Updated 14 Jan 23483 views
Amazon Glue grew in popularity as many companies began using managed data integration services. Primarily, data engineers and ETL developers use Glue to create, run, and monitor ETL workflows.
So before you move on to AWS Glue, it's best to review your ETL concepts. Please check out this blog:What is ETLfor more details. In this Amazon Glue blog, we will cover the following topics in detail:
- Was ist AWS Glue?
- AWS Glue Pricing
- When to use AWS Glue?
- Recursos use AWS Glue
- Components make AWS Glue
- AWS Glue architecture
- Pros and cons of AWS Glue
First, watch this informative video from the AWS Glue tutorial on YouTube:
Was ist AWS Glue?
AWS Glue is a serverless ETL and data integration service that simplifies discovering, preparing, and merging data for data analysis, machine learning, and application development. To simplify the data integration process, Glue offers both visual and code-based tools.
Amazon Glue consists of three components, namely the AWS Glue data catalog, an ETL engine that automatically creates Python or Scala code, and a configurable scheduler that manages dependency resolution, task monitoring, and restarts.
Glue's data catalog allows users to find and retrieve data quickly. Customization, orchestration, and monitoring of complicated data streams are also available through the Glue service.
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AWS Glue Pricing
Amazon Glue starts at $0.44. There are four different plans available here:
- ETL tasks and development endpoints are available for $0.44.
- Interactive crawlers and DataBrew sessions are available for $0.44.
- Jobs at DataBrew start at $0.48
- Monthly Storage and Data Catalog requests are $1.00.
There is no free plan for the Glue service atAWS. It costs about $0.44 per DPU per hour. So, on average, you would have to spend $21 a day. However, prices may vary by region.
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When to use AWS Glue?
It's not enough to know all the information about Amazon Glue, you should also know where to use it. Here are some AWS Glue use cases to consider.
- To run serverless queries against your Amazon S3 data lake, you can use Glue. Amazon Glue can help you get started right away by having all your data available for analysis in a single interface, without you having to move it around.
- To understand your data assets, you can use Amazon Glue. The data catalog makes it easy to find different sets of AWS data. Additionally, by using this data catalog, you can store your data across multiple AWS services while maintaining a consistent view of your data.
- Glue comes in handy when building event-driven ETL workflows. By invoking your Glue ETL jobs from an AWS Lambda service, you can run your ETL operations as soon as new data is availableAmazon S3.
- AWS Glue is also useful for organizing, cleaning, validating, and formatting data in preparation for storage in a data warehouse or data lake.
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Recursos use AWS Glue
Amazon Glue gives you all the data integration capabilities you need, so you can gain insights and apply your knowledge to create new breakthroughs in minutes instead of months. Below are some features you need to know about.
- Drag and drop interface:A drag-and-drop task editor lets you create the ETL process, and AWS Glue instantly creates the code to extract, convert, and upload the data.
- Automatic scheme detection:You can use the Glue service to create trackers connected to different data sources. It organizes the data, extracts schema-related information and efficiently stores it in the data catalog. This data can then be used by ETL tasks to monitor ETL processes.
- Work schedule:The adhesive can be used on a schedule, as needed, or in response to an event. You can also use the scheduler to create sophisticated ETL pipelines by creating dependencies between tasks.
- Code generation:Without having to write proprietary code, Glue Elastic Views makes it easy to create materialized views that aggregate and replicate data across different data stores.
- Integrated machine learning:Glue has a built-in machine learning feature called FindMatches. It detects records that are incomplete copies of each other and deduplicates them.
- Developer Endpoints:Glue provides developer endpoints that you can use to modify, debug, and test the code you create if you want to actively build your ETL code.
- Cola DataBrew:It is a data preparation tool for users like data analysts and data scientists that helps them clean and normalize data using Glue DataBrew's live visual interface.
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Components make AWS Glue
Before understanding Glue's architecture, we need to know a few components. To design and maintain your ETL workflow, AWS Glue relies on the interaction of several components. The main components of the Glue architecture are listed below.
Permanent metadata is stored in the Glue Data Catalog. To manage your Glue environment, it provides table, job and other control data. AWS provides a catalog of glue data for each account in each region.
A classifier is the schema of your data that is determined by the classifier. AWS Glue provides classifiers for popular relational database management systems and file types such as CSV, JSON, AVRO, XML, and others.
