AWS Glue Tutorial for Beginners - Intelligence (2023)

AWS Glue Tutorial for Beginners - Intelligence (1)

Updated on 03/23120 views

Amazon Glue has grown in popularity as more companies have started using managed data integration services. The glue is mainly used by data technicians andETLDevelopers to create, run and monitor ETL workflows.

We will discuss these topics in this AWS Glue tutorial:

  • Was ist AWS Glue?
  • Benefits of using AWS Glue
  • AWS Glue use cases
  • AWS Data Pipeline versus AWS Glue
  • AWS Glue Components
  • AWS peg architecture
  • Benefits of AWS Glue
  • AWS Glue Pricing
  • Diploma

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Was ist AWS Glue?

AWSGlue is a precise and expertly crafted ETL (Extract, Transform, and Load) tool for automating data analysis. The time required to prepare data for analysis has been drastically reduced. Automatically discover and list data using the AWS Glue Data Catalog. It recommends, curates, and builds Python or Scala code to stream data from source, loads and transforms work based on timed events, provides configurable schedules, and creates an Apache Spark environment that scales to specific data loads.

The AWS Glue service transforms, balances, secures, and monitors complex streams of data. It offers a serverless solution by simplifying the complicated activities of application development.

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AWS Glue also provides fast integration techniques to combine multiple legitimate datasets and quickly disassemble and approve the data.

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Benefits of using AWS Glue

Faster data integration

AWS Glue enables different groups in your organization to collaborate on data integration tasks such as extract, cleanse, normalize, merge, load, and run scalable ETL workflows. This reduces the time it takes to review and use your data from months to minutes.

Automate data integration

AWS Glue automates most of the work related to data integration. scans your data sources, recognizes data formats and recommends data storage schemes.

It generates the code needed to perform your data transformations and loads automatically. It simplifies running and managing hundreds of ETL procedures, combining and duplicating data across multiple data warehouses using SQL.

no servers

AWS Glue works in a serverless mode. There is no infrastructure to manage, and it allocates, configures, and scales the resources needed to run your data integration operations. You only pay for the resources your jobs consume while they are running.

AWS Glue use cases

Build event-based ETL pipelines

AWS Glue can run your ETL processes as new data arrives. For example, you can use an AWS Lambda function to run your ETL operations as soon as new data is availableAmazonas S3. You can also include this new dataset in your ETL operations by registering it with the AWS Glue Data Catalog.

AWS Glue Tutorial for Beginners - Intelligence (2)

Create a unified catalog

With the AWS Glue Data Catalog, you can discover and search numerous AWS datasets without having to move the data. Once the data is cataloged, it is immediately available for search and queryAthenian Amazon, Amazon EMR andAmazon RedshiftSpectrum.

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AWS Glue Tutorial for Beginners - Intelligence (3)

Create, run and monitor ETL jobs

AWS Glue Studio simplifies the graphical development, execution, and monitoring of AWS Glue ETL operations. Automatically creates code for ETL tasks that transport and convert data.

You can then use the AWS Glue Studio job execution dashboard to monitor ETL execution and confirm that your jobs are working properly.

AWS Glue Tutorial for Beginners - Intelligence (4)

explore data

With AWS Glue DataBrew, you can explore and experiment with data directly from your data lake, data warehouses, and databases such as Amazon S3, Amazon Redshift, AWS Lake Formation, Amazon Aurora, and Amazon RDS, and choose from over 250 transformations Simplify data prep tasks, such as B. filtering out anomalies, standardizing formats and correcting inaccurate values.

Once the data is prepared, it can be used immediately for analysis and machine learning.

AWS Glue Tutorial for Beginners - Intelligence (5)

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AWS Data Pipeline versus AWS Glue

ParameterAWS data channelizationAWS service area
specialization data transferETL, data catalogue
PricesPricing is based on frequency of use and whether you are using AWS or an on-premises agreement.AWS Data Catalog charges for storage monthly, while AWS Glue ETL charges by the hour.
data replicationComplete table; incremental replication via timestamp fieldfull mesa; incremental use of AWS Database Migration Service (DMS)Change Data Capture (CDC).
Connector AvailabilityAWS Data Pipeline supports only four data sources: DynamoDB, SQL, Redshift, and S3.It uses JDBC to connect to Amazon platforms like Redshift, S3, RDS, DynamoDB, AWS targets and other databases.

