Delta Lake Quickstart

This guide helps you quickly explore the main features of Delta Lake. It provides code snippets that show how to read from and write to Delta Lake tables from interactive, batch, and streaming queries.

Set up Apache Spark with Delta Lake

Delta Lake requires Apache Spark version 2.4.2 or above. Follow the instructions below to set up Delta Lake with Apache Spark. You can run the steps in this guide on your local machine in the following two ways:

  1. Run interactively: Start the Spark Shell (Scala or Python) with Delta Lake and run the code snippets interactively in the shell.
  2. Run as a project: Set up a Maven or SBT project (Scala or Java) with Delta Lake, copy the code snippets into a source file, and run the project.

Set up interactive shell

To use Delta Lake interactively within the Spark shell you need a local installation of Apache Spark. Depending on whether you want to use Python or Scala language, you can set up either PySpark or the Spark shell, respectively.


If you need to install or upgrade PySpark, run:

pip install --upgrade pyspark

Run PySpark with the Delta Lake package:

pyspark --packages

Spark Scala Shell

Download the latest version of Apache Spark (2.4.2 or above) by following instructions from Downloading Spark, either using pip or by downloading and extracting the archive and running spark-shell in the extracted directory.

Run spark-shell with the Delta Lake package:

bin/spark-shell --packages


If you are seeing the following error, make sure that Apache Spark and delta-core is built for the same Scala version (2.11 or 2.12). The pre-built distributions of Apache Spark 2.4.3 from the download page are built with Scala 2.11:

java.util.ServiceConfigurationError: org.apache.spark.sql.sources.DataSourceRegister: Provider could not be instantiated

See this issue for details.

Set up project

If you want to build a project using Delta Lake binaries from Maven Central Repository, you can use the following Maven coordinates.


You include Delta Lake in your Maven project by adding it as a dependency in your POM file. Delta Lake is cross compiled with Scala versions 2.11 and 2.12; choose the version that matches your project. If you are writing a Java project, you can use either version.



You include Delta Lake in your SBT project by adding the following line to your build.sbt file:

libraryDependencies += "" %% "delta-core" % "0.2.0"

Create a table

To create a Delta Lake table, write a DataFrame out in the delta format. You can use existing Spark SQL code and change the format from parquet, csv, json, and so on, to delta.

data = spark.range(0, 5)
val data = spark.range(0, 5)
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;

SparkSession spark = ...   // create SparkSession

Dataset<Row> data = data = spark.range(0, 5);

These operations create a new Delta Lake table using the schema that was inferred from your DataFrame. For the full set of options available when you create a new Delta Lake table, see Create a table and Write to a table.


We use local paths for Delta Lake table locations throughout the Quickstart. For configuring HDFS or cloud storage for Delta Lake, see Storage Configuration.

Update the table data

Delta Lake supports several operations to modify tables using standard DataFrame APIs. This example runs a batch job to overwrite the data in the table:

data = spark.range(5, 10)
val data = spark.range(5, 10)
Dataset<Row> data = data = spark.range(5, 10);

Read data

You read data in your Delta Lake table by specifying the path to the files: "/tmp/delta-table":

df ="delta").load("/tmp/delta-table")
val df ="delta").load("/tmp/delta-table")
Dataset<Row> df ="delta").load("/tmp/delta-table");;

You should see only the values 5-9 you have added because you overwrote the previous data.

Read older versions of data using Time Travel

You can query previous snapshots of your Delta Lake table by using a feature called Time Travel. If you want to access the data that you overwrote, you can query a snapshot of the table before you overwrote the first set of data using the versionAsOf option.

df ="delta").option("versionAsOf", 0).load("/tmp/delta-table")
val df ="delta").option("versionAsOf", 0).load("/tmp/delta-table")
Dataset<Row> df ="delta").option("versionAsOf", 0).load("/tmp/delta-table");;

You should see the first set of data, from before you overwrote it! Time Travel is an extremely powerful feature that takes advantage of the power of the Delta Lake transaction log to access data that is no longer in the table. Removing the version 0 option (or specifying version 1) would let you see the newer data again. For more information, see Query an older snapshot of a table (time travel).

Write a stream of data to a table

You can also write to a Delta Lake table using Structured Streaming. The Delta Lake transaction log guarantees exactly-once processing, even when there are other streams or batch queries running concurrently against the table. By default, streams run in append mode, which adds new records to the table:

streamingDf = spark.readStream.format("rate").load()
stream = streamingDf.selectExpr("value as id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table")
val streamingDf = spark.readStream.format("rate").load()
val stream =$"value" as "id").writeStream.format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table")
import org.apache.spark.sql.streaming.StreamingQuery;

Dataset<Row> streamingDf = spark.readStream().format("rate").load();
StreamingQuery stream = streamingDf.selectExpr("value as id").writeStream().format("delta").option("checkpointLocation", "/tmp/checkpoint").start("/tmp/delta-table");

While the stream is running, you can read the table using the earlier commands.


If you’re running this in a shell, you may see the streaming task progress, which make it hard to type commands in that shell. It may be useful to start another shell in a new terminal for querying the table.

You can stop the stream by running stream.stop() in the same terminal that started the stream.

For more information about Delta Lake integration with Structured Streaming, see Table Streaming Reads and Writes.

Read a stream of changes from a table

While the stream is writing to the Delta Lake table, you can also read from that table as streaming source. For example, you can start another streaming query that prints all the changes made to the Delta Lake table.

stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()
val stream2 = spark.readStream.format("delta").load("/tmp/delta-table").writeStream.format("console").start()
StreamingQuery stream2 = spark.readStream().format("delta").load("/tmp/delta-table").writeStream().format("console").start();