In the age of big data, companies face the challenge of consolidating data from multiple sources into a single system. This is why so many companies are turning to AWS Glue. Examples of its effectiveness are its speed, ease of use, and cost compared to traditional extraction, transformation, and loading (ETL) processes.
AWS Glue makes the move easyS3 dataand many other data sources into a data warehouse for analysis. It can handle complicated and time-consuming tasks, especially if you know how to use the system effectively. However, AWS Glue has a learning curve. If you don't avoid common mistakes, you produce incorrect data and contribute to poor decision-making.
Learn more about AWS Glue, when to use it, and how to maximize its potential while avoiding common mistakes.
Was ist AWS Glue?
AWS Glue is a fully managed, serverless data integration service that simplifies the process of preparing and loading data for analysis. It offers a flexible and cost-effective way to move and transform data between on-premises and cloud-based data stores.
You can use AWS Glue to create sophisticated cloud-based data lakes or centralized data repositories. The service also automates the time-consuming tasks of data discovery, mapping, and job scheduling so your business can focus on the data.
AWS Glue is a set of features including:
- AWS Glue-Datenkatalog:Allows you to catalog data assets and make them available to all AWS analytics services.
- Rastreadores use AWS Glue:Perform data discovery on data sources.
- AWS Glue-Jobs:Run the ETL on your pipeline in Python or Scala. Python scripts use an extension of the PySpark Python dialect for ETL jobs.
Users can also interact with AWS Glue through a graphical user interface using services such as AWS Glue DataBrew and AWS Glue Studio. This step makes the service more accessible for complex tasks like data processing without advanced technical knowledge like code generation or editing.
With AWS Glue, you only pay for the resources you use. There is no minimum fee and no prepayment. The service is one of several tools offered by AWS for ETL processes and can be used in conjunction with other tools such asServerloses Amazon EMR.
What are the benefits of AWS Glue?
AWS Glue is a low-cost, easy-to-use ETL service that can process both structured and unstructured data. The time it takes to develop an ETL solution can be reduced from months to just minutes. Here are the main benefits of using AWS Glue.
automated data collection
Data discovery, the process of identifying and categorizing data, can be time-consuming, especially with large amounts of data spread across multiple sources. AWS Glue helps automate this process by tracking your data sources and identifying your schema.
Simple data cleansing
Cleaning up inaccurate or incomplete data is critical, especially before data is loaded into a new system or prepared for analysis. AWS Glue can help you cleanse your data with its built-in transformations.
ETL is a process many data-driven companies use to consolidate data from multiple sources. This process can be time-consuming and expensive, but AWS Glue can help you automate it. With AWS Glue, you only need to define the ETL job once. From there, the system extracts the data from the source, completes the data transformation process, and automatically generates the desired format in the destination.
Help with data preparation
Data preparation is essential for any analysis project. Raw data often needs to be cleaned, normalized and aggregated before it can be analyzed. AWS Glue helps you prepare your data with its built-in transformations.
Data migration support
AWS Glue can migrate on-premises data stores to Amazon S3. This usually happens as part of a largerCloud-Migrationsstrategie. With AWS Glue, you can migrate your data without having to rewrite existing ETL jobs.
What are the challenges of using AWS Glue?
AWS Glue offers time-saving benefits for your operational ETL pipeline, but it's important to be aware of the common mistakes even experienced users make. These errors are often due to a failure to understand the nuances of the service.
Learn about three key places where mistakes most often occur.
Source data is a critical aspect of a data pipeline and must be managed appropriately. To ensure a smooth pipeline, review common mistakes and invest time in checking data quality.
A common mistake is using the wrong file format. File formats play a critical role in the pipeline and must be divisible to fit in Spark executor memory. Common sharable formats are CSV, Parquet, and ORC, while non-sharable formats include XML and JSON. While AWS Glueautomatically supports file splitting, consider using column styles like Parquet for better performance.
Another mistake is when the data quality is not checked. This causes AWS Glue crawlers to misinterpret the data and other unexpected behavior. Use a service like AWS Glue DataBrew to examine the dataset. Integrate data cleansing into your ETL processes. If needed, use the Amazon Athena $path pseudo column to locate fields in the underlying data.
Another common mistake is working with small files, which leads to performance issues. During the creation of the dynamic frame or DataFrame, the Spark driver builds an in-memory list of all files to include. Until this list is complete, the object will not be created. Select the useS3ListImplementation function in AWS Glue to enable lazy loading of S3 objects and reduce driver memory pressure. The grouping option in AWS Glue can also help by consolidating files for group-level reads, reducing the number of tasks.
Rastreader tun AWS Glue
The AWS Glue Crawler is a valuable tool for companies that want to offload the task of determining and defining the schema of structured and semi-structured datasets.
The right tracker starts with the right configuration and the correct definition of the data catalog. Crawlers typically come before or after ETL jobs, which means the time it takes to crawl a record affects the total time it takes to complete the pipeline. The record metadata discovered by the crawler is now used in most AWS analytics services.
