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Get started: Ingest and insert additional data

  • Article

This get started article walks you through using a Azure 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:

Tip

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:

  1. Click New Icon New in the sidebar, and then click Notebook.
  2. On the Create Notebook page:
    • Specify a unique name for your notebook.
    • Set the default language for your notebook and then click Confirm if prompted.
    • Click Connect and select a compute resource. To create a new compute resource, see Use compute.

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.

  1. 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.

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

    Python

    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

    Scala

    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

    R

    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.

Note

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.

  1. 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.

    Python

    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}"))

    Scala

    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}")

    R

    # 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")

  2. Press Shift+Enter to run the cell and then move to the next cell.

Step 4: Load data into DataFrame from CSV file

Note

This step simulates loading data from cloud storage.

  1. 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.

    Python

    df1 = spark.read.csv(f"{path_volume}/{file_name}",
    header=True,
    inferSchema=True,
    sep=",")
display(df1)

    Scala

    val df1 = spark.read
    .option("header", "true")
    .option("inferSchema", "true")
    .option("delimiter", ",")
    .csv(s"$pathVolume/$fileName")
display(df1)

    R

    df1 <- read.df(paste0(path_volume, "/", file_name),
    source = "csv",
    header = TRUE,
    inferSchema = TRUE)
display(df1)

  2. Press Shift+Enter to run the cell and then move to the next cell.

Step 5: Insert into existing table

  1. 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.

    Python

    df.write.mode("append").insertInto(f"{path_table}.{table_name}")
display(spark.sql(f"SELECT * FROM {path_table}.{table_name} WHERE Year = 2022"))

    Scala

    df1.write.mode("append").insertInto(s"${pathTable}.${tableName}")
display(spark.sql(s"SELECT * FROM ${pathTable}.${tableName} WHERE Year = 2022"))

    R

    # 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")))

  2. Press Ctrl+Enter to run the cell.

Ingest additional data notebooks

Use one of the following notebooks to perform the steps in this article.

Python

Ingest and insert additional data using Python

Get notebook

Scala

Ingest and insert additional data using Scala

Get notebook

R

Ingest and insert additional data using R

Get notebook

Next steps

To learn about cleansing and enhancing data, see Get started: Enhance and cleanse data.

Additional resources