AWS Glue FAQs

AWS Glue is a serverless data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning (ML), and application development. AWS Glue provides all the capabilities needed for data integration, so you can start analyzing your data and putting it to use in minutes instead of months. AWS Glue provides both visual and code-based interfaces to make data integration easier. Users can more easily find and access data using the AWS Glue Data Catalog. Data engineers and ETL (extract, transform, and load) developers can visually create, run, and monitor ETL workflows in a few steps in AWS Glue Studio. Data analysts and data scientists can use AWS Glue DataBrew to visually enrich, clean, and normalize data without writing code.

To start using AWS Glue, sign in to the AWS Management Console and navigate to Glue under the Analytics category. You can follow one of our guided tutorials that will walk you through an example use case for AWS Glue. You can also find sample ETL code in our GitHub repository under AWS Labs.

AWS Glue consists of a Data Catalog, which is a central metadata repository; a data processing engine that runs Scala or Python code; a flexible scheduler that handles dependency resolution, job monitoring, and retries; Together, these features automate much of the undifferentiated heavy lifting involved with discovering, categorizing, cleaning, enriching, and moving data, so you can spend more time analyzing your data.

Use AWS Glue to discover properties of the data you own, transform it, and prepare it for analytics. AWS Glue can automatically discover both structured and semi-structured data stored in your data lake on Amazon Simple Storage Service (Amazon S3), data warehouse in Amazon Redshift, Amazon SageMaker Lakehouse, and various databases running on AWS. It provides a unified view of your data via the Data Catalog that is available for ETL, querying, and reporting using services like Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. AWS Glue automatically generates Scala or Python code for your ETL jobs that you can further customize using tools that you are already familiar with.

AWS Glue can integrate with more than 80 data sources on AWS, on premises, and on other clouds. The service natively supports data stored in the following databases in your Amazon Virtual Private Cloud (Amazon VPC) running on Amazon Elastic Compute Cloud (Amazon EC2):

• Amazon Aurora

• Amazon RDS for MySQL

• Amazon RDS for Oracle

• Amazon RDS for PostgreSQL

• Amazon RDS for SQL Server

• Amazon Redshift

• Amazon DynamoDB

• Amazon S3

• MySQL, Oracle, Microsoft SQL Server, and PostgreSQL

AWS Glue also supports data streams from Amazon Managed Streaming for Apache Kafka (Amazon MSK), Amazon Kinesis Data Streams, and Apache Kafka. AWS Glue natively supports 20 SaaS applications such as Salesforce, SAP, Zendesk and Service now. In addition, you can add connectors, including Snowflake, GCP BigQuery, and Teradata, from the AWS Marketplace. You can also write custom Scala or Python code and import custom libraries and Jar files into your AWS Glue ETL jobs to access data sources not natively supported by AWS Glue. For more details on importing custom libraries, refer to our documentation.

To create a connection to a data source, navigate to the data connections page in the AWS Glue console and use the Create connection wizard functionality to create a connection. As part of connection creation wizard, you can test connections and validate access credentials. On the data connections page, you can access all data connections you have permission to including the data connection created in SageMaker Unified Studio (Preview).

Lake Formation uses a shared infrastructure with AWS Glue, including console controls, ETL code creation and job monitoring, a common Data Catalog, and serverless architecture. While AWS Glue is still focused on these types of functions, Lake Formation encompasses AWS Glue features and provides additional capabilities designed to help build, secure, and manage a data lake. See the AWS Lake Formation pages for more details.

Amazon SageMaker Data Processing and Analytics analyzes, prepares, integrates and orchestrates your data with a unified experience in Amazon SageMaker, bringing together data processing capabilities fromAmazon Athena, Amazon EMR, AWS Glue, and Amazon Managed Workflows for Apache Airflow.

The Data Catalog is a central repository to store structural and operational metadata for all your data assets. For a given dataset, you can store its table definition and physical location, add business-relevant attributes, and track how this data has changed over time.

The Data Catalog is Apache Hive Metastore compatible and is a drop-in replacement for the Apache Hive Metastore for Big Data applications running on Amazon EMR. For more information about setting up your Amazon EMR cluster to use the Data Catalog as an Apache Hive Metastore, read the AWS Glue Documentation.

The Data Catalog also provides built-in integration with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. Once you add your table definitions to the Data Catalog, they are available for ETL and also readily available for querying in Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum so that you can have a common view of your data between these services.

