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.
A data lake is a scalable centralized repository in Amazon S3 that is optimized to make data from many diverse data stores accessible in one place to support analytical applications and queries. A data lake enables analytics and machine learning across all your organization’s data for improved business insights and decision making. AWS Glue Elastic Views, on the other hand, is a service that enables you to combine and replicate data across multiple databases and your Amazon S3 data lake. If you are building application functionality that needs to access specific data from one or more existing data stores in near-real time, AWS Glue Elastic Views enables you to replicate data from multiple data stores and keep the data up-to-date. You can also use AWS Glue Elastic Views to load data from operational databases into a data lake by creating views over your operational databases and materializing them into your data lake.
Currently supported sources for the preview include Amazon DynamoDB, with support for Amazon Aurora MySQL, Amazon Aurora PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for PostgreSQL to follow. Currently supported targets are Amazon Redshift, Amazon S3, and Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) with support for Amazon Aurora MySQL, Amazon Aurora PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for PostgreSQL to follow.
Yes. With AWS Glue Elastic Views, you can replicate data from one data store to another in near-real time. This enables high performance operational applications that need access to up-to-date data from multiple data stores. AWS Glue Elastic Views also enables you to integrate your operational and analytical systems without having to build and maintain complex data integration pipelines. Using AWS Glue Elastic Views, you can create database views over data in your operational databases and materialize those views in your data warehouse or data lake. AWS Glue Elastic Views keeps track of changes in your operational databases and ensures that data in your data warehouse and data lake is kept in sync. You can now run analytical queries on your most recent operational data.
AWS Glue Elastic Views lets you connect to multiple data store sources in AWS and create views over these sources using familiar SQL. You can materialize these views into target data stores. As an example, you can create views that access restaurant information in Amazon Aurora and customer reviews in Amazon DynamoDB and materialize those views to Amazon Redshift. You can then build an application combining food preferences and popular restaurants on top of Amazon Redshift. Also, because AWS Glue Elastic Views sources are separate from targets, if you have read heavy applications, you can offload read requests to an AWS Glue Elastic Views target that maintains a consistent copy of the source. You can visualize the data in AWS Glue Elastic Views target data stores using services like Amazon QuickSight or partner visualization tools like Tableau.
Yes. You can visually track all the changes made to your data in the AWS Glue DataBrew Management Console. The visual view makes it easy to trace the changes and relationships made to the datasets, projects and recipes, and all other associated jobs. In addition, Glue DataBrew keeps all account activities as logs in the AWS CloudTrail.
No. You can use AWS Glue DataBrew without using either the AWS Glue Data Catalog or AWS Lake Formation. However, if you use either the AWS Glue Data Catalog or AWS Lake Formation, DataBrew users can select the data sets available to them from their centralized data catalog.
Yes. Sign up for an AWS Free Tier account, then visit the AWS Glue DataBrew Management Console, and get started instantly for free. If you are a first-time user of Glue DataBrew, the first 40 interactive sessions are free.
For input data, AWS Glue 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, AWS Glue DataBrew supports comma-separated values (.csv), JSON, Apache Parquet, Apache Avro, Apache ORC and XML.
You can choose from over 250 built-in transformations to combine, pivot, and transpose the data without writing code. AWS Glue 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, Glue DataBrew provides transformations that use advanced machine learning 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.
AWS Glue DataBrew is built for users who need to clean and normalize data for analytics and machine learning. 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.
AWS Glue 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. AWS Glue DataBrew is generally available today in US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), Asia Pacific (Sydney), and Asia Pacific (Tokyo).
ML Transforms provide a destination for creating and managing machine-learned transforms. Once created and trained, these ML Transforms can then be executed in standard AWS Glue scripts. Customers select a particular algorithm (for example, the FindMatches ML Transform) and input datasets and training examples, and the tuning parameters needed by that algorithm. AWS Glue uses those inputs to build an ML Transform that can be incorporated into a normal ETL Job workflow.
