In this part 1 of this blog series we began by looking into some critical capabilities of AWS Glue that make ETL tasks easier when it comes to data modernization. AWS Glue is a new addition to the world of fully managed serverless ETL services. It stands apart from the rest by combining the speed and power of Apache Spark with the feather light data organization of Hive metastores.Let’s take a look at some of the main capabilities of AWS Glue.
1. AWS Glue ETL Functions
When you talk about ETL operations or functions you are essentially referring to the system element in charge of autogenerating code on Python or Scala. This facet can additionally provide for the specifications of the created code.The entire pipeline begins with data sources from which data crawlers assess these sources and targets while extracting the required data that pertains to them. The data catalogue takes care of holding all of this information.With the help of the management console users can interact with this information. This code is the reason behind all data transformations from data sources to data targets. The job scheduler can set off ETL jobs until the ETL pipeline automatically extract the needed data straight from the sources, transforms as required and loads it into data end points.
2. Job Timetabling
This component called the job scheduling system enables users to automate ETL pipelines by generating a completion timetable or action-based job triggering. It additionally enables the chaining of ETL pipelines.
3. AWS Glue Console
The AWS Glue console is an indispensable management tool for the AWS Glue system. This feature allows you to establish tables, connections, define jobs and so on. It also helps with searching for objects, modifying scripts for transformations, outline events for triggering jobs, put up the scheduler and more. In addition, internal APIs endpoints help engage with the backend.
4. Data Classifiers and Crawlers
This task determines how the catalogue gets updated, it falls on the shoulders of data classifiers and crawlers. They go through various data reserves in various holdings and evaluate what is the best suited schema for the data. Following which the meta-information is saved into the data catalogue.
A Time to Implement AWS Glue and When to Not
Three Core Advantages from AWS Glue
It becomes cheaper since you are only entitled to pay for the resources utilized. Your ETL computing requirements may change during the course of the project, yet you are not expected to pay for time resources other than your scheduled bookings.
Amazon services come tuned to work with AWS Glue. This makes it easy to integrate with Amazon MSK, Amazon S3, Amazon Redshift and Amazon Kinesis. There are other commonly used data storages that can be used with Amazon EC2 for instance.
Since you are only paying for what you used it becomes cheaper in general.
AWS Glue coordinates jobs on Apache Spark meaning that the engineers associated with this part of the process must know Spark well. Developers would be best equipped to handle such situations with adequate knowledge of Scala or Python. This automatically means that not everyone will be able to work with ETL jobs for particular briefs.
Spark is not the best when it comes to high cardinality joins. However when it comes to gaming, fraud detection and advertising they are required. Tasks above and beyond those specified will be required to be completed in order to make such joins efficient.
It is not an easy task to combine stream and batch process since AWS Glue requires stream and batch processes to be mutually exclusive. This results in the same code being churned out and fine-tuned twice. The developer also has to ensure that no duplications or contradictions exist between batch and stream processes. This makes it a complex and inherently limiting process.
Even though AWS Glue is designed to collaborate with external AWS services it falls short of integrations with products that are not encompassed by the AWS sphere.
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