A common mistake is not crawling a subset of data, resulting in long-running crawlers. To work around this, enable incremental scans with the "Only scan new folders" option. Alternatively, use inclusion and exclusion patterns to identify specific data that needs to be tracked.
For large datasets, instead of using one large tracker, you can use multiple smaller trackers, each pointing to a subset of data. Another solution is to use data sampling for databases and "sample size" for S3 buckets. With this configuration, only a sample of data is tracked, reducing overall time.
Another common problem is failing to optimally configure tracker detection. The result is more tables than expected. To avoid this, select the option "Create a single scheme for each S3 path". The crawler considers both data compatibility and schema similarity and groups the data into a single table when both requirements are met.
Sometimes this fix doesn't work. If the crawler keeps creating more tables than expected, look at the underlying data to determine the cause.
AWS Glue jobs are the heart of the AWS Glue service as they run the ETL. The AWS Glue console and services like AWS Glue Studio generate scripts for this task. However, many customers choose to write their own Spark code, which poses a higher risk of errors.
A common mistake is not to useDynamic framescorrectly. DynamicFrames are specific to AWS Glue. They are an improvement over traditional Spark DataFrames because they describe themselves and are better at handling unexpected values. However, some operations still require DataFrames, which can result in costly conversions. The goal should be to start with a DynamicFrame, convert to a DataFrame only when needed, and end with a DynamicFrame.
Another common mistake is using job markers incorrectly. Job markers enable tracking of data processed each time an ETL job is run from sources such as S3 or JDBC. Bookmarks allow you to "rewind" a job to reprocess a subset of data, or reset the bookmark to fill it. When custom designs are missing key components, the markers will not function properly. Consider using the AWS Glue generated ETL script for reference.
Another flaw with AWS Glue jobs is that the data is not partitioned properly. By default, scripts generated by AWS Glue are not partitioned when writing the job output. Use pushdown predicates to filter partitions without having to list and read every file in the dataset. Pushdown predicates are evaluated before S3 Lister, making them a valuable tool for improving performance.
What are the best practices for using AWS Glue?
Just as there are best practices for avoiding failures with AWS Glue, there are also general tips for getting the most out of this service. Here are five of them.
Use partitions to parallelize reads and writes
Partitions are a fundamental part of how AWS Glue processes data. By dividing your data into partitions, you can parallelize reads and writes, improving performance and reducing costs. When creating partitions, consider the size of your data, the required partitions, and the system load.
Improve performance and compression with columnar file formats
Columnar file formats are a type of file format optimized for columnar data stores. These formats are commonly used in data warehouses and analytics applications because of their excellent performance and compression. When using columnar file formats with AWS Glue, consider the size of your data, the number of columns, and the available compression codecs.
Optimize your data layout
Data layout can have a big impact on performance. When designing a layout, consider the size of your data, the number of columns, how it will be stored, and whether you need to support multiple versions of your data.
By compressing your data, you can improve performance and save on storage costs. There are many compression codecs available, so look for codecs that are compatible with AWS Glue and work well with your specific data set.
Focus on incremental changes with step-by-step commits
When committing changes to Amazon S3, use staged commits rather than large commits. Staggered commits allow you to commit changes in small batches, reducing the risk of errors and rollbacks. This approach is particularly useful if you are new to AWS Glue.
Aproveite o AWS Glue Auto Scaling
With Auto Scaling, you can automatically scale AWS Glue Spark jobs based on dynamically calculated requirements as jobs run, improving efficiency and performance while reducing costs. This is particularly useful when dealing with large and unpredictable amounts of data in the cloud.
This means you no longer have to manually plan capacity in advance or experiment with data to determine how much capacity is needed. Instead, you simply specify the maximum number of workers you need, and AWS Glue dynamically allocates resources based on workload requirements during job execution, adding more worker nodes to the cluster in near real-time as Spark requests more.
Use interactive sessions for Jupyter
Interactive Sessions provides a highly scalable, serverless Spark backend for Jupyter notebooks and Jupyter-based IDEs, enabling efficient on-demand development of interactive jobs using AWS Glue. When using Interactive Sessions, there are no clusters to deploy or manage, no clusters to pay for unused clusters, and no initial setup required, making it a simple and cost-effective choice. It also eliminates resource contention for the same development environment and uses the exact same runtime and Spark serverless platform as the AWS Glue ETL jobs.
Using interactive sessions for Jupyter can greatly improve AWS Glue development efficiency and help you save costs.
Succeed with AWS Glue
AWS Glue is a powerful service with defined best practices to guide you. It can be one of the most useful, versatile, and robust tools in your AWS data ecosystem. With its serverless architecture, automatic scaling and data discovery, and catalog capabilities, AWS Glue offers an efficient and cost-effective solution for your data integration needs.
While AWS Glue has tremendous potential, it is only as good as the user. Be sure to avoid rookie mistakes and learn from AWS Glue examples to maximize the potential in your business.
By partnering with an AWS Premier Consulting Partner like Mission Cloud, you can streamline your data integrations and easily access mission-critical analytics. learn howFind support and gain trustinto your data strategy by implementing a low-cost, serverless solution like AWS Glue.