AWS Glue's FindMatches ML Transform makes it easy to find and link records that refer to the same entity but don’t share a reliable identifier. Before FindMatches, developers would commonly solve data-matching problems deterministically, by writing huge numbers of hand-tuned rules. FindMatches uses machine learning algorithms behind the scenes to learn how to match records according to each developer's own business criteria. FindMatches first identifies records for the customer to label as to whether they match or do not match and then uses machine learning to create an ML Transform. Customers can then execute this Transform on their database to find matching records or they can ask FindMatches to give them additional records to label to push their ML Transform to higher levels of accuracy.
Both AWS Glue and Amazon 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. Amazon 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 flight and has built-in functionality to process streaming data that is semi-structured or has an evolving schema. Use 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.
Streaming ETL in Amazon Kinesis Data Firehose enables you to easily capture, transform, and deliver streaming data. Amazon 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 does not include the advanced ETL capabilities that AWS Glue supports.
Both AWS Glue and Amazon Kinesis Data Analytics can be used to process streaming data. AWS Glue is recommended when your use cases are primarily ETL and when you want to run jobs on a serverless Apache Spark-based platform. Amazon Kinesis Data Analytics is recommended when your use cases are primarily analytics and when 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 flight and has built-in functionality to process streaming data that is semi-structured or has an evolving schema. Use Glue to apply both its built-in and Spark-native transforms to data streams and load them into your data lake or data warehouse.
Amazon Kinesis Data Analytics enables you 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 Amazon Kinesis Data Analytics for real-time analytics and more general stream data processing.
No. While we do believe that using both the AWS Glue Data Catalog and ETL provides an end-to-end ETL experience, you can use either one of them independently without using the other.
AWS Glue supports ETL on streams from Amazon Kinesis Data Streams, Apache Kafka, and Amazon MSK. Add the stream to the Glue Data Catalog and then choose it as the data source when setting up your AWS Glue job.
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 monitors job event metrics and errors, and pushes all notifications to Amazon CloudWatch. With Amazon 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 Glue, you can trigger an AWS Lambda function. Glue also provides default retry behavior that will retry all failures three times before sending out an error notification.
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.
In addition to the ETL library and code generation, AWS Glue provides a robust set of orchestration features that allow you to manage dependencies between multiple jobs to build end-to-end 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 Glue jobs from an external source such as an AWS Lambda function.
You can create and connect to development endpoints that offer ways to connect your notebooks and IDEs.
AWS Glue’s ETL script recommendation system generates Scala or Python code. It leverages Glue’s custom ETL library to simplify access to data sources as well as manage job execution. You can find more details about the library in our documentation. You can write ETL code using AWS Glue’s custom library or write arbitrary code in Scala or Python by using inline editing via the AWS Glue Console script editor, downloading the auto-generated code, and editing it in your own IDE. You can also start with one of the many samples hosted in our Github repository and customize that code.
Yes. AWS Glue Studio offers a graphical interface for authoring 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.
Yes, the Schema Registry supports both resource-level permissions and identity-based IAM policies.
AWS CloudWatch metrics are available as part of CloudWatch’s free tier. You can access these metrics in the CloudWatch Console.
You can use AWS PrivateLink to connect your data producer’s 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.
Yes, your clients communicate with the Schema Registry via 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.
AWS Glue Schema Registry storage is an AWS service, while the serializers and deserializers are Apache-licensed open-source components.
The Schema Registry storage and control plane is designed for high availability and is backed by the AWS Glue SLA, and the serializers and deserializers leverage best-practice caching techniques to maximize schema availability within clients.
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 Schema Registry user documentation to learn more about compatibility rules.
The Schema Registry supports Apache Avro and JSON Schema data formats and Java client applications. We plan to continue expanding support for other data formats and non-Java clients. The Schema Registry integrates with applications developed for Apache Kafka, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda.
AWS Glue Schema Registry, a serverless feature of AWS Glue, enables you 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 Managed Streaming for Apache Kafka (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.
Before you can start using 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 AWS Glue Data Catalog.
No. AWS Glue Data Catalog is Apache Hive Metastore compatible. You can point to the Glue Data Catalog endpoint and use it as an Apache Hive Metastore replacement.
You simply run an ETL job that reads from your Apache Hive Metastore, exports the data to an intermediate format in Amazon S3, and then imports that data into the AWS Glue Data Catalog.
