Consistent data in data lake gen2

First I invite you to read https://video2.skills-academy.com/en-us/azure/storage/blobs/data-lake-storage-best-practices.

The concept of data consistency in ADLS Gen2 is that data is accurately written, read, and listen without anomalies or errors, even in the presence of concurrent operations.

Strong Consistency

ADLS Gen2 provides strong consistency guarantees, meaning that once a write operation is acknowledged, the written data is immediately visible to subsequent read and listing operations. This eliminates the uncertainties associated with eventual consistency, where data might not immediately reflect recent writes, making ADLS Gen2 reliable for analytical and transactional workloads that require immediate consistency.

Atomic File Operations

File operations in ADLS Gen2, such as creation, deletion, and renaming, are atomic at the file level. This means that these operations either fully succeed or fail, without leaving the system in an intermediate state. For example, if you rename a file, the change is instantly visible to all clients, and there's never a moment when the file is accessible by both the old and new names.

Concurrent Append

ADLS Gen2 supports concurrent append operations, allowing multiple clients to append data to the same file simultaneously without data corruption. This feature is particularly useful for logging scenarios where data from multiple sources needs to be aggregated into a single file.

Directory and File Atomicity

The hierarchical namespace enables atomicity at the directory level for certain operations. For instance, moving a directory within the same file system is an atomic operation. This ensures that the directory move is immediately visible and consistent across all clients.

Transactional Support

While ADLS Gen2 itself does not provide transactional support akin to traditional database systems, it integrates well with services like Azure Data Factory, which can orchestrate transaction-like behavior across multiple steps in a data processing pipeline. This is achieved through careful planning and the implementation of idempotent operations, ensuring that data flows remain consistent even in complex processing scenarios.

How does it work in Practise ?

In practice, maintaining data consistency in ADLS Gen2 involves leveraging these features and understanding their implications. For instance, when designing a data ingestion pipeline, knowing that file operations are atomic can inform how you structure data ingestion batches or handle errors. Similarly, the strong consistency model simplifies data processing logic, as you can rely on the immediate visibility of written data.