Chapter 9: Delta Sharing (Multiple Choice)

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Questions and Answers

What is the primary advantage of open source data sharing over proprietary solutions?

Avoiding unnecessary limitations and financial burdens (correct)

Scalability for massive datasets

Vendor lock-in

Financial savings

What is the primary focus of the Delta Sharing protocol?

Cross-platform data sharing (correct)

Machine learning and AL

Data movement and duplication

Vendor lock-in avoidance

What is the benefit of Delta Sharing for data recipients?

Vendor-specific technology is required

Data movement is necessary for sharing

Data replication is required

Direct connection without data replication is possible (correct)

What is a characteristic of Delta Sharing in terms of supported clients?

Supports a diverse range of clients Signup and view all the answers

What is the benefit of Delta Sharing in terms of governance?

Centralized control and compliance Signup and view all the answers

What is the scalability of Delta Sharing in terms of datasets?

Designed to handle massive structured datasets Signup and view all the answers

What is the key feature of Delta Sharing that allows for easy data sharing?

Direct connection without data replication Signup and view all the answers

What is the benefit of Delta Sharing in terms of data access control?

Granular control over data access Signup and view all the answers

What is the role of the sharing server in the Delta Sharing protocol?

To authenticate the recipient's client and manage access Signup and view all the answers

What is the purpose of filters provided by the client in the Delta Sharing protocol?

To request a specific subset of the data Signup and view all the answers

What is the benefit of using short-lived pre-signed URLs in the Delta Sharing protocol?

To enable fast, cheap, and reliable data transfer Signup and view all the answers

What is the role of the data provider in the Delta Sharing protocol?

To manage access and decide what data to share Signup and view all the answers

What is the format of the data stored on the cloud data lake?

Delta Lake Signup and view all the answers

What is the purpose of the Delta Sharing protocol?

To facilitate efficient data sharing between data providers and recipients Signup and view all the answers

What is the benefit of the Delta Sharing protocol for data recipients?

They can use one of the many Delta Sharing clients supporting the protocol Signup and view all the answers

What is the advantage of using cloud storage systems in the Delta Sharing protocol?

It enables fast, cheap, and reliable data transfer Signup and view all the answers

What is the name of the JSON file property that contains the server credentials?

shareCredentialsVersion Signup and view all the answers

What command is used to upload the share file to a dbfs:/ location?

dbfs cp Signup and view all the answers

What is the purpose of the profile_path variable in the notebook?

To specify the path to the shared table file Signup and view all the answers

What is the correct syntax for accessing a shared table with Spark?

dbfs:/mnt/datalake/book/delta-sharing Signup and view all the answers

Where is the share file uploaded to in the Databricks filesystem?

dbfs:/mnt/.../delta-sharing/ Signup and view all the answers

What is the purpose of the SharingClient in Delta Sharing?

To list all shared Delta tables Signup and view all the answers

What is the output of the list_all_tables() method?

A list of all shared Delta tables Signup and view all the answers

What is the format of the URL to access a shared Delta table?

profile_path + #delta_sharing.default.table_name Signup and view all the answers

What is the purpose of the load_as_pandas() method?

To load a shared table as a pandas DataFrame Signup and view all the answers

What is the default limit of the load_as_pandas() method?

10 Signup and view all the answers

What is the purpose of the load_as_spark() method?

To load a shared table as a Spark DataFrame Signup and view all the answers

What is the format of the output of the load_as_pandas() method?

A pandas DataFrame Signup and view all the answers

What is the benefit of using the load_as_spark() method?

It enables loading shared tables as Spark DataFrames Signup and view all the answers

What is a major drawback of managing and maintaining proprietary in-house solutions?

High cost Signup and view all the answers

What is a benefit of using commercial data-sharing solutions?

Simplicity Signup and view all the answers

What is a limitation of commercial data-sharing solutions?

Lack of interoperability Signup and view all the answers

What is a consequence of platform disparities in commercial data-sharing solutions?

Introducing complexities in data sharing Signup and view all the answers

What is a step involved in loading data onto a commercial data-sharing platform?

ETL and creating copies of the data Signup and view all the answers

What is a characteristic of cloud object storage?

Elastic nature and seamless scalability Signup and view all the answers

What is a consequence of the challenges in commercial data-sharing solutions?

Additional costs for sharing data Signup and view all the answers

What is a benefit of using cloud object storage for data sharing?

Effortless accommodation of unlimited growth Signup and view all the answers

Study Notes

Commercial data-sharing solutions are widely chosen by companies as an alternative to building in-house solutions.
They offer a balance between not wanting to allocate extensive time and resources to developing a proprietary solution and desiring greater control than what cloud object storage can provide.
These solutions provide simplicity for users to share data with others on the same platform.

Vendor lock-in: Commercial solutions often lack interoperability with other platforms, making it difficult to share data with users of competing solutions.
Data movement: Data needs to be loaded onto a specific platform, which involves additional steps, such as ETL and creating copies of the data.
Scalability: Commercial data-sharing solutions may have limitations on scaling imposed by the vendors.
Cost: The challenges mentioned above contribute to additional costs for sharing data with potential customers, as data providers need to replicate data for different recipients across various cloud platforms.

Cloud Object Storage

Object storage is highly regarded as a well-suited solution for cloud environments due to its elastic nature and seamless scalability.
It allows handling vast amounts of data and effortlessly accommodating unlimited growth.

Open source data sharing is not associated with a vendor-specific technology that introduces unnecessary limitations and financial burdens.
Delta Sharing is an open source protocol designed to provide open cross-platform data sharing, allowing data sharing in Delta Lake and Apache Parquet formats with any platform, whether on premises or another cloud.

Open cross-platform data sharing: Delta Sharing provides an open source, cross-platform solution that avoids vendor lock-in.
Share live data without data movement: Data recipients can directly connect to Delta Sharing without replicating the data.
Support a wide range of clients: Delta Sharing supports a diverse range of clients, including popular tools like Power BI, Tableau, Apache Spark, pandas, and Java.
Centralized governance: Delta Sharing provides robust security, auditing, and governance capabilities, allowing data providers to have granular control over data access.

Delta Sharing lets data providers share existing tables or parts thereof stored on their cloud data lake in Delta Lake format.
The data provider decides what data they want to share and runs a sharing server in front of it that implements the Delta Sharing protocol and manages access for recipients.
As a data recipient, you only need one of the many Delta Sharing clients supporting the protocol.

The recipient's client authenticates to the sharing server and asks to query a specific table.
The server verifies whether the client is allowed to access the data, logs the request, and then determines which data to send back.
The server generates short-lived pre-signed URLs that allow the client to read these Parquet files directly from the cloud provider.

Reading a Shared Table

A shared table can be accessed using a Delta Sharing client, passing it the profile path, and listing all shared Delta tables.
A URL to access a shared table can be created using the syntax: profile_path + "#delta_sharing..".
The shared table can be read as a pandas DataFrame or a standard PySpark DataFrame using the load_as_pandas() or load_as_spark() method, respectively.

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Description

This quiz assesses your understanding of scalability issues in data management and commercial data-sharing solutions. It covers the limitations of in-house and cloud-based solutions.