Right now, we’re happy to announce new AWS Glue connectors for Azure Blob Storage and Azure Knowledge Lake Storage that will let you transfer knowledge bi-directionally between Azure Blob Storage, Azure Knowledge Lake Storage, and Amazon Easy Storage Service (Amazon S3).

We’ve seen a requirement to design functions that allow knowledge to be transportable throughout cloud environments and provide the means to derive insights from a number of knowledge sources. One of many knowledge sources now you can rapidly combine with is Azure Blob Storage, a managed service for storing each unstructured knowledge and structured knowledge, and Azure Knowledge Lake Storage, an information lake for analytics workloads. With these connectors, you may deliver the info from Azure Blob Storage and Azure Knowledge Lake Storage individually to Amazon S3.

On this submit, we use Azure Blob Storage for example and exhibit how the brand new connector works, introduce the connector’s features, and give you key steps to set it up. We give you conditions, share methods to subscribe to this connector in AWS Market, and describe methods to create and run AWS Glue for Apache Spark jobs with it. Concerning the Azure Knowledge Lake Storage Gen2 Connector, we spotlight any main variations on this submit.

AWS Glue is a serverless knowledge integration service that makes it easy to find, put together, and mix knowledge for analytics, machine studying, and utility growth. AWS Glue natively integrates with numerous knowledge shops resembling MySQL, PostgreSQL, MongoDB, and Apache Kafka, together with AWS knowledge shops resembling Amazon S3, Amazon Redshift, Amazon Relational Database Service (Amazon RDS), and Amazon DynamoDB. AWS Glue Market connectors will let you uncover and combine further knowledge sources, resembling software program as a service (SaaS) functions and your customized knowledge sources. With only a few clicks, you may seek for and choose connectors from AWS Market and start your knowledge preparation workflow in minutes.

How the connectors work

On this part, we focus on how the brand new connectors work.

Azure Blob Storage connector

This connector depends on the Spark DataSource API and calls Hadoop’s FileSystem interface. The latter has applied libraries for studying and writing numerous distributed or conventional storage. This connector additionally contains the hadoop-azure module, which helps you to run Apache Hadoop or Apache Spark jobs straight with knowledge in Azure Blob Storage. AWS Glue hundreds the library from the Amazon Elastic Container Registry (Amazon ECR) repository throughout initialization (as a connector), reads the connection credentials utilizing AWS Secrets and techniques Supervisor, and reads knowledge supply configurations from enter parameters. When AWS Glue has web entry, the Spark job in AWS Glue can learn from and write to Azure Blob Storage.

We assist the next two strategies for authentication: the authentication key for Shared Key and shared entry signature (SAS) tokens:

#Technique 1:Shared Key, 
spark.conf.set("fs.azure.account.key.youraccountname.blob.core.home windows.internet", "*Your account key")

df = spark.learn.format("csv").possibility("header","true").load("wasbs://[email protected] windows.internet/loadingtest-input/100mb")

df.write.format("csv").possibility("compression","snappy").mode("overwrite").save("wasbs://<container_name>@<account_name>.blob.core.home windows.internet/output-CSV/20210831/")

#Technique 2:Shared Entry Signature (SAS) Tokens
spark.conf.set("fs.azure.sas.yourblob.youraccountname.blob.core.home windows.internet", "Your SAS token*")

df = spark.learn.format("csv").possibility("header","true").load("wasbs://[email protected] windows.internet/loadingtest-input/100mb")

df.write.format("csv").possibility("compression","snappy").mode("overwrite").save("wasbs://<container_name>@<account_name>.blob.core.home windows.internet/output-CSV/20210831/")

Azure Knowledge Lake Storage Gen2 connector

The utilization of Azure Knowledge Lake Storage Gen2 is way the identical because the Azure Blob Storage connector. The Azure Knowledge Lake Storage Gen2 connector makes use of the identical library because the Azure Blob Storage connector, and depends on the Spark DataSource API, Hadoop’s FileSystem interface, and the Azure Blob Storage connector for Hadoop.

As of this writing, we solely assist the Shared Key authentication methodology:

#Technique: Shared Key 
spark.conf.set("fs.azure.account.key.youraccountname.dfs.core.home windows.internet", "*Your account key")

# Learn file from ADLS instance
df= spark.learn.format("csv").possibility("header","true").load("abfss://<container_name>@<account_name>.dfs.core.home windows.internet/input-csv/covid/") 

# Write file to ADLS instance
df.write.format("parquet").possibility("compression","snappy").partitionBy("state").mode("overwrite").save("abfss://<container_name>@<account_name>.dfs.core.home windows.internet/output-glue3/csv-partitioned/") 

Resolution overview

The next structure diagram reveals how AWS Glue connects to Azure Blob Storage for knowledge ingestion.

