Sr. Consultant Sales - AWS at a tech services company with 51-200 employees
Real User
Top 5
Apr 5, 2026
If your team is thinking about Azure Data Lake Storage, I’d say it’s a really solid choice if you’re dealing with growing amounts of data and plan to do analytics, reporting, or machine learning—but it’s not something you just “turn on and use” like basic storage. It works best when you have a clear structure, good naming, and some governance in place; otherwise, it can quickly become messy and more expensive than expected. The biggest upside is how well it scales and integrates with tools like Databricks, Synapse, and Power BI, but you’ll get the most value only if you treat it like a long-term data platform rather than just a storage dump. Overall, I’d rate it around 8.5/10—powerful and flexible, but it rewards teams that plan ahead.
We are mostly working with S3, RDS, and AWS Glue jobs. We are using Amazon S3. For Microsoft products, we are using Databricks, Data Factory, Azure Data Lake services, Power BI, and Power Automate. For Azure Data Lake Analytics, we use data lake storage but do not use analytics, as it is external for us. We use Azure Data Lake Storage and Databricks. We do not use the hierarchical namespace feature because we have separate namespaces for each division and separate namespaces for each project. For assessing the effectiveness of Azure Data Lake Storage encryption and RBAC for maintaining data security, we do not do anything because we are on a private network and Azure is also privately hosted for us, so it is taken care of by the networking team. We use HDFS compatibility in Azure Data Lake Storage. We are still working on managing data silos through Azure Data Lake Storage and are working on making it a unified data lake storage. Currently, different projects use different data lake storage, so we still operate some projects in silos. We are building data products to overcome this by having each product and project own a certain section of data that will be shared with all other projects. This way we do not maintain duplicate data. We are still working on the data mesh concept and everything, so it is not yet complete. We are midway through. All our requirements are currently met by Azure Data Lake Storage. We have been using it only for the data lake and are not much concerned about advanced features. Most of the features we use from the data lake to track the lineage and everything are mostly covered from the Databricks side. Integration with third-party solutions is always compatible with whatever is on the approved list of third-party solutions on the Azure platform. If we are doing something custom, we have other options to build it. We can build a Linux machine and do it. I do not face any difficulty with integration. It will be time-consuming if it is not approved by Azure or AWS; otherwise, it is fine. I give this review an overall rating of nine out of ten.
Vice President, Technology at a tech vendor with 11-50 employees
Real User
Top 20
Oct 17, 2025
I don't deal with DDoS protection or Network Watcher. Is that the on-prem version? With Azure, I just deal with Azure Stack. It's not an AWS product, so it wasn't purchased through the AWS marketplace. The overall rating for Azure Data Lake Storage is 9 out of 10.
EPM Practice Manager at a tech services company with 1,001-5,000 employees
Real User
Top 5
Apr 1, 2025
Overall, I would rate Azure Data Lake Storage as an eight out of ten. I have not encountered any complex or critical issues with Data Lake so far. I recommend it to others due to its integration capabilities and stability.
Data Engineer at a financial services firm with 1,001-5,000 employees
Real User
Top 5
Nov 15, 2024
I would recommend Azure Data Lake Storage because it is straightforward to set up and versatile for various projects, especially AI solutions requiring document storage. I rate the product around nine out of ten.
DevOps Manager at a computer software company with 5,001-10,000 employees
MSP
Top 5
Nov 11, 2024
It is essential to enable lifecycle management in Data Lake Storage for better cost management. Proper setup according to your use case and employing strategies like using cold or archive tiers can save cost and optimize usage. I'd rate the solution nine out of ten.
Data Strategist, Cloud Solutions Architect at BiTQ
Real User
Top 5
Sep 11, 2024
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.
I use Azure Data Lake Storage in a cloud-only setup, not on-premises. We receive API calls and store the responses in the product. Then, we process these files using the tool. For first-time users, I recommend learning from Microsoft materials or YouTube videos before using the tool. It is better to gain some knowledge before using it. It's easy for beginners to learn and use, especially compared to AWS and other services. I'd rate Azure Data Lake Storage eight out of ten. I find it user-friendly as a fresher with about two point eight years in my tech career. I started my career with this tool, gained much knowledge, and now I can lead a team.
Strategy Consultant at a computer software company with 201-500 employees
Consultant
Top 10
May 27, 2024
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.
Data Engineer at Universidad Peruana de Ciencias Aplicadas
Real User
Top 10
May 10, 2024
For ETL solutions, it’s essential to stay updated with new and recurring events that might arise. Reviewing forums, articles, and media can help you identify trends and troubleshooting tips relevant to your project. This proactive approach can help you anticipate and address potential issues effectively. Overall, I rate the solution a nine out of ten.
Senior Software Engineer at a tech consulting company with 10,001+ employees
Real User
Top 20
May 3, 2024
Integrating Azure Data Lake Storage into my company's current workflow was easy. I recommend the product to those who plan to use it. I rate the tool an eight to nine out of ten.
