Introduction
Delta Lake is an open-source storage layer built on Apache Spark for building reliable, performant, and scalable data lakes. It provides ACID (Atomicity, Consistency, Isolation, and Durability) transactions, data versioning, schema enforcement, and data lineage for big data workloads.
Features of Delta Lake
Delta Lake provides several key features for building scalable data lakes on top of Apache Spark,
ACID Transactions
Delta Lake provides full support for ACID transactions. This means that multiple concurrent readers and writers can safely modify data in a Delta Lake table without data inconsistency issues. Delta Lake ensures that all transactions are committed or rolled back as a single unit, providing strong consistency guarantees.
Schema Enforcement
Delta Lake enforces schema validation at the right time. This means that any data that is written to a Delta Lake table must conform to the table's schema. If any data is incompatible with the schema, the write operation fails, and the data is not written to the table.
Data Versioning
Delta Lake supports data versioning, which allows you to keep track of changes to data over time. This feature enables you to query and analyze data at any point in history. Delta Lake also provides several APIs for managing data versions, including the ability to roll back to a previous data version.
Data Lineage
Delta Lake provides data lineage, which allows you to track the history of changes to a Delta Lake table. This feature enables you to understand how your data has evolved and provides valuable insights for debugging and auditing purposes.
Scalability
Delta Lake is designed to be highly scalable and can easily handle large data volumes. It is built on Apache Spark, a highly scalable and performant data processing engine.
The code block below contains commands to showcase the above features with some sample data.
from pyspark.sql import SparkSession
from delta.tables import *
# Create a SparkSession
spark = SparkSession.builder.appName("DeltaLakeDemo").getOrCreate()
# Set the location of the Delta Lake table
delta_table_path = "/path/to/delta/table"# Create a Delta Lake table
data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)]
df = spark.createDataFrame(data, ["name", "age"])
df.write.format("delta").save(delta_table_path)
# Read from the Delta Lake table
df = spark.read.format("delta").load(delta_table_path)
df.show()
# Perform a write operation that violates schema validation
data = [("Alice", 25, "female"), ("Bob", 30, "male"), ("Charlie", 35, "male")]
df = spark.createDataFrame(data, ["name", "age", "gender"])
try:
df.write.format("delta").mode("overwrite").save(delta_table_path)
except AnalysisException as e:
print("Schema validation error:", e)
# Perform a write operation that enforces schema validation
data = [("Alice", 25), ("Bob", 30), ("Charlie", 35)]
df = spark.createDataFrame(data, ["name", "age"])
df.write.format("delta").mode("overwrite").option("mergeSchema", "true").save(delta_table_path)
# Add a new version to the Delta Lake table
data = [("Alice", 25, "female"), ("Bob", 30, "male"), ("Charlie", 35, "male")]
df = spark.createDataFrame(data, ["name", "age", "gender"])
df.write.format("delta").mode("append").save(delta_table_path)
# Roll back to a previous version of the Delta Lake table
delta_table = DeltaTable.forPath(spark, delta_table_path)
delta_table.restoreToVersion(0)
# Show the current version of the Delta Lake table
df = delta_table.toDF()
df.show()
# Update a record in the Delta Lake table
delta_table.update("name = 'Bob'", {"age": "31"})
# Delete a record from the Delta Lake table
delta_table.delete("name = 'Charlie'")
# Show the current version of the Delta Lake table
df = delta_table.toDF()
df.show()
# Display the data lineage for the Delta Lake table
delta_table.history().show()
# Stop the SparkSession
spark.stop()
Use Cases for Delta Lake
Delta Lake is a versatile storage layer that can be used in a wide range of use cases, including:
Data Lake
Delta Lake can be used to build scalable data lakes for storing large volumes of structured and unstructured data. It provides a reliable and performant storage layer for big data workloads.
Data Warehousing
Delta Lake can be used to build data warehouses for storing and analyzing large volumes of structured data. It provides ACID transactions and data versioning, important features for data warehousing workloads.
Machine Learning
Delta Lake can store large volumes of data for Machine Learning workloads. It provides ACID transactions and data versioning, important features for machine learning workloads.
