Showing posts with label frequently. Show all posts
Showing posts with label frequently. Show all posts

November 01, 2022

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).

 

AWS(Amazon Web Services) Interview Questions and Answers

AWS Cloud Interview Questions and Answers


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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June 07, 2022

Top 20 Amazon OpenSearch Interview Questions and Answers

  

    Amazon OpenSearch Service is used to do interactive log analytics, real-time application monitoring, internet search, and other tasks. OpenSearch is a distributed search and analytics package based on Elasticsearch that is open source. Amazon OpenSearch Service is the successor of Amazon Elasticsearch Service, and it includes the most recent versions of OpenSearch, as well as support for 19 different versions of Elasticsearch (from 1.5 to 7.10), as well as visualisation features via OpenSearch Dashboards and Kibana (1.5 to 7.10 versions).


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AWS Cloud Interview Questions and Answers


Ques. 1): What is an Amazon OpenSearch Service domain?

Answer:

Elasticsearch (1.5 to 7.10) or OpenSearch clusters built with the Amazon OpenSearch Service dashboard, CLI, or API are considered Amazon OpenSearch Service domains. Each domain is a cloud-based OpenSearch or Elasticsearch cluster with the computation and storage resources you choose. Domains may be created and deleted, infrastructure attributes can be defined, and access and security can be controlled. One or more Amazon OpenSearch Service domains can be used.


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Ques. 2): Why should I store my items in cold storage?

Answer:

Cold storage allows you to increase the data you wish to examine on Amazon OpenSearch Service at a lower cost and acquire significant insights into data that was previously purged or archived. If you need to undertake research or forensic analysis on older data and want to access all of the features of Amazon OpenSearch Service at an affordable price, cold storage is a wonderful choice. Cold storage is designed for large-scale deployments and is supported by Amazon S3. Find and discover the data you want, then link it to your cluster's UltraWarm nodes and make it available for analysis in seconds. The same fine-grained access control restrictions that limit access at the index, document, and field level apply to attached cold data.


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Ques. 3): What types of error logs does Amazon OpenSearch Service expose?

Answer:

OpenSearch makes use of Apache Log4j 2 and its built-in log levels of TRACE, DEBUG, INFO, WARN, ERROR, and FATAL (from least to most severe). If you enable error logs, Amazon OpenSearch Service sends WARN, ERROR, and FATAL log lines to CloudWatch, as well as select failures from the DEBUG level.  


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Ques. 4): Is it true that enabling slow logs in Amazon OpenSearch Service also enables logging for all indexes?

Answer:

No. When slow logs are enabled in Amazon OpenSearch Service, the option to publish the generated logs to Amazon CloudWatch Logs for indices in the provided domain becomes available. However, in order to begin the logging process, you must first adjust the parameters for one or more indices.


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Ques. 5): Is it possible to make more snapshots of my Amazon OpenSearch Service domains as needed?

Answer:

Yes. In addition to the daily-automated snapshots made by Amazon OpenSearch Service, you may utilise the snapshot API to make extra manual snapshots. Manual snapshots are saved in your S3 bucket and are subject to Amazon S3 use fees.


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Ques. 6): Is there any performance data available from Amazon OpenSearch Service via Amazon CloudWatch?

Answer:

Yes. Several performance indicators for data and master nodes are exposed by Amazon CloudWatch, including number of nodes, cluster health, searchable documents, EBS metrics (if relevant), CPU, memory, and disc use.


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Ques. 7): Can my Amazon OpenSearch Service domains be accessed by applications operating on servers in my own data centre?

Answer:

Yes. Through a public endpoint, applications having public Internet access can access Amazon OpenSearch Service domains. You can utilise VPC access if your data centre is already linked to Amazon VPC using Direct Connect or SSH tunnelling. In both circumstances, you may use IAM rules and security groups to grant access to your Amazon OpenSearch Service domains to applications operating on non-AWS servers.  


