 9.6 out of 10
N/A
Azure Data Lake Storage Gen2 is a highly scalable and cost-effective data lake solution for big data analytics. It combines the power of a high-performance file system with massive scale and economy to help you speed your time to insight. Data Lake Storage Gen2 extends Azure Blob Storage capabilities and is optimized for analytics workloads.
As described in the use case, it is perfect for backup data storage where you do not expect to retrieve the data often. Think of it as a data dump; it is nice to know you have a backup, but it actually is expensive and somewhat difficult to retrieve everything.
Azure Data Lake storage is well suited for applications/use cases within organizations where capturing and storing large amounts of data in any format is required, primarily for storing and processing purposes. It's an easy and cost-effective cloud solution for your application data. The ability to integrate with other Azure Services like Azure Databricks and Azure Data Factory is superb.
Azure Data Lake Storage is extremely scalable. It allows us to scale up or down endlessly based on what we need including replication.
In terms of security, Azure Data Lake Storage fits our requirements really well as we can monitor and encrypt seamlessly. We can also assign permissions through roles and grant network-level access.
Due to the fact that it can scale, we are able to monitor the cost of storage and any given time and make financial decisions about our infrastructure based on how small or big we want to scale.
I'd like to see a better cross-platform native client. Azure Data Explorer is fine, but it's far from the "SSMS" kind of experience SQL Server users are used to.
Listing a large number of file is somewhat problematic and slow. Using the native C# library, running directly on an Azure VM, it can take several hours to list just a couple million files.
Switching from V1 to V2 requires the creation of a new Storage Account and that's pretty inconvenient.
Amazon Glacier isn't a direct competitor to the products I've listed; it could compare to the clouds/data warehouses each of these products use to store their data. In the case of CloudBerry, Amazon Glacier is used with it to create a complete archival backup system. That said, when using Amazon Glacier along with a product like CloudBerry, you can create a reliable, inexpensive cloud backup system for retaining HUGE quantities of data for much less than these other cloud backup solutions. HOWEVER, if you want to restore said data, the cost and complexity begin to become a major concern, so Amazon Glacier should only be considered in situations where you don't plan to touch the data regularly, if at all, once it's at Glacier.
The Azure Data Lake solution is designed for organizations that want to take advantage of big data. It provides a data platform that can help developers, data scientists, and analysts store data of any size and format and perform all types of processing and analytics across multiple platforms and programming languages. It can work with your existing solutions, such as identity management and security solutions. It also integrates with other data warehouses and cloud environments. It can be useful for organizations that need the above softwares.
The cost can be high for more advanced work. In some cases, for instance, time limits and lab runtimes may be too short if you are too slow to learn what is explained as you go along.
promote flexible team communication. You can create different spaces for different teams, and share files and tasks.
