Get started: Ingest and insert additional data

This get started article walks you through using a Databricks notebook to ingest a CSV file containing additional baby name data into your Unity Catalog volume and then import the new baby name data into an existing table by using Python, Scala, and R.

Important

This get started article builds on Get started: Import and visualize CSV data from a notebook . You must complete the steps in that article in order to complete this article. For the complete notebook for that getting started article, see Import and visualize data notebooks .

Requirements

To complete the tasks in this article, you must meet the following requirements:

Your workspace must have Unity Catalog enabled. For information on getting started with Unity Catalog, see Set up and manage Unity Catalog .

You must have permission to use an existing compute resource or create a new compute resource. See Get started: Account and workspace setup or see your Databricks administrator.

For a completed notebook for this article, see Ingest additional data notebooks .

Step 1: Create a new notebook

To create a notebook in your workspace, click New in the sidebar, and then click Notebook . A blank notebook opens in the workspace.

To learn more about creating and managing notebooks, see Manage notebooks .

Step 2: Define variables

In this step, you define variables for use in the example notebook you create in this article.

Copy and paste the following code into the new empty notebook cell. Replace <catalog-name> , <schema-name> , and <volume-name> with the catalog, schema, and volume names for a Unity Catalog volume. Replace <table_name> with a table name of your choice. You will save the baby name data into this table later in this article.

Press Shift+Enter to run the cell and create a new blank cell.

catalog = "<catalog_name>"
schema = "<schema_name>"
volume = "<volume_name>"
file_name = "new_baby_names.csv"
table_name = "baby_names"
path_volume = "/Volumes/" + catalog + "/" + schema + "/" + volume
path_table = catalog + "." + schema
print(path_table) # Show the complete path
print(path_volume) # Show the complete path
val catalog = "<catalog_name>"
val schema = "<schema_name>"
val volume = "<volume_name>"
val fileName = "new_baby_names.csv"
val tableName = "baby_names"
val pathVolume = s"/Volumes/${catalog}/${schema}/${volume}"
val pathTable = s"${catalog}.${schema}"
print(pathVolume) // Show the complete path
print(pathTable) // Show the complete path
catalog <- "<catalog_name>"
schema <- "<schema_name>"
volume <- "<volume_name>"
file_name <- "new_baby_names.csv"
table_name <- "baby_names"
path_volume <- paste0("/Volumes/", catalog, "/", schema, "/", volume, sep = "")
path_table <- paste0(catalog, ".", schema, sep = "")
print(path_volume) # Show the complete path
print(path_table) # Show the complete path
Step 3: Add new CSV file of data to your Unity Catalog volume 
This step creates a DataFrame named df with a new baby name for 2022 and then saves that data into a new CSV file in your Unity Catalog volume.
This step simulates adding new yearly data to the existing data loaded for previous years. In your production environment, this incremental data would be stored in cloud storage.
Copy and paste the following code into the new empty notebook cell. This code creates the DataFrame with additional baby name data, and then writes that data to a CSV file in your Unity Catalog volume.
data = [[2022, "CARL", "Albany", "M", 42]]
df = spark.createDataFrame(data, schema="Year int, First_Name STRING, County STRING, Sex STRING, Count int")
# display(df)
(df.coalesce(1)
    .write
    .option("header", "true")
    .mode("overwrite")
    .csv(f"{path_volume}/{file_name}"))
val data = Seq((2022, "CARL", "Albany", "M", 42))
val columns = Seq("Year", "First_Name", "County", "Sex", "Count")
val df = data.toDF(columns: _*)
// display(df)
df.coalesce(1)
    .write
    .option("header", "true")
    .mode("overwrite")
    .csv(f"{pathVolume}/{fileName}")
# Load the SparkR package that is already preinstalled on the cluster.
library(SparkR)
data <- data.frame(Year = 2022,
    First_Name = "CARL",
    County = "Albany",
    Sex = "M",
    Count = 42)
df <- createDataFrame(data)
# display(df)
write.df(df, path = paste0(path_volume, "/", file_name),
    source = "csv",
    mode = "overwrite",
    header = "true")
Copy and paste the following code into an empty notebook cell. This code loads the new baby names data into a new DataFrame from the CSV file.
df1 = spark.read.csv(f"{path_volume}/{file_name}",
    header=True,
    inferSchema=True,
    sep=",")
display(df1)
val df1 = spark.read
    .option("header", "true")
    .option("inferSchema", "true")
    .option("delimiter", ",")
    .csv(s"$pathVolume/$fileName")
display(df1)
df1 <- read.df(paste0(path_volume, "/", file_name),
    source = "csv",
    header = TRUE,
    inferSchema = TRUE)
display(df1)
Step 5: Insert into existing table 
Copy and paste the following code into an empty notebook cell. This code appends the new baby names data from the DataFrame into the existing table.
df.write.mode("append").insertInto(f"{path_table}.{table_name}")
display(spark.sql(f"SELECT * FROM {path_table}.{table_name} WHERE Year = 2022"))
df1.write.mode("append").insertInto(s"${pathTable}.${tableName}")
display(spark.sql(s"SELECT * FROM ${pathTable}.${tableName} WHERE Year = 2022"))
# The write.df function in R, as provided by the SparkR package, does not directly support writing to Unity Catalog.
# In this example, you write the DataFrame into a temporary view and then use the SQL command to insert data from the temporary view to the Unity Catalog table
createOrReplaceTempView(df1, "temp_view")
sql(paste0("INSERT INTO ", path_table, ".", table_name, " SELECT * FROM temp_view"))
display(sql(paste0("SELECT * FROM ", path_table, ".", table_name, " WHERE Year = 2022")))
Next steps 
To learn about cleansing and enhancing data, see Get started: Enhance and cleanse data.
Additional resources 
Get started: Query and visualize data from a notebook
Get started: Import and visualize CSV data from a notebook
Tutorial: Load and transform data using Apache Spark DataFrames