AWS Glue provides numerous ways to populate metadata into the Data Catalog. AWS Glue crawlers scan various data stores that you own to automatically infer schemas, partition structure, and populate the Data Catalog with corresponding table definitions and statistics. You can also schedule crawlers to run periodically so that your metadata is always up to date and i -sync with the underlying data. Alternately, you can add and update table details manually by using the AWS Glue console or by calling the API. You can also run Hive DDL statements through the Amazon Athena console or a Hive client on an Amazon EMR cluster. Finally, if you already have a persistent Apache Hive Metastore, you can perform a bulk import of that metadata into the Data Catalog by using our import script.

An AWS Glue crawler connects to a data store, progresses through a prioritized list of classifiers to extract the schema of your data and other statistics, and then populates the Data Catalog with this metadata. Crawlers can run periodically to detect the availability of new data as well as changes to existing data, including table definition changes. Crawlers automatically add new tables, new partitions to existing table, and new versions of table definitions. You can customize AWS Glue crawlers to classify your own file types.

You can run an ETL job that reads from your Hive Metastore, exports the data to an intermediate format in Amazon S3, and then imports that data into the Data Catalog.

No. The Data Catalog is Hive Metastore compatible. You can point to the Data Catalog endpoint and use it as a Hive Metastore replacement. For more information about how to configure your cluster to use the Data Catalog as a Hive Metastore, read the AWS Glue Documentation.

Before you can start using the AWS Glue Data Catalog as a common metadata repository between Amazon Athena, Amazon Redshift Spectrum, and AWS Glue, you must upgrade your Amazon Athena data catalog to the AWS Glue Data Catalog. For steps required for the upgrade, read the Amazon Athena User Guide.

The metadata stored in the Data Catalog can be readily accessed from AWS Glue ETL, Amazon Athena, Amazon EMR, Amazon Redshift Spectrum, and third-party services.

Yes. AWS Glue Studio offers a graphical interface for authoring AWS Glue jobs to process your data. After you define the flow of your data sources, transformations and targets in the visual interface, AWS Glue Studio will generate Apache Spark code on your behalf.

You can use either Scala or Python.

The AWS Glue ETL script recommendation system generates Scala or Python code. It uses the AWS Glue custom ETL library to simplify access to data sources and manage job execution. You can find more details about the library in the AWS Glue Documentation. You can write ETL code using the AWS Glue custom library. You can also write arbitrary code in Scala or Python using inline editing through the AWS Glue console script editor, downloading the auto-generated code, and editing it in your own integrated development environment (IDE). You can also start with one of the many samples hosted in our GitHub repository and customize that code.

Yes. You can import custom Python libraries and Jar files into your AWS Glue ETL job. For more details, read the AWS Glue Documentation.

Yes. You can write your own code using the AWS Glue ETL library. You can also write your own Scala or Python code and upload it to an AWS Glue ETL job. For more details, read the AWS Glue Documentation.

You can create and connect to development endpoints that offer ways to connect your notebooks and IDEs.

In addition to the ETL library and code generation, AWS Glue provides a robust set of orchestration features that helps you manage dependencies between multiple jobs to build complete ETL workflows. AWS Glue ETL jobs can either be triggered on a schedule or on a job completion event. Multiple jobs can be triggered in parallel or sequentially by triggering them on a job completion event. You can also trigger one or more AWS Glue jobs from an external source such as an AWS Lambda function.

AWS Glue manages dependencies between two or more jobs or dependencies on external events using triggers. Triggers can watch one or more jobs as well as invoke one or more jobs. You can either have a scheduled trigger that invokes jobs periodically, an on-demand trigger, or a job completion trigger.

AWS Glue monitors job event metrics and errors, and pushes all notifications to Amazon CloudWatch. With CloudWatch, you can configure a host of actions that can be triggered based on specific notifications from AWS Glue. For example, if you get an error or a success notification from AWS Glue, you can trigger an AWS Lambda function. AWS Glue also provides default retry behavior that will retry all failures three times before sending an error notification.

Yes. You can run your existing Scala or Python code on AWS Glue. Simply upload the code to Amazon S3 and create one or more jobs that use that code. You can reuse the same code across multiple jobs by pointing them to the same code location on Amazon S3.

AWS Glue supports ETL on streams from Amazon Kinesis Data Streams, Apache Kafka, and Amazon MSK. Add the stream to the Data Catalog, and then choose it as the data source when setting up your AWS Glue job.

No. While we do believe that using both the Data Catalog and ETL provides a complete ETL experience, you can use either one of them independently without using the other.

