Top 20 AWS SQS Interview Questions and Answers

 

Ques: 1). What distinguishes Amazon SQS from Amazon MQ?

Answer:

Consider Amazon MQ if you want to rapidly and simply migrate your messaging to the cloud while still utilising it with your current apps. You may transition from any standards-based message broker to Amazon MQ without having to completely rewrite the messaging code in your apps since it supports industry-standard APIs and protocols. We advise you to take Amazon SQS and Amazon SNS into account while developing brand-new cloud-based apps. Amazon SQS and SNS are lightweight, fully managed message queue and topic services that offer straightforward, user-friendly APIs and almost indefinite scalability.

 

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Ques: 2). What distinguishes Amazon SQS from Amazon Kinesis Streams?

Answer:

For storing messages as they go between applications or microservices, Amazon SQS provides a dependable, highly scalable hosted queue. It transfers data among various application components and aids in decoupling them. Common middleware components like dead-letter queues and poison-pill management are offered by Amazon SQS. Any programming language that the AWS SDK supports can access it, and it also offers a general web services API. Standard and FIFO queues are both supported by Amazon SQS.

Real-time processing of streaming large data and the ability to read and replay records to numerous Amazon Kinesis Applications are made possible by Amazon Kinesis Streams. The Amazon Kinesis Client Library (KCL) makes it simpler to create numerous apps that read from the same Amazon Kinesis stream by sending all records for a particular partition key to the same record processor (for example, to perform counting, aggregation, and filtering).

 

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Ques: 3). Dead-letter queues: what are they?

Answer:

If the consumer application for the source queue is unable to effectively consume the messages, the source queue may send the messages to a dead-letter queue on Amazon SQS. You can manage the life cycle of unconsumed messages and handle message consumption errors more easily with dead-letter queues. To identify problems with consumer applications, you may create an alert for any messages that are delivered to a dead-letter queue, go through the logs for any errors that led to their delivery to the queue, and check the substance of the messages. You can redrive the messages from your dead-letter queue to the source queue after your consumer application has been restored.

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Ques: 4). How dependable is the data storage in Amazon SQS?

Answer:

In order to prevent message inaccessibility due to a single computer, network, or Availability Zone (AZ) failure, Amazon SQS stores all message queues and messages in a single, highly-available AWS region with many redundant Availability Zones (AZs). See Regions and Availability Zones in the Amazon Relational Database Service User Guide for further details.

AWS Identity and Access Management (IAM) rules are similar to the policies used by Amazon SQS's resource-based permissions system in that both employ policies defined in the same language. For instance, both use variables.

The Transport Layer Security (TLS) and HTTP over SSL (HTTPS) protocols are supported by Amazon SQS. Most clients can automatically negotiate to use newer versions of TLS without any code or configuration change. Amazon SQS supports versions 1.0, 1.1, and 1.2 of the Transport Layer Security (TLS) protocol in all regions.

 

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Ques: 5). Do you provide message purchasing using Amazon SQS?

Answer:

Yes. First-in, first-out (FIFO) queues maintain the precise sequence of messages delivered and received. You don't need to provide sequencing information in your messages if you utilise a FIFO queue. Standard queues include a loose-FIFO feature that makes an effort to maintain the message order. Receiving messages in the precise sequence they are sent is not guaranteed, however, because conventional queues are intended to be immensely scalable utilising a widely dispersed architecture.

 

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Ques: 6). What advantages does Amazon SQS have over custom-built or pre-packaged message queuing systems?

Answer:

Compared to developing your own message queue management software or utilising open-source or commercial message queuing solutions, which take a substantial amount of setup time, Amazon SQS offers a number of benefits.

These options demand continuing system management and hardware maintenance resources. The requirement for redundant message storage, which guarantees that messages are not lost in the event of hardware failure, further increases the complexity of creating and operating these systems.

