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Course of Apache Hudi, Delta Lake, Apache Iceberg datasets at scale, half 1: AWS Glue Studio Pocket book


Cloud knowledge lakes supplies a scalable and low-cost knowledge repository that permits prospects to simply retailer knowledge from quite a lot of knowledge sources. Knowledge scientists, enterprise analysts, and line of enterprise customers leverage knowledge lake to discover, refine, and analyze petabytes of information. AWS Glue is a serverless knowledge integration service that makes it simple to find, put together, and mix knowledge for analytics, machine studying, and utility growth. Clients use AWS Glue to find and extract knowledge from quite a lot of knowledge sources, enrich and cleanse the information earlier than storing it in knowledge lakes and knowledge warehouses.

Over years, many desk codecs have emerged to help ACID transaction, governance, and catalog usecases. For instance, codecs reminiscent of Apache Hudi, Delta Lake, Apache Iceberg, and AWS Lake Formation ruled tables, enabled prospects to run ACID transactions on Amazon Easy Storage Service (Amazon S3). AWS Glue helps these desk codecs for batch and streaming workloads. This put up focuses on Apache Hudi, Delta Lake, and Apache Iceberg, and summarizes methods to use them in AWS Glue 3.0 jobs. For those who’re taken with AWS Lake Formation ruled tables, then go to Efficient knowledge lakes utilizing AWS Lake Formation sequence.

Deliver libraries for the information lake codecs

In the present day, there are three out there choices for bringing libraries for the information lake codecs on the AWS Glue job platform: Market connectors, customized connectors (BYOL), and additional library dependencies.

Market connectors

AWS Glue Connector Market is the centralized repository for cataloging the out there Glue connectors supplied by a number of distributors. You possibly can subscribe to greater than 60 connectors provided in AWS Glue Connector Market as of in the present day. There are market connectors out there for Apache Hudi, Delta Lake, and Apache Iceberg. Moreover, {the marketplace} connectors are hosted on Amazon Elastic Container Registry (Amazon ECR) repository, and downloaded to the Glue job system in runtime. If you choose easy consumer expertise by subscribing to the connectors and utilizing them in your Glue ETL jobs, {the marketplace} connector is an efficient possibility.

Customized connectors as bring-your-own-connector (BYOC)

AWS Glue customized connector lets you add and register your personal libraries situated in Amazon S3 as Glue connectors. You have got extra management over the library variations, patches, and dependencies. Because it makes use of your S3 bucket, you possibly can configure the S3 bucket coverage to share the libraries solely with particular customers, you possibly can configure non-public community entry to obtain the libraries utilizing VPC Endpoints, and so on. If you choose having extra management over these configurations, the customized connector as BYOC is an efficient possibility.

Further library dependencies

There’s another choice – to obtain the information lake format libraries, add them to your S3 bucket, and add additional library dependencies to them. With this feature, you possibly can add libraries on to the job with no connector and use them. In Glue job, you possibly can configure in Dependent JARs path. In API, it’s the --extra-jars parameter. In Glue Studio pocket book, you possibly can configure within the %extra_jars magic. To obtain the related JAR recordsdata, see the library areas within the part Create a Customized connection (BYOC).

Create a Market connection

To create a brand new market connection for Apache Hudi, Delta Lake, or Apache Iceberg, full the next steps.

Apache Hudi 0.10.1

Full the next steps to create a market connection for Apache Hudi 0.10.1:

  1. Open AWS Glue Studio.
  2. Select Connectors.
  3. Select Go to AWS Market.
  4. Seek for Apache Hudi Connector for AWS Glue, and select Apache Hudi Connector for AWS Glue.
  5. Select Proceed to Subscribe.
  6. Evaluate the Phrases and circumstances, pricing, and different particulars, and select the Settle for Phrases button to proceed.
  7. Ensure that the subscription is full and also you see the Efficient date populated subsequent to the product, after which select Proceed to Configuration.
  8. For Supply Methodology, select Glue 3.0.
  9. For Software program model, select 0.10.1.
  10. Select Proceed to Launch.
  11. Underneath Utilization instructions, select Activate the Glue connector in AWS Glue Studio. You’re redirected to AWS Glue Studio.
  12. For Title, enter a reputation in your connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Delta Lake 1.0.0

