Entry Amazon Redshift Managed Storage tables by Apache Spark on AWS Glue and Amazon EMR utilizing Amazon SageMaker Lakehouse

Knowledge environments in data-driven organizations are altering to satisfy the rising calls for for analytics, together with enterprise intelligence (BI) dashboarding, one-time querying, information science, machine studying (ML), and generative AI. These organizations have an enormous demand for lakehouse options that mix the very best of information warehouses and information lakes to simplify information administration with quick access to all information from their most popular engines.

Amazon SageMaker Lakehouse unifies all of your information throughout Amazon Easy Storage Service (Amazon S3) information lakes and Amazon Redshift information warehouses, serving to you construct highly effective analytics and synthetic intelligence and machine studying (AI/ML) purposes on a single copy of information. SageMaker Lakehouse offers you the flexibleness to entry and question your information  in place with all Apache Iceberg appropriate instruments and engines. It secures your information within the lakehouse by defining fine-grained permissions, that are persistently utilized throughout all analytics and ML instruments and engines. You’ll be able to deliver information from operational databases and purposes into your lakehouse in close to actual time by zero-ETL integrations. It accesses and queries information in-place with federated question capabilities throughout third-party information sources by Amazon Athena.

With SageMaker Lakehouse, you may entry tables saved in Amazon Redshift managed storage (RMS) by Iceberg APIs, utilizing the Iceberg REST catalog backed by AWS Glue Knowledge Catalog. This expands your information integration workload throughout information lakes and information warehouses, enabling seamless entry to various information sources.

Amazon SageMaker Unified Studio, Amazon EMR 7.5.0 and better, and AWS Glue 5.0 natively help SageMaker Lakehouse. This submit describes easy methods to combine information on RMS tables by Apache Spark utilizing SageMaker Unified Studio, Amazon EMR 7.5.0 and better, and AWS Glue 5.0.

The best way to entry RMS tables by Apache Spark on AWS Glue and Amazon EMR

With SageMaker Lakehouse, RMS tables are accessible by the Apache Iceberg REST catalog. Open supply engines equivalent to Apache Spark are appropriate with Apache Iceberg, they usually can work together with RMS tables by configuring this Iceberg REST catalog. You’ll be able to be taught extra in Connecting to the Knowledge Catalog utilizing AWS Glue Iceberg REST extension endpoint.

Word that the Iceberg REST extensions endpoint is used while you entry RMS tables. This endpoint is accessible by the Apache Iceberg AWS Glue Knowledge Catalog extensions, which comes preinstalled on AWS Glue 5.0 and Amazon EMR 7.5.0 or larger. The extension library allows entry to RMS tables utilizing the Amazon Redshift connector for Apache Spark.

To entry RMS backed catalog databases from Spark, every RMS database requires its personal Spark session catalog configuration. Listed here are the required Spark configurations:

Configuration parameters:

• {catalog_name}: Your chosen identify for referencing the RMS catalog database in your utility code
• {rms_catalog_name}: The RMS catalog identify as proven within the AWS Lake Formation catalogs part
• {database_name}: The RMS database identify
• {aws_region}: The AWS Area the place the RMS catalog is positioned

For a deeper understanding of how the Amazon Redshift hierarchy (databases, schemas, and tables) is mapped to the AWS Glue multilevel catalogs, you may seek advice from the Bringing Amazon Redshift information into the AWS Glue Knowledge Catalog documentation.

Within the following part, we exhibit easy methods to entry RMS tables by Apache Spark utilizing SageMaker Unified Studio JupyterLab notebooks with the AWS Glue 5.0 runtime and Amazon EMR Serverless.

Though we will deliver present Amazon Redshift tables into the AWS Glue Knowledge catalog by making a Lakehouse Redshift catalog from an present Redshift namespace and supply entry to a SageMaker Unified Studio venture, within the following instance, you’ll create a managed Amazon Redshift Lakehouse catalog instantly from SageMaker Unified Studio and work with that.

Conditions

To observe these directions, you could have the next conditions:

Create a SageMaker Unified Studio venture

Full the next steps to create a SageMaker Unified Studio venture:

1. Register to SageMaker Unified Studio.
2. Select Choose a venture on the highest menu and select Create venture.
3. For Mission identify, enter demo.
4. For Mission profile, select All capabilities.
5. Select Proceed.

6. Go away the default values and select Proceed.
7. Overview the configurations and select Create venture.

That you must anticipate the venture to be created. Mission creation can take about 5 minutes. When the venture standing modifications to Energetic, choose the venture identify to entry the venture’s house web page.

8. Make observe of the Mission position ARN since you’ll want it for subsequent steps.

You’ve efficiently created the venture and famous the venture position ARN. The following step is to configure a Lakehouse catalog to your RMS.