AWS Glue provides a number of ways to populate metadata into the AWS Glue Data Catalog. Glue crawlers scan various data stores you own to automatically infer schemas and partition structure and populate the Glue 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 in-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 via 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 AWS Glue Data Catalog by using our import script.
AWS Glue Schema Registry Storage is a service used while serializing and deserializing Apache Licensed open sources components.
AWS Tags are labels used in assigning us to AWS Resources.
Each tag contains a Key and an Optional Value, which we can define. We can also use tags in AWS Glue for organizing and identifying our resources. All the tags are used in creating cost accounting reports and restricting access to resources.
Development Endpoints are used in describing the AWS Glue API that is related to testing by using Custom DevEndpoint.The endpoint is where a developer can debug the extract, transforming, and loading ETL Scripts.
AWS Glue consists of:
* Data Catalog is a Central Metadata Repository.
* ETL Engine helps in generating Python and Scala Code.
* Flexible Scheduler helps in handling Dependency Resolution, Job Monitoring and Retring.
* AWS Glue DataBrew helps in Normalizing and Cleaning Data with visual interface.
* AWS Glue Elastic View used in Replicating and Combining Data through multiple Data Stores.
AWS Glue helps in enabling ETL operations on streaming data by using continuously-running jobs.It can also be built on the Apache Spark Structured Streaming engine, and can ingest streams from Kinesis Data Streams and Apache Kafka using Amazon Managed Streaming for Apache Kafka.It can clean and transform streaming data and load it into S3 and JDBC data stores and can process event data like IoT streams, clickstreams, and network logs.
AWS Glue Schema Registry helps by enabling us for validating and controlling the evolution of streaming data using the registered Apache Avro schemas with no additional charge.Schema Registry helps in integrating with Java Applications developed for Apache Kafka, Amazon Managed Streaming for Apache Kafka (MSK), Amazon Kinesis Data Streams, Apache Flink, Amazon Kinesis Data Analytics for Apache Flink, and AWS Lambda.
AWS Glue Crawlers used for storing data and progressing through a prioritized list of classifiers for extracting the schema of our data and other statistics and populates the Glue Data Catalog with this metadata.They helps us by running periodically for detecting the availability for new data and also changes the existing data, including table definition changes.Crawlers automatically add new tables, new partitions to existing table, and new versions of table definitions.
AWS Glue Data Catalog is a persist metadata store used for storing structural and operational metadata for all data sets, also provides uniform repository where disparate systems helps in storing and finding metadata for keeping track of data in data silos.It uses metadata to query and transform the data.It also helps in tracking data that has changed overtime, is a drop in replacement for the Apache Hive Metastore for Big Data Applications running on AWS EMR.AWS Glue Data Catalog also helps by providing out of box integration with Athena, EMR, and Redshift Spectrum.
* Limited Compatibility - used for working with variety of commonly used data sources and works with services running on AWS.
* No incremental data sync - Glue is not the best option for real-time ETL jobs.
* Learning curve - used for supporting queries of traditional relational database.
The use cases of AWS Glue are as follows:
Data extraction - helps in extracting data in variety of formats.
Data transformation - helps in reformating data for storage.
Data integration - helps in interagting data into enterprise data lakes and warehouse.
* Automatic Schema Discovery - Allows in automating crawlers to obtain schema related information and also in storing in data catalog.
* Job Scheduler - Several jobs can be started in parallel, and users can specify dependencies between jobs.
* Developer Endpoints - helps in creating custom readers, writers and transformations.
* Automatic Code Generation - helps in generating code.
* Integrated Data Catalog - stores data from a disparate source in the AWS pipeline.
AWS Glue is a service which helps in making simple and cost effective for categorizing our data, clean it and move it reliably between various data stores and data streams.It consists of central metadat repository called as SWA Glue Catalog.AWS Glue helps in generating Python or Scala code, by handling dependency resolution, job monitoring, and retries.AWS Glue is serverless infrastructure for set up or manage, it is a component known as dynamic frame that will help us using in our ETL scripts.Dynamic Frame is same as Apache Spark dataframe and the data abstraction which is used for organizing data into rows and columns.