Azure Data Lake Storage Reviews

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.

We use the Azure Data Lake to store raw customer files exported from their databases. Our pipelines then pick up this data and process it in various ways. For instance, we use Databricks to handle the data processing, transformation, and ETL tasks. The processed data is then stored in SQL Server or converted into other file formats.

The tool's most valuable feature is security access policies. 

The solution needs to improve APIs and make them more accessible. 

The tool is scalable. We currently manage around 200 GB of data, and recently, we've optimized and synchronized additional data, which is approximately 500 GB. My company has 1000-2000 users who use it weekly. 

We've had to seek help several times, particularly with Power BI and Databricks integrations. However, the initial level of support hasn't always been as knowledgeable as we'd like. They usually gather information and then schedule follow-up appointments, which can take a few days. Improving the expertise and responsiveness of the first-level support team could speed up the resolution process.

We use templates for deployment and automate the process. 

The tool's licensing model is pay-as-you-go. Regarding pricing, there's always competition between Azure Data Lake Storage and AWS. They're quite similar, but to attract more customers, Azure Data Lake Storage could consider adjusting its pricing to be more competitive with AWS. This might make Azure Data Lake Storage a more appealing option for users.

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. 

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.