Amazon SQS, in comparison, needs very minimal configuration and no expense in terms of administration. On a huge scale, Amazon SQS processes billions of messages every day. Without any configuration, you may adjust the amount of traffic you send to Amazon SQS. Amazon SQS also provides extremely high message durability, giving you and your stakeholders added confidence.

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Ques: 7). Is Amazon SQS compatible with other AWS services?

Answer:

Yes. By combining Amazon SQS with computing services like Amazon EC2, Amazon Elastic Container Service (ECS), and AWS Lambda as well as storage and database services like Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB, you can increase the scalability and flexibility of your applications.

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Ques: 8). What is lengthy polling for Amazon SQS?

Answer:

You may obtain messages from your Amazon SQS queues using extended polling. Long polling doesn't respond until a message enters the message queue or the long poll times out, whereas conventional short polling responds, even if the message queue being polled is empty.

It is cheap to collect messages from your Amazon SQS queue as soon as they become available thanks to long polling. If you employ lengthy polling, you can cut down on the amount of empty receives, which might lower the cost of utilising SQS.

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Ques: 9). How can I monitor and control the expenses related to my Amazon SQS queues?

Answer:

Utilizing cost allocation tags, you may label and monitor your queues for resource and expense control. A key-value pair makes up a tag, which is a type of metadata label. You may, for instance, categorise and track your expenditures depending on the cost centres you use to tag your queues.

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Ques: 10). What advantages does SSE have for Amazon SQS?

Answer:

Sensitive data may be sent in encrypted queues using SSE. Using keys maintained by the AWS Key Management Service, SSE secures the contents of messages in Amazon SQS queues (AWS KMS). As soon as messages are received by Amazon SQS, SSE encrypts them. The messages are stored in encrypted form and Amazon SQS decrypts messages only when they are sent to an authorized consumer.

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Ques: 11). What distinguishes Amazon Simple Notification Service (SNS) from Amazon SQS?

Answer:

Applications can use Amazon SNS to deliver time-sensitive messages to a large number of subscribers instead of periodically checking or polling for updates. A message queuing service called Amazon SQS may be used to separate the sending and receiving parts of distributed applications so that messages can be exchanged using a polling mechanism.

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Ques: 12). What number of copies will I get of a message?

Answer:

FIFO queues are made to ensure that no messages are ever introduced twice. In some circumstances, though, your message producer could add duplicates. For instance, if the producer sends a message, doesn't get a reply, and then sends the identical message again. The deduplication mechanism offered by the Amazon SQS APIs stops your message producer from sending duplicate messages. Within a 5-minute deduplication interval, the message producer's duplicates are eliminated.

You could occasionally get a duplicate copy of a message for regular queues (at-least-once delivery). If you utilise a normal queue, you must create idempotent apps (that is, they must not be affected adversely when processing the same message more than once).

 

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Ques: 13). How are unprocessable messages handled by Amazon SQS?

Answer:

Dead letter queues in Amazon SQS may be set up using the console or the API to accept messages from other source queues. You must use RedriveAllowPolicy to specify the correct permissions for the dead letter queue redrive when configuring a dead letter queue.

The dead-letter queue redrive permission's parameters are included in RedriveAllowPolicy. As a JSON object, it specifies which source queues are permitted to declare dead-letter queues.

After creating a dead letter queue, messages are sent to it if the processing cannot be finished within a certain number of attempts. Dead letter queues can be used to collect unprocessable messages for subsequent examination.

 

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Ques: 14). Does message metadata support Amazon SQS?

Answer:

Yes. A message from Amazon SQS may have up to 10 metadata characteristics. The substance of a message may be distinguished from the metadata that describes it using message attributes. Because your apps don't have to analyse a complete message before knowing how to process it, information is processed and stored more quickly and efficiently.

Name-type-value triples are used for Amazon SQS message characteristics. The available types are numeric, binary, and string (including integer, floating-point, and double).

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Ques: 15). Do I need to update my application in order to use the Java version of the AmazonSQSBufferedAsyncClient?