Both AWS Glue and Kinesis Data Analytics can be used to process streaming data. AWS Glue is recommended when your use cases are primarily ETL and you want to run jobs on a serverless Apache Spark-based platform. Kinesis Data Analytics is recommended when your use cases are primarily analytics and you want to run jobs on a serverless Apache Flink-based platform.

Streaming ETL in AWS Glue enables advanced ETL on streaming data using the same serverless pay-as-you-go platform that you currently use for your batch jobs. AWS Glue generates customizable ETL code to prepare your data while in transit and has built-in functionality to process streaming data that is semi-structured or has an evolving schema. Use AWS Glue to apply both its built-in and Spark-native transforms to data streams and load them into your data lake or data warehouse.

Use Kinesis Data Analytics to build sophisticated streaming applications to analyze streaming data in real time. It provides a serverless Apache Flink runtime that automatically scales without servers and durably saves application state. Use Kinesis Data Analytics for real-time analytics and more general stream data processing.

Both AWS Glue and Kinesis Data Firehose can be used for streaming ETL. AWS Glue is recommended for complex ETL, including joining streams, and partitioning the output in Amazon S3 based on the data content. Kinesis Data Firehose is recommended when your use cases focus on data delivery and preparing data to be processed after it is delivered.

Streaming ETL in AWS Glue enables advanced ETL on streaming data using the same serverless pay-as-you-go platform that you currently use for your batch jobs. AWS Glue generates customizable ETL code to prepare your data while in transit and has built-in functionality to process streaming data that is semi-structured or has an evolving schema. Use AWS Glue to apply complex transforms to data streams, enrich records with information from other streams and persistent data stores, and then load records into your data lake or data warehouse.

Stream ETL in Kinesis Data Firehose to more easily capture, transform, and deliver streaming data. Kinesis Data Firehose provides ETL capabilities including serverless data transformation through AWS Lambda and format conversion from JSON to Parquet. It includes ETL capabilities that are designed to make data easier to process after delivery, but it does not include the advanced ETL capabilities that AWS Glue supports.

AWS Glue Data Quality is a feature of AWS Glue that reduces manual data quality effort by automatically measuring and monitoring the quality of data in data lakes and pipelines. In data lakes, it then automatically recommends data quality rules. You can modify these rules, add additional rules from built-in rule types, and configure actions to alert teams when quality issues occur. Rules can also be included in AWS Glue data pipelines and scheduled to run periodically. This feature then measures data quality by evaluating these rules and calculates data quality scores. You can view these data quality scores in the Data Catalog. Data quality issues can be remediated by modifying data pipelines and tracking quality score improvements using AWS Glue Data Quality.

Data quality is the measure of how well suited a dataset is to serve its specific purpose, such as analytics to improve operations, business decision making, and planning. Hundreds of thousands of customers use data lakes on AWS, but customers struggle to use their data assets effectively due to poor data quality.

AWS Glue Data Quality reduces the manual effort and time that it takes to set up data quality checks in your data lakes and pipelines. It automates the process of analyzing data to determine the appropriate data quality rules, and then it applies those checks on a schedule that you choose. It also provides built-in options for monitoring and alerting.

AWS Glue Data Quality currently supports 18 built-in rule types under four categories:

1. Consistency rules check if data across different columns agrees by looking at column correlations.

2. Accuracy rules check if record counts meet a set threshold and if columns are not empty, match certain patterns, have valid data types, and have valid values.

3. Integrity rules check if duplicates exist in a dataset.

4. Completeness rules check if data in your datasets do not have missing values.

These rule types are high-level groupings of data quality rules that address several use cases. For any missing requirements, you can author custom rules using SQL.

To get started, go to Data Quality in the Data Catalog and select a table. Then choose the Data Quality tab to get started. Alternatively, you can set up data quality rules within your pipelines by adding a Data Quality transform on AWS Glue Studio. You can also use APIs to set up data quality rules and run them.

AWS Glue Data Quality uses Deequ, an Amazon developed open-source framework that many Amazon teams use to manage the quality of Amazon internal datasets at petabyte scale. One Amazon team uses Deequ to check dataset quality in their 60 PB data lake. Deequ uses Apache Spark to gather data statistics, such as averages, correlations, patterns, and other advanced statistics. It then uses these statistics to identify the right set of checks or rules to validate data quality.

You can view and edit recommended rules in the Data Catalog. If you are using other AWS services, you can programmatically access your recommendations using the AWS Glue Data Quality API. You can also add new rules in the Data Catalog.

