Azure Data Lake Storage Reviews

At our company, we strategize and design solutions that have an impact on our client's business. Our company is majorly focused on technological projects for development through business strategies. Azure Data Lake Storage is one of the tools that helps our company develop business solutions for our customers. 

The solution is used primarily for storing Big Data and not for any analytics tasks. If the client's system is already on AWS, then we don't recommend Azure Data Lake Storage. 

Azure Data Lake Storage offers high scalability for data storage, whereas some storage systems in the market offer limited data capacity. Using the product, multiple documents can be read simultaneously. In products like Azure Data Lake Storage, storage scalability is a basic requirement to handle Big Data use cases. 

Almost any storage issue with the solution can be solved if you click on specific properties of the configuration.  

The Azure Data Lake Storage should not only be compatible with the Azure platforms but also with other vendor solutions. I have faced a lack of clarity in the migration process when Azure Synapse is being used with the solution to migrate data to Microsoft Fabric.

Traditionally, when our organization uses Azure Data Lake Storage, we also need to use Azure Synapse. But when data needs to be imported to Microsoft Fabric from the solution using Synapse, Microsoft does not provide a clear migration passage.

Thus integrators face difficulty in migrating data from Azure Data Lake Storage to other products from Microsoft. The parent owner of Azure products is focused on launching new products in a span of a few years but is not focusing on how customers will migrate to their latest product from other data storage systems. Microsoft is not developing the older products once they launch new solutions; they are just providing basic support for the former products. 

I am a user of Azure Data Lake Storage. 

Azure Data Lake Storage is one of the rare services with SLAs that show major stability. On the other hand, if I consider GPT and OpenAI services in Azure, half of the SLAs remain locked. 

I would rate the scalability a ten out of ten. I haven't encountered any data use cases so large that Azure Data Lake Storage wasn't able to manage and scale. For instance, when it comes to storing global transactions of Visa, even for such big use cases, Azure Data Lake Storage will be able to handle the data volume. 

At our company, we work with Azure Data Lake Storage for top-level enterprise companies. 

Instead of reaching out to customer support every time an issue occurs with the product, our organization members use the documentation to resolve the problem. The technical specialists of our company are capable of solving most of the issues on their own, leveraging the documentation around Azure Data Lake Storage. 

The initial setup of the product was super easy. There is a very straightforward process that involves visiting the Azure portal, creating accounts, and configuring the needed ratio of Azure Data Lake Storage. The security configuration of the solution is a bit complex and has multiple technicalities compared to the rest of the setup process. 

The solution's setup process can probably be improved by embedding a feature that guides users through the selections necessary to achieve a secure configuration.

Most of the networking configurations are easily available, and to avail of advanced security options, a user needs to visit the advanced setup section. If Azure Data Lake Storage needs to be integrated with a pre-built network and there is a requirement for authenticated user access and the accessible medium, all such sections of security need to be better guided by the solution provider.  

There needs to be additional assistance provided by the solution during the setup process for a highly secure configuration, especially for individuals who are not networking experts.

Azure Data Lake Storage is one of the most affordable products available from the vendor. When the storage capacity offered by the solution is compared with the computation, Azure Data Lake Storage turns out to be a more affordable option than other databases. 

Azure Data Lake Storage can be used not only for Big Data but also for normal-sized data because it's more cost-effective than other database solutions. The solution offers almost the same features as Snowflake but at a lower price. The cost-effectiveness is the major reason why in our company, we use Azure Data Lake Storage instead of Snowflake and Synapse. 

In our organization, we use Azure Data Lake Storage in integration with Spark and Azure Databricks. When you are working with an extremely large volume of data, you should prefer Azure Data Lake Storage over other data warehouses; other solutions may process data and queries faster but wouldn't be able to manage the data size. I would overall rate Azure Data Lake Storage a ten out of ten. 

It is for data warehousing. We currently work on using Databricks and Hadoop for data warehousing – figuring out initial data migrations is our focus right now.

