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Azure Data Lake Storage Reviews

Vendor: Microsoft
4.3 out of 5
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What is Azure Data Lake Storage?

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.

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Buyer's Guide
Azure Data Lake Storage
March 2025
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PeerAnalyst reports based on Azure Data Lake Storage reviews

TypeTitleDate
CategoryCloud StorageMar 3, 2025Download
ProductReviews, tips, and advice from real usersMar 3, 2025Download
ComparisonAzure Data Lake Storage vs NetApp Cloud Volumes ONTAPMar 3, 2025Download
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ComparisonAzure Data Lake Storage vs NasuniMar 3, 2025Download
 
 
Key learnings from peers

Valuable Features

Azure Data Lake Storage's key attributes include security with SAS tokens, hierarchical namespace for efficient data organization, scalability, and integration with Azure services. It provides different data storage tiers and allows data partitioning with directories for improved data handling. Users appreciate its cost-effectiveness, user-friendliness, and ability to scale storage up or down. Its compatibility with tools like Databricks and Azure Data Factory enhances data management and analytics capabilities.
  • "Azure Data Lake Storage is user-friendly and easy to use."
  • "Azure Data Lake Storage is user-friendly and easy to use."
  • "I'd rate the solution nine out of ten."

Room for Improvement

Azure Data Lake Storage's pricing is high, SAP connectivity is lacking, and APIs need better accessibility. Users face technical challenges due to unclear instructions, complex migrations, and limited connector functionalities. Data security and format support, such as Iceberg, requires enhancement, along with establishing version control. Simplifying licensing, improving Microsoft support, faster data access, and incorporating AI features would be advantageous. Enhanced tutorials and direct file management within storage could elevate the user experience.
  • "The scalability is limited. However, it's easy to set up."
  • "Maybe the solution could be a bit more user-friendly."
  • "Improvement is needed in the migration process from Lakehouse to Enterprise Data Lake (EDL). Currently, migration is only one-way possible, and it would be beneficial if this aspect could be improved."

ROI

Pricing

Azure Data Lake Storage utilizes a pay-as-you-go model, with charges based on data storage amount. Users experienced varied pricing perceptions, with some finding it affordable compared to AWS and others noting higher costs, particularly with added services like Microsoft Fabric. Pricing depends on features, data tiers, and geolocation settings, with competitive advantages in cost over competitors. Licensing complexity is a noted issue, although some users are satisfied with overall pricing management and affordability for basic storage needs.
  • "It costs just a few dollars per terabyte per month."
  • "Pricing is tricky because it depends on the solution you're building and the type of Data Lake storage you use—hot or cold."
  • "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."

Popular Use Cases

Azure Data Lake Storage is used for storing and processing large volumes of static, raw, and processed data from various sources. It supports data warehousing, integration, and reporting, allowing secure data transfer and storage. Companies utilize it for operational reporting, analytics, and machine learning, integrating tools like Databricks, SQL Server, and Azure Synapse. It serves as a central hub for storing unstructured data, enabling seamless data processing and consumption across multiple environments.

Service and Support

Azure Data Lake Storage support varies among users. Some find the service responsive and effective, while others report needing improved first-level assistance. Many leverage extensive documentation and community resources, reducing reliance on direct support. Response times differ, influenced by support agreements. Users often rate support highly, though some have had mixed experiences, utilizing external consultants or internal expertise. Overall satisfaction tends to increase post-initial deployment issues with clear instructions aiding the process.

Deployment

Many find Azure Data Lake Storage's initial setup straightforward, while others describe it as moderately complex. Time estimates range from under an hour to several weeks, influenced by data architecture planning and security configurations. Using templates and automation aids the process. Security aspects may require additional guidance. User experience varies, with some requiring more personnel and expertise. Ratings for ease range from five to ten out of ten.

Scalability

Azure Data Lake Storage is praised for its scalability, handling large data volumes efficiently for enterprises. It offers flexibility with features like disk selection and scale-out options. Gen 2 and Gen 3 versions enhance scalability, integrating with other cloud storages. Some variability in scalability exists based on process complexity and pricing. Users rate scalability highly, often mentioning it supports hundreds to thousands of users. Some challenges reported during initial migrations, particularly from Hadoop environments.

Stability

Azure Data Lake Storage is praised for its stability, consistently rated between eight to ten out of ten. Users find it stable and reliable with rare, brief outages that minimally impact service. Many appreciate its SLAs for stability, though factors like configuration, geographical redundancy, and features play a role. Organizations highlight the absence of major stability issues and rate it highly for reliability and dependability.
These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Azure Data Lake Storage Buyer's Guide for additional reliable information.

Learn more about Azure Data Lake Storage

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 Data capabilities for developing technological solutions. With strong security features, high scalability, hierarchical namespace for better performance, and efficient data partitioning, it integrates seamlessly with tools like Databricks. Supporting structured, unstructured, and semi-structured data, it is ideally suited for data lakes.

What are the key features of Azure Data Lake Storage?
  • Strong security features: Includes SAS tokens and access policies
  • High scalability: Able to manage vast amounts of data
  • Hierarchical namespace: Improves performance
  • Efficient data partitioning: Enhances data management
  • User-friendly configuration and integration: Supports tools like Databricks
  • Support for various data formats: Ideal for data lakes
What are the expected benefits and ROI?
  • Hybrid cloud directories: Enhances data management
  • Enhanced big data handling: Improves data analytics processes
  • Policy management for data retention: Facilitates compliance

Azure Data Lake Storage finds its application in several industries by enabling technological solutions that leverage its Big Data capabilities. For instance, businesses in finance use it for aggregating financial reports, while retail companies leverage it for customer data analytics. Healthcare industries use it to store and analyze patient data securely. The manufacturing sector benefits by integrating data from different sources to optimize production processes.

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