Within the following sections, we present you methods to create a brand new secret for Azure Blob Storage in Secrets and techniques Supervisor, subscribe to the AWS Glue connector, and transfer knowledge from Azure Blob Storage to Amazon S3.

Stipulations

You want the next conditions:

• A storage account in Microsoft Azure and your knowledge path in Azure Blob Storage. Put together the storage account credentials prematurely. For directions, check with Create a storage account shared key.
• A Secrets and techniques Supervisor secret to retailer a Shared Key secret, utilizing one of many supporting authentication strategies.
• An AWS Identification and Entry Administration (IAM) function for the AWS Glue job with the next insurance policies:
  • AWSGlueServiceRole, which permits the AWS Glue service function entry to associated companies.
  • AmazonEC2ContainerRegistryReadOnly, which gives read-only entry to Amazon EC2 Container Registry repositories. This coverage is for utilizing AWS Market’s connector libraries.
  • A Secrets and techniques Supervisor coverage, which gives learn entry to the key in Secrets and techniques Supervisor.
  • An S3 bucket coverage for the S3 bucket that you want to load ETL (extract, remodel, and cargo) knowledge from Azure Blob Storage.

Create a brand new secret for Azure Blob Storage in Secrets and techniques Supervisor

Full the next steps to create a secret in Secrets and techniques Supervisor to retailer the Azure Blob Storage connection strings utilizing the Shared Key authentication methodology:

1. On the Secrets and techniques Supervisor console, select Secrets and techniques within the navigation pane.
2. Select Retailer a brand new secret.
3. For Secret sort, choose Different sort of secret.
4. Exchange the values for accountName, accountKey, and container with your personal values.
   
5. Depart the remainder of the choices at their default.
6. Select Subsequent.
7. Present a reputation for the key, resembling azureblobstorage_credentials.
8. Comply with the remainder of the steps to retailer the key.

Subscribe to the AWS Glue connector for Azure Blob Storage

To subscribe to the connector, full the next steps:

1. Navigate to the Azure Blob Storage Connector for AWS Glue on AWS Market.
2. On the product web page for the connector, use the tabs to view details about the connector, then select Proceed to Subscribe.
   
3. Evaluation the pricing phrases and the vendor’s Finish Person License Settlement, then select Settle for Phrases.
   
4. Proceed to the subsequent step by selecting Proceed to Configuration.
   
5. On the Configure this software program web page, select the achievement choices and the model of the connector to make use of.

Now we have supplied two choices for the Azure Blob Storage Connector: AWS Glue 3.0 and AWS Glue 4.0. On this instance, we deal with AWS Glue 4.0. Select Proceed to Launch.

6. On the Launch this software program web page, select Utilization directions to overview the utilization directions supplied by AWS.
7. If you’re able to proceed, select Activate the Glue connector from AWS Glue Studio.
   

The console will show the Create market connection web page in AWS Glue Studio.

Transfer knowledge from Azure Blob Storage to Amazon S3

To maneuver your knowledge to Amazon S3, you need to configure the customized connection after which arrange an AWS Glue job.

Create a customized connection in AWS Glue

An AWS Glue connection shops connection info for a specific knowledge retailer, together with login credentials, URI strings, digital non-public cloud (VPC) info, and extra. Full the next steps to create your connection:

1. On the AWS Glue console, select Connectors within the navigation pane.
2. Select Create connection.
3. For Connector, select Azure Blob Storage Connector for AWS Glue.
4. For Title, enter a reputation for the connection (for instance, AzureBlobStorageConnection).
5. Enter an elective description.
6. For AWS secret, enter the key you created (azureblobstorage_credentials).
7. Select Create connection and activate connector.

The connector and connection info is now seen on the Connectors web page.

Create an AWS Glue job and configure connection choices

Full the next steps:

1. On the AWS Glue console, select Connectors within the navigation pane.
2. Select the connection you created (AzureBlobStorageConnection).
3. Select Create job.
   
4. For Title, enter Azure Blob Storage Connector for AWS Glue. This title needs to be distinctive amongst all of the nodes for this job.
5. For Connection, select the connection you created (AzureBlobStorageConnection).
6. For Key, enter path, and for Worth, enter your Azure Blob Storage URI. For instance, after we created our new secret, we already set a container worth for the Azure Blob Storage. Right here, we enter the file path /input_data/.
7. Enter one other key-value pair. For Key, enter fileFormat. For Worth, enter csv, as a result of our pattern knowledge is on this format.
8. Optionally, if the CSV file incorporates a header line, enter one other key-value pair. For Key, enter header. For Worth, enter true.
9. To preview your knowledge, select the Knowledge preview tab, then select Begin knowledge preview session and select the IAM function outlined within the conditions.
10. Select Affirm and await the outcomes to show.
    
11. Choose S3 as Goal Location.
12. Select Browse S3 to see the S3 buckets that you’ve got entry to and select one because the goal vacation spot for the info output.
    
