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Companies nowadays are tasked with processing immense amounts of data to draw valuable insights and stand out on the market. While the volumes of data continue to grow, handling such amounts of information is becoming increasingly more challenging. There are some tools offered by the AWS professional services that allow businesses to streamline their extract, transform, load (ETL) tasks. From Amazon Lambda function and Elastic MapReduce to AWS Glue, organizations can manage their data integration in multiple ways.
So, what does AWS Glue do and why is it such a popular choice for ETL workloads? In this article, we’ll explore AWS Glue architecture, features, and components, discover its benefits and limitations, and take a look at best practices.
Before we dive into features, components, benefits, and other aspects, let’s answer the question of what is AWS Glue. Amazon Glue is a fully managed ETL service used to prepare data for various purposes such as data analysis, application development, and machine learning. The service enables data engineers to transform data and make it available for querying, analyzing, and other purposes. From generating and collecting data from multiple sources and cleaning and merging it to loading data in warehouses and lakes, AWS Glue helps you at every step.
In other words, Amazon Glue is a feature that provides a streamlined method of data integration process throughout the entire data pipeline.
So what is AWS Glue used for? The service offers a wide range of features aimed at optimizing and accelerating data integration. Let’s take a closer look at the essential functions that Amazon Glue delivers.
ETL process: AWS Glue allows you to automatically run ETL jobs as soon as new data is available in your Amazon Simple Storage Service (S3) buckets. Additionally, users can schedule the ETL processes or choose between on-demand or event-based methods.
Data Catalog: This serverless data integration service also allows you to organize your data in catalogs to quickly find the required dataset.
AWS Glue Studio: This feature enables no-code building and running of ETL jobs. In fact, it can automatically generate Python or Scala code lines for your ETL process based on a data source.
Data quality assurance: AWS Glue makes the management and monitoring of your data easier, ensuring the utmost quality and results.
Data schema discovery: Another feature that streamlines data integration and organization is automatic schema discovery. It automatically identifies the schema of your data and hosts it in the catalog to help you find the required information faster.
Integration with AWS services: Being an Amazon service, Glue seamlessly integrates with other vital offerings like Amazon S3, Athena, EMR, Redshift, and many more. The compatibility extends your service portfolio and facilitates the development of end-to-end data processing solutions.
How AWS Glue works is that it consists of several components that work together to aid users in streamlining their ETL workflows. Here is an overview of the most pivotal elements of this Amazon service:
The console is the operational hub of AWS Glue. Here users can define and manage their data integration processes.
The Glue data catalog is a repository where users can store their data and metadata. A glossary of your data schema and metrics, the catalog simplifies both the discovery and management of your data.
The job scheduling system is a tool that allows users to automate their AWS Glue ETL processes. The workflows can be triggered by events and perform on-demand tasks.
Scripts contain information that helps you extract and transform your data properly. Using AWS Glue, you can generate a script that executes data integration following your predefined rules.
ETL engine takes care of ETL code by automatically writing Python or Scala code lines including various customization options.
Crawler is a tool that retrieves data from multiple sources using classifiers. By relying on metadata tables, the crawler extracts valuable data and stores it in the catalog.
Job is the business logic that executes the ETL operations. There are different types of jobs that play an important role in AWS Glue services:
The Amazon Glue architecture is designed to perform ETL tasks from exploring data to storing it in data warehouses or data lakes. The difference between data lake and data warehouse lies in the type of stored data. While data lakes store unstructured data, data warehouses are built for cleaned and processed datasets.
The first step is to identify the data sources you’ll be using for your goals. In the case of using a data store source, you can build a crawler to create a Glue data catalog with metadata table definitions. If you’re relying on streaming sources, create data catalog tables and assign attributes to the data stream.
Now, the data catalog is a central hub with all your data assets, categorized, organized, and optimized for ETL jobs. Using the console, AWS Glue can convert your data by generating and following a script. According to the script, you can execute on-demand or event-based ETL jobs by extracting data, transforming it, and loading it to the target.
From streamlined data discovery, simplified data cleansing, and superior ETL jobs execution to cloud migration, AWS Glue can provide an array of benefits to your business. In this section, we’ll focus on the advantages that you’ll yield when adopting Amazon Glue services.
One of the main advantages of using Amazon Glue is for identifying and categorizing data. By leveraging crawlers, users can automatically discover and catalog metadata. Crawlers scan multiple data sources such as Amazon S3, Amazon Relational Database Service (RDS), and Amazon Redshift to discover data schema. Later, the discovered data can be stored in the catalog which acts as a central hub for your data assets. Automated data discovery saves a lot of resources and streamlines metadata gathering and storage.
