Introduction to Delta Lake: What it is and how it works

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In the world of big data processing, the reliability and consistency of data are critical. This is where Delta Lake comes in - a storage layer that provides reliability, performance, and scalability to your data lake. In this article, we will explore what Delta Lake is and how it works, with ample examples in PySpark.

What is Delta Lake?

Delta Lake is an open-source storage layer that provides ACID transactions, schema enforcement, and other features on top of cloud and on-premises storage systems. Delta Lake provides a set of tools that allow developers to build high-quality data lakes with reliability and performance. It was developed by Databricks and is now a part of the Linux Foundation.

Delta Lake is built on top of Apache Spark, and it stores data in a columnar format like Parquet or ORC. Delta Lake provides a set of features that make it easy to work with big data, including:

1. ACID transactions: Delta Lake provides full ACID transactions to ensure data consistency, even in the face of failures. This means that if a transaction fails, Delta Lake rolls back the changes, ensuring that the data remains consistent.
2. Schema enforcement: Delta Lake provides schema enforcement to ensure that data is written in the correct format. This means that if a schema is defined, Delta Lake checks that the data is written in the correct format before storing it.
3. Data versioning: Delta Lake provides data versioning, allowing you to keep track of changes to your data. This means that you can easily rollback to a previous version of your data if necessary.
4. Time travel: Delta Lake provides time travel, allowing you to query your data as it existed at any point in time. This means that you can easily analyze changes in your data over time.

How Does Delta Lake Work?

Delta Lake is built on top of Apache Spark and uses Spark’s storage layer to store data. Delta Lake stores data in a columnar format, which is an efficient and compressed way to store data. Delta Lake is designed to work with different storage systems, including cloud-based storage systems like Amazon S3 and Azure Data Lake Storage, and on-premises storage systems like Hadoop Distributed File System (HDFS) and Apache Cassandra.

Delta Lake uses a transaction log to store all the changes made to the data. The transaction log is stored alongside the data in the storage system, and it allows Delta Lake to provide full ACID transactions. When a write operation is performed on a Delta Lake table, Delta Lake writes the data to the storage system and appends the changes to the transaction log. If a transaction fails, Delta Lake can roll back the changes by replaying the transaction log.

Delta Lake also provides schema enforcement, which ensures that data is written in the correct format. When you create a Delta Lake table, you can define a schema for the table. Delta Lake checks that the data being written to the table matches the schema before storing it. This ensures that the data is written in a consistent format, which makes it easier to query and analyze.

Delta Lake provides data versioning, which allows you to keep track of changes to your data. Delta Lake stores multiple versions of the data, and you can query the data as it existed at any point in time. This makes it easy to analyze changes in the data over time.

Delta Lake also provides time travel, which allows you to query your data as it existed at any point in time. This makes it easy to analyze changes in the data over time. To query your data as it existed at a specific point in time, you can specify a version or timestamp when you read the data. Delta Lake uses the transaction log to retrieve the data as it existed at that point in time.

Examples in PySpark:

Let's walk through some examples in PySpark to see how Delta Lake works in practice.

Creating a Delta Lake table:
To create a Delta Lake table, you can use the following PySpark code:

In this example, we create a SparkSession and use it to create a DataFrame with some sample data. We then write the data to a Delta Lake table stored at /mnt/delta-lake/example. The format("delta") option specifies that we want to use Delta Lake as the storage layer.

Updating data in a Delta Lake table:
To update data in a Delta Lake table, you can use the following PySpark code:

In this example, we load the data from the Delta Lake table stored at /mnt/delta-lake/example. We then filter out the row with id = 2 and overwrite the existing data in the Delta Lake table.

Reading data from a specific version of a Delta Lake table:
To read data from a specific version of a Delta Lake table, you can use the following PySpark code:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("read_delta_table_v1") \
    .config("spark.sql.extensions", "io.delta.sql.DeltaSparkSessionExtension") \
    .config("spark.sql.catalog.spark_catalog", "org.apache.spark.sql.delta.catalog.DeltaCatalog") \
    .getOrCreate()

df_v1 = spark.read.format("delta").option("versionAsOf", 1).load("/mnt/delta-lake/example")

df_v1.show()

In this example, we updated the value of the row with id=1 to "updated_foo". We then read the Delta Lake table as it existed at version 1 (i.e., after the update). The output shows the updated version of the data.

Links to official documentation:

If you want to learn more about Delta Lake and how it works, you can refer to the official documentation provided by Databricks:

Delta Lake official documentation: https://docs.delta.io/latest/index.html

In conclusion, Delta Lake is a powerful storage layer that provides ACID transactions, schema enforcement, data versioning, and time travel on top of cloud and on-premises storage systems. Delta Lake is built on top of Apache Spark and is designed to work with big data processing frameworks. In this article, we explored what Delta Lake is and how it works, with examples in PySpark. If you are working with big data and looking for a reliable and scalable storage layer, Delta Lake is definitely worth exploring.