Azure Data Lake Storage Reviews

It is typically used in my data analytics workflows. I use it as a data lake. 

The hierarchical namespace is okay. It's not the kind of product I would have any issue with.

RLS (Row-Level Security) and these kinds of things have been effective for protecting data. 

Microsoft is quite good when it comes to integration. They have multiple connectors available and different ingestion and integration processes available.

There is room for improvement in Microsoft support. I didn't have a good experience with it. 

Microsoft, in general, needs to simplify its licensing model. That's one of the biggest issues with Microsoft. The licensing model is either quite difficult to understand or is constantly evolving.

I like the move from Data Lake to Lakehouse. I think it's more up to Microsoft, regarding the trends of the market and what organizations need.

I use it, but it's integrated into Microsoft Fabric. 

I don't have any performance issues. It's a reliable kind of product.

I would rate the stability an eight out of ten. 

Technical support is not something I'm using a lot because I had a poor experience with it in the past, but not on these products, technically. I'm not using a lot of Microsoft support currently.

So, I don't really need it anymore because I've benefited from colleagues and the community to help, and it helps a lot.

I work with Microsoft products in general, but mostly Fabric. There is also Data Factory, Databricks, and Synapse. My company is a Microsoft partner only.

Microsoft, in general, needs to simplify its licensing model. That's one of the biggest issues with Microsoft. The licensing model is either quite difficult to understand or is constantly evolving. I think there's a will to simplify it because one of the biggest client complaints is that the licensing model is always messy and evolving. It's quite difficult to understand.

However, I'm quite satisfied with the pricing. It's quite good.

I would recommend it to other users. I would not recommend it to the smallest companies because you have an entry ticket while using this kind of tool. It's based on usage, but it's mostly beneficial for companies that have big architecture to build, that are looking for a big, complex architecture. It's quite relevant for that. 

If you, as a small company, would like to simplify and rationalize, you may have some packaged products that would not only provide storage or transformation, but a different kind of end-to-end experience that would be better.

Overall, I would rate the solution an eight out of ten. 

We use the tool for multiple processes. We use it as a storage layer for files coming in from relational systems and data from real-time streaming systems. We also use it as a staging area for data scientists to consume.

The tool offers a big storage layer. The security aspect is quite good. In Azure, there's an option for soft deletes and policy management. This allows us to store only the most up-to-date data while everything else can be policy-managed. This makes handling and management easier.

If I had to nitpick, maybe the throughput could be faster - how quickly you can access data and how fast data can be written onto the Azure Data Lake Storage.

I have been working with the product for five years. 

I rate the tool's stability a nine out of ten. 

The tool is scalable as long as you pay more. 

Support depends on what agreements you have with Microsoft. Many consultants and companies outside Microsoft can also provide expertise in maintaining and managing the Azure environment, especially the Data Lake environment. It doesn't have to be Microsoft. But if you're raising tickets with Microsoft to fix issues, they're pretty reasonable.

Neutral

The tool's deployment is simple. I've worked with Azure Data Lake Storage in different scenarios. It can be on-premises, in the cloud, or a hybrid solution—it depends on the design. I've worked with it in both on-premises and cloud-based scenarios. For the last few years, as everyone's been transitioning to the cloud, we've mainly worked with cloud-based solutions.

Pricing is tricky because it depends on the solution you're building and the type of Data Lake storage you use—hot or cold.

The tool can be used by small and large companies. It's not restricted by price, so it's not just for high-end companies. Especially with cloud options available now, any company can potentially use it. 

For competitors, from a cloud-based provider perspective, you have Amazon, Google, and other cloud providers. If you are building your custom solution, you can use traditional SAN drives on-premise for data lake storage, which becomes expensive. I'd say the main competitors of the cloud options are Microsoft, AWS, and Google. There are potentially other providers like Alibaba, but I haven't used them, so I can't provide more information about them.

I have experience integrating AI solutions with Azure Data Lake Storage and helped design some of them. AI solutions access data similarly to downstream systems like ETL tools. For cloud providers, the connections to AI tools are typically built into their products.

I rate the overall solution a nine out of ten. I definitely recommend Azure Data Lake Storage. I have recommended it for all the solutions I've designed and built for my clients. I would recommend it to anybody considering entering the data space or looking at building warehouses, AI solutions, etc.