Answer:

No. A drop-in replacement for the current AmazonSQSAsyncClient is provided by the AmazonSQSBufferedAsyncClient for Java.

Your application will gain the advantages of automated batching and prefetching if you update it to utilise the most recent AWS SDK and switch your client to use the AmazonSQSBufferedAsyncClient for Java instead of the AmazonSQSAsyncClient.

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Ques: 16). What timeout setting should I use for my long-poll?

Answer:

A long-poll timeout should generally be limited to 20 seconds. Set your long-poll timeout as high as you can since it will result in fewer empty ReceiveMessageResponse objects being returned.

Set a shorter long-poll timeout, as low as 1 second, if the 20-second limit is insufficient for your application.

By default, all AWS SDKs operate with polls that last 20 seconds. You might need to change your Amazon SQS client to enable longer queries or to use a shorter long-poll timeout if you don't use an AWS SDK or if you set your AWS SDK to explicitly have a lower timeout.

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Ques: 17). How can I set the Amazon SQS limit message size?

Answer:

Use the console or the SetQueueAttributes method to set the MaximumMessageSize property to the desired value. The maximum amount of bytes that an Amazon SQS message may contain is specified by this parameter. Set this attribute's value to anything between 1 KB (1,024 bytes) to 262,144 bytes (256 KB).

Make advantage of the Amazon SQS Extended Client Library for Java to transmit messages bigger than 256 KB. With the help of this library, you are able to send an Amazon SQS message with a reference to a message payload in Amazon S3 that is up to 2 GB in size.

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Ques: 18). Why do the actions of ReceiveMessage and DeleteMessage exist separately?

Answer:

Whether or not you get a message that Amazon SQS returns to you, the message remains in the message queue. The deletion request confirms that you have finished processing the message, and it is your responsibility to delete the message.

If you don't remove the message, Amazon SQS will resend it when it gets a new request to accept it.

 

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Ques: 19). Is it possible to remove every message from a message queue without removing the queue itself?

Answer:

Yes. The PurgeQueue action allows you to remove every message from an Amazon SQS message queue.

All of the messages that have already been sent to a message queue are erased when you purge it. There is no need to change the message queue's configuration because your message queue and its properties are still there. Use the DeleteMessage or DeleteMessageBatch actions to specifically delete particular messages.

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Ques: 20). How do messaging groups work?

Answer:

Within a FIFO queue, messages are organised into discrete, sequential "bundles." All messages are sent and received in exact sequence for each message group ID. Messages with various message group ID values, nevertheless, could be sent and received out of chronological order. A message must be linked to a message group ID. The action fails if a message group ID is not supplied.

If messages with the same message group ID are transmitted to a FIFO queue by several hosts (or separate threads on the same host), Amazon SQS distributes the messages in the order in which they arrive for processing.

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Top 20 AWS QuickSight Interview Questions and Answers

 

                        Amazon QuickSight, a very quick, simple-to-use, cloud-powered business analytics service. All employees within an organisation can easily create visualisations, carry out ad-hoc analysis, and quickly gain business insights from their data with the help of AWS QuickSight. This can be done anytime, anywhere, and on any device. Access on-premises databases like SQL Server, MySQL, and PostgreSQL, upload CSV and Excel files, connect to SaaS programmes like Salesforce, and easily find your AWS data sources like Amazon Redshift, Amazon RDS, Amazon Aurora, Amazon Athena, and Amazon S3. With the help of a powerful in-memory engine, QuickSight allows businesses to grow their business analytics capabilities to hundreds of thousands of users while providing quick and responsive query performance (SPICE).

 

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Ques: 1).  Can you describe SPICE?

