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, knowledge science, machine studying (ML), and generative AI. These organizations have an enormous demand for lakehouse options that mix the perfect of knowledge warehouses and knowledge lakes to simplify knowledge administration with easy accessibility to all knowledge from their most popular engines.

Amazon SageMaker Lakehouse unifies all of your knowledge throughout Amazon Easy Storage Service (Amazon S3) knowledge lakes and Amazon Redshift knowledge warehouses, serving to you construct highly effective analytics and synthetic intelligence and machine studying (AI/ML) purposes on a single copy of knowledge. SageMaker Lakehouse provides you the flexibleness to entry and question your knowledge  in place with all Apache Iceberg appropriate instruments and engines. It secures your knowledge 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 carry knowledge from operational databases and purposes into your lakehouse in close to actual time by zero-ETL integrations. It accesses and queries knowledge in-place with federated question capabilities throughout third-party knowledge sources by Amazon Athena.

With SageMaker Lakehouse, you possibly can 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 knowledge integration workload throughout knowledge lakes and knowledge warehouses, enabling seamless entry to numerous knowledge sources.

Amazon SageMaker Unified Studio, Amazon EMR 7.5.0 and better, and AWS Glue 5.0 natively assist SageMaker Lakehouse. This submit describes the best way to combine knowledge on RMS tables by Apache Spark utilizing SageMaker Unified Studio, Amazon EMR 7.5.0 and better, and AWS Glue 5.0.

The 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 akin 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.

Be aware that the Iceberg REST extensions endpoint is used if 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 increased. 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 software 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 situated

For a deeper understanding of how the Amazon Redshift hierarchy (databases, schemas, and tables) is mapped to the AWS Glue multilevel catalogs, you possibly can check with the Bringing Amazon Redshift knowledge into the AWS Glue Knowledge Catalog documentation.

Within the following part, we exhibit the best way 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 are able to carry current Amazon Redshift tables into the AWS Glue Knowledge catalog by making a Lakehouse Redshift catalog from an current Redshift namespace and supply entry to a SageMaker Unified Studio challenge, within the following instance, you’ll create a managed Amazon Redshift Lakehouse catalog straight from SageMaker Unified Studio and work with that.

Stipulations

To observe these directions, it’s essential to have the next conditions:

Create a SageMaker Unified Studio challenge

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

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

6. Go away the default values and select Proceed.
7. Evaluation the configurations and select Create challenge.

It is advisable to look forward to the challenge to be created. Venture creation can take about 5 minutes. When the challenge standing modifications to Energetic, choose the challenge identify to entry the challenge’s dwelling web page.

8. Make be aware of the Venture position ARN since you’ll want it for subsequent steps.

You’ve efficiently created the challenge and famous the challenge position ARN. The subsequent 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. Watch for 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, below Construct, select Question Editor.
9. On the highest proper, select Choose knowledge 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 knowledge:

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;

(Elective) Create an Amazon EMR Serverless software

IMPORTANT: This part is just required in case you plan to check additionally utilizing Amazon EMR Serverless. When you intend to make use of AWS Glue completely, you possibly can skip this part totally.

1. Navigate to the challenge 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. Watch for the Amazon EMR Serverless software 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 the best way to question tables saved in RMS utilizing a SageMaker Unified Studio pocket book.

1. Within the navigation pane, select Knowledge
2. Beneath Lakehouse, choose the down arrow subsequent to rms-catalog-demo
3. Beneath 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 possibly can specify totally different compute varieties for every pocket book cell. Though this instance demonstrates utilizing AWS Glue compute (challenge.spark.compatibility), the identical code might be executed utilizing Amazon EMR Serverless by deciding on the suitable compute within the cell settings. The next desk exhibits the connection kind and compute values to specify when operating PySpark code or Spark SQL code with totally 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 below the rms-catalog-demo RMS catalog:

from pyspark.sql import SparkSession

catalog_name = "rmscatalog"
#Change  along with your AWS account ID
rms_catalog_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 straight
9. Enter the next SQL assertion to view all tables below 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 about 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 the best way 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 expenses, clear up all created assets:

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

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 the best way to use Apache Spark to work together with Amazon Redshift Managed Storage tables by Amazon SageMaker Lakehouse utilizing the Iceberg REST catalog. This integration offers a unified view of your knowledge throughout Amazon S3 knowledge lakes and Amazon Redshift knowledge warehouses, so you possibly can construct highly effective analytics and AI/ML purposes whereas sustaining a single copy of your knowledge.

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

In regards to the Authors

Noritaka Sekiyama is a Principal Large Knowledge Architect with Amazon Net Providers (AWS) Analytics companies. He’s liable for constructing software program artifacts to assist clients. In his spare time, he enjoys biking on his highway bike.

Stefano Sandonà is a Senior Large Knowledge Specialist Resolution Architect at Amazon Net Providers (AWS). Captivated with knowledge, distributed methods, and safety, he helps clients worldwide architect high-performance, environment friendly, and safe knowledge options.

Derek Liu is a Senior Options Architect based mostly out of Vancouver, BC. He enjoys serving to clients resolve large knowledge challenges by Amazon Net Providers (AWS) analytic companies.

Raj Ramasubbu is a Senior Analytics Specialist Options Architect centered on large knowledge and analytics and AI/ML with Amazon Net Providers (AWS). He helps clients architect and construct extremely scalable, performant, and safe cloud-based options on AWS. Raj offered technical experience and management in constructing knowledge engineering, large knowledge analytics, enterprise intelligence, and knowledge science options for over 18 years previous to becoming a member of AWS. He helped clients in varied trade 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, based mostly in Madrid. He has beforehand labored on analysis associated to knowledge analytics and AI in numerous European analysis tasks. In his present position, Angel helps companions develop companies centered on knowledge 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, check with 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()