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.
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Qumulo Core
Score 7.0 out of 10
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Qumulo Core Hybrid Cloud File Storage delivers real-time visibility, scale and control of data across on-prem and cloud. Qumulo customers can manage storage at a granular level, programmatically configuring and managing usage, capacity and performance.
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.
Qumulo is great for media and entertainment companies, that need simple and easy management of a NAS. The product can also scale and be sized to larger shops too. The support that comes with it also can act as an extension of internal IT. Their support is always watching the box when it phones home with hardware errors or if it would go offline. Qumulo is not initially set up well-running virtual workloads, while Vmware supports NFS, some settings need to be adjusted on Qumulo to allow for upgrades to happen while virtual machines are running
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.
Their Slack-based support is like nothing I have experienced. They are fast, helpful, and willing to go the extra mile, even when an issue is not clearly their's to solve. They are committed to your success. The engineers on the Slack channel are oftentimes the engineers that programmed the very same feature you are asking questions about or having issues with.
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.
Qumulo was not the least expensive but we were blown away by support offered and pre-sales support to ensure questions were answered. Our main challenges were mixed end-user platforms and a diverse set of use cases on those end-user computers. Having the dual access to volumes, over NFS and SMB, helps us greatly in this area.
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.