Answer:

The "SPICE" super-fast, parallel, in-memory calculation engine is used in the construction of Amazon QuickSight. Built specifically for the cloud, SPICE runs interactive queries on massive datasets and provides quick results by combining columnar storage, in-memory technologies made possible by the newest hardware advancements, and machine code generation. With the support of SPICE, you can do complex calculations to get the most out of your study without having to worry about provisioning or managing infrastructure. Until a user manually deletes data, it is persistent in SPICE. SPICE also enables QuickSight to grow to hundreds of thousands of users who can all concurrently undertake quick interactive analysis across a wide range of AWS data sources, and it replicates data automatically for high availability.

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Ques: 2). With Amazon QuickSight, how can I make an analysis?

Answer:

Making an analysis is straightforward. Within your AWS account, Amazon QuickSight automatically finds data in well-known AWS data repositories. Simply target one of the found data sources at Amazon QuickSight. You can specify the connection information of the source to connect to another AWS data source that is not in your AWS account or in a different zone. After that, pick a table and begin examining your data. Additionally, you may upload CSV and spreadsheet files, and Amazon QuickSight can be used to examine your data. Start by choosing the data fields you wish to examine, dragging the fields into the visual canvas, or performing a combination of these two activities. Amazon QuickSight will automatically select the appropriate visualization to display based on the data you’ve selected.

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Ques: 3). If QuickSight is running in the background of a browser, will a Reader be charged?

Answer:

No, there won't be any use fees if Amazon QuickSight is active in a background tab. Only when there is explicit Reader action on the QuickSight web application does a session start. No further sessions (beyond those started when the Reader was active on the window or tab) will be charged if the QuickSight page is minimised or moved to the background until the Reader interacts with QuickSight once again.

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Ques: 4). Is Amazon QuickSight compatible with my mobile device?

Answer:

For quick access to your data and insights so that you can make choices while you're out and about, use the QuickSight mobile applications (available on iOS and Android). Utilize your dashboards to browse, search, and take action. Dashboards can be added to Favorites for fast access. Using dig downs, filters, and other methods, explore your data. Any mobile device with a web browser may be used to access Amazon QuickSight.

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Ques: 5). How can I get access to my AWS data sources data?

Answer:

Your AWS data sources that are accessible in your account and have your approval are effortlessly discovered by Amazon QuickSight. You may start viewing the data and creating visualisations right now. By supplying connection information for such sources, you may also explicitly connect to additional AWS data sources that are not in your account or in a different region.

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Ques: 6). Can I use JDBC or ODBC to connect to hosted or local databases and specify the AWS region?

Answer:

Yes. Customers are urged to utilise the area where your data is housed for better performance and user interaction. Only the AWS region of the Amazon QuickSight endpoint to which you are connected is used by the auto discovery function of Amazon QuickSight to identify data sources.

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Ques: 7). Row-level security: what is it?

Answer:

With row-level security (RLS), QuickSight dataset owners may restrict access to data at the row level based on the user's rights while interacting with the data. Users of Amazon QuickSight need to maintain one set of data and apply the proper row-level dataset rules to it with RLS. These guidelines will be enforced by all connected dashboards and analytics, making it easier to manage datasets and eliminating the need to keep separate datasets for users with various levels of data access privileges.

 

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Ques: 8). Who are QuickSight's "Authors" and "Readers"?

Answer:

A person who can connect to data sources (inside or outside of AWS), produce graphics, and evaluate data is known as a QuickSight Author. Authors may publish dashboards with other account users and construct interactive dashboards utilising sophisticated QuickSight features like parameters and computed fields.

A user that consumes interactive dashboards is known as a QuickSight Reader. Using a web browser or mobile app, readers may log in using the desired authentication method for their company (QuickSight username/password, SAML portal, or AD auth), view shared dashboards, filter data, dig down to details, or export data as a CSV file. Readers are not allotted any SPICE capacity.

It is possible to grant certain end users access to QuickSight as Readers. Reader price is only valid for manual session interactions. If, in its discretion, it would discovers that you are using reader sessions for other purposes, it has the right to charge the reader at the higher monthly author fee (e.g., programmatic or automated queries).