You can schedule the recommendation process to get new recommendations based on recent data. AWS Glue Data Quality will provide new recommendations based on recent data patterns.

You can use actions to respond to a data quality issue. In the Data Catalog, you can write the metrics to Amazon CloudWatch and set up alerts in CloudWatch to notify you when scores go below a threshold. On AWS Glue Studio, you can fail a job when quality deteriorates, preventing bad data from moving into data lakes. 

After you create data quality rules in the Data Catalog, you can create a data quality task and run it immediately or schedule it to run at certain intervals. Data quality rules on your pipelines evaluate your data quality as data is brought into your data lake through your pipelines.

You can make confident data-driven decisions using data quality scores. You can view data quality scores on the Data Quality tab of your table from the Data Catalog. You can view your data pipeline scores on AWS Glue Studio by opening an AWS Glue Studio job and choosing Data Quality. You can configure your data quality tasks to write results to an Amazon S3 bucket. You can then query this data using Amazon Athena or Amazon QuickSight.

Business analysts and data analysts use DataBrew to transform data without writing any code. Data stewards and data engineers use the Data Catalog to manage metadata. Data engineers use AWS Glue Studio to author scalable data integration pipelines. These user types must manage data quality in their workflows. Also, data engineers need more technical data quality rules compared to business analysts who write functional rules. Therefore, data quality features are made available in each of these experiences to meet unique user requirements.

Yes. Within AWS Glue Studio you may select the data preparation node that offers a visual data preparation tool that makes it easy for data analysts and data scientists to prepare data with an interactive, point-and-click visual interface without writing code. 

AWS Glue data preparation is built for users who need to clean and normalize data for analytics and ML. Data analysts and data scientists are the primary users. For data analysts, examples of job functions are business intelligence analysts, operations analysts, market intelligence analysts, legal analysts, financial analysts, economists, quants, or accountants. For data scientists, examples of job functions are materials scientists, bioanalytical scientists, and scientific researchers.

Yes. Within AWS Glue Studio you may select the data preparation node that offers a visual data preparation tool, import your existing DataBrew recipes and then continue to author them in AWS Glue Studio. You then can run them and enjoy the additional transformations that AWS Glue Studio offers.

DataBrew is a visual data preparation tool that makes it easy for data analysts and data scientists to prepare data with an interactive, point-and-click visual interface without writing code. With Glue DataBrew, you can easily visualize, clean, and normalize terabytes, and even petabytes of data directly from your data lake, data warehouses, and databases, including Amazon S3, Amazon Redshift, Amazon Aurora, and Amazon RDS. 

DataBrew is built for users who need to clean and normalize data for analytics and ML. Data analysts and data scientists are the primary users. For data analysts, examples of job functions are business intelligence analysts, operations analysts, market intelligence analysts, legal analysts, financial analysts, economists, quants, or accountants. For data scientists, examples of job functions are materials scientists, bioanalytical scientists, and scientific researchers.

You can choose from over 250 built-in transformations to combine, pivot, and transpose data without writing code. DataBrew also automatically recommends transformations such as filtering anomalies; correcting invalid, incorrectly classified, or duplicate data; normalizing data to standard date and time values; or generating aggregates for analyses. For complex transformations, such as converting words to a common base or root word, DataBrew provides transformations that use advanced ML techniques such as natural language processing (NLP). You can group multiple transformations together, save them as recipes, and apply the recipes directly to the new incoming data.

For input data, DataBrew supports commonly used file formats, such as comma-separated values (.csv), JSON and nested JSON, Apache Parquet and nested Apache Parquet, and Excel sheets. For output data, DataBrew supports comma-separated values (.csv), JSON, Apache Parquet, Apache Avro, Apache ORC, and XML.

Yes. Sign up for an AWS Free Tier account, and then visit the AWS Glue DataBrew console. If you are a first-time user of DataBrew, the first 40 interactive sessions are free. For more information, see AWS Glue Pricing.

No. You can use DataBrew without using either the Data Catalog or Lake Formation. However, if you use either the Data Catalog or Lake Formation, DataBrew users can select the datasets available to them from their centralized data catalog.

Yes. You can visually track all the changes made to your data in the AWS Glue DataBrew console. The visual view makes it easier to trace the changes and relationships made to the datasets, projects and recipes, and all other associated jobs. In addition, DataBrew keeps all account activities as logs in AWS CloudTrail.