Full the next steps to create a market connection for Delta Lake 1.0.0:

  1. Open AWS Glue Studio.
  2. Select Connectors.
  3. Select Go to AWS Market.
  4. Seek for Delta Lake Connector for AWS Glue, and select Delta Lake Connector for AWS Glue.
  5. Select Proceed to Subscribe.
  6. Evaluate the Phrases and circumstances, pricing, and different particulars, and select the Settle for Phrases button to proceed.
  7. Ensure that the subscription is full and also you see the Efficient date populated subsequent to the product, after which select Proceed to Configuration.
  8. For Supply Methodology, select Glue 3.0.
  9. For Software program model, select 1.0.0-2.
  10. Select Proceed to Launch.
  11. Underneath Utilization instructions, select Activate the Glue connector in AWS Glue Studio. You’re redirected to AWS Glue Studio.
  12. For Title, enter a reputation in your connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Apache Iceberg 0.12.0

Full the next steps to create a market connection for Apache Iceberg 0.12.0:

  1. Open AWS Glue Studio.
  2. Select Connectors.
  3. Select Go to AWS Market.
  4. Seek for Apache Iceberg Connector for AWS Glue, and select Apache Iceberg Connector for AWS Glue.
  5. Select Proceed to Subscribe.
  6. Evaluate the Phrases and circumstances, pricing, and different particulars, and select the Settle for Phrases button to proceed.
  7. Ensure that the subscription is full and also you see the Efficient date populated subsequent to the product, after which select Proceed to Configuration.
  8. For Supply Methodology, select Glue 3.0.
  9. For Software program model, select 0.12.0-2.
  10. Select Proceed to Launch.
  11. Underneath Utilization instructions, select Activate the Glue connector in AWS Glue Studio. You’re redirected to AWS Glue Studio.
  12. For Title, enter iceberg-0120-mp-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Create a Customized connection (BYOC)

You possibly can create your personal customized connectors from JAR recordsdata. On this part, you possibly can see the precise JAR recordsdata which are used within the market connectors. You possibly can simply use the recordsdata in your customized connectors for Apache Hudi, Delta Lake, and Apache Iceberg.

To create a brand new customized connection for Apache Hudi, Delta Lake, or Apache Iceberg, full the next steps.

Apache Hudi 0.9.0

Full following steps to create a customized connection for Apache Hudi 0.9.0:

  1. Obtain the next JAR recordsdata, and add them to your S3 bucket.
    1. https://repo1.maven.org/maven2/org/apache/hudi/hudi-spark3-bundle_2.12/0.9.0/hudi-spark3-bundle_2.12-0.9.0.jar
    2. https://repo1.maven.org/maven2/org/apache/hudi/hudi-utilities-bundle_2.12/0.9.0/hudi-utilities-bundle_2.12-0.9.0.jar
    3. https://repo1.maven.org/maven2/org/apache/parquet/parquet-avro/1.10.1/parquet-avro-1.10.1.jar
    4. https://repo1.maven.org/maven2/org/apache/spark/spark-avro_2.12/3.1.1/spark-avro_2.12-3.1.1.jar
    5. https://repo1.maven.org/maven2/org/apache/calcite/calcite-core/1.10.0/calcite-core-1.10.0.jar
    6. https://repo1.maven.org/maven2/org/datanucleus/datanucleus-core/4.1.17/datanucleus-core-4.1.17.jar
    7. https://repo1.maven.org/maven2/org/apache/thrift/libfb303/0.9.3/libfb303-0.9.3.jar
  2. Open AWS Glue Studio.
  3. Select Connectors.
  4. Select Create customized connector.
  5. For Connector S3 URL, enter comma separated Amazon S3 paths for the above JAR recordsdata.
  6. For Title, enter hudi-090-byoc-connector.
  7. For Connector Kind, select Spark.
  8. For Class identify, enter org.apache.hudi.
  9. Select Create connector.
  10. Select hudi-090-byoc-connector.
  11. Select Create connection.
  12. For Title, enter hudi-090-byoc-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Apache Hudi 0.10.1

