100 Days of Azure Product Management
How to Efficiently Utilize Azure Storage in a Scrum Sprint
Product managers overseeing multiple teams may find managing data challenging during a Scrum sprint. This article outlines a strategic plan for using Azure Storage Explorer to streamline data handling and enhance team productivity within a sprint cycle.
Identify Your Data Needs
Begin by evaluating the data types and volumes your product requires. Determine which data should reside in the cloud and how often your team accesses this data. This assessment will help you prioritize your actions in Azure Storage Explorer.
Configure Storage Containers
Based on your earlier assessment, create and configure storage containers in Azure Storage Explorer. This step involves setting access permissions and deciding on the appropriate data tier—hot, cool, or archive—to optimize cost and access speed (See video below).
Implement Security Measures
Generate and manage Shared Access Signatures (SAS) for your containers. Ensure that access to sensitive data is controlled and time-limited, securing your product data during the sprint.
Monitor and Adjust
Monitor data usage and access patterns regularly. Adjust storage settings and permissions to respond to changing project requirements or team needs.
Automate Data Operations
Set up scripts using Azure Storage Explorer to automate routine data operations such as backups and transfers. This reduces manual workloads and allows your team to focus on sprint goals.
Have you found specific features of Azure Storage Explorer handy for your Scrum projects? Please share your insights in the comments, and if this article was helpful, clap to let others know! Your claps help this content reach more readers.
PS: Flexible Data Storage
This video by Adam Marczak provides three key takeaways:
- CHOOSE THE RIGHT STORAGE TYPE
Understand the distinct purposes of each Azure storage type: Blob for unstructured data, Queue for messaging, Table for NoSQL data, and File Share for managed file storage. Match these options to your product’s specific needs.
2. OPTIMIZE COST AND PERFORMANCE
Consider the access frequency and storage duration when choosing between the various performance tiers in Azure Storage—Hot, Cool, and Archive. This helps balance cost efficiency with performance requirements.
3. LEVERAGE AZURE SECURITY FEATURES
Implement Azure’s robust security features, such as shared access signatures (SAS) and role-based access controls, to manage who can access your data and how they can interact with it. This is critical for maintaining data integrity and compliance.
PS: Azure storage solution for big data analytics
These key takeaways focus on the enhancements in Azure Data Lake Storage Gen 2 that support big data analytics, highlighting its adaptability, integration capabilities, and improved accessibility.
MULTI-CONTAINER CAPABILITY:
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Azure Data Lake Storage Gen 2 allows the creation of multiple containers within each Data Lake, each acting as a separate file system. This flexibility enables users to organize their data into various file and folder structures according to their needs, enhancing manageability and accessibility for big data analytics.
HADOOP COMPATIBILITY:
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The service features a Hadoop-compatible file system known as ABFS (Azure Blob File System), which ensures seamless integration with existing big data solutions like Hortonworks, Databricks, HDInsight, Cloudera, or Hadoop. This compatibility reduces the complexity and coding requirements for connecting and deploying big data solutions.
MULTI-PROTOCOL ACCESS:
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A recent update to Azure Data Lake includes multi-protocol access, allowing users to access data using the ABFS and the classic Windows Azure Storage Blob (WASB). This dual access feature enables applications that previously couldn’t connect to Azure Data Lake, such as Power BI and other analysis services, to quickly integrate and utilize the data stored in the lake.
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