Question

What is the difference between Azure Data Lake store and Blob storage?

Answer 1

	Azure Data Lake Storage Gen1	Azure Blob Storage
Purpose	Optimized storage for big data analytics workloads	General-purpose object store for a wide variety of storage scenarios, including big data analytics
Structure	Hierarchical file system	Object store with flat namespace
Key Concepts	Data Lake Storage Gen1 account contains folders, which in turn contains data stored as files	Storage account has containers, which in turn has data in the form of blobs
Use Cases	Batch, interactive, streaming analytics, and machine learning data such as log files, IoT data, clickstreams, large datasets	Any type of text or binary data, such as application back end, backup data, media storage for streaming, and general-purpose data. Additionally, full support for analytics workloads; batch, interactive, streaming analytics, and machine learning data such as log files, IoT data, clickstreams, large datasets
Server-side API	WebHDFS-compatible REST API	Azure Blob Storage REST API
Data Operations – Authentication	Based on Azure Active Directory Identities	Based on shared secrets – Account Access Keys and Shared Access Signature Keys.

Answer 2

Following is the list of key differences between Data Lake Storage and Blob Storage:

Azure Data Lake Storage	Azure Blob Storage
The main purpose of Azure Data Lake Storage is to provide optimized storage for big data analytics workloads. So, we can say that it is an optimized storage solution for big data analytics workloads.	The Azure Blob Storage is a general-purpose object storage system for a wide variety of storage scenarios and big data analytics.
The structure of Azure Data Lake Storage follows the hierarchical file system.	The structure of Azure Blob Storage follows an object store with a flat namespace.
Azure Data Lake Storage contains folders in which the data is stored as files.	Azure Blob Storage facilitates us to create a storage account with containers in which the data is stored.
It is mainly used to store batch, interactive, streaming analytics, and machine learning data such as log files, IoT data, clickstreams, large datasets, etc.	It can be used to store any text or binary data, such as application back end, backup data, media storage for streaming, and general-purpose data. It also provides full support for analytics workloads; batch, interactive, streaming analytics, and machine learning data such as log files, IoT data, clickstreams, large datasets, etc.
It uses WebHDFS-compatible REST API as the Server-side API.	It uses Azure Blob Storage REST API as the Server-side API.
Its data operations and authentication is based on Azure Active Directory Identities.	Its data operations and authentication is based on shared secrets, Account Access Keys, and Shared Access Signature Keys.