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Ques: 9). Who is a QuickSight “Admin”? Can I make an Author or Reader an Admin?

Answer:

A person who has the ability to manage QuickSight users, account-level preferences, and buy SPICE capacity and yearly subscriptions for the account is known as a QuickSight Admin. Administrators have access to all QuickSight writing features. If necessary, administrators can also upgrade accounts from Standard Edition to Enterprise Edition.

Authors and Readers of Amazon QuickSight can at any moment become Admins.

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Ques: 10). Can more users be invited by Qucksight "Authors" or "Readers"?

Answer:

No, QuickSight Authors and Readers are the only user categories that are unable to modify account permissions or extend an invitation to other users. One may acquire SPICE capacity and yearly subscriptions for the account as well as manage QuickSight users and account-level options using the Admin user that QuickSight gives. Administrators have access to all QuickSight writing features. If necessary, administrators can also upgrade accounts from Standard Edition to Enterprise Edition.

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Ques: 11). My data's source is not in a tidy format. How should the data be formatted and transformed before visualisation?

Answer:

You can prepare data that isn't ready for visualisation using Amazon QuickSight. The connection dialog's "Edit/Preview Data" button should be selected. To format and alter your data, Amazon QuickSight includes a number of features. You can alter data types and alias data fields. You can use drag and drop to conduct database join operations and built-in filters to subset your data. Using mathematical operations and built-in functions like conditional statements, text, numerical, and date functions, you can also build calculated fields.

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Ques: 12). Can QuickSight dashboards be displayed and refreshed scriptedly on monitors or other big displays using my QuickSight Reader account?

Answer:

The reader price for Amazon QuickSight is applicable to interactive data consumption by end users inside an enterprise. It is advisable that using an Author account to adhere to the QuickSight Reader's fair use standards for automated refresh and programmatic access.

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Ques: 13). A recommended visualisation is what? How are suggestions generated by Amazon QuickSight?

Answer:

A built-in recommendation engine in Amazon QuickSight offers you potential representations depending on the characteristics of the underlying datasets. Suggestions act as potential first or next steps in an analysis, eliminating the time-consuming job of querying and comprehending your data's structure. The recommendations will change as you work with more precise data to reflect the subsequent actions that are appropriate for your current research.

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Ques: 14). How does SageMaker's interaction with QuickSight work?

Answer:

Connecting the data source from which you wish to pull data is the first step. Once you've established a connection to a data source, choose "Augment using SageMaker." The next step is to choose the model you wish to use from a list of SageMaker models in your AWS account and supply the schema file, which is a JSON-formatted file containing the input, output, and run-time parameters. Compare the columns in your data collection with the input schema mapping. When you're finished, you may run this task and begin the inference.

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Ques: 15). What types of visualizations are supported in Amazon QuickSight?

Answer:

Amazon QuickSight supports assorted visualizations that facilitate different analytical approaches:

• Comparison and distribution
• Bar charts (several assorted variants)
• Changes over time
• Line graphs
• Area line charts
• Correlation
• Scatter plots
• Heat maps
• Aggregation
• Pie graphs
• Tree maps
• Tabular
• Pivot tables

 

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Ques: 16). How do stories work?

Answer:

Stories act as walking tours of certain analyses. In order to facilitate cooperation, they are used to communicate significant ideas, a thinking process, or the development of an analysis. They may be built in Amazon QuickSight by recording and annotating particular analysis states. Readers of the tale are directed to the analysis when they click on a story image, where they can further investigate on their own.

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Ques: 17). Which data sources can I use with Amazon QuickSight?

Answer:

AWS data sources including Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon Athena, and Amazon S3 are all accessible through connections. Additionally, you may connect to on-premises databases like SQL Server, MySQL, and PostgreSQL, upload Excel spreadsheets or flat files (CSV, TSV, CLF, and ELF), and import data from SaaS programmes like Salesforce.

