Azure Data Lake Storage Reviews

From my perspective, it is secure transfer storage out there.

In terms of the use of Azure Data Lake Storage by customers for data analytics and processing workflows, I would say that my role is to convince customers that it is the safest tool for the storage of data. You can securely connect to remote regions with the tool Azure Storage Explorer, which gives the options and possibilities to safely transfer your data from your existing storage premises and send it to Azure Cloud.

The solution's most valuable features are the tools and functions, which are primarily hosted in Azure Storage Explorer. However, you can also facilitate them from within the backup. The tool is very safe to use. It is impossible to hack the product.

Some customers residing in former Eastern European countries operate in an independent and very weak IT environment. If tools like Azure Data Lake Storage are enabled within the tool named Azure Storage Explorer, then it would be of tremendous help, but it can be really tricky. It would be great if some of the aforementioned features could be enabled, but I fully understand the complexities involved.

I used to like Azure Data Lake Storage previously. Presently, I like the fact Azure Data Lake Storage is improving rapidly by investing and honestly in assets, resources, top personnel, along with a lot of money for making Azure's storage part a bigger concept. Azure Data Lake Storage can be a danger for the large storage products.

I have been using Azure Data Lake Storage for a couple of years. I am an Azure solution architect. I work with Azure Data Lake Storage Gen2.

I am very confident that it is a stable solution. Stability-wise, I rate the solution a ten out of ten.

There was a major issue during mid-October, which affected many global businesses just for a few hours. It was the biggest issue with the tool I had been involved in for many years.

Scalability-wise, I rate the solution a ten out of ten.

Azure Lake Dade Data Lake Storage scalability has very much impacted our customer's data storage strategy since it offers options to choose the disk, scale-out, and DRC options, making everything fantastic.

All customers I have worked with over the last year are using the tool and assigning me to look after it, so it could be seven or eight businesses over the last three years, some of which are global leaders in the market, having over 12,000 employees globally.

On a scale of one to ten, where one is difficult and ten is easy, I rate the product's initial setup phase as ten. You have to understand what to do since you can be lucky and just go and click a few buttons to do the setup process. Knowing the tool's setup phase can make the product cost-effective, but if you don't know about it, then it can be costly. Combining the tool with the features of Azure Cost Management can make things much easier for you. The upcoming edition of Microsoft Copilot should make everything in the tool way easier and also other things not so expensive.

I was not directly involved in the product's deployment process, but I am subscribed to all channels associated with the deployment part, and I have many friends in Microsoft in Northern Europe, and in Sweden, where I live. Storage is one of my top skills, and my friends want to help me become a champion.

The solution is deployed on the cloud and in the on-premises version.

From one to ten, where one is cheap and ten is expensive, I rate the product price as five. It costs money, but it is cheaper by at least thirty percent if you consider the other equivalent solutions from AWS. Considering the aforementioned perspective, the tool is cheap, but you have to pay a certain amount.

There are options to choose from depending on the subscription you have and the amount of features you consume from Microsoft, so it can vary quite a bit.

For my organization, the most valuable part of the tool stems from a variety of features within SQL tables and also data, which is a combination of Azure Data Lake Storage and Azure Blob Storage. Azure Data Lake Storage Gen2 is the best, and I think it is fantastic. From my point of view, the tool is considered to be very competitive here.

I have been working with storage tools for over twenty years, so I am developing my skill sets related to cloud solution providers, mainly on Azure since I began with that in 2008.

Take a deep dive into all the possibilities and options you get because you won't be disappointed. You need to do comparisons with other CSPs and other storage vendors, like NetApp and Dell EMC.

I rate the tool a ten out of ten.

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.

The most valuable feature of Azure Data Lake Storage is the ability to partition data into various datasets using a directory hierarchy. This folder structure is key for any delivery. Currently, we're not doing much with the data in the tool, but when Databricks comes along, we'll convert it to Parquet format. It's a two-step process: raw data is moved to Parquet, which Databricks can manipulate easily.

One improvement I'd suggest is the out-of-the-box conversion of input data, like spreadsheet or table data, to various formats. We'll be using Parquet, which enables transactional integrity.

I have been using the product for a year. 

Stability is good if you build your Azure Data Lake Storage well in the first place.

Scalability depends on process complexity—it is high for simple processes and low for complex ones. This is due to the architecture of a data lake, but once converted to a data lakehouse, scalability is high across the board. I think Azure Data Lake Storage would suit medium—to large enterprises. 

Microsoft's documentation is superb, and support is good, especially if you have a relevant intermediate supplier.

We haven't compared Azure Data Lake Storage with products from other vendors because we're an Azure shop. We did check that the Azure product was good enough for our needs, and it was, so we didn't explore alternatives like AWS, Google, or Snowflake.

The initial setup is fairly complex, but if you get your data architecture right from the start, it's not a problem. We're using a totally cloud-based deployment with Azure.

Integration capabilities are fairly smooth and comparable to AWS in terms of cloud integration. Some might say it's slightly better, others slightly worse, but I think it's good. I'd rate Azure Data Lake Storage an eight out of ten. However, it's important to note that it's only eventually consistent, so don't expect immediate consistency when changes are made. It works well as a data storage bucket for future use, but it's unsuitable for transactional work. You need to use a data lakehouse like Databricks for transactional processes, which can handle transactional work once the data is in the correct format (like Parquet). The tool is great for storing data you want to put into a data lakehouse, but not for frequent transactions. It's suitable for daily archiving, but anything more frequent than that might cause issues.

We use Azure Data Lake Storage for managing large data volumes in our big data projects.

I have configured the tool to automate the deletion of data and transfer data from one repository to another automatically.