AWS Glue Connection is the Data Catalog object that contains the properties required to connect to a specific data store.
It is a component that tracks multiple data stores in a single encounter. It determines the schema for your data using a prioritized set of classifiers, and then generates tables of metadata in the Glue data catalog.
A formal group of related Data Catalog table definitions is called a database.
A data storage is a place where you can keep your data for a long time. Relational databases and Amazon S3 buckets are two examples.
A data source is a collection of data that is used as input to a process or transformation.
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A data target is the data store to which the job writes the transformed data.
Transform is the logic in code used to change the format of your data.
You can use the development endpoint environment to build and test your AWS Glue ETL programs.
A DynamicFrame is identical to a DataFrame except that each entry is self-describing. So, initially, no circuit diagram is required. In addition, Dynamic Frame has a number of sophisticated data cleaning and ETL processes.
The AWS Glue job is business logic required by the ETL job. A transformation script, data sources and data targets are the components of a job.
Trigger starts an ETL process. Triggers can be configured to occur at a specific time or in response to an event.
It's a web-based environment for running PySpark commands. On a development endpoint, a notebook enables active creation and testing of ETL scripts.
A script is a piece of code that extracts data from sources, modifies it, and loads it into targets.PySparkNameor Scala scripts are generated using AWS Glue. Apache Zeppelin notebooks and notebook servers are powered by Amazon Glue.
In data warehousing, a table is the metadata definition that describes the data. Column names, data type definitions, partition information, and other metadata about a base record are all stored in a table.
In the future, let's see how AWS Glue works.
AWS Glue architecture
The architecture of Glue is shown in the figure below.
In AWS Glue, you define jobs to extract, transform, and load (ETL) data from a data source to a data target. Below are the steps you need to follow:
- First you need to decide which data source you are going to use.
- If you use a data warehouse source, you must create a crawler to populate the AWS Glue Data Catalog with metadata table definitions.
- When you point your crawler at a data store, the crawler populates the data catalog with metadata.
- When using streaming sources, you must explicitly create data catalog tables and data stream properties.
- Once the data catalog is categorized, the data is instantly searchable, queryable, and available for ETL.
- AWS Glue then converts the data by generating a script. You can also deploy the script through the Glue console or API. (On AWS Glue, the script runs in an Apache Spark environment.)
- After generating the script, you can run the task on-demand or schedule it to start when a specific event occurs. A time-based schedule or an event can be used as a trigger.
- As you run the job, the script extracts the data from the data source, transforms it, and loads that data into the data target as shown in the image above. In this way, the ETL (Extract, Transform, Load) job in AWS Glue is successful.
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Pros and cons of AWS Glue
Like everything else in the world of big data computing, AWS Glue has advantages and disadvantages.
Here are some benefits of AWS Glue:
- Glue is aServerless data integrationSolution that eliminates the need to build and manage infrastructure.
- It offers simple tools to generate and track work activities that are in placetriggered by schedules and events, or on request.
- It is acost-benefitSolution. You only have to pay for the resources you use while running the jobs.
- Based on your data sources and goals, Glue will do thisAutomatically generate ETL pipeline codein Scala or Python.
- AWS Glue can be used by multiple organizations within the company to collaborate on various data integration projects. Thereduces the time requiredneeded to analyze the data.
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While Glue has a lot of cool features, it also has some downsides. So let's look at some of the limitations of AWS Glue.
- has glueintegration restrictions.Only JDBC and S3 (CSV) ETL data sources work properly with Glue. If you want to load data from other cloud services like File Storage Base, Glue can't help.
- Individual spreadsheet tasks are not trackedwith glue. The ETL process is only used to process the entire database.
- Only certain data sources like S3 are supported by AWS Glue. This means that incremental synchronization with the data source is not possible. That means younot be able to have real-time datafor complicated processes.
- Supports AWS Glueonly two programming languageslike Python and Scala to modify ETL scripts.
In this post, we examine AWS Glue, a powerful cloud-based solution for working with ETL pipelines. There are only three main phases of the user interaction process. You start using data crawlers to create a data catalog. Then write the ETL code that does theAWS-Datenkanalrequires. Finally, create the ETL job schedule.
We hope this blog gives you a full understanding of Amazon Glue.
If you still have questions or concerns about this technology, please post them belowAWS-Community.
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