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Ques: 18). How can I control who may access Amazon QuickSight?

Answer:

By default, you are given administrator rights when you establish a new Amazon QuickSight account. Whoever invites you assigns you either an ADMIN or a USER role if they want you to utilise Amazon QuickSight. If you have the ADMIN position, you may also buy yearly subscriptions, SPICE capacity, and create and remove user accounts in addition to utilising the service.

Sending an email invitation to the user using an in-app interface allows you to establish a user account. The user then completes the account creation process by choosing a password and logging in.

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Ques: 19). In what ways can I establish a dashboard?

Answer:

Dashboards are groups of visual displays that are grouped and made visible at once, such as tables and visualisations. By selecting the sizes and layouts of the visualisations in an analysis, you may create a dashboard using Amazon QuickSight, which you can then share with a group of people inside your company.

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Ques: 20). What does "private VPC access" entail in regard to Amazon QuickSight?

Answer:

This functionality is for you if you have data in AWS (perhaps in Amazon Redshift, Amazon Relational Database Service (RDS), or on EC2) or locally on Teradata or SQL Server servers on servers without public connection. Elastic Network Interface (ENI) is used by Private VPC (Virtual Private Cloud) Access for QuickSight for secure, private connection with data sources in a VPC. You may also utilise AWS Direct Connect to establish a private, secure connection with your on-premises resources.

 

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        Amazon Athena is an interactive query service that makes it simple to use normal SQL to evaluate data in Amazon S3. Because Athena is serverless, you don't have to worry about maintaining infrastructure, and you just pay for the queries you run.

Athena is simple to operate. Simply point to your Amazon S3 data, define the schema, and begin querying using regular SQL. The majority of results arrive in seconds. There's no need for complicated ETL procedures to prepare your data for analysis with Athena. This makes it simple for anyone with SQL expertise to study massive datasets fast.

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Ques. 1): What is Amazon Athena all about?

Answer:

Amazon Athena is an interactive query service that makes it simple to use normal SQL to evaluate data in Amazon S3. Because Athena is serverless, there is no infrastructure to set up or operate, and you can immediately begin analysing data. You don't even have to load your data into Athena; it works with S3 data immediately. Simply log into the Athena Management Console, create your schema, and begin querying. Amazon Athena works with a range of standard data formats, including CSV, JSON, ORC, Apache Parquet, and Avro, and leverages Presto with full SQL support. While Amazon Athena is great for interactive analytics and interacts with Amazon QuickSight for quick visualisation, it's not the most user-friendly platform. it can also handle complex analysis, including large joins, window functions, and arrays.

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Ques. 2): What makes Amazon Athena, Amazon EMR, and Amazon Redshift different?

Answer:

Different demands and use cases are addressed by query services like Amazon Athena, data warehouses like Amazon Redshift, and advanced data processing frameworks like Amazon EMR. All you have to do now is pick the correct tool for the job. For enterprise reporting and business intelligence workloads, Amazon Redshift provides the fastest query performance, especially for those utilising extremely sophisticated SQL with numerous joins and sub-queries. When compared to on-premises deployments, Amazon EMR makes running highly distributed processing frameworks like Hadoop, Spark, and Presto straightforward and cost effective. You can execute bespoke apps and code on Amazon EMR, as well as configure particular computing, memory, storage, and application parameters to maximise your analytic needs. Amazon Athena makes it simple to execute interactive queries over S3 data without having to set up or manage any servers.

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Ques. 3): When should I utilise Amazon EMR and when should I use Amazon Athena?

Answer:

Amazon EMR is capable of much more than just conducting SQL queries. You can use EMR to conduct a variety of scale-out data processing activities for applications like machine learning, graph analytics, data transformation, streaming data, and almost anything else you can think of. If you utilise custom code to handle and analyse extremely huge datasets with the latest big data processing frameworks like Spark, Hadoop, Presto, or Hbase, you should use Amazon EMR. Amazon EMR allows you complete control over the configuration and applications installed on your clusters.

