Delta Lake and Delta table are related concepts in the Apache Delta Lake project. which extends Apache Spark with ACID (Atomicity, Consistency, Isolation, Durability) capabilities for data lakes.

Delta Lake provides a storage layer that enables transactional and scalable data processing on top of cloud storage systems like Hadoop Distributed File System (HDFS)/Amazon S3/ADLS.

Reference: https://docs.delta.io/latest/delta-intro.html

A Delta table is a collection of data organized in a tabular format within Delta Lake. It represents a table structure with schema and associated data stored in a Delta Lake format. There are 2 types of delta tables

1. Managed table
2. Unmanaged table

Please refer to the following document for more information about managed and unmanaged delta tables:

https://docs.databricks.com/lakehouse/data-objects.html#managed-table

Key features of Delta Lake and Delta tables are the same and they include:

ACID transactions

Schema enforcement and evolution

Time travel

Data reliability

Metadata management

In summary, Delta Lake is the underlying storage layer that provides transactional and reliability features, while Delta tables represent the tabular structures within Delta Lake, offering ACID properties, schema enforcement, versioning, and other Delta Lake capabilities. Delta tables are the primary means of working with structured data in Delta Lake.