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Ques. 8): How does Amazon OpenSearch Service handle AZ outages and instance failures?

Answer:

When one or more instances in an AZ become unavailable or unusable, Amazon OpenSearch Service attempts to put up new instances in the same AZ to take their place. If the domain has been set to deploy instances over several AZs, and fresh instances cannot be brought up in the AZ, Amazon OpenSearch Service brings up new instances in the other available AZs. When the AZ problem is resolved, Amazon OpenSearch Service rebalances the instances so that they are evenly distributed among the domain's AZs.


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Ques. 9): What is the distribution of dedicated master instances among AZs?

Answer:

When you deploy your data instances in a single AZ, you must also deploy your dedicated master instances in the same AZ. If you divide your data instances over two or three AZs, Amazon OpenSearch Service distributes the dedicated master instances across three AZs automatically. If a region only has two AZs, or if you choose an older-generation instance type for the master instances that isn't accessible in all AZs, this rule does not apply.


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Ques. 10): Is it possible to integrate Amazon OpenSearch Service with Logstash?

Answer:

Yes. Logstash is compatible with Amazon OpenSearch Service. You may use your Amazon OpenSearch Service domain as the backend repository for all Logstash logs. You may use request signing to authenticate calls from your Logstash implementation or resource-based IAM policies to include IP addresses of instances running your Logstash implementation when configuring access control on your Amazon OpenSearch Service domain.


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Ques. 11): What does the Amazon OpenSearch Service accomplish for me?

Answer:

From delivering infrastructure capacity in the network environment you require to installing the OpenSearch or Elasticsearch software, Amazon OpenSearch Service automates the work needed in setting up a domain. Once your domain is up and running, Amazon OpenSearch Service automates standard administration chores like backups, instance monitoring, and software patching. The Amazon OpenSearch Service and Amazon CloudWatch work together to provide metrics that offer information about the condition of domains. To make customising your domain to your application's needs easier, Amazon OpenSearch Service provides tools to adjust your domain instance and storage settings.


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Ques. 12): What data sources is Trace Analytics compatible with?

Answer:

Trace Analytics now enables the collection of trace data from open source OpenTelemetry Collector-compatible application libraries and SDKs, such as the Jaeger, Zipkin, and X-Ray SDKs. AWS Distro for OpenTelemetry, a distribution of OpenTelemetry APIs, SDKs, and agents/collectors, is also integrated with Trace Analytics. It is an AWS-supported, high-performance and secure distribution of OpenTelemetry components that has been tested for production usage. Customers may utilise AWS Distro for OpenTelemetry to gather traces and metrics for a variety of monitoring solutions, including Amazon OpenSearch Service, AWS X-Ray, and Amazon CloudWatch for trace data and metrics, respectively.


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Ques. 13): What is the relationship between Open Distro for Elasticsearch and the Amazon OpenSearch Service?

Answer:

Open Distro for Elasticsearch has a new home in the OpenSearch project. Amazon OpenSearch Service now supports OpenSearch and provides capabilities such as corporate security, alerting, machine learning, SQL, index state management, and more that were previously only accessible through Open Distro.


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Ques. 14): What are UltraWarm's performance characteristics?

Answer:

UltraWarm implements granular I/O caching, prefetching, and query engine improvements in OpenSearch Dashboards and Kibana to give performance comparable to high-density installations using local storage.


AWS Elastic Block Store (EBS) Interview Questions and Answers


Ques. 15): Is it possible to cancel a Reserved Instance?

Answer:

No, Reserved Instances cannot be cancelled, and the one-time payment (if applicable) and discounted hourly usage rate (if applicable) are non-refundable. You also won't be able to move the Reserved Instance to another account. Regardless matter how much time you use your Reserved Instance, you must pay for each hour.


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Ques. 16): What happens to my reservation if I scale my Reserved Instance up or down?

Answer:

Each Reserved Instance is linked to the instance type and region that you choose. You will not receive lower pricing if you change the instance type in the Region where you have the Reserved Instance. You must double-check that your reservation corresponds to the instance type you intend to utilise.  