If you want to conduct interactive SQL queries against data on Amazon S3 without having to manage any infrastructure or clusters, you should utilise Amazon Athena.

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Ques. 4): What data formats is Amazon Athena compatible with?

Answer:

Amazon Athena can handle a wide range of data formats, including CSV, TSV, JSON, and Textfiles, as well as open source columnar formats like Apache ORC and Apache Parquet. Snappy, Zlib, LZO, and GZIP compressed data formats are also supported by Athena. You can increase speed and lower costs by compressing, dividing, and adopting columnar formats.

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Ques. 5): I'm getting data from Kinesis Firehose. How can I use Athena to query it?

Answer:

You can use Amazon Athena to query your Kinesis Firehose data if it's hosted on Amazon S3. Simply construct an Athena schema for your data and begin querying. To improve efficiency, we recommend dividing the data into parts. ALTER TABLE DDL instructions can be used to add partitions produced by Kinesis Firehose. Get more information on partitions.

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Ques. 6): How can I make my query perform better?

Answer:

By compressing, splitting, or turning your data into columnar formats, you can increase the performance of your query. Apache Parquet and Apache ORC are two open source columnar data formats that Amazon Athena supports. By allowing Athena to scan less data from S3 when executing your query, converting your data into a compressed, columnar format minimizes your costs and increases query performance.

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Ques. 7): What is a federated query, exactly?

Answer:

If you have data in places other than Amazon S3, you may use Athena to query it or create pipelines to extract data from numerous sources and put it in Amazon S3. You can perform SQL queries against data stored in relational, non-relational, object, and custom data sources using Athena Federated Query.

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Ques. 8): Can I do ETL (Extract, Transform, Load) using federated queries?

Answer:

Athena stores query results in an Amazon S3 file. This means Athena may be used to make federated data accessible to other users and apps. Use Athena's CREATE TABLE AS function to perform analysis on the data without having to query the underlying source frequently. You may also query the data using Athena's UNLOAD function and save the results in a specific file format to Amazon S3.

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Ques. 9): What embedded ML use cases does Athena support?

Answer:

The following examples show how Athena can be used in a variety of sectors. What-if analysis and Monte Carlo simulations are available to financial risk data analysts. To aid in the creation of richer and forward-looking business dashboards that forecast revenues, business analysts may use linear regression or forecasting models to predict future values. K-means clustering methods could aid marketing analysts in determining their various client categories. Logical regression models could be used by security analysts to uncover abnormalities and detect security incidents in logs.

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Ques. 10): What capabilities does Athena ML have?

Answer:

Athena provides machine learning inference (prediction) capabilities using a SQL interface. You can also use an Athena UDF to perform pre- or post-processing logic on your result set. Multiple calls can be batched together for increased scalability, and inputs can be any column, record, or table. Inference can be performed during the Select or Filter phases.

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Ques. 11): Is Athena highly available?

Answer:

Yes. Amazon Athena is highly available, executing queries across many facilities and intelligently routing queries correctly if one of the facilities is unavailable. Athena's underlying data store is Amazon S3, which makes your data highly available and durable. Amazon S3 provides a reliable infrastructure for storing essential data, with 99.999999999 percent object durability. Your information is duplicated across numerous facilities and devices inside each facility.

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Ques. 12): What should I do to lower the costs?

Answer:

By compressing, splitting, and turning your data into columnar formats, you can save 30 percent to 90 percent on query costs while also improving performance. Each of these actions reduces the quantity of data that Amazon Athena must scan in order to complete a query. Apache Parquet and ORC, two of the most popular open-source columnar formats, are supported by Amazon Athena. On the Athena console, you can view how much data was scanned for each query.

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Ques. 13): Are there any other fees related with Amazon Athena?

