What are Delta tables and how are they advantageous to data frames?

Delta Tables are a type of storage layer built on top of data lakes like AWS S3, Azure Data Lake Storage, and others. They are part of the Delta Lake technology developed by Databricks. Delta Tables bring transactional capabilities to data lakes, enabling data engineers and analysts to ensure high performance, reliability, and consistency, which are typically challenges when working with large datasets in data lakes.

Key Features and Advantages Over DataFrames:

• ACID Transactions: Delta Tables ensure ACID (Atomicity, Consistency, Isolation, Durability) transactions, making operations like insert, update, and delete consistent and reliable. This is not available in regular Data Frames, where operations can be non-transactional.

• Schema Enforcement: Delta Tables enforce schema on write, meaning that data must adhere to a defined schema. This prevents errors from mismatched data types or missing fields, unlike standard Data Frames where schema enforcement is not guaranteed.

• Time Travel: Delta Lake allows users to query historical data using time travel. This is especially useful for auditing, debugging, or reverting to previous data versions, which is not possible in regular Data Frames.

Querying and Time Travel:

Delta Lake automatically stores versions every time there’s a modification. To query a previous version, you can specify the versionAsOf or timestampAsOf options.

• versionAsOf: Query the table as it was at a specific version number.
• timestampAsOf: Query the table as it was at a specific point in time.

# Load Delta Table as of a specific version
version_1_df = spark.read.format("delta").option("versionAsOf", 1).load(delta_table_path)

# Save this version to another location if needed
version_1_df.write.format("delta").save("/tmp/delta-table-version1")

# Alternatively, you can query by timestamp
version_at_time_df = spark.read.format("delta").option("timestampAsOf", "2023-10-15T00:00:00.000Z").load(delta_table_path)        

• Optimized Performance: Delta Tables support data skipping and Z-ordering, which optimizes data layout for faster queries. These performance improvements can make reading and writing data significantly faster compared to standard Data Frames, especially in large datasets.

Data Skipping is an optimization technique used by Delta Lake to avoid reading unnecessary data during queries. When you store data in a Delta Table, Delta Lake automatically collects metadata for each file, such as the minimum and maximum values of each column in the file. During a query, instead of scanning all the data files, Delta Lake can skip over files that do not match the query conditions based on this metadata.

How it works: If you’re querying for records where, for example, id = 5, Delta Lake can skip files where the id column’s range (e.g., 10 to 20) does not match the query condition, thus avoiding a full scan of the entire dataset.

Z-ordering is a technique that optimizes the physical layout of data files on disk by clustering related information together. When you Z-order data, it rearranges the data files so that the records with similar column values (usually a frequently queried column like a date or an ID) are stored close together. This enhances query performance because it reduces the number of files that need to be scanned when filtering on these columns.

How it works: In Z-ordering, Delta Lake sorts the data based on one or more columns. This reorganization helps when you frequently run queries that filter based on those columns.

# Optimize Delta Table with Z-ordering
DeltaTable.forPath(spark, "/tmp/delta-table").optimize().executeZOrderBy("id")        

• Unified Batch and Stream Processing: Delta Lake allows seamless unification of batch and streaming data. You can write streaming data into Delta Tables and then perform batch queries or further streaming reads. DataFrames on their own do not have this capability.

• Data Compaction and Clean-up: Delta Tables support automatic data compaction and clean-up via the VACUUM command, which removes old data and keeps the storage clean. Data Frames, on their own, do not offer such cleaning or compaction functionalities