Another pivotal benefit of Amazon Glue is simplified data cleansing. Before you can load your data into the system and apply it for analysis, it’s important to properly clean inaccurate datasets. AWS Glue provides an array of functions to accelerate and streamline data cleansing ensuring data quality, consistency, and reliability.
Among the major benefits of Amazon Glue are simplified extract, transform, load (ETL) processes. ETL allows companies to centralize data from various data targets and sources. AWS Glue Studio enables users to create, run, and monitor ETL jobs with its intuitive drag-and-drop interface. By defining the ETL job once, you allow the system to extract data, execute data transformation, and create the required format.
Another application for AWS Glue is data preparation. Using its built-in transforms, you can prepare your data for analysis and machine learning, among other purposes. Data preparation includes data partitioning, format conversions, and data normalization.
As a part of a cloud migration journey, AWS Glue can assist you in migrating your data from on-premise to the cloud. It offers a collection of integrations with popular data sources like on-prem databases, various cloud providers, and SaaS applications. Additionally, Amazon Glue offers incremental data migration allowing you to slowly and efficiently execute your strategies. Finally, it supports data deduplication and validation to guarantee data integrity and consistency.
All tools, no matter how advanced, have limitations and challenges. In this part, we’ll investigate the most common obstacles that companies face when working with AWS Glue.
The challenges of source data are three: improper file formats, lackluster data quality, and operating over small files. Using incorrect data formats may require additional processing power and steps which can lead to inefficiencies and delays. Acceptable formats of data sources are CSV, Parquet, and ORC, as well as XML and JSON.
Poor quality of data can also impact the accuracy and reliability of the AWS Glue ETL processes. Dealing with unverified data sources can produce data misinterpretation, which can jeopardize the end results. You can avoid these issues by integrating data cleansing and validation into your ETL pipeline.
Finally, AWS Glue is designed for large datasets, which makes dealing with small files inefficient. From increased overhead and slower processing times to severe performance issues, smaller datasets can pose a threat to the entire ETL process. Consolidate smaller files into larger datasets before utilizing Glue features.
Using AWS Glue Crawler, companies can determine and define the data schema. However, without proper configuration and definition of data catalogs, you may run into some obstacles. Failure to configure crawler discovery is a prominent issue that companies can face. It can lead to suboptimal performance, redundant crawling of irrelevant data, or even missing datasets. Try to carefully configure AWS Glue Crawler settings to optimize the performance.
Another common issue is associated with failing to crawl a data subset within a data source. Either due to incorrect permissions or unsupported and corrupt files, a crawler may fail to discover a subset of data, leading to incomplete data discovery and erroneous data representation. To prevent these issues, you can rely on include and exclude patterns or use multiple crawlers.
AWS Glue Jobs is a function that executes the ETL pipeline. A common issue that derives from the jobs function is the failure to apply DynamicFrames correctly. As a data abstraction tool providing a robust way to deal with structured and semi-structured data, DynamicFrames should be handled properly. Organizations that fail to do so face challenges associated with job failures, incorrect access to data structures, poor data quality, and job errors. You can avoid these bottlenecks by starting with a DynamicFrame, converting to a DataFrame, and completing the cycle with a DynamicFrame.
Companies also struggle with incorrect usage of job bookmarks. Their purpose is to aid users in tracking their progress and resuming where they left off. Using bookmarks inaccurately can result in redundant data processing, inconsistent data, and unexpected system behavior. You can battle these issues by learning the purpose and limitations of bookmarks. Focus on handling data updates, reset bookmarks when changes were made, and enable them when incremental processing is necessary.
Finally, additional problems may arise when AWS Glue Jobs fail to partition data. Data partitioning is utilized to organize and optimize data storage based on specific attributes. Failure to partition data properly can lead to subpar query performance, small file problems, and additional data scanning. To mitigate these challenges, choose the correct partition keys, find the right balance between the number of partitions and their sizes, and regularly monitor the partitioning strategy.
Using Amazon Glue can be quite a learning curve. To ensure you’re employing strategies to extract the most value from the service, rely on the following best practices.
Companies rely on data partitioning to parallelize read and write operations in AWS Glue. This process enables more efficient and smooth data processing. Instead of reading the entire dataset, partitioning allows you to only scan the relevant partitions, thus reducing the time and effort that goes into scanning. When setting partitions, focus on the correct dataset sizes and system load.