You can use the Schema Registry, a serverless feature of AWS Glue, to validate and control the evolution of streaming data using schemas registered in Apache Avro and JSON Schema data formats, at no additional charge. Through Apache-licensed serializers and deserializers, the Schema Registry integrates with Java applications developed for Apache Kafka, Amazon MSK, Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda. When data streaming applications are integrated with the Schema Registry, you can improve data quality and safeguard against unexpected changes using compatibility checks that govern schema evolution. Additionally, you can create or update AWS Glue tables and partitions using Apache Avro schemas stored within the registry.

With the Schema Registry, you can do the following:

1. Validate schemas. When data streaming applications are integrated with the Schema Registry, schemas used for data production are validated against schemas within a central registry, allowing you to centrally control data quality.

2. Safeguard schema evolution. You can set rules on how schemas can and cannot evolve using one of eight compatibility modes.

3. Improve data quality. Serializers validate schemas used by data producers against those stored in the registry, improving data quality when it originates and reducing downstream issues from unexpected schema drift.

4. Save costs. Serializers convert data into a binary format and can compress it before it is delivered, reducing data transfer and storage costs.

5. Improve processing efficiency. In many cases, a data stream contains records of different schemas. The Schema Registry enables applications that read from data streams to selectively process each record based on the schema without having to parse its contents, which increases processing efficiency.

The Schema Registry supports Apache Avro and JSON Schema data formats and Java client applications. There are plans to continue expanding support for other data formats and non-Java clients. The Schema Registry integrates with applications developed for Apache Kafka, Amazon MSK, Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda.

The following compatibility modes are available for you to manage your schema evolution: Backward, Backward All, Forward, Forward All, Full, Full All, None, and Disabled. Visit the AWS Glue Schema Registry user documentation to learn more about compatibility rules.

The Schema Registry storage and control plane is designed for high availability and is backed by the AWS Glue Service Level Agreement. The serializers and deserializers use best-practice caching techniques to maximize schema availability within clients.

Schema Registry storage is an AWS service, while the serializers and deserializers are Apache-licensed open-source components.

Yes. Your clients communicate with the Schema Registry through API calls, which encrypt data in transit using TLS encryption over HTTPS. Schemas stored in the Schema Registry are always encrypted at rest using a service-managed KMS key.

You can use AWS PrivateLink to connect your data producer’s virtual private cloud (VPC) to AWS Glue by defining an interface VPC endpoint for AWS Glue. When you use a VPC interface endpoint, communication between your VPC and AWS Glue is conducted entirely within the AWS network. For more information, please read the AWS Glue Developer Guide. 

AWS CloudWatch metrics are available as part of the CloudWatch free tier. You can access these metrics in the CloudWatch console. Visit the AWS Glue Schema Registry user documentation for more information.

Yes. The Schema Registry supports both resource-level permissions and identity-based IAM policies.

Steps to migrate from a third-party schema registry to the Schema Registry are available in the AWS Glue Schema Registry user documentation.

AWS Glue Flex is a flexible execution job class that allows you to reduce the cost of your nonurgent data integration workloads (for example, preproduction jobs, testing, and data loads) by up to 35%. AWS Glue has two job execution classes: standard and flexible. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources. The flexible execution class is appropriate for nonurgent jobs whose start and completion times may vary. AWS Glue Flex can reduce the cost of your non-time-sensitive workloads (for example, nightly batch ETL jobs, weekend jobs, and one-time bulk data ingestion jobs).

The AWS Glue standard and flexible execution classes have different execution properties. With the standard execution class, jobs start immediately and have dedicated resources while running. Flexible execution class jobs run on non-dedicated compute resources on AWS that can be reclaimed while a job is running, and their start and completion times vary. As a result, the two execution classes are appropriate for different workloads. The standard execution class is ideal for time-sensitive workloads that require fast job startup and dedicated resources. The flexible execution class is less expensive and suitable for nonurgent jobs where variance in start and completion times is acceptable.

The flexible execution class is available for AWS Glue Spark jobs. To use the flexible execution class, you change the default setting of the execution class parameter from STANDARD to FLEX. You can do this through AWS Glue Studio or a command line interface (CLI). Read the AWS Glue Documentation for more information.

AWS Glue Flex flexible execution class is not appropriate for time-sensitive workloads that require consistent job start and run times, or for jobs that must complete execution by a specific time. AWS Glue Flex is also not recommended for long-running data integration workloads because they are more likely to get interrupted, resulting in frequent cancellations.

