April 21, 2022

Top 20 AWS Glue Interview Questions and Answers


                AWS Glue is a serverless data integration tool that makes finding, preparing, and combining data for analytics, machine learning, and application development a breeze. AWS Glue has all of the data integration features you'll need, so you can start analysing and using your data in minutes rather than months. To make data integration easier, AWS Glue offers both visual and code-based interfaces. The AWS Glue Data Catalog allows users to quickly locate and retrieve data. With a few clicks in AWS Glue Studio, data engineers and ETL (extract, transform, and load) developers can graphically construct, run, and monitor ETL workflows. Data analysts and scientists can use AWS Glue DataBrew to visually enrich, clean, and standardise data without having to write code. Application developers may utilise Structured Query Language (SQL) to mix and replicate data across disparate data stores with AWS Glue Elastic Views.


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Ques. 1): What are your thoughts on AWS Glue?


AWS Glue is a service that makes categorising, cleaning, and reliably moving data across various data stores and data streams simple and cost effective.

  • It comprises of the SWA Glue Catalog, a central metadat repository.
  • By handling dependency resolution, task monitoring, and retries, AWS Glue assists in the generation of Python or Scala code.
  • AWS Glue is a serverless infrastructure that is easy to set up and manage, and it has a dynamic frame component that we can use in our ETL scripts.
  • Dynamic Frame is the same as an Apache Spark dataframe and is a data abstraction for organising data into rows and columns.


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Ques. 2): Which Data Stores Can I Crawl using Glue?


  • Crawlers can crawl both file-based and table-based data stores.
  • Crawlers can crawl the following data stores through their respective native interfaces:
  • Amazon Simple Storage Service (Amazon S3)
  • Amazon DynamoDB
  • Crawlers can crawl the following data stores through a JDBC connection:
  • Amazon Redshift
  • Amazon Relational Database Service (Amazon RDS)
  • Amazon Aurora
  • Microsoft SQL Server
  • MySQL
  • Oracle
  • PostgreSQL
  • Publicly accessible databases
  • Aurora
  • Microsoft SQL Server
  • MySQL
  • Oracle
  • PostgreSQL


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Ques. 3): What components does AWS Glue make use of?


AWS Glue is made up of the following ingredients:

  • Data Catalog is a Metadata Repository on the Cloud.
  • The ETL Engine assists with the generation of Python and Scala code.
  • Flexible Scheduler aids in the resolution of dependencies, job monitoring, and retring.
  • AWS Glue DataBrew provides a visual interface for normalising and cleaning data.
  • Replicating and combining data across various data stores with AWS Glue Elastic View.


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Ques. 4): What is AWS Glue DataBrew, and how does it work?


AWS Glue DataBrew is a visual data preparation solution that allows data analysts and scientists to prepare data without writing code using an interactive, point-and-click visual interface. You can easily view, clean, and normalise terabytes, if not petabytes, of data directly from your data lake, data warehouses, and databases, including Amazon S3, Amazon Redshift, Amazon Aurora, and Amazon RDS, using Glue DataBrew. AWS Glue DataBrew is now broadly available in the US East (North Carolina), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), Asia Pacific (Sydney), and Asia Pacific (Frankfurt) regions (Tokyo).


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Ques. 5): What steps do I need to take to get my metadata into the AWS Glue Data Catalog?


There are several ways to populate metadata into the AWS Glue Data Catalog with AWS Glue. Glue crawlers automatically deduce schemas and partition structure from various data sources you control, populating the Glue Data Catalog with corresponding table definitions and statistics. You can also schedule crawlers to run on a regular basis to keep your metadata current and in sync with the underlying data. Alternatively, you can use the AWS Glue Console or the API to manually add and change table details. On an Amazon EMR cluster, you can also run Hive DDL statements via the Amazon Athena Console or a Hive client. Finally, if you already have a persistent Apache Hive Metastore, you can perform a bulk import of that metadata into the AWS Glue Data Catalog by using our import script.


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Ques. 6): What steps does AWS Glue take to deduplicate my data?


The FindMatches ML Transform in AWS Glue makes it simple to locate and link records that refer to the same entity but lack a unique identifier. Before FindMatches, data-matching problems were usually solved deterministically by constructing a large number of hand-tuned rules. Behind the scenes, FindMatches employs machine learning algorithms to learn how to match records based on each developer's specific business criteria. FindMatches first selects records for the client to categorise as matching or not matching, and then creates an ML Transform using machine learning.


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Ques. 7): To use AWS Glue DataBrew, do I need to use AWS Glue Data Catalog or AWS Lake Formation?


No. You don't need the AWS Glue Data Catalog or AWS Lake Formation to use AWS Glue DataBrew. If you utilise either the AWS Glue Data Catalog or AWS Lake Formation, DataBrew users can choose from a centralised data catalogue of data sets available to them.


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Ques. 8): What are the benefits of using AWS Glue Schema Registry?


  • Validate schemas using the AWS Glue Schema Registry. Schemas used for data production are checked against schemas in a central registry when data streaming apps are linked with AWS Glue Schema Registry, allowing you to centrally regulate data quality.
  • Maintain the evolution of the schema. One of eight compatibility modes can be used to specify criteria for how schemas can and cannot grow.
  • Improve the quality of your data. Serializers compare data producers' schemas to those in the registry, enhancing data quality at the source and avoiding downstream difficulties caused by unexpected schema drift.
  • Save money. Serializers transform data into a binary format, which can then be compressed before being provided, lowering data transit and storage costs.
  • Increase the speed of processing. A data stream often contains records with multiple schemas. The Schema Registry allows applications that read data streams to choose process each record based on the schema rather than parsing its contents, improving processing efficiency.


