Azure Data Lake Storage Reviews

We use containers for DevOps when implementing solutions, primarily with Azure.

Azure Data Lake Storage offers comprehensive integration with a variety of Azure tools, such as SQL servers and the Azure DevOps platform. It is a solid platform for constructing solutions due to its quality and support. This integration across Microsoft's suite of tools provides a scenario conducive to developing different solutions.

The speed of support response needs to improve, especially for critical, high-priority issues. Having a quick SLA response time is crucial.

I have been working with Azure Data Lake Storage since last year.

My experience with the initial setup was moderate, and I rated it as a seven out of ten.

I find the stability of Azure Data Lake Storage to be excellent and would rate it as an eight out of ten.

I believe the scalability of Azure Data Lake Storage is good and would rate it as a seven out of ten.

Azure support is akin to AWS, with a rating of seven or eight out of ten. A good support experience is marked by the speed of reply and the relevancy of resolution tips.

Positive

I found the initial setup to be straightforward and I rated it as a seven.

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.

Public Cloud

Microsoft Azure

We are using Azure Data Lake Storage as a secondary storage solution. We use it for operational reporting and machine learning implementations. We also use the security and encryption features.

We can use the solution to implement a data warehouse. I am working on machine learning use cases. I have implemented a reusable framework. We can implement one framework and utilize it for multiple use cases.

Microsoft Fabric costs more since we are charged for all the components.

I rate the tool’s scalability an eight out of ten. There are a lot of customizable codes. I have created multiple frameworks. We have more than 300 users. After we start with our ML use cases, there will be more users.

The support team provides quick support. It provides quick solutions when we create tickets.

Positive

I rate the ease of setup a nine out of ten. It is pretty straightforward. We need three people to deploy the product. There are two developers on our team. Everyone on our team has more than 12 years of experience.

The pricing is dependent on what we use and how we use it. In my previous project, we paid a lot of money for the tool. Now, we only use the product for storage purposes. The price will increase if the machine learning use cases are productionised.

The product is bundled with Microsoft. The vendor charges us based on services. Recently, they introduced Microsoft Fabric, which includes all components. It costs more because we are charged for all the services available. We will not choose it for the initial stages of implementation.

Whenever we provide solutions to our customers, we often compare the product to Amazon Web Services. I prefer Microsoft for mid-sized companies.

I have been using Azure Synapse for the last seven years. In my current organization, we are exploring more options because SAP does not have machine-learning capabilities. We are planning to use Microsoft Purview to share data.

We don't have any maintenance needs. We do a little bit of monitoring. We have automated everything. Maintenance is mostly done by Microsoft. I started my career in Microsoft. I gradually moved from Microsoft Stack on-premises to the cloud. Overall, I rate the product a nine out of ten.

I use the solution in my company as per our project requirements. In my company, we are only putting data from on-premises RDBMS into Azure Data Lake Storage Gen2, and then the file is stored in parquet format. After the aforementioned process is followed, my company has another data engineering team, which reads those data further.

For writing data to data lake, my company uses Oracle GoldenGate for Big Data. With Oracle GoldenGate for Big Data, my company had to use Handler (Java Platform SE 8), but now we use HDFS Handler, and then using it, we have to configure some files and open some ports between a bank's private network to Azure Data Lake Storage Gen2. After opening all the aforementioned areas, my company is able to push the data to Azure Data Lake Storage Gen2.

In my company, we are not facing any slowness or other kinds of issues with the product. Each day in my company, we create new directories and put the current files into them, so there is the segregation part that is taken care of, and because of this, there are no issues with the tool.

In our company, one of the teams use Azure Databricks to read data from Azure Data Lake Storage's account and as per the business use case, they move data or take the data further. The project I am currently doing has only limited work. I haven't explored all the points associated with the tool.

The high price of the product is an area of concern where improvements are required.

I have been using Azure Data Lake Storage for a year.

The product's stability is good.

The scalability part of the product is very good, and my company has not faced any issues with it.

At present, 15 to 16 percent of the company uses the tool, but it will increase by a percent in the future.

The response time and quality offered by the support team are good. I rate the technical support as nine out of ten.

Positive

The product's initial setup phase is not too simple, and it can be described as a moderate process.

The solution is deployed on the cloud.