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Ques. 17): For the Amazon OpenSearch Service, what constitutes billable instance hours?

Answer:

Instance hours are invoiced for each hour your instance is running in an available state on Amazon OpenSearch Service. To prevent being paid for extra instance hours, you must deactivate the domain if you no longer want to be charged for your Amazon OpenSearch Service instance. Instance hours utilised in part by Amazon OpenSearch Service are invoiced as full hours.


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Ques. 18): Is it possible to update the domain swiftly without losing any data?

Answer:

No. All of the data in your cluster is recovered as part of the in-place version upgrading procedure. You can take a snapshot of your data, erase all your indexes from the domain, and then do an in-place version upgrade if you simply want to upgrade the domain. You may also establish a new domain using the newest version and then restore your data to that domain.


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Ques. 19): How does Amazon OpenSearch Service protect itself from problems that may arise during version upgrades?

Answer:

Before triggering the update, Amazon OpenSearch Service conducts a series of checks to look for known problems that might prevent the upgrade. If no problems are found, the service takes a snapshot of the domain and, if the snapshot is successful, begins the upgrading process. If there are any problems with any of the stages, the upgrade will not take place.


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Ques. 20): When logging is turned on or off, will the cluster experience any downtime?

Answer:

No. There will be no lulls in the action. We will install a new cluster in the background every time the log status is changed, and replace the old cluster with the new one. There will be no downtime as a result of this procedure. However, because a new cluster has been installed, the log status will not be updated immediately.

 

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Top 20 Amazon EMR Interview Questions and Answers

 

    Using open source frameworks like as Apache Spark, Apache Hive, and Presto, Amazon EMR is the industry-leading cloud big data platform for data processing, interactive analysis, and machine learning. With EMR, you can perform petabyte-scale analysis for half the price of typical on-premises solutions and over 1.7 times quicker than ordinary Apache Spark.


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AWS Cloud Interview Questions and Answers


Ques. 1): What are the benefits of using Amazon EMR?

Answer:

Amazon EMR frees you up to focus on data transformation and analysis rather than maintaining computing resources or open-source apps, and it saves you money. You may supply as much or as little capacity on Amazon EC2 as you want using EMR, and build up scaling rules to handle changing compute demand. CloudWatch notifications may be set up to notify you of changes in your infrastructure so you can react quickly. You may use EMR to submit your workloads to Amazon EKS clusters if you utilise Kubernetes. Whether you employ EC2 or EKS, EMR's optimised runtimes help you save time and money by speeding up your analysis.


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Ques. 2): How do I troubleshoot a query that keeps failing after each iteration?

Answer:

You may use the same tools that they use to troubleshoot Hadoop Jobs in the case of a processing failure. The Amazon EMR web portal, for example, may be used to locate and view error logs. Here's where you can learn more about troubleshooting an EMR task.


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Ques. 3): What is the best way to create a data processing application?

Answer:

In Amazon EMR Studio, you can create, display, and debug data science and data engineering applications written in R, Python, Scala, and PySpark. You may also create a data processing task on your desktop and run it on Amazon EMR using Eclipse, Spyder, PyCharm, or RStudio. When spinning up a new cluster, you may also pick JupyterHub or Zeppelin in the software configuration and build your application on Amazon EMR utilising one or more instances.


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Ques. 4): Is it possible to perform many queries in a single iteration?

Answer:

Yes, you may specify a previously ran iteration in subsequent processing by specifying the kinesis.checkpoint.iteration.no option. The approach ensures that subsequent runs on the same iteration use the exact same input records from the Kinesis stream as earlier runs.


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Ques. 5): In Amazon EMR, how is a computation done?

