Quick Start

Understanding Table Paths

Before accessing shared data, you need to understand how Delta Sharing table paths work. A table path consists of two parts joined by #:

<profile-file-path>#<share-name>.<schema-name>.<table-name>

Profile file path

The path to your credential file (can be local or remote):

Local: /path/to/profile.share
S3: s3a://my-bucket/profile.share
ADLS: abfss://container@account.dfs.core.windows.net/profile.share

Fully qualified table name

The three-part identifier for the shared table: <share>.<schema>.<table>

The profile file path must use Hadoop FileSystem-compatible URLs. Use s3a:// (not s3://) for S3 paths.

Reading Shared Tables

Once you have your table path, reading shared data is simple in any language.

Python
Scala
Java
R
SQL

# Define the table path
table_path = "<profile-file-path>#<share-name>.<schema-name>.<table-name>"

# Load the shared table as a DataFrame
df = spark.read.format("deltaSharing").load(table_path)

# Display the data
df.show()

# Perform operations
df.filter(df.age > 21).select("name", "age").show()

Example with Real Path

# Using the example Delta Sharing server
table_path = "/tmp/open-datasets.share#delta_sharing.default.boston-housing"

df = spark.read.format("deltaSharing").load(table_path)
print(f"Rows: {df.count()}, Columns: {len(df.columns)}")
df.printSchema()

// Define the table path
val tablePath = "<profile-file-path>#<share-name>.<schema-name>.<table-name>"

// Load the shared table as a DataFrame
val df = spark.read.format("deltaSharing").load(tablePath)

// Display the data
df.show()

// Perform operations
df.filter($"age" > 21).select("name", "age").show()

Example with Real Path

// Using the example Delta Sharing server
val tablePath = "/tmp/open-datasets.share#delta_sharing.default.boston-housing"

val df = spark.read.format("deltaSharing").load(tablePath)
println(s"Rows: ${df.count()}, Columns: ${df.columns.length}")
df.printSchema()

// Define the table path
String tablePath = "<profile-file-path>#<share-name>.<schema-name>.<table-name>";

// Load the shared table as a DataFrame
Dataset<Row> df = spark.read().format("deltaSharing").load(tablePath);

// Display the data
df.show();

// Perform operations
df.filter(df.col("age").gt(21))
  .select("name", "age")
  .show();

Example with Real Path

// Using the example Delta Sharing server
String tablePath = "/tmp/open-datasets.share#delta_sharing.default.boston-housing";

Dataset<Row> df = spark.read().format("deltaSharing").load(tablePath);
System.out.println("Rows: " + df.count() + ", Columns: " + df.columns().length);
df.printSchema();

# Define the table path
table_path <- "<profile-file-path>#<share-name>.<schema-name>.<table-name>"

# Load the shared table as a DataFrame
df <- read.df(table_path, "deltaSharing")

# Display the data
showDF(df)

# Perform operations
filtered_df <- filter(df, df$age > 21)
showDF(select(filtered_df, "name", "age"))

Example with Real Path

# Using the example Delta Sharing server
table_path <- "/tmp/open-datasets.share#delta_sharing.default.boston-housing"

df <- read.df(table_path, "deltaSharing")
cat("Rows:", nrow(df), "Columns:", ncol(df), "\n")
printSchema(df)

-- First, create a table reference
CREATE TABLE shared_table 
USING deltaSharing 
LOCATION '<profile-file-path>#<share-name>.<schema-name>.<table-name>';

-- Now you can query it like any other table
SELECT * FROM shared_table;

-- Perform operations
SELECT name, age 
FROM shared_table 
WHERE age > 21;

Example with Real Path

-- Using the example Delta Sharing server
CREATE TABLE boston_housing 
USING deltaSharing 
LOCATION '/tmp/open-datasets.share#delta_sharing.default.boston-housing';

SELECT COUNT(*) as row_count FROM boston_housing;
DESCRIBE boston_housing;

Working with DataFrames

Once loaded, shared tables behave like regular Spark DataFrames:

# Standard DataFrame operations work seamlessly
df = spark.read.format("deltaSharing").load(table_path)

# Aggregations
df.groupBy("category").count().show()

# Joins with other DataFrames
local_df = spark.read.parquet("/path/to/local/data")
joined = df.join(local_df, "id")

# Write results to storage
df.filter(df.status == "active").write.parquet("/output/path")

Common Patterns

Reading specific columns

# Select only needed columns for better performance
df = spark.read.format("deltaSharing").load(table_path)
result = df.select("id", "name", "timestamp")

Filtering data

# Apply filters to reduce data transfer
df = spark.read.format("deltaSharing").load(table_path)
filtered = df.filter((df.date >= "2024-01-01") & (df.status == "active"))

Caching for multiple queries

# Cache the DataFrame if you'll use it multiple times
df = spark.read.format("deltaSharing").load(table_path)
df.cache()

# Now multiple operations won't re-fetch the data
df.count()
df.groupBy("category").count().show()

Creating temporary views

# Create a temporary view for SQL access
df = spark.read.format("deltaSharing").load(table_path)
df.createOrReplaceTempView("shared_data")

# Query with SQL
spark.sql("SELECT * FROM shared_data WHERE age > 21").show()

Profile File Locations

Profile files can be stored in various locations:

table_path = "/home/user/credentials/profile.share#myshare.myschema.mytable"
df = spark.read.format("deltaSharing").load(table_path)

Error Handling

Common Issues:

Authentication errors: Verify your profile file credentials are correct
Table not found: Check the share, schema, and table names
Invalid path: Ensure you’re using # to separate the profile path from the table name
Network issues: Confirm connectivity to the Delta Sharing server

# Example error handling
try:
    df = spark.read.format("deltaSharing").load(table_path)
    df.show()
except Exception as e:
    print(f"Error accessing shared table: {e}")
    # Check profile file exists and is valid
    # Verify table path format
    # Confirm network connectivity

Next Steps

SQL Usage

Learn advanced SQL patterns for Delta Sharing

Streaming

Stream data from shared tables

Change Data Feed

Query table changes over time

Get Started

Core Concepts

Python Connector

Apache Spark Connector

Reference Server

Community Connectors

Understanding Table Paths

Reading Shared Tables

Example with Real Path

Example with Real Path

Example with Real Path

Example with Real Path

Example with Real Path

Working with DataFrames

Common Patterns

Profile File Locations

Error Handling

Next Steps

SQL Usage

Streaming

Change Data Feed

Build docs developers (and LLMs) love

Get Started

Core Concepts

Python Connector

Apache Spark Connector

Reference Server

Community Connectors

​Understanding Table Paths

​Reading Shared Tables

​Example with Real Path

​Example with Real Path

​Example with Real Path

​Example with Real Path

​Example with Real Path

​Working with DataFrames

​Common Patterns

​Profile File Locations

​Error Handling

​Next Steps

SQL Usage

Streaming

Change Data Feed

Build docs developers (and LLMs) love

Understanding Table Paths

Reading Shared Tables

Example with Real Path

Example with Real Path

Example with Real Path

Example with Real Path

Example with Real Path

Working with DataFrames

Common Patterns

Profile File Locations

Error Handling

Next Steps