Azure Data Lake Storage is user-friendly and easy to use. It effectively manages large data volumes and allows for automated configuration of data operations such as deletion and transfer between repositories.

Maybe the solution could be a bit more user-friendly.

It is very stable and reliable. It is a good solution that doesn't crash.

The scalability is limited. However, it's easy to set up.

The support from Microsoft for Azure products is good. It's timely.

Positive

The initial setup is very easy with this tool.

I am not familiar with the pricing.

Overall, I would rate the Azure Data Lake Storage as nine out of ten.

We have parameters to create three types of data storage. The first is staging, the second is the intermediate, and the third is the target. The target storage contains the cleanest data used for reporting tools. For specific cases in Data Lake projects, we often use files stored in formats such as CSV, which are the most useful for this type of data processing.

Most of our clients use Excel. We prioritize making data accessible in formats compatible with Excel. We aim to meet these client requirements, for example, with Excel files. However, for big data solutions, it's often more efficient to use formats, which we store in the Data Lake.

In some projects, you can usually access files, enabling accessibility, mixing, and transformation. This is useful for both our team and for data engineers. For clients, it reduces costs by optimizing performance and calls and allows for implementing a security model. Additionally, using tools like Global Storage, you can create a hybrid cloud directory or restructure data, making it more organized and easier for clients to integrate with ETL tools.

When you store your files manually, you can't ensure complete data integrity, which can impact data security.

When you make these types of releases or improvements in this solution, you can enhance the data's stability. You can also include features like security integration with Active Directory for data access and ensure compatibility for various integrations. This approach complements both structured and unstructured data, making it more suitable for big data solutions.

I have been using Azure Data Lake Storage for two years

There are some limitations regarding data scalability. In such cases, you can complement it with Databricks. Databricks has a powerful engine that can enhance your security solutions. This combination provides great data performance and a better overall solution.

Once you pass the initial two weeks, it becomes simpler to manage technical support. However, before this period, it is important to have clear instructions, documentation, or videos prepared to assist with technical support and ensure a smooth deployment process.

It's great, but it can be challenging if you don't have all the necessary documents or lack one-on-one discussions about the solutions.

Positive

The initial setup depends on the project, but when storing this type of project, you need to have access activities to provide the source of the data. It's important to have a core level of security to access and transform the data. After storing the data, you need a solution for quality assurance to ensure data integration and quality. This is crucial because poor data quality can affect the future of your solution. Implementing data quality measures is essential for the success of your solutions.

Deployment depends, but it takes about two weeks. For example, one week is typically for deployment, and the other week is for checking the data flow to ensure no errors. If there is an error, you will receive an alert to check the status of the operation.

We need an Azure DevOps professional who makes many configurations to pass for a developer in a production environment.

The solution is worth the money because it allows you to gain insights into your business and implement forecasting solutions to predict future trends. Investing in such solutions is valuable for understanding and planning for future developments.

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.

When dealing with data from various sources or servers, Azure Data Lake Storage allows for seamless integration and secure storage through features like Azure BitLocker encryption. This ensures data integrity and protection, regardless of its origin or destination.

We focus on the regional messaging for our data housed within it ensures better data management and quality control.

The price for Azure is decent. However, the reliability of the provider and their efficient collaboration are positive aspects. Using Azure Data services, we've successfully deployed support systems that facilitate seamless data confirmation across various resources within our company.

Azure offers a comprehensive suite of tools beneficial for both development and delivery. Performance, usability, and scalability are strong points. Recently, I acquired a new tool called AWS Storage Gateway for seamless data transfer between AWS storage and Azure Data Lake, enhancing database operations and developer workflows.

One feature could be added is the ability to create and manage files within the same storage using serverless query integration with Data Lake Analytics.

When comparing your account with AWS, the financial aspect will be the deciding factor for the customer. If it's a favorable response, we will continue working with Azure. We were awaiting the HTTPS integration.

There's a strong community for learning how to use Azure Data Lake. We've encountered an issue while testing AzureProtect.

Overall, I rate the solution a nine out of ten.

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

The solution is stable.

The product’s scalability is good. 45 users are using this solution.

The initial setup is easy. Our team consisting of two individuals initially, with the option for a third support member, making it easier to utilize Infiniti. It takes four months to deploy. 

We have ongoing plans for building more deposits. Additionally, they can send project details via calls, as all projects are managed within the same system.

Four members were involved during deployment.

The pricing is reasonable.

We're planning to utilize machine learning techniques in Azure Data Lake.

Maintenance is easy.

Overall, I rate the solution 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 use the tool to aggregate all our information into one particular environment. We have separate environments, but we aggregate the important pieces into one environment so they can be used for reporting and analytics.

Azure Data Lake Storage is a user-friendly and easy-to-learn solution.

The solution's instructions could be improved. Some terminology is not easy to understand when using some interfaces. Microsoft must provide more examples because everybody uses the tool differently.

I have been using Azure Data Lake Storage for 5 years.

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

Around 300 users use the solution in our organization, including developers and people who use the data through report interfaces.

I rate the solution ten out of ten for scalability.

The solution’s initial setup was straightforward.

We implemented the solution through an in-house team. Certain features are turned on to deploy the tool. You go in there and set up what you need, like the database or data factory. Once they turn it on and give you permission, you go in there and set it up yourself.

The tool is worth the money because I've worked in other environments, and this one seems to be the most flexible and easy to use.

I use Azure Data Lake Storage version 2. We integrate the solution with other internal and external sources through REST and SQL. We have external sites that we integrate or pull the information into. We can see all the data in one place and report off of it.

The tool can be expanded at a moment's notice. Once I'm done, I can lower the threshold back down. The solution's flexibility allows me to increase my CPU or database size at a moment's notice.

Overall, I rate the solution 10 out of 10.