Azure Data Lake Storage Reviews and Ratings

Use Cases and Deployment Scope

Stored Terabytes of Healthcare data in a cost-optimized solution on-cloud using Azure Data Lake Storage Gen2 in containerized fashion. We utilized Azure Data Lake Storage containers as a Destination in our Data Engineering Streasmets Pipelines. Loaded Data became available further to multiple downstream applications in an automated and faster way using Azure Data Factory. Also turned out a better, cost-optimized, and faster solution than HDFS for our different business use cases like the migration of huge data from RDBMS to Data Lake.

Likelihood to Recommend

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.

Use Cases and Deployment Scope

We had all of our storage located within a single datacenter, which caused an issue should it go down. Azure Data Lake Storage allowed us to move some of the storage there, keeping that piece online and active if we lost communication to the main datacenter. It's nice, but not the most reliable.

Likelihood to Recommend

Azure Data Lake Storage is great if it's used in a company where once configured no one goes in and make changes, and there is little to no need for growth over time. If it's a business that will be modifying it daily and constantly expanding, other vendors may have a more feasible option available.

Use Cases and Deployment Scope

Overall it is easy to learn and would be useful for any home care service. Another thing that I like about it is that there is a phone call system where they help you with all the questions you may have. Audio and video calls are possible, with PC screen sharing its other systems

Allows saving documents for some members or sharing them in general channels

Likelihood to Recommend

Having short videos allows me to go back and study precisely

the topics I need without sifting through 30-minute videos to find the

vignettes I need. Using the labs, I got Associate Developer and Cloud

Practitioner authorization. study for the certifications also to have them as a

reference for work when you have any questions about applying a configuration

to the equipment.

Use Cases and Deployment Scope

In our environment, we use Azure Data Lake Storage as a basis for data analytics workflows. Since we want to move away from single storage solutions we chose Azure Data lake as it integrates seamlessly with our current Azure environment. We are able to process large amounts of data and visualize and present it accordingly while ensuring availability and security.

Likelihood to Recommend

Azure Data Lake is best suited for organizations looking to capture large amounts of data to process and store it. Azure Data Lake will make this process easy and cost-effective as it allows you to also report on this data. It provides tools to clean and present it into tables, charts, and graphs, and will easily integrate with any existing Azure environments.

Use Cases and Deployment Scope

We need to store large amount of data that flow daily from our processes as well from external APIs, and we need to keep them for long period of times to perform historical analysis for our clients. Azure Data Lake Storage helps us achieve this goal by providing a secure, fast and large data store for our needs.

Likelihood to Recommend

You should use Azure Data Lake Storage if you need to store large amount of data for analytical purposes, especially if combined with analytical solutions that support its API, like Azure Databricks or Azure Synapse Analytics. You should not use it for transactional workload, of course.

Use Cases and Deployment Scope

We use the Azure Data Lake Storage unlimited capacity for storing real big IoT data. The major problem with the real-time data is the scalability and when the business demands more resources the database e.g. increasing number of connected sensors, the Azure Data Lake Storage is highly scalable and helps us to manage such big data.

Likelihood to Recommend

For handling big data, it works very well, the interface is friendly also it provides a great feature and capability for adding security layers in working with data, so good security features.

The big data compute clusters are easy to set up and the learning curve is somehow easy but still Microsoft needs to provide more intractive instructions.

Use Cases and Deployment Scope

Azure Data Lake Storage is a huge storage repository service that can be used to store information in the public cloud by Microsoft.

Our business scope is to work on a large data analytic project where we have to extract a large amount of structured and unstructured data for the data analysis and transformation.

Since we are also hosted our business application on Azure Cloud, the Azure Data Lake Storage is very helpful to use as it can be integrate with other Azure services and we do our analysis on the real-time at one place. Azure Data Lake Storage is built on the Hadoop file system which means it can process massive pentabytes of data in an efficient way.

It helps in streamline the overall efficiency of our requirement and business outcomes.

Except for some query performance improvements, we have faced no issues so far.

Likelihood to Recommend

Azure Data Lake Storage is built to help with large data analytics and transformations. It can be used as a single data repository for any structured and unstructured data extracted from almost any source. As it is built on top of the Hadoop file system (HDFS), jobs' overall analytic performance and running is quite impressive.

As some features are still in development phase, there are some improvements required to make this a unified storage solution for the organizations.

Use Cases and Deployment Scope

Azure Data Lake is being utilized in a number of ways for our company, most of all tracking employee meal plans, and other analytical sales data. This is the best solution for our use case, and has worked extremely well. We love that it also integrates with Power BI, which our sales team and marketing folks use heavily.

Likelihood to Recommend

ADL is great for structured and unstructured data backups: files, folders, disks, VMs, and databases. It does this better than any platform we ever vetted, and Microsoft is the industry standard typically.

It may not be feasible or cost effective if you don't have that much data to implement, or if you're a smaller organization with two or less VMs/production servers.

Use Cases and Deployment Scope

We use Azure Data Lake Storage as a repository for our larger data analytics. Azure Data Lake has enabled us to capture data of various sizes (from small to large) and types, relatively fast for us to do our analytics. It integrated without issue into our existing data warehouses. Was able to set it up with minimal help.

Likelihood to Recommend

While I did not think it was the most difficult to deploy, I did feel that there were some steps that took time when migrating from our data warehouse to data lakes. More documentation would have been helpful but I expect this to improve as the user base grows. I find that I need to be more careful in writing my queries for optimization when using a data lake. Can be a little more cavalier in a warehouse.

Use Cases and Deployment Scope

Microsoft Azure Data Lake Storage is the best among all available Hadoop data Software in the market. It has the best end-to-end security for our data. It is used by our company to analyze our large data. It is highly cost-efficient and also highly scalable with gives us a visualization of our dataset and makes it easy to understand the data properly.

Likelihood to Recommend

We have used Azure Data Lake Storage to analyze our large dataset to understand and visualize it thoroughly. It is highly encrypted and is highly scalable. It helps us to organize our data well and distribute it basis on its size.