The availability and interruption frequency of AWS Glue Flex depends on several factors, including the AWS Region and Availability Zone, time of day, and day of week. Resource availability determines whether AWS Glue Flex jobs will start at all. While the interruption rate can be between 5% and 10% during peak hours, we expect the interruption rate of AWS Glue Flex jobs to be about 5% or the failure rate due to interruption to be less than 5%.

Yes. You can always choose the flexible execution class to run your AWS Glue jobs. However, the ability of AWS Glue to execute these jobs is based on the availability of non-dedicated AWS capacity and the number of workers selected for your job. It is possible that, during peak times, AWS Glue may not have adequate capacity for your job. In that case, your job will not start. You can specify a timeout value after which AWS Glue will cancel the job. The longer the timeout value, the greater the chance that your job will be executed.

If an AWS Glue Flex job is interrupted because there are no longer sufficient workers to complete the job based on the number of workers specified, the job will fail. AWS Glue will retry failed jobs up to the specified maximum number of retries on the job definition before canceling the job. You should not use the flexible execution class for any job that has a downstream dependency on other systems or processes.

The flexible execution class supports only AWS Glue Spark jobs. Python Shell and streaming are not supported. AWS Glue Flex is supported by AWS Glue version 3.0 and later. The flexible execution class does not currently support streaming workloads.

AWS Glue works on top of the Apache Spark environment to provide a scale-out execution environment for your data transformation jobs. AWS Glue infers, evolves, and monitors your ETL jobs to greatly simplify the process of creating and maintaining jobs.

Amazon EMR provides you with direct access to your Hadoop environment, affording you lower-level access and greater flexibility in using tools beyond Spark.

AWS DMS helps you migrate databases to AWS more easily and securely. AWS DMS is recommended for use cases that require a database migration from on premises to AWS or database replication between on-premises sources and sources on AWS. Once your data is on AWS, you can use AWS Glue to move, combine, replicate, and transform data from your data source into another database or data warehouse, such as Amazon Redshift.

Use AWS Batch to efficiently run any batch computing job on AWS, regardless of the nature of the job. AWS Batch creates and manages the compute resources in your AWS account, giving you full control and visibility into the resources being used.

AWS Glue is a fully managed ETL service that provides a serverless Apache Spark environment to run your ETL jobs. For your ETL use cases, we recommend you explore using AWS Glue. For other batch-oriented use cases, including some ETL use cases, AWS Batch might be a better fit.

You will pay a simple monthly fee, above the AWS Glue Data Catalog free tier, for storing and accessing the metadata in the Data Catalog. You will pay an hourly rate, billed per second, for the crawler run with a 10-minute minimum. If you choose to use a development endpoint to interactively develop your ETL code, you will pay an hourly rate, billed per second, for the time that your development endpoint is provisioned, with a 10-minute minimum. Additionally, you will pay an hourly rate, billed per second, for the ETL job with a 1-minute minimum. For more details, see AWS Glue Pricing.

Billing commences as soon as the job is scheduled for execution and continues until the entire job completes. With AWS Glue, you only pay for the time for which your job runs and not for the environment provisioning or shutdown time.

Yes. AWS Glue offers Usage Profiles, a cost control capability that allows admins to set preventatives controls and limits over resources consumed by their Glue jobs and Notebook sessions. With AWS Glue Usage Profiles, admins can create different cost profiles for different classes of users. Each profile is a unique set of parameters that can be assigned to different types of users.

We provide server-side encryption for data at rest and SSL for data in motion.

Please refer the AWS Glue Documentation to learn more about service limits.

Please refer to the AWS Region Table for details of AWS Glue service availability by Region.

You can specify the number of DPUs that you want to allocate to your ETL job. An AWS Glue ETL job requires a minimum of 2 DPUs. By default, AWS Glue allocates 10 DPUs to each ETL job.

AWS Glue provides the status of each job and pushes all notifications to CloudWatch. You can set up SNS notifications through CloudWatch actions to be informed of job failures or completions.

Our AWS Glue SLA guarantees a Monthly Uptime Percentage of at least 99.9% for AWS Glue.

You are eligible for an SLA credit for AWS Glue under the AWS Glue SLA if more than one Availability Zone in which you are running a task, within the same Region, has a Monthly Uptime Percentage of less than 99.9% during any monthly billing cycle.

For full details on all the terms and conditions of the SLA, as well as details on how to submit a claim, read the AWS Glue Service Level Agreement.