The tool is the best platform for storing all kinds of data; I've never experienced any downtime with it. Plus, it offers secure access and security features, which I appreciate. I rate the product a nine out of ten.
Azure Data Lake Storage is widely used for data warehousing, storing processed data, raw customer files, and integrating data from multiple sources, supporting analytics, reporting, and machine learning by securely storing JSON, CSV, and other formats.
Organizations use Azure Data Lake Storage to aggregate information for reporting, integrate it into data pipelines, and benefit from secure transfer capabilities. It serves data scientists as a staging area and businesses leverage its Big...
If your team is thinking about Azure Data Lake Storage, I’d say it’s a really solid choice if you’re dealing with growing amounts of data and plan to do analytics, reporting, or machine learning—but it’s not something you just “turn on and use” like basic storage. It works best when you have a clear structure, good naming, and some governance in place; otherwise, it can quickly become messy and more expensive than expected. The biggest upside is how well it scales and integrates with tools like Databricks, Synapse, and Power BI, but you’ll get the most value only if you treat it like a long-term data platform rather than just a storage dump. Overall, I’d rate it around 8.5/10—powerful and flexible, but it rewards teams that plan ahead.
We are mostly working with S3, RDS, and AWS Glue jobs. We are using Amazon S3. For Microsoft products, we are using Databricks, Data Factory, Azure Data Lake services, Power BI, and Power Automate. For Azure Data Lake Analytics, we use data lake storage but do not use analytics, as it is external for us. We use Azure Data Lake Storage and Databricks. We do not use the hierarchical namespace feature because we have separate namespaces for each division and separate namespaces for each project. For assessing the effectiveness of Azure Data Lake Storage encryption and RBAC for maintaining data security, we do not do anything because we are on a private network and Azure is also privately hosted for us, so it is taken care of by the networking team. We use HDFS compatibility in Azure Data Lake Storage. We are still working on managing data silos through Azure Data Lake Storage and are working on making it a unified data lake storage. Currently, different projects use different data lake storage, so we still operate some projects in silos. We are building data products to overcome this by having each product and project own a certain section of data that will be shared with all other projects. This way we do not maintain duplicate data. We are still working on the data mesh concept and everything, so it is not yet complete. We are midway through. All our requirements are currently met by Azure Data Lake Storage. We have been using it only for the data lake and are not much concerned about advanced features. Most of the features we use from the data lake to track the lineage and everything are mostly covered from the Databricks side. Integration with third-party solutions is always compatible with whatever is on the approved list of third-party solutions on the Azure platform. If we are doing something custom, we have other options to build it. We can build a Linux machine and do it. I do not face any difficulty with integration. It will be time-consuming if it is not approved by Azure or AWS; otherwise, it is fine. I give this review an overall rating of nine out of ten.
I don't deal with DDoS protection or Network Watcher. Is that the on-prem version? With Azure, I just deal with Azure Stack. It's not an AWS product, so it wasn't purchased through the AWS marketplace. The overall rating for Azure Data Lake Storage is 9 out of 10.
Overall, I would rate Azure Data Lake Storage as an eight out of ten. I have not encountered any complex or critical issues with Data Lake so far. I recommend it to others due to its integration capabilities and stability.
Overall, I would rate the Azure Data Lake Storage as nine out of ten.
Exploring more AI features might enhance the solution. I'd rate the solution seven out of ten.
I would recommend Azure Data Lake Storage because it is straightforward to set up and versatile for various projects, especially AI solutions requiring document storage. I rate the product around nine out of ten.
It is essential to enable lifecycle management in Data Lake Storage for better cost management. Proper setup according to your use case and employing strategies like using cold or archive tiers can save cost and optimize usage. I'd rate the solution nine out of ten.
I'd rate the solution eight out of ten.
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.
I use Azure Data Lake Storage in a cloud-only setup, not on-premises. We receive API calls and store the responses in the product. Then, we process these files using the tool. For first-time users, I recommend learning from Microsoft materials or YouTube videos before using the tool. It is better to gain some knowledge before using it. It's easy for beginners to learn and use, especially compared to AWS and other services. I'd rate Azure Data Lake Storage eight out of ten. I find it user-friendly as a fresher with about two point eight years in my tech career. I started my career with this tool, gained much knowledge, and now I can lead a team.
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.
For ETL solutions, it’s essential to stay updated with new and recurring events that might arise. Reviewing forums, articles, and media can help you identify trends and troubleshooting tips relevant to your project. This proactive approach can help you anticipate and address potential issues effectively. Overall, I rate the solution a nine out of ten.
Integrating Azure Data Lake Storage into my company's current workflow was easy. I recommend the product to those who plan to use it. I rate the tool an eight to nine out of ten.
The tool is the best platform for storing all kinds of data; I've never experienced any downtime with it. Plus, it offers secure access and security features, which I appreciate. I rate the product a nine out of ten.