Azure Storage: 

Table: A NoSQL key-value store for rapid development using massive semi-structured dataset. Highly scalable to PBs, and has dynamic scaling based on load. Has fast key/value lookups. We can consider this one for alternative for relational DB which is highly scalable and schema less.

Queue: When applications absorb unexpected traffic bursts and can prevent servers from being overwhelmed by a sudden flood of requests.  Instead of getting dropped, incoming requests are buffered in the queue until servers catch up so traffic bursts don’t take down your applications. 

File: File storage offers shared storage for applications using the standard SMB 2.1 or SMB 3.0 protocol. Microsoft Azure virtual machines and cloud services can share file data across application components via mounted shares, and on-premises applications can access file data in a share via the File storage API.When we want multiple virtual machine shared file system.

Blob: A reliable, cost-effective cloud storage for large amounts of unstructured data, such as documents and media files.  It is a highly scalable, REST-based cloud object store.  The storage service offers three types of blobs: block blobs, page blobs, and append blobs.  Block blobs are best for sequential file I/O, page blobs are best for random-write pattern data, and append blobs are optimized for append operations. This is suitable to store all our unstructured data ie the files what we have .xml, .pdf, .chm , .xls etc

Data Lake Store: It is a new flavor of Azure Blob Storage which can handle streaming data (low latency, high volume, short updates), data-locality aware and allows individual files to be sized at petabyte scale. It a basically is a HDFS as a service. We can store all type of data here (structure data like relational DB, unstructured like logs, and semi structure data like json and xml file). While storing data inside azure data lake no need to define the schema. It only support schema on read.

While storing any data it will stored as a file in HDFS.

Data lake is suitable basically for doing analytics on petabytes of data where we can leverage parallel processing and performing aggregation on huge volume of data using spark engine. But how ever in our repository meta data store we need to store huge volume of data and performing mapping for that azure table storage will satisfy the requirement.

The Azure Data Lake is optimized for Analytical workload and thereby gives you more IOPs than a blob storage.

DocumentDB: Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service for mission-critical applications. Azure Cosmos DB provides turn-key global distribution, elastic scaling of throughput and storage worldwide, single-digit millisecond latencies at the 99th percentile, five well-defined consistency levels, and guaranteed high availability, all backed by industry-leading SLAs. Azure Cosmos DB automatically indexes data without requiring you to deal with schema and index management. It is multi-model and supports document, key-value, graph, and columnar data models. It is one of the alternative of MongoDB. it can store massive volumes of JSON data, query them within order of milliseconds latency, and evolve the schema easily.

Important Links: 

Comparing Azure Data Lake Store and Azure Blob Storage

Azure Storage Scalability and Performance Targets

Microsoft Azure DocumentDB vs Azure Table Storage