Getting Started with Delta Lake
To get started with Delta Lake, you must have an Apache Spark cluster running. You can install Apache Spark on your local machine or a cloud-based platform like AWS, GCP, or Azure.
Once you have an Apache Spark cluster running, you can install the Delta Lake package using the following command:
$ spark-shell --packages io.delta:delta-core_2.12:<version>
Replace <version> with the latest version of Delta Lake. You can find the latest version on the Delta Lake GitHub repository.
Once you have installed the Delta Lake package, you can start using Delta Lake to build scalable and reliable data lakes. Here are some key steps for working with Delta Lake:
Creating a Delta Lake Table
You can create a Delta Lake table using the CREATE TABLE statement. Here's an example:
CREATE TABLE delta.`/path/to/table`
USING delta
AS SELECT * FROM my_table;
This statement creates a Delta Lake table at the specified path and populates it with data from an existing table (my_table).
Writing Data to a Delta Lake Table
You can write data to a Delta Lake table using the INSERT INTO statement. Here's an example.
INSERT INTO delta.`/path/to/table`
VALUES (1, 'John', 'Doe'), (2, 'Jane', 'Doe');
This statement inserts two rows into the Delta Lake table at the specified path.
Updating Data in a Delta Lake Table
You can update data in a Delta Lake table using the UPDATE statement. Here's an example:
UPDATE delta.`/path/to/table`
SET last_name ='Smith'WHERE first_name ='John';
This statement updates the last name of all rows where the first name is "John" in the Delta Lake table at the specified path.
Deleting Data from a Delta Lake Table
You can delete data from a Delta Lake table using the DELETE statement. Here's an example:
DELETE FROM delta.`/path/to/table`
WHERE last_name ='Doe';
This statement deletes all rows with the last name "Doe" in the Delta Lake table at the specified path.
Querying a Delta Lake Table
You can query a Delta Lake table using standard SQL statements. Here's an example:
SELECT * FROM delta.`/path/to/table`
WHERE first_name ='John';
This statement selects all rows where the first name is "John" in the Delta Lake table at the specified path.
Data Versioning
Delta Lake provides several APIs for managing data versions. Here are some key APIs:
DESCRIBE HISTORY- This API displays the version history of a Delta Lake table.CREATE VERSION: This API creates a new version of a Delta Lake table.DELETE: This API deletes a version of a Delta Lake table.
Schema Evolution
Delta Lake supports schema evolution, which allows you to evolve the schema of a Delta Lake table over time. Here's an example of how to evolve a schema:
ALTER TABLE delta.`/path/to/table`
ADD COLUMN age INT;
This statement adds a new column called "age" to the schema of the Delta Lake table at the specified path.
Delta Sharing
Delta Sharing is an open-source protocol for secure and scalable data sharing across organizations. It allows organizations to easily share data in a low-latency, high-throughput manner while maintaining control over their data.
Here's a basic Python code example for publishing data to Delta Sharing.
from delta_sharing import DeltaSharingClient
import pandas as pd
# create a pandas DataFrame
df = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['A', 'B', 'C']})
# create a DeltaSharingClient object
client = DeltaSharingClient("delta-sharing://my-sharing-service-url")
# create a Delta table from the DataFrame
delta_table = client.create_table("my_table_name", df)
# publish the Delta table to the sharing service
client.publish_table(delta_table)
And here's a basic Python code example for consuming data from Delta Sharing:
from delta_sharing import DeltaSharingClient
# create a DeltaSharingClient object
client = DeltaSharingClient("delta-sharing://my-sharing-service-url")
# get a reference to the Delta table by name
delta_table = client.get_table("my_table_name")
# load the Delta table into a pandas DataFrame
df = delta_table.to_pandas()
Conclusion
Delta Lake provides a reliable, performant, and scalable storage layer for building data lakes on top of Apache Spark. It provides several key features, including ACID transactions, data versioning, schema enforcement, and data lineage. Delta Lake can be used in many use cases, including data lakes, data warehousing, and machine learning.