Delta Table

Dependencies & cluster requirements

Dependencies:

ProphecySparkBasicsPython 0.0.1+
ProphecySparkBasicsScala 0.0.1+

Cluster requirements:

UC dedicated clusters 14.3+ supported
UC standard clusters 14.3+ supported
Livy clusters not supported

Reads from and writes to Delta tables that your execution environment’s Metadata catalog manages.

Prerequisites

Before you specify parameters and properties, select the Delta table type:

Open the Source or Target gem configuration.
On the Type & Format page, select Catalog Table.
On the Properties page, set the provider property to delta.

Parameters

Parameter	Tab	Description
Use Unity Catalog	Location	Whether to use a Unity catalog.
Catalog	Location	If you use a unity catalog, specify which catalog to use.
Database	Location	Name of the database to connect to.
Table	Location	Name of the table to connect to.
Use file path	Location	Whether to use a custom file path to store underlying files in the Target gem.
Schema	Properties	Schema to apply on the loaded data. In the Source gem, you can define or edit the schema visually or in JSON code. In the Target gem, you can view the schema visually or as JSON code.

Source

The Source gem reads data from Delta tables and allows you to optionally specify the following additional properties.

Source properties

Properties	Description	Default
Description	Description of your dataset.	None
Provider	Provider to use. You must set this to `delta`.	`delta`
Filter Predicate	Where clause to filter the table by.	None
Read timestamp	Time travel in milliseconds to a specific timestamp. This value should be between the first commit timestamp and the latest commit timestamp in the table.	None
Read version	Time travel to a specific version of the table.	None

You can only select Read Timestamp or Read Version, not both.If you don’t use a time travel option, the Source gem fetches the most recent version of each row by default.

To learn more about Delta time travel and its use cases, see Introducing Delta Time Travel for Large Scale Data Lakes.

Source example

Compiled code

To see the compiled code of your project, switch to the Code view in the project header.

Without filter predicate

def Source(spark: SparkSession) -> DataFrame:
 return spark.read.table(f"test_db.test_table")

object Source {

 def apply(spark: SparkSession): DataFrame = {
 spark.read.table("test_db.test_table")
 }

}

With filter predicate

def Source(spark: SparkSession) -> DataFrame:
 return spark.sql("SELECT * FROM test_db.test_table WHERE col > 10")

object Source {

 def apply(spark: SparkSession): DataFrame =
 spark.sql("SELECT * FROM test_db.test_table WHERE col > 10")

}

Target

The Target gem writes data to Delta tables and allows you to optionally specify the following additional properties.

Target properties

Property	Description	Default
Description	Description of your dataset.	None
Provider	Provider to use. You must set this to `delta`.	`delta`
Write Mode	How to handle existing data. For a list of the possible values, see Supported write modes.	`error`
Use insert into	Whether to use the `insertInto()` method to write instead of the `save()` method.	false
Overwrite table schema	Whether to overwrite the schema of the Delta table.	false
Merge DataFrame schema into table schema	Whether to automatically add columns that are present in the `DataFrame` but not in the Target table to the end of the schema as part of a write transaction.	false
Partition Columns	List of columns to partition the Delta table table by.	None
Overwrite partition predicate	Selectively overwrite only the data that satisfies the given where clause expression.	None
Optimize write	Whether to optimize Spark partition sizes based on the actual data.	false

Supported write modes

Write mode	Description
overwrite	If the data already exists, overwrite the data with the contents of the `DataFrame`.
error	If the data already exists, throw an exception.
append	If the data already exists, append the contents of the `DataFrame`.
ignore	If the data already exists, do nothing with the contents of the `DataFrame`. This is similar to the `CREATE TABLE IF NOT EXISTS` clause in SQL.
merge	Use the Delta `merge` command to insert, delete and update data. For more information, see Delta MERGE.
scd2 merge	Store and manage the current and historical data over time. For more information, see Delta MERGE.

These overwrite, append, ignore, and error write modes operate the same way as with other native Spark-supported formats such as Parquet.

Target example

Compiled code

To see the compiled code of your project, switch to the Code view in the project header.

def Target(spark: SparkSession, in0: DataFrame):
 in0.write\
 .format("delta")\
 .mode("overwrite")\
 .saveAsTable("test_db.test_table")

object Target {

 def apply(spark: SparkSession, in: DataFrame): DataFrame = {
 in.write
 .format("delta")
 .mode("overwrite")
 .saveAsTable("test_db.test_table")
 }

}

Overview

Sources and targets

Data processing

Advanced

Prerequisites

Parameters

Source

Source properties

Source example

Compiled code

Without filter predicate

With filter predicate

Target

Target properties

Supported write modes

Target example

Compiled code

​Prerequisites

​Parameters

​Source

​Source properties

​Source example

​Compiled code

​Without filter predicate

​With filter predicate

​Target

​Target properties

​Supported write modes

​Target example

​Compiled code

Prerequisites

Parameters

Source

Source properties

Source example

Compiled code

Without filter predicate

With filter predicate

Target

Target properties

Supported write modes

Target example

Compiled code