13. For the opposite choices, use the default values.
    
14. On the Job particulars tab, for IAM Position, select the IAM function outlined within the conditions.
15. For Glue model, select your AWS Glue model.
16. Proceed to create your ETL job. For directions, check with Creating ETL jobs with AWS Glue Studio.
17. Select Run to run your job.
    

When the job is full, you may navigate to the Run particulars web page on the AWS Glue console and test the logs in Amazon CloudWatch.

The information is ingested into Amazon S3, as proven within the following screenshot. We are actually capable of import knowledge from Azure Blob Storage to Amazon S3.

Scaling issues

On this instance, we use the default AWS Glue capability, 10 DPU (Knowledge Processing Items). A DPU is a standardized unit of processing capability that consists of 4 vCPUs of compute capability and 16 GB of reminiscence. To scale your AWS Glue job, you may enhance the variety of DPU, and in addition reap the benefits of Auto Scaling. With Auto Scaling enabled, AWS Glue robotically provides and removes employees from the cluster relying on the workload. After you select the utmost variety of employees, AWS Glue will adapt the appropriate measurement of assets for the workload.

Clear up

To scrub up your assets, full the next steps:

1. Take away the AWS Glue job and secret in Secrets and techniques Supervisor with the next command:

   aws glue delete-job —job-name <your_job_name>
   
   aws glue delete-connection —connection-name <your_connection_name>
   
   aws secretsmanager delete-secret —secret-id <your_secretsmanager_id>

2. In case you are now not going to make use of this connector, you may cancel the subscription to the Azure Blob Storage connector:
   1. On the AWS Market console, go to the Handle subscriptions web page.
   2. Choose the subscription for the product that you just wish to cancel.
   3. On the Actions menu, select Cancel subscription.
   4. Learn the knowledge supplied and choose the acknowledgement test field.
   5. Select Sure, cancel subscription.
3. Delete the info within the S3 bucket that you just used within the earlier steps.

Conclusion

On this submit, we confirmed methods to use AWS Glue and the brand new connector for ingesting knowledge from Azure Blob Storage to Amazon S3. This connector gives entry to Azure Blob Storage, facilitating cloud ETL processes for operational reporting, backup and catastrophe restoration, knowledge governance, and extra.

We welcome any suggestions or questions within the feedback part.

Appendix

If you want SAS token authentication for Azure Knowledge Lake Storage Gen 2, you need to use Azure SAS Token Supplier for Hadoop. To try this, add the JAR file to your S3 bucket and configure your AWS Glue job to set the S3 location within the job parameter --extra-jars (in AWS Glue Studio, Dependent JARs path). Then save the SAS token in Secrets and techniques Supervisor and set the worth to spark.hadoop.fs.azure.sas.mounted.token.<azure storage account>.dfs.core.home windows.internet in SparkConf utilizing script mode at runtime. Be taught extra in README.

from pyspark.sql import SparkSession
spark = SparkSession.builder 
.config("spark.hadoop.fs.azure.sas.mounted.token.<azure storage account>.dfs.core.home windows.ne", sas_token) 
.getOrCreate()

References

Concerning the authors

Qiushuang Feng is a Options Architect at AWS, liable for Enterprise clients’ technical structure design, consulting, and design optimization on AWS Cloud companies. Earlier than becoming a member of AWS, Qiushuang labored in IT firms resembling IBM and Oracle, and accrued wealthy sensible expertise in growth and analytics.

Noritaka Sekiyama is a Principal Large Knowledge Architect on the AWS Glue crew. He’s enthusiastic about architecting fast-growing knowledge environments, diving deep into distributed huge knowledge software program like Apache Spark, constructing reusable software program artifacts for knowledge lakes, and sharing information in AWS Large Knowledge weblog posts.

Shengjie Luo is a Large Knowledge Architect on the Amazon Cloud Know-how skilled service crew. They’re liable for options consulting, structure, and supply of AWS-based knowledge warehouses and knowledge lakes. They’re expert in serverless computing, knowledge migration, cloud knowledge integration, knowledge warehouse planning, and knowledge service structure design and implementation.

Greg Huang is a Senior Options Architect at AWS with experience in technical structure design and consulting for the China G1000 crew. He’s devoted to deploying and using enterprise-level functions on AWS Cloud companies. He possesses practically 20 years of wealthy expertise in large-scale enterprise utility growth and implementation, having labored within the cloud computing discipline for a few years. He has in depth expertise in serving to numerous varieties of enterprises migrate to the cloud. Previous to becoming a member of AWS, he labored for well-known IT enterprises resembling Baidu and Oracle.

Maciej Torbus is a Principal Buyer Options Supervisor inside Strategic Accounts at Amazon Net Providers. With in depth expertise in large-scale migrations, he focuses on serving to clients transfer their functions and programs to extremely dependable and scalable architectures in AWS. Exterior of labor, he enjoys crusing, touring, and restoring classic mechanical watches.