Delta Lake is emerging as a leading open protocol for serverless ACID databases, particularly suited for analytics workloads. Its simplicity and scalability have made it a popular choice, akin to DuckDB in the realm of serverless transactional databases. While Iceberg is a similar contender, Delta Lake's straightforward approach is the focus of this discussion. The implementation of a Delta Lake-inspired serverless ACID database can be achieved with just 500 lines of Go code, requiring no external dependencies. This implementation will allow for the creation of tables, insertion of rows, and scanning of all rows while supporting concurrent readers and writers, all while maintaining snapshot isolation. However, certain features of Delta Lake, such as updating and deleting rows, checkpointing transaction metadata, and compaction, will not be covered in this initial exploration. Delta Lake operates by writing immutable data files to blob storage and managing transaction metadata through a naming scheme that incorporates transaction IDs. This method allows for concurrency control via an atomic PutIfAbsent operation on the metadata file. When a transaction is initiated, it identifies the highest existing transaction ID and increments it for its own use. If a transaction attempts to commit with a duplicate ID, the write operation fails, ensuring that concurrent transactions do not interfere with each other. The implementation begins with setting up basic assertion methods, a debug method, and a UUID generator in Go. The core requirement for the blob storage is to support atomic writes, listing locations by prefix, and reading bytes from specific locations. The design diverges from Delta Lake by using a single directory for all files, simplifying the implementation of listing files by prefix. The atomic PutIfAbsent functionality can be implemented using various cloud storage providers, but for simplicity, a filesystem-based approach is adopted. This leverages POSIX file creation semantics to ensure atomicity. The file storage implementation includes methods for writing, listing, and reading files, with careful error handling to maintain data integrity. The ACID properties of Delta Lake are maintained through the immutability of data files, allowing for consistent snapshots even when concurrent transactions are in progress. The implementation sketches out the structure of transactions, which consist of a list of actions, including defining table schemas and adding data files. A client structure is established to manage transactions, with methods for starting transactions, creating tables, writing rows, and committing transactions. The transaction management ensures that all operations are performed within the context of a transaction, maintaining the integrity of the database. When writing data, rows are buffered in memory until a threshold is reached, at which point they are flushed to disk as immutable data objects. The scanning functionality is designed to return an iterator, allowing for efficient traversal of both in-memory and on-disk data. Committing a transaction involves flushing any remaining data and attempting to write the transaction's metadata atomically. If another concurrent transaction has modified the state, the commit will fail, ensuring that only one transaction can succeed at a time. Testing the implementation reveals that concurrent writers will fail if they attempt to commit overlapping transactions, while readers can still access a consistent snapshot of the data. This behavior aligns with the principles of snapshot isolation, allowing for safe concurrent operations. The exploration concludes with an invitation to further investigate the Delta Lake paper and its specifications, encouraging readers to consider enhancements such as handling updates and deletes, transaction log checkpoints, and data object compaction. The simplicity and effectiveness of the approach highlight the potential for building robust serverless ACID databases using straightforward techniques.