# 4. Key Features of Delta Lake

![](/files/-MFijPRmwFHOgobTzWmW)

DataLakes typically have mutiple Data Pipelines reading & writing data concurrently & data engineer have to go through a tedious process to ensure Data Integrity due to lack of Transaction .

\>Delta Lake bring transactions to our Data Lake . It provides Searializability , the strongest level of Isolation levels.

\>>**Scala Metadata Handling :**  In Big Data even the metadata itself can be Big Data . Delta Lake treats metadata just like data leverage Spark distributed processing power to handle all its metadata. As a result Data Lake can handle petabytes scala table with billions of partitions & file easily.

\>>**Time Versioning :  Delta Lake provides snapshot of data enabling developers to access and revert to earlier version of data for Audits , Rollbacks or to reproduce experiment.**

**>>>Open Format : All data in Delta Lake is stored in Apache Parquet format enabling Delta Lake to leverage the effeiciency , compression and encoding schemas that are native to Parquet .**

**>>Unified Batch & Streaming Source & Sink : They are a table in a Delta Lake both batch & as well as streaming source  & sink .Streaming data ingest batch historical back fill & interactive queries are all just work out of the box .**

**>>Schema Enforcement : Delta Lake provides the ability to specify our schema & enforce it .It helps ensure that the datatypes are correct & required columns are present , preventing bad data from causing Data corruption.**

**>>Schema Evolution : Big Data is continously changing  Delta Lake enables us to make changes to table schema that can be applied automatically without the need for cumbersome DDL .**

**>>Audit History :  Delta Lake transctions logs records details about every changes made to the data provided a fully audit trail of the chnages .**

**>>Updates & Deletes : Delta Lake suports Scala , Java API to merge update & delete datasets .** 

**>>100**% Compatible with Apache Spark API : Developers can use Delta Lake with their existing Data pipelines with minimal change as it is fully compatible with Spark .

 


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://architectures.gitbook.io/project/delta-lake-with-apache-spark-using-scala/introduction/4.-key-features-of-delta-lake.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.