I rate the product price as two or three, where one is high, and ten is low. The product's price is really high.

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.

We leverage its hierarchical storage structure to store pre-generated static data in JSON format. This improves performance significantly. Instead of pulling data from on-premises sources, we pull the pre-processed JSON files from Azure Data Lake Storage.

Our primary use case is storing processed data for direct access by end users. It's a good solution for that.

We have both an education product and several financial products. With our education product, we store pre-generated JSON files in Azure Data Lake Storage, representing different student tests in a hierarchical structure. This allows us to serve pre-made tests to thousands of students without hitting performance bottlenecks.

Similarly, for our financial products, we store the final calculated output in Azure Data Lake Storage for use with Power BI Embedded. Users get their Power BI data directly from the data lake. We’ve offloaded a lot of load from our core Azure SQL Server by using Azure Data Lake Storage.

Overall, we primarily use Azure Data Lake Storage to serve data to end-users, not for complex calculations or analytics.

The security features are great, especially the ability to use SAS tokens.

I also like its speed. It's basically built on Azure Blob Storage, which was already fast. But Azure Data Lake Storage adds the hierarchical structure for even better performance.

That's the biggest benefit of Azure Data Lake Storage – the hierarchical namespace. That structure is what makes it truly suitable for data lake scenarios.

Pricing is always a factor. It could be more affordable.

We've been using Azure Data Lake Storage for about two years now.

The stability is excellent. I'd rate the stability a ten out of ten. 

It is a highly scalable solution. 

We haven't needed to contact support. Everything has worked smoothly.

We used to use Databricks, but our license expired.

The initial setup is straightforward. 

It took less than an hour. The most time-consuming part is deciding your folder structure and how you want to organize data. 

The actual creation of the hierarchical structure and data storage is simple; it shouldn't take more than an hour or two.

Azure Data Lake Storage is cloud-based. We handled the implementation in-house

The performance of Azure Data Lake Storage has had a significant positive impact on the solution's cost management. 

By using Azure Data Lake Storage, we've been able to reduce our reliance on Azure SQL Database, which was running on higher tiers. This has led to cost savings.

We've seen benefits in both cost and performance.

It's a pay-as-you-go model. Your charges are based on the amount of data you store.

There are no extra costs.

It's great for improving the speed and accessibility of your static data.

For my use case, it is a good solution. So, I would rate it a ten out of ten. 

The main use cases are for people who don't want their data to be siloed. They want to integrate it into one place, which is a data lake, where they can have data coming from different sources and formats. They want to be able to report on the data from one place, have different personnel work in the same place, and utilize the modern data platform.

Azure Data Lake itself doesn't have built-in features for handling data on its own. It's used as a collection of data. Tools you can use with it include Azure Data Factory and Databricks. Most recently, Microsoft Fabric has been the main tool I have recommended.

Azure Data Lake Storage should support other formats apart from the Data Lake format, such as the Iceberg format. Additionally, improvements in supporting various formats would be beneficial.

I've been working with Azure Data Lake Storage for about three years now.

I would rate stability between nine and ten. It is very stable, however, it depends on settings like geographical redundancy. It's also dependent on expertise and the company's willingness to pay for specific features.

It is very scalable, especially the Gen 2 version. Azure Data Lake Gen 2 is very flexible and uses hierarchical file structures. The newest version, Gen 3, also supports a lake house approach and integrates with other cloud storage like Amazon S3, Google Storage, and Snowflake.

In terms of community support, they respond between one to two weeks. Organization-paid technical support almost gets an immediate response. Overall, their service is rated eight.

Positive

Microsoft Fabric is easier to use than Databricks. Fabric is a software as a service (SaaS), while Databricks is a platform as a service (PaaS, making Fabric easier for starters. Setting up Azure Data Lake Storage can be quick if done manually, but using code ensures scalability.

It's very cheap to store large terabytes of data. It costs just a few dollars per terabyte per month. Computational costs could vary based on usage and are generally more expensive than storage.

For startups, I recommend using Microsoft Fabric as it integrates well with other tools and is cost-efficient. The pricing model is easy to understand. I'd rate the solution nine out of ten.

Hybrid Cloud

Microsoft Azure

We use the solution for the default data platform situation.

The solution's most valuable feature is its simplicity of configuration and setup. It also has good scalability, allowing you to add more data.