Full the next steps to create a customized connection for Apache Hudi 0.10.1:

  1. Obtain following JAR recordsdata, and add them to your S3 bucket.
    1. hudi-utilities-bundle_2.12-0.10.1.jar
    2. hudi-spark3.1.1-bundle_2.12-0.10.1.jar
    3. spark-avro_2.12-3.1.1.jar
  2. Open AWS Glue Studio.
  3. Select Connectors.
  4. Select Create customized connector.
  5. For Connector S3 URL, enter comma separated Amazon S3 paths for the above JAR recordsdata.
  6. For Title, enter hudi-0101-byoc-connector.
  7. For Connector Kind, select Spark.
  8. For Class identify, enter org.apache.hudi.
  9. Select Create connector.
  10. Select hudi-0101-byoc-connector.
  11. Select Create connection.
  12. For Title, enter hudi-0101-byoc-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Word that the above Hudi 0.10.1 set up on Glue 3.0 doesn’t absolutely help Merge On Learn (MoR) tables.

Delta Lake 1.0.0

Full the next steps to create a customized connector for Delta Lake 1.0.0:

  1. Obtain the next JAR file, and add it to your S3 bucket.
    1. https://repo1.maven.org/maven2/io/delta/delta-core_2.12/1.0.0/delta-core_2.12-1.0.0.jar
  2. Open AWS Glue Studio.
  3. Select Connectors.
  4. Select Create customized connector.
  5. For Connector S3 URL, enter a comma separated Amazon S3 path for the above JAR file.
  6. For Title, enter delta-100-byoc-connector.
  7. For Connector Kind, select Spark.
  8. For Class identify, enter org.apache.spark.sql.delta.sources.DeltaDataSource.
  9. Select Create connector.
  10. Select delta-100-byoc-connector.
  11. Select Create connection.
  12. For Title, enter delta-100-byoc-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Apache Iceberg 0.12.0

Full the next steps to create a customized connection for Apache Iceberg 0.12.0:

  1. Obtain the next JAR recordsdata, and add them to your S3 bucket.
    1. https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark3-runtime/0.12.0/iceberg-spark3-runtime-0.12.0.jar
    2. https://repo1.maven.org/maven2/software program/amazon/awssdk/bundle/2.15.40/bundle-2.15.40.jar
    3. https://repo1.maven.org/maven2/software program/amazon/awssdk/url-connection-client/2.15.40/url-connection-client-2.15.40.jar
  2. Open AWS Glue Studio.
  3. Select Connectors.
  4. Select Create customized connector.
  5. For Connector S3 URL, enter comma separated Amazon S3 paths for the above JAR recordsdata.
  6. For Title, enter iceberg-0120-byoc-connector.
  7. For Connector Kind, select Spark.
  8. For Class identify, enter iceberg.
  9. Select Create connector.
  10. Select iceberg-0120-byoc-connector.
  11. Select Create connection.
  12. For Title, enter iceberg-0120-byoc-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Apache Iceberg 0.13.1

Full the next steps to create a customized connection for Apache Iceberg 0.13.1:

  1. Obtain the next JAR recordsdata, and add them to your S3 bucket.
    1. iceberg-spark-runtime-3.1_2.12-0.13.1.jar
    2. https://repo1.maven.org/maven2/software program/amazon/awssdk/bundle/2.17.161/bundle-2.17.161.jar
    3. https://repo1.maven.org/maven2/software program/amazon/awssdk/url-connection-client/2.17.161/url-connection-client-2.17.161.jar
  2. Open AWS Glue Studio.
  3. Select Connectors.
  4. Select Create customized connector.
  5. For Connector S3 URL, enter comma separated Amazon S3 paths for the above JAR recordsdata.
  6. For Title, enter iceberg-0131-byoc-connector.
  7. For Connector Kind, select Spark.
  8. For Class identify, enter iceberg.
  9. Select Create connector.
  10. Select iceberg-0131-byoc-connector.
  11. Select Create connection.
  12. For Title, enter iceberg-0131-byoc-connection.
  13. Optionally, select a VPC, subnet, and safety group.
  14. Select Create connection.