Answer:

Your source data is invoiced at S3 rates because Amazon Athena queries data directly from Amazon S3. When you perform a query through Amazon Athena, the results are saved in an S3 bucket of your choosing, and you are charged at standard S3 rates for these result sets. We recommend that you keep an eye on these buckets and utilise lifecycle policies to limit how much data is kept.

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Ques. 14): Does the User Defined Functions (UDFs) are supported by Athena?

Answer:

User-defined functions (UDFs) in Amazon Athena allow you to create new scalar functions and utilise them in SQL queries. While Athena has built-in capabilities, UDFs allow you to conduct custom processing such as data compression and decompression, redaction of sensitive material, and bespoke decryption.

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Ques. 15): In Amazon Athena, how can I add new data to an existing table?

Answer:

If your data is partitioned, you'll need to run an ALTER TABLE ADD PARTITION metadata query to add the partition to Athena once new data is available on Amazon S3. If your data isn't partitioned, simply adding new data (or files) to an existing prefix will add them to Athena.

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Ques. 16): What exactly is a SerDe?

Answer:

Serializer/Deserializer are libraries that teach Hive how to understand different data formats. You must mention a SerDe in Hive DDL statements so that the system knows how to interpret the data you're pointing to. SerDes is used by Amazon Athena to analyse data read from Amazon S3. SerDes is the same notion in Athena as it is in Hive. The following SerDes are supported by Amazon Athena:

Apache Web Logs: "org.apache.hadoop.hive.serde2.RegexSerDe"

CSV: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

TSV: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

Custom Delimiters: "org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe"

Parquet: "org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe"

Orc: "org.apache.hadoop.hive.ql.io.orc.OrcSerde"

JSON: “org.apache.hive.hcatalog.data.JsonSerDe” OR org.openx.data.jsonserde.JsonSerDe

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Ques. 17): Can I query data processed with Amazon EMR using Amazon Athena?

Answer:

Yes, Amazon Athena and Amazon EMR both support many of the same data formats. The Athena data catalogue is compatible with the Hive metastore. If you're utilising EMR and already have a Hive metastore, you can query your data straight away without affecting your Amazon EMR operations by executing your DDL statements on Amazon Athena.

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Ques. 18): How are table definitions and schema stored in Amazon Athena?

Answer:

To keep information and schemas about the databases and tables you create for your data saved in Amazon S3, Amazon Athena employs a managed Data Catalog. You can use the AWS Glue Data Catalog with Amazon Athena in regions where AWS Glue is accessible. Athena uses an internal Catalog in regions where AWS Glue is not available.

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The catalogue can be modified using DDL statements or the AWS Management Console. Unless you delete them directly, any schemas you define are automatically stored. Athena leverages schema-on-read technology, which means that when queries are run, your table definitions are applied to your data on S3. There’s no data loading or transformation required. You can delete table definitions and schema without impacting the underlying data stored on Amazon S3.

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Ques. 19): Can I use Athena to run any Hive query?

Answer:

Hive is only used by Amazon Athena for DDL (Data Definition Language) and for creating, modifying, and deleting tables and partitions. For a complete list of statements that are supported, please check here. When you run SQL queries on Amazon S3, Athena uses Presto. To query your data in Amazon S3, you can use ANSI-Compliant SQL SELECT queries.

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Ques. 20): Is data partitioning possible with Amazon Athena?

Answer:

Yes. You can segment your data on any column with Amazon Athena. Partitions reduce the quantity of data scanned by each query, resulting in cost savings and faster performance. The PARTITIONED BY clause in the CREATE TABLE statement allows you to specify your partitioning plan.  

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Ques. 21): What is the purpose of data source connectors?

Answer:

A data source connector is a piece of AWS Lambda code that bridges the gap between your target data source and Athena. You can conduct SQL queries on federated data stores after using a data source connector to register a data store with Athena. When a query is conducted on a federated source, Athena invokes the Lambda function, which is tasked with executing the parts of your query that are unique to the federated source.

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