It's a cloud-based tool within the Microsoft ecosystem, offering many benefits for data handling.

Our focus is on standardization. We're currently analyzing how it could work with Databricks and haven't explored Azure Data Lake Storage for storage extensively.

We've been actively working with big data analytics for the past three years. Initially, we used Microsoft APS (Analytics Platform System).

From an SAP perspective, direct connectivity to SAP systems is an area that could be enhanced. Our landscape heavily relies on SAP, and we find solutions like Snowflake and Databricks more integrated. Azure Data Lake Storage could improve by providing stronger connectivity options for SAP databases.

My company is in the initialization stage. Like, we've only completed the initial setup phase.

So, we're in the analysis phase, working with a sandbox environment at the moment. It has been six months now. 

I would rate the stability an eight out of ten. 

I would rate the scalability a six out of ten. We've primarily encountered issues during the initial migration from Hadoop to Azure Data Lake Storage.

There are more than 30 end users using it. It's more of a real-time kind of setup. We're focusing on continuous data replication.

We had some troubles. 

Neutral

We used different solutions. We switched to Azure because of the increases data volume we needed to handle. 

I would rate the experience with the initial setup a five out of ten, with ten being easy to set up. 

It took us a few weeks to set up. We haven't started that integration process yet. We're currently in the sandbox testing phase.

Our landscape administrator team handles initial setups. We have a separate infra team for the deployment processes. 

There are around ten members in the team. 

It's quite expensive. Compared to other options we've explored, I would rate the pricing a seven out of ten, with ten being expensive. 

Overall, I would rate the solution a seven out of ten. 

We are using Databricks along with some other tools to have an automated process. The data from different sources gets loaded into Data Lake. My use case for Data Lake Storage is as an integration for various sources of data that are processed and loaded into the lake for subsequent analysis.

Using Azure Data Lake Storage has provided us with a cost-effective solution for data storage, which allows us to manage large volumes of data efficiently.

Storage within Azure Data Lake is cheaper, which is one of the reasons we moved to it. Another valuable feature is the flexibility to scale storage up or down as needed.

The documentation could be more user-friendly with better tutorials. While the initial setup is not too complex, it requires understanding various options and their implications. Improving this can help users understand the configuration process better.

I have been using this solution for one to two years.

I would rate the stability an eight out of ten. It is quite stable.

From a scalability point of view, it is easy to scale. The flexibility to expand or reduce capacity according to requirements is well-handled.

I haven't engaged much with the technical support. I cannot provide an accurate mark for it.

Neutral

We moved from Matillion ETL to Snowflake and Azure Data Lake Storage.

The initial setup process is not very complex but requires detailed knowledge of the various configuration options. One needs to understand the implications of each option, such as cost and performance, which can make the setup process somewhat challenging without adequate documentation support.

The pricing is average, not too high and not too cheap.

Depending on your use case, Azure Data Lake Storage can benefit your organization. It is suitable for medium to large scale companies.

I'd rate the solution seven out of ten.

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.

We are restoring external tables and data in Databricks, accessing those tables to read and write data using Azure Data Lake Storage. We use it for data quality purposes and store data to form external tables.

The hierarchical structure allows us to create multiple hierarchies inside, such as storage containers, directories, and subdirectories. It provides multiple edges and access control. We can define who can access which directory and restrict read and write operations.

Version control would be a great improvement. Currently, there is no version control, and if something is deleted, it's permanently gone. The addition of a trash item would help in recovering data deleted by mistake.

We have been using the solution for about three to four years.

There is no downtime, and everything is superior. The SLA is 99.99%.

It's good, so I would rate it as eight or nine out of ten. It handles large amounts of data efficiently.

I would rate technical support ten out of ten.

Positive

The setup is too complex since we handle a large amount of data.

Our team handles everything. We don't have any consultants or third-party integrators.

We did evaluate AWS as it has S3 buckets.

I'd rate the solution eight out of ten.