Configure a Lakehouse catalog to your RMS

Full the next steps to configure a Lakehouse catalog to your RMS:

1. Within the navigation pane, select Knowledge.
2. Select the + (plus) signal.
3. Choose Create Lakehouse catalog to create a brand new catalog and select Subsequent.

4. For Lakehouse catalog identify, enter rms-catalog-demo.
5. Select Add catalog.

6. Await the catalog to be created.

7. In SageMaker Unified Studio, select Knowledge within the left navigation pane, then choose the three vertical dots subsequent to Redshift (Lakehouse) and select Refresh to verify the Amazon Redshift compute is energetic.

Create a brand new desk within the RMS Lakehouse catalog:

8. In SageMaker Unified Studio, on the highest menu, underneath Construct, select Question Editor.
9. On the highest proper, select Choose information supply.
10. For CONNECTIONS, select Redshift (Lakehouse).
11. For DATABASES, select dev@rms-catalog-demo.
12. For SCHEMAS, select public.
13. Select Select.

14. Within the question cell, enter and execute the next question to create a brand new schema:

create schema "dev@rms-catalog-demo".salesdb

15. In a brand new cell, enter and execute the next question to create a brand new desk:

create desk salesdb.store_sales (ss_sold_timestamp timestamp, ss_item textual content, ss_sales_price float);

16. In a brand new cell, enter and execute the next question to populate the desk with pattern information:

insert into salesdb.store_sales values ('2024-12-01T09:00:00Z', 'Product 1', 100.0),
('2024-12-01T11:00:00Z', 'Product 2', 500.0),
('2024-12-01T15:00:00Z', 'Product 3', 20.0),
('2024-12-01T17:00:00Z', 'Product 4', 1000.0),
('2024-12-01T18:00:00Z', 'Product 5', 30.0),
('2024-12-02T10:00:00Z', 'Product 6', 5000.0),
('2024-12-02T16:00:00Z', 'Product 7', 5.0);

17. In a brand new cell, enter and run the next question to confirm the desk contents:

choose * from salesdb.store_sales;

(Optionally available) Create an Amazon EMR Serverless utility

IMPORTANT: This part is simply required in case you plan to check additionally utilizing Amazon EMR Serverless. For those who intend to make use of AWS Glue completely, you may skip this part completely.

1. Navigate to the venture web page. Within the left navigation pane, choose Compute, then choose the Knowledge processing Select Add compute.

2. Select Create new compute assets, then select Subsequent.

3. Choose EMR Serverless.

4. Specify emr_serverless_application as Compute identify, choose Compatibility as Permission mode, and select Add compute.

5. Monitor the deployment progress. Await the Amazon EMR Serverless utility to finish its deployment. This course of can take a minute.

Entry Amazon Redshift Managed Storage tables by Apache Spark

On this part, we exhibit easy methods to question tables saved in RMS utilizing a SageMaker Unified Studio pocket book.

1. Within the navigation pane, select Knowledge
2. Below Lakehouse, choose the down arrow subsequent to rms-catalog-demo
3. Below dev, choose the down arrow subsequent salesdb, select store_sales, and select the three dots

SageMaker Lakehouse offers a number of evaluation choices: Question with Athena, Question with Redshift, and Open in Jupyter Lab pocket book.

4. Select Open in Jupyter Lab pocket book
5. On the Launcher tab, select Python 3 (ipykernel)

In SageMaker Unified Studio JupyterLab, you may specify completely different compute varieties for every pocket book cell. Though this instance demonstrates utilizing AWS Glue compute (venture.spark.compatibility), the identical code may be executed utilizing Amazon EMR Serverless by deciding on the suitable compute within the cell settings. The next desk reveals the connection kind and compute values to specify when operating PySpark code or Spark SQL code with completely different engines:

6. Within the pocket book cell’s prime left nook, set Connection Kind to PySpark and choose spark.compatibility (AWS Glue 5.0) as Compute
7. Execute the next code to initialize the SparkSession and configure rmscatalog because the session catalog for accessing the dev database underneath the rms-catalog-demo RMS catalog:

from pyspark.sql import SparkSession

catalog_name = "rmscatalog"
#Change <your_account_id> along with your AWS account ID
rms_catalog_id = "<your_account_id>:rms-catalog-demo/dev"

#Change along with your AWS area
aws_region="us-east-2"

spark = SparkSession.builder.appName('rms_demo') 
    .config(f'spark.sql.catalog.{catalog_name}', 'org.apache.iceberg.spark.SparkCatalog') 
    .config(f'spark.sql.catalog.{catalog_name}.kind', 'glue') 
    .config(f'spark.sql.catalog.{catalog_name}.glue.id', rms_catalog_id) 
    .config(f'spark.sql.catalog.{catalog_name}.shopper.area', aws_region) 
    .config('spark.sql.extensions','org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions') 
    .getOrCreate()

8. Create a brand new cell and swap the connection kind from PySpark to SQL to execute Spark SQL instructions instantly
9. Enter the next SQL assertion to view all tables underneath salesdb (RMS schema) inside rmscatalog:

SHOW TABLES IN rmscatalog.salesdb

10. In a brand new SQL cell, enter the next DESCRIBE EXTENDED assertion to view detailed details about the store_sales desk within the salesdb schema:

DESCRIBE EXTENDED rmscatalog.salesdb.store_sales

Within the output, you’ll observe that the Supplier is ready to iceberg. This means that the desk is acknowledged as an Iceberg desk, regardless of being saved in Amazon Redshift managed storage.