Conditions

To proceed this tutorial, it’s essential to create the next AWS sources prematurely:

  • AWS Id and Entry Administration (IAM) function in your ETL job or pocket book as instructed in Arrange IAM permissions for AWS Glue Studio. Word that AmazonEC2ContainerRegistryReadOnly or equal permissions are wanted once you use {the marketplace} connectors.
  • Amazon S3 bucket for storing knowledge.
  • Glue connection (one of many market connector or the customized connector equivalent to the information lake format).

Reads/writes utilizing the connector on AWS Glue Studio Pocket book

The next are the directions to learn/write tables utilizing every knowledge lake format on AWS Glue Studio Pocket book. As a prerequisite, just remember to have created a connector and a connection for the connector utilizing the data above.
The instance notebooks are hosted on AWS Glue Samples GitHub repository. You will discover 7 notebooks out there. Within the following directions, we’ll use one pocket book per knowledge lake format.

Apache Hudi

To learn/write Apache Hudi tables within the AWS Glue Studio pocket book, full the next:

  1. Obtain hudi_dataframe.ipynb.
  2. Open AWS Glue Studio.
  3. Select Jobs.
  4. Select Jupyter pocket book after which select Add and edit an current pocket book. From Select file, choose your ipynb file and select Open, then select Create.
  5. On the Pocket book setup web page, for Job identify, enter your job identify.
  6. For IAM function, choose your IAM function. Select Create job. After a short while interval, the Jupyter pocket book editor seems.
  7. Within the first cell, substitute the placeholder together with your Hudi connection identify, and run the cell:
    %connections hudi-0101-byoc-connection (Alternatively you need to use your connection identify created from {the marketplace} connector).
  8. Within the second cell, substitute the S3 bucket identify placeholder together with your S3 bucket identify, and run the cell.
  9. Run the cells within the part Initialize SparkSession.
  10. Run the cells within the part Clear up current sources.
  11. Run the cells within the part Create Hudi desk with pattern knowledge utilizing catalog sync to create a brand new Hudi desk with pattern knowledge.
  12. Run the cells within the part Learn from Hudi desk to confirm the brand new Hudi desk. There are 5 data on this desk.
  13. Run the cells within the part Upsert data into Hudi desk to see how upsert works on Hudi. This code inserts one new report, and updates the one current report. You possibly can confirm that there’s a new report product_id=00006, and the prevailing report product_id=00001’s value has been up to date from 250 to 400.
  14. Run the cells within the part Delete a Document. You possibly can confirm that the prevailing report product_id=00001 has been deleted.
  15. Run the cells within the part Time limit question. You possibly can confirm that you simply’re seeing the earlier model of the desk the place the upsert and delete operations haven’t been utilized but.
  16. Run the cells within the part Incremental Question. You possibly can confirm that you simply’re seeing solely the latest commit about product_id=00006.

On this pocket book, you possibly can full the essential Spark DataFrame operations on Hudi tables.

Delta Lake

To learn/write Delta Lake tables within the AWS Glue Studio pocket book, full following:

  1. Obtain delta_sql.ipynb.
  2. Open AWS Glue Studio.
  3. Select Jobs.
  4. Select Jupyter pocket book, after which select Add and edit an current pocket book. From Select file, choose your ipynb file and select Open, then select Create.
  5. On the Pocket book setup web page, for Job identify, enter your job identify.
  6. For IAM function, choose your IAM function. Select Create job. After a short while interval, the Jupyter pocket book editor seems.
  7. Within the first cell, substitute the placeholder together with your Delta connection identify, and run the cell:
    %connections delta-100-byoc-connection
  8. Within the second cell, substitute the S3 bucket identify placeholder together with your S3 bucket identify, and run the cell.
  9. Run the cells within the part Initialize SparkSession.
  10. Run the cells within the part Clear up current sources.
  11. Run the cells within the part Create Delta desk with pattern knowledge to create a brand new Delta desk with pattern knowledge.
  12. Run the cells within the part Create a Delta Lake desk.
  13. Run the cells within the part Learn from Delta Lake desk to confirm the brand new Delta desk. There are 5 data on this desk.
  14. Run the cells within the part Insert data. The question inserts two new data: record_id=00006, and record_id=00007.
  15. Run the cells within the part Replace data. The question updates the value of the prevailing data record_id=00007, and record_id=00007 from 500 to 300.
  16. Run the cells within the part Upsert data. to see how upsert works on Delta. This code inserts one new report, and updates the one current report. You possibly can confirm that there’s a new report product_id=00008, and the prevailing report product_id=00001’s value has been up to date from 250 to 400.
  17. Run the cells within the part Alter DeltaLake desk. The queries add one new column, and replace the values within the column.
  18. Run the cells within the part Delete data. You possibly can confirm that the report product_id=00006 as a result of it’s product_name is Pen.
  19. Run the cells within the part View Historical past to explain the historical past of operations that was triggered in opposition to the goal Delta desk.

On this pocket book, you possibly can full the essential Spark SQL operations on Delta tables.

Apache Iceberg

To learn/write Apache Iceberg tables within the AWS Glue Studio pocket book, full the next:

  1. Obtain iceberg_sql.ipynb.
  2. Open AWS Glue Studio.
  3. Select Jobs.
  4. Select Jupyter pocket book after which select Add and edit an current pocket book. From Select file, choose your ipynb file and select Open, then select Create.
  5. On the Pocket book setup web page, for Job identify, enter your job identify.
  6. For IAM function, choose your IAM function. Select Create job. After a short while interval, the Jupyter pocket book editor seems.
  7. Within the first cell, substitute the placeholder together with your Delta connection identify, and run the cell:
    %connections iceberg-0131-byoc-connection (Alternatively you need to use your connection identify created from {the marketplace} connector).
  8. Within the second cell, substitute the S3 bucket identify placeholder together with your S3 bucket identify, and run the cell.
  9. Run the cells within the part Initialize SparkSession.
  10. Run the cells within the part Clear up current sources.
  11. Run the cells within the part Create Iceberg desk with pattern knowledge to create a brand new Iceberg desk with pattern knowledge.
  12. Run the cells within the part Learn from Iceberg desk.
  13. Run the cells within the part Upsert data into Iceberg desk.
  14. Run the cells within the part Delete data.
  15. Run the cells within the part View Historical past and Snapshots.

On this pocket book, you possibly can full the essential Spark SQL operations on Iceberg tables.

Conclusion

This put up summarized methods to make the most of Apache Hudi, Delta Lake, and Apache Iceberg on AWS Glue platform, in addition to display how every format works with a Glue Studio pocket book. You can begin utilizing these knowledge lake codecs simply in Spark DataFrames and Spark SQL on the Glue jobs or the Glue Studio notebooks.

This put up targeted on interactive coding and querying on notebooks. The upcoming half 2 will concentrate on the expertise utilizing AWS Glue Studio Visible Editor and Glue DynamicFrames for purchasers preferring visible authoring with out the necessity to write code.


In regards to the Authors

Noritaka Sekiyama is a Principal Large Knowledge Architect on the AWS Glue crew. He enjoys studying completely different use circumstances from prospects and sharing information about huge knowledge applied sciences with the broader neighborhood.

Dylan Qu is a Specialist Options Architect targeted on Large Knowledge & Analytics with AWS. He helps prospects architect and construct extremely scalable, performant, and safe cloud-based options on AWS.

Monjumi Sarma is a Knowledge Lab Options Architect at AWS. She helps prospects architect knowledge analytics options, which supplies them an accelerated path in direction of modernization initiatives.

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