My Overall experience with azure data lake storage is very amazing till now, i am yet to use it to its full extent but it's worth it.

The high availability and scalability is best feature of this service.

There is as such nothing has not pleased me the whole service is very useful and progressive.

Azure Data Lake Storage also known as ADLS Gen2 is my primary storage when integrating to a synapse analytics workspace in Azure, it is my daily work now to read and write files using Apache spark notebook to the Gen2 account. It is very speedy and secure.

This product is truly fantastic, below is the list of features that I love:- Individual file-level fine-grained access control via access and permissions control.- Hierarchical namespace enabled storage, easy to run ETL in spark analytics.- Very low cost and maintainability.- We get two endpoint : DFS (dfs.core.windows.net) and BLOB.- One of the very few vendors which actually supports HADOOP.- It also provides Diagnostic logging for running good investigation in support tickets.

There are no such cons but will enlist few complexities.- The plan of Blob soft delete container enablement is very complicated.- When container is made private, the purview policy are difficult to organize.But apart from that, no such setbacks for this products.

As a learning data analyst, I find Azure Data Lake Storage one of the most efficient solutions for big-data analysis on the Microsoft Azure platform. It allows me to process and query massive datasets regarding students learning records on MOOC platforms. The service empowers me to use advanced data science tools and applications without being a data scientist or spending much time managing the infrastructure. I can run an advanced data analysis workflow by writing a few lines of code and following specific instructions available on the service documentation webpages. Azure Data Lake Storage helps me analyze big educational data using advanced technologies and connect big data to Power Bi with minimum effort and affordable cost.

Azure Data Lake Storage service allows data analysts to clean, transform, and process big data in less time with a few lines of code. It allows users to focus more on the data analysis and less on the infrastructure. The service has seamless integration with many big-data file management systems like Hadoop. And has API connectors to data analytics applications like Power Bi. Azure Data Lake Storage is scalable, cost-effective, and facilitates the entire data analysis workload.

Although the Azure Data Lake Storage service is relatively easy-to-use, it requires much time walking through the service documentation and the support forum to learn about the service features.

Azure Data Lake Storage is an exceptional cloud storage solution. What I like the most is the seamless integration with other Azure services, such as Azure Data Factory, Azure Databricks, and Azure HDInsight which makes it easy to manage and analyze large data sets.

Overall Azure Data Lake Storage is great but the learning curve for using Azure Data Lake Storage can be steep, especially for those who are new to cloud-based data storage solutions. Also, the cost seems a little high for small-budget projects.

Highly versatile, secure, and cost-effective with unmatched ease of use and maintainability.

Lacks optimization for analytic use cases, presenting slow data processing and missing built-in file recovery options.