Columnar file formats like Parquet and ORC are suitable for a data warehouse due to their elevated performance and compression. These formats store data in a column-oriented manner, which increases the efficiency of your encoding techniques. If you decide to use columnar file formats, pay attention to the size of your data, the number of columns, and compression technologies.
Focus on optimizing your data layout to improve the performance of your AWS Glue jobs. Design your layouts in a way that aligns with the size of your data, the number of columns, and the type of storage.
Compression can minimize your data store expenses and enhance transfer speeds by reducing data volumes. Data compression types such as Gzip, Snappy, Bzip2, etc. are efficient and can be used for your data files in AWS Glue.
Staged commits is a function that allows AWS Glue users to enable changes in smaller batches, thus minimizing the risks of failures. This feature is especially relevant and useful for new users of Amazon Glue.
AWS Glue auto-scaling capabilities automatically adjust the number of jobs based on the current requirements. This feature helps companies enhance efficiency and performance while keeping costs low. Especially if your system is prone to varying and unpredictable data volumes, auto-scaling ensures optimal resource distribution.
Interactive sessions allow you to efficiently develop and test AWS Glue jobs. They help users experiment with various transformations and optimize their jobs before going into the production phase. Using interactive sessions for Jupyter, you remove idle clusters, reduce costs, and eliminate the necessity to configure.
Amazon Web Services is known for flexible pay-as-you-go pricing models that allow users to only pay for the services they utilize. However, without proper cost management practices, you may run into unnecessary spending. To mitigate the ever-growing cloud bill, you can adopt some strategies that help companies keep their spending under control.
For example, you can leverage AWS Cost Explorer, which provides a visual representation of your expenses and resource usage over time. AWS Budgets is another helpful tool that enables the setting of custom thresholds and alerts allowing you to keep a close eye on your expenditure. Furthermore, AWS offers cost and usage reports that showcase detailed information about your current services, including Amazon Glue.
Another way of measuring and monitoring your AWS Glue expenses is by partnering up with a reliable IT agency. Consider collaborating with a trustworthy outsourcing company that specializes in cloud services. Make sure your future ally is a certified AWS partner who showcases expert-level understanding of Amazon services, including AWS Glue.
When used correctly, AWS Glue can be a game-changer for your cloud ecosystem. The Glue data catalog capabilities coupled with auto-scaling and serverless data integration make this service a robust and cost-effective solution. If you’re interested in adopting Amazon Glue, consider partnering up with our experts at NIX. We’re a software development agency that focuses on cloud services. Our extensive experience and technical know-how allow us to help clients enhance their data integrations and leverage their data. Get in touch with NIX to learn more about our background and identify valuable insights into your data.
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AWS Glue is perfect for ETL processes as it provides a fully managed environment for creating, executing, and tracking ETL jobs.
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The main use cases for AWS Glue are data integration and ETL jobs. From data discovery, preparation, and transformation to loading it into data target destinations, Amazon Glue helps businesses better perform analytics and machine learning functions.
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AWS Glue does not use SQL but supports SQL-like operations using Apache Spark. The ETL jobs are written in Python or Scala offering APIs for data transformation.
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Although AWS Lambda can be used to execute ETL workloads, this service requires more setup and management compared to Amazon Glue. AWS Glue was built specifically to handle ETL jobs, making it easier to configure and use.
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AWS Glue supports Python and Scala programming languages with Python (PySpark) being more common. However, AWS Glue Studio provides a drag-and-drop interface, allowing employees without in-depth technical background to easily use it.
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Amazon S3 is a common data target destination for data after having been processed by Glue ETL jobs. You can transfer data to S3 buckets in different formats like CSV, Parquet, and JSON, depending on your requirements.
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Instead of using a specific database, AWS Glue utilizes a metadata repository called Glue Data Catalog. It stores a data catalog object associated with your data like tables, sources, and schemas. However, you can connect your Amazon Glue to various databases, including Amazon Redshift, RDS, S3, and others.
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Using Java Database Connectivity (JDBC) or Open Database Connectivity (ODBC), you can connect AWS Glue to your Azure data source.
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The main difference between AWS Glue and EMR is the complexity of your processing scenarios. While Amazon Glue is perfect for simpler ETL workloads, EMR is more suitable for complex and large-scale data processing tasks.
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AWS Glue is not suitable for real-time data processing purposes, complex workloads that require customer libraries, and extremely large tasks.
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