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Ques. 9): What are the benefits of using AWS Glue Elastic Views?


To aggregate and constantly replicate data across various data stores in near-real time, you should use AWS Glue Elastic Views. This is often the case when developing new application functionality that requires access to data from one or more current data stores. An organisation might, for example, utilise a customer relationship management (CRM) programme to keep track of client connections and an e-commerce website to conduct online transactions. These applications would store data in one or more data stores. The organisation is now developing a new bespoke application that generates and presents unique offers to active website visitors. This programme accomplishes this by combining customer data from the CRM application with online clickstream data from the e-commerce application. A developer can create new functionality in three phases using AWS Glue Elastic Views. First, they use AWS Glue Elastic Views to connect the CRM and e-commerce application data stores. Then, using SQL, they choose the appropriate data from the CRM and e-commerce data databases. Finally, they connect the data storage of the custom application to the results.


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Ques. 10): Which AWS services and open source projects make use of AWS Glue Data Catalog?


Following are the AWS services and open source projects that make use of the AWS Glue Data Catalog include:

  • AWS Lake Formation
  • Amazon Athena
  • Amazon Redshift Spectrum
  • Amazon EMR
  • AWS Glue Data Catalog Client for Apache Hive Metastore


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Ques. 11): When should I employ a Glue Classifier?


When you crawl a data store to define metadata tables in the AWS Glue Data Catalog, you employ classifiers. You can use an ordered set of classifiers to set up your crawler. When a crawler calls a classifier, the classifier determines whether or not the data has been identified. If the first classifier fails to recognise the data or is unsure, the crawler moves on to the next classifier in the list to see if it can recognise the data.


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Ques. 12): How can I connect to the AWS Glue Schema Registry in a secure manner?


By configuring an interface VPC endpoint for AWS Glue, you may use AWS PrivateLink to link your data producer's VPC to AWS Glue. Communication between your VPC and AWS Glue occurs entirely within the AWS network when you use a VPC interface endpoint.


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Ques. 13): When a crawler runs, what happens?


To examine a data storage, a crawler performs the following actions:

  • Create a custom classifier to customise the results of classification in order to determine the raw data's format, schema, and associated attributes.
  • Data is organised into tables or partitions based on crawler algorithms.
  • You can control how the crawler adds, changes, and deletes tables and partitions by configuring how it writes metadata to the Data Catalog.


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Ques. 14): When should I utilise Amazon EMR vs. AWS Glue?


AWS Glue is a scale-out execution environment for your data transformation activities that runs on top of the Apache Spark ecosystem. AWS Glue infers, adapts, and monitors your ETL jobs, making job creation and maintenance more easier. Amazon EMR gives you direct access to your Hadoop environment, allowing you to access it at a lower level and use tools other than Spark.


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Ques. 15): What options do I have for customising the ETL code provided by AWS Glue?


Scala or Python code is generated by AWS Glue's ETL script suggestion algorithm. It makes use of Glue's custom ETL framework to make it easier to access data sources and manage job execution. More information about the library can be found in our documentation. You can use AWS Glue's own library to write ETL code, or you can use inline editing in the AWS Glue Console script editor to write arbitrary code in Scala or Python, then download the auto-generated code and edit it in your own IDE.   


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Ques. 16): How am I charged for AWS Glue?


Over and beyond the AWS Glue Data Catalog free tier, you'll pay a basic monthly cost to store and retrieve metadata in the AWS Glue Data Catalog. The crawler run will cost you an hourly charge, paid per second, with a 10-minute minimum. If you want to use a development endpoint to create your ETL code interactively, you will be charged an hourly rate, billed per second, for the time it takes to provision your development endpoint, with a 10-minute minimum. In addition, depending on the Glue version you choose, you'll pay an hourly cost, billed per second, for the ETL process, with a 1-minute or 10-minute minimum.


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Ques. 17): Is it possible to monitor and troubleshoot AWS Glue ETL operations using the Apache Spark web UI?


Yes, you can monitor and debug AWS Glue ETL processes running on the AWS Glue job system, as well as Spark applications running on AWS Glue development endpoints, using the Apache Spark web UI. For each job, the Spark UI allows you to verify the following:

  • Each Spark stage's event chronology
  • The job's directed acyclic graph (DAG).
  • SparkSQL query physical and logical plans
  • For each job, the underlying Spark environmental variables


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Ques. 18): What happens if AWS Glue encounters an ETL error?


AWS Glue keeps track of job event metrics and faults and sends all alerts to Amazon CloudWatch. With Amazon CloudWatch, you can set up a variety of actions to be triggered in response to certain AWS Glue notifications. You may use an AWS Lambda function to handle an error or success notification from Glue, for example. The default retry behaviour in Glue is to retry all failures three times before sending an error notification.


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Ques. 19): When a glue crawler decides to construct partitions, how does it do so?


When an AWS Glue crawler searches an Amazon S3 path and finds several folders in a bucket, it determines which folders are table partitions and the root of a table in the folder structure. The table's name is derived from the Amazon S3 prefix, or folder name. You specify an Include path that points to the crawled folder level. The crawler makes divisions of a table instead of two independent tables when the majority of schemas at a folder level are similar.


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Ques. 20): AWS Glue Elastic Views currently supports which sources and targets?


Amazon DynamoDB is now supported for the preview, with Amazon Aurora MySQL, Amazon Aurora PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for PostgreSQL to follow. Amazon Redshift, Amazon S3, and Amazon OpenSearch Service are now supported targets, with support for Amazon Aurora MySQL, Amazon Aurora PostgreSQL, Amazon RDS for MySQL, and Amazon RDS for PostgreSQL on the way.


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