Simple migrations from one data lake to another take too much time and could be improved.

I have been using Azure Data Lake Storage for a couple of months.

The solution’s stability is very good.

I rate the solution’s stability a nine out of ten.

Our clients for Azure Data Lake Storage are usually enterprise businesses.

I rate the solution’s scalability a nine out of ten.

The solution’s deployment takes a few weeks.

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's initial setup an eight out of ten.

On a scale from one to ten, where one is cheap and ten is expensive, I rate the solution's pricing a seven out of ten.

Azure Data Lake Storage has slightly impacted the speed of data access. The solution's integration capability is very good, and I rate it an eight out of ten. All AI runs on data, and it has to be stored. It is usually stored in such an environment. I would recommend the solution to other users because it has good price quality.

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

Public Cloud

Microsoft Azure

We use Azure Data Lake Storage for sources like HubSpot (CRM software) and Xero (invoice software). We call their APIs, get the data, and store it in the product. From there, we use it to get the responses and load them into Azure SQL DB.

The tool's best feature is that it can store different types of data - structured, unstructured, and semi-structured—in one lake. We can use the required data for analytics and dashboard design.

I suggest enhancing the connectors for improvement. Some software, like HubSpot and Xero, has connectors, but they're limited to a few fields. That's why we use REST API calls instead. It would be better if the connectors could retrieve all data.

I have been using the product for a year. 

The tool is a stable product; they've been improving it recently.

About 400 to 500 people (40 to 50 percent of employees) use Azure Data Lake Storage for ETL or development. It's scalable - we can add or remove users and change permissions within minutes.

I haven't talked directly to Microsoft support, but I use blogs and forums to find answers. 

Setting up and deploying Azure Data Lake Storage is easy. We use Azure DevOps to connect and deploy all our services.

The tool is cheap, depending on the services and requirements you need.

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.

We use the solution for storing data but don’t use Synapse to store data directly in it. Instead, Azure Synapse Analytics is utilized to analyze and process data in Data Lake Storage. Data Lake Storage is a large, scalable solution that handles extensive volumes of structured and unstructured data rather than a direct disk storage system.

In Azure Data Lake Storage, the tool we're using, Spark, handles the management, storage, retrieval, and organization of data. Spark employs its algorithms to abstract the underlying complexities. We don’t work with a large amount of data. If we were to handle larger datasets, we would need to focus more on optimizing storage and retrieval processes, as the efficiency of these operations would become more critical.

The version was a bit outdated compared to the newer Microsoft Data Fabric offerings. For instance, the directory services are already available in Data Fabric, so I don't think adding them to Azure Data Lake Storage would be necessary. For example, Snowflake, a cloud data analytics platform, adds its capabilities and optimizations to Azure Data Lake Storage, such as improved performance or easier integration with SQL. Compared to other similar services, Azure Data Lake Storage remains very competitive.

I have been using Azure Data Lake Storage for over a year.

Azure is a stable platform. These interruptions are relatively rare and usually last only a few minutes. It is good for data-oriented applications that don’t require continuous online processing.

These brief outages do not significantly impact the quality of service. We haven’t experienced major stability issues with Azure Storage. 

It is scalable.

Any issues are handled by the team responsible for managing the platform.

We primarily use Azure Synapse, which integrates with Azure Data Lake Storage. Synapse leverages the storage provided by Data Lake Storage, so both are part of the Azure ecosystem but remain distinct services.

Another integration involves SQL Server, which serves data to various consumers as an SQL database. The main consumer is Power BI, which provides extensive reporting capabilities. Additionally, Azure Functions integrates with internal systems at the client’s end.

It is a cost-effective solution.

Using a cloud platform generally allows for flexible capacity management, meaning you can use and pay for resources only when needed. This is particularly useful for our customers, who can run Spark clusters in serverless mode. They only pay for the time they use the service, which is cost-effective since they don’t need constant access to high power and typically run jobs for shorter periods, like half an hour.

It is available continuously and supports data archiving. However, since the current volume of data is not large, the client doesn’t need to focus on archiving or optimization. As their data grows and becomes more historical, they may need to optimize storage and archiving practices.

The other team manages the integration tasks. The process is straightforward as long as the systems, functions, or other components interact with external systems. The ease of integration can depend on the intensity of the integration requirements.

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