11. In a brand new SQL cell, enter the next SELECT assertion to view the content material of the desk

SELECT * FROM rmscatalog.salesdb.store_sales

All through this instance, we demonstrated easy methods to create a desk in Amazon Redshift Serverless and seamlessly question it as an Iceberg desk utilizing Apache Spark inside a SageMaker Unified Studio pocket book.

Clear up

To keep away from incurring future costs, clear up all created assets:

1. Delete the created SageMaker Unified Studio venture. This step will routinely delete Amazon EMR compute (for instance, the Amazon EMR Serverless utility) that was provisioned from the venture:
   1. Inside SageMaker Studio, navigate to the demo venture’s Mission overview part.
   2. Select Actions, then choose Delete venture.
   3. Kind verify and select Delete venture.

2. Delete the created Lakehouse catalog:
   1. Navigate to the AWS Lake Formation web page within the Catalogs part.
   2. Choose the rms-catalog-demo catalog, select Actions, then choose Delete.
   3. Within the affirmation window kind rms-catalog-demo after which select Drop.

Conclusion

On this submit, we demonstrated easy methods to use Apache Spark to work together with Amazon Redshift Managed Storage tables by Amazon SageMaker Lakehouse utilizing the Iceberg REST catalog. This integration gives a unified view of your information throughout Amazon S3 information lakes and Amazon Redshift information warehouses, so you may construct highly effective analytics and AI/ML purposes whereas sustaining a single copy of your information.

For added workloads and implementations, go to Simplify information entry to your enterprise utilizing Amazon SageMaker Lakehouse.

Concerning the Authors

Noritaka Sekiyama is a Principal Large Knowledge Architect with Amazon Internet Companies (AWS) Analytics companies. He’s chargeable for constructing software program artifacts to assist prospects. In his spare time, he enjoys biking on his street bike.

Stefano Sandonà is a Senior Large Knowledge Specialist Answer Architect at Amazon Internet Companies (AWS). Obsessed with information, distributed methods, and safety, he helps prospects worldwide architect high-performance, environment friendly, and safe information options.

Derek Liu is a Senior Options Architect primarily based out of Vancouver, BC. He enjoys serving to prospects clear up large information challenges by Amazon Internet Companies (AWS) analytic companies.

Raj Ramasubbu is a Senior Analytics Specialist Options Architect centered on large information and analytics and AI/ML with Amazon Internet Companies (AWS). He helps prospects architect and construct extremely scalable, performant, and safe cloud-based options on AWS. Raj offered technical experience and management in constructing information engineering, large information analytics, enterprise intelligence, and information science options for over 18 years previous to becoming a member of AWS. He helped prospects in varied business verticals like healthcare, medical gadgets, life science, retail, asset administration, automotive insurance coverage, residential REIT, agriculture, title insurance coverage, provide chain, doc administration, and actual property.

Angel Conde Manjon is a Sr. EMEA Knowledge & AI PSA, primarily based in Madrid. He has beforehand labored on analysis associated to information analytics and AI in various European analysis tasks. In his present position, Angel helps companions develop companies centered on information and AI.

Appendix: Pattern script for Lake Formation FGAC enabled Spark cluster

If you wish to entry RMS tables from Lake Formation FGAC enabled Spark cluster on AWS Glue or Amazon EMR, seek advice from the next code instance:

from pyspark.sql import SparkSession

catalog_name = "rmscatalog"
rms_catalog_name = "123456789012:rms-catalog-demo/dev"
account_id = "123456789012"
area = "us-east-2"

spark = SparkSession.builder.appName('rms_demo') 
.config('spark.sql.defaultCatalog', catalog_name) 
.config(f'spark.sql.catalog.{catalog_name}', 'org.apache.iceberg.spark.SparkCatalog') 
.config(f'spark.sql.catalog.{catalog_name}.kind', 'glue') 
.config(f'spark.sql.catalog.{catalog_name}.glue.id', rms_catalog_name) 
.config(f'spark.sql.catalog.{catalog_name}.shopper.area', area) 
.config(f'spark.sql.catalog.{catalog_name}.glue.account-id', account_id) 
.config(f'spark.sql.catalog.{catalog_name}.glue.catalog-arn',f'arn:aws:glue:{area}:{rms_catalog_name}') 
.config('spark.sql.extensions','org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions') 
.getOrCreate()