Answer:

The Hadoop data processing engine is used by Amazon EMR to perform calculations using the MapReduce programming methodology. The customer uses the map() and reduce() methods to create their algorithm. A customer-specified number of Amazon EC2 instances, consisting of one master and several additional nodes, are started by the service. On these instances, Amazon EMR runs Hadoop software. The master node separates the input data into blocks and distributes the block processing to the subordinate nodes. The map function is then applied to the data that has been assigned to each node, resulting in intermediate data. The intermediate data is then sorted and partitioned before being transmitted to processes on the nodes that perform the reduction function locally.


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Ques. 6): What distinguishes EMR Studio from EMR Notebooks?

Answer:

There are five major differences:

EMR Studio does not require access to the AWS Management Console. The EMR Studio server is not part of the AWS Management Console. If you don't want data scientists or engineers to have access to the AWS Management Console, this is a good option.

To log in to EMR Studio, you can utilise enterprise credentials from your identity provider using AWS Single Sign-On (SSO).

EMR Studio provides you with your first notebook encounter. Because EMR Studio kernels and applications operate on EMR clusters, you receive the benefit of distributed data processing with the Amazon EMR runtime for Apache Spark, which is designed for performance.

Attaching the laptop to an existing cluster or establishing a new one is all it takes to run code on a cluster.

EMR Studio features a user interface that is simple to use and abstracts hardware specifications. For instance, you can create cluster templates once and then utilise them to create future clusters.

EMR Studio facilitates debugging by allowing you to access native application user interfaces in one location with as few clicks as feasible.


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Ques. 7): What tools are available to me for debugging?

Answer:

You may use a variety of tools to gather information about your cluster and figure out what went wrong. If you utilise Amazon EMR studio, you can leverage debugging tools like Spark UI and YARN Timeline Service. You can gain off-cluster access to persistent application user interfaces for Apache Spark, Tez UI, and the YARN timeline server through the Amazon EMR Console, as well as multiple on-cluster application user interfaces and a summary view of application history for all YARN apps. You may also use SSH to connect to your Master Node and inspect cluster instances using these web interfaces. See our docs for additional details.


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Ques. 8): What are the advantages of utilising Command Line Tools or APIs rather than the AWS Management Console?

Answer:

The Command Line Tools or APIs allow you to programmatically launch and monitor the progress of running clusters, as well as build custom functionality for other Amazon EMR customers (such as sequences with multiple processing steps, scheduling, workflow, or monitoring) or build value-added tools or applications. The AWS Management Console, on the other hand, offers a simple graphical interface for starting and monitoring your clusters from a web browser.


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Ques. 9): What distinguishes EMR Studio from SageMaker Studio?

Answer:

With Amazon EMR, you may utilise both EMR Studio and SageMaker Studio. EMR Studio is an integrated development environment (IDE) for developing, visualising, and debugging data engineering and data science applications in R, Python, Scala, and PySpark. Amazon SageMaker Studio is a web-based visual interface that allows you to complete all machine learning development phases in one place. SageMaker Studio provides you total control, visibility, and access to every step of the model development, training, and deployment process. You can upload data, create new notebooks, train and tune models, travel back and forth between phases to change experiments, compare findings, and push models to production all in one spot, increasing your productivity significantly.


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Ques. 10): Is it possible to establish or open a workspace in EMR Studio without a cluster?

Answer:

Yes, a workspace may be created or opened without being attached to a cluster. You should only join them to a cluster when you need to execute. EMR Studio kernels and apps run on Amazon EMR clusters, allowing you to take advantage of distributed data processing with the Amazon EMR runtime for Apache Spark.


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Ques. 11): What computational resources can I use in EMR Studio to execute notebooks?

Answer:

You may execute notebook code on Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2) or Amazon EMR on Amazon Elastic Kubernetes Service using EMR Studio (Amazon EKS). Notebooks can be added to either existing or new clusters. In EMR Studio, you can construct EMR clusters in two ways: by using an AWS Service Catalog pre-configured cluster template or by defining the cluster name, number of instances, and instance type.


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Ques. 12): What IAM policies are required to utilise EMR