AWS Glue Components

AWS Glue relies on the interaction of various components to build and maintain its ETL operations. The essential components of the Glue architecture are the following:

  • AWS Glue-Datenkatalog:Persistent metadata is stored in the Glue Data Catalog. It provides tables, tasks, and other control data to help you maintain your Glue environment. AWS offers a catalog of Glue data by account and region.
  • Sorter:A classifier is the data structure determined by the classifier. It includes classifiers for popular relational database management systems and file formats such as CSV, JSON, AVRO, and XML.
  • Connection:The AWS Glue connection data catalog object contains the properties required to connect to a specific data store.
  • Traktor:It is a component that searches many data repositories in a single encounter. Builds metadata tables in Glue's data catalog after determining the schema for your data using a set of prioritized classifiers.
  • Database:A database is a logically organized collection of Data Catalog table definitions that are linked together.
  • Data storage:A data storage is a place where you can keep your data for a longer period of time. Examples are relational databases and Amazon S3 buckets.
  • Data Source:A data source is a set of data that is used as input to a process or transformation.
  • Transform:The logic in the code used to change the format of your data is called a transformation.
  • End of development:You can create and test your AWS Glue ETL scripts using the development endpoint environment.
  • Dynamic frame:A DynamicFrame is similar to a DataFrame, except that each element is self-describing. Accordingly, no outline is initially required. In addition, Dynamic Frame has a number of advanced ETL and data cleaning techniques.
  • Work:AWS Glue Job is a type of business logic required for ETL tasks. The components of a job include a transformation script, data sources, and data targets.
  • Deduction:Trigger starts the ETL process. Triggers can be set to occur at a predetermined time or in response to an event.
  • portable server:It's a web-based environment where you can run PySpark commands. A notebook on a development endpoint allows for active creation and testing of ETL scripts.
  • Script:A script is a piece of code that collects information from sources, modifies it, and loads it to targets. AWS Glue is used to create PySpark or Scala scripts. Amazon Glue offers Apache Zeppelin laptops and laptop servers.
  • Mesa:A table in the data warehouse is the metadata description that describes the data. A table stores column names, data type definitions, partitioning information, and other metadata about a base record.

AWS peg architecture

AWS Glue Tutorial for Beginners - Intelligence (6)

AWS Glue tasks are used to extract, transform, and load (ETL) data from a data source to a data target. The steps are the following:

  • First you need to choose which data source you want to use.
  • If you use a data warehouse, you must create a crawler to send metadata table definitions to AWS Glue Data Catalog.
  • When you point your crawler at a data store, it adds metadata to the data catalog.
  • When using streaming sources, you must explicitly set the data catalog tables and stream properties.
  • Once the data catalog has been categorized, the data is immediately searchable, queryable and ETL ready.
  • After you create the script, you can run it on-demand or schedule it to run when a specific event occurs. The trigger can be a timed program or an event.
  • As the task runs, the script extracts data from the data source, transforms it, and loads it into the data target as shown in the diagram above. As a result, the ETL (Extract, Transform, Load) process in AWS Glue is successful.

career change

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Benefits of AWS Glue

  • Glue is a serverless data integration solution with no infrastructure to build or manage.
  • It provides simple tools to create and track work activities triggered by schedules, events, or on demand.
  • It's an inexpensive solution. You only have to pay for the resources you use during the job execution process.
  • Depending on your data sources and targets, Glue creates an ETL code pipeline in Scala or Python.
  • Multiple organizations within the enterprise can use AWS Glue to collaborate on various data integration initiatives. This reduces the time required to analyze the data.

Learn more about AWS through ourTutorials from AWS.

AWS Glue Pricing

Amazon Glue's starting price is $0.44. The four plans available are the following:

  • Development endpoints and ETL jobs are offered for $0.44.
  • Interactive crawlers and DataBrew sessions are offered for $0.44 per session.
  • At DataBrew, starting salaries are $0.48.
  • Data catalog requirements and monthly storage costs are $1.00.

AWS does not offer a free plan for the Glue service. It costs about $0.44 per DPU per hour. So you have to spend an average of $21 a day. However, the prices may vary regionally.

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AWS Glue differentiates itself from other competitors as a cost-effective serverless service provider. Amazon Glue provides simple tools to categorize, classify, validate, enhance, and move data stored in data warehouses and data lakes.

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It is possible to work with semi-structured or clustered data using AWS Glue. Compatible with other Amazon services, this service provides centralized storage by combining data from numerous sources and preparing it for various stages such as reporting and data analysis.

By seamlessly interacting with different platforms for fast and fast data analysis at low cost, the AWS Glue service achieves excellent efficiency and performance.

If you have any questions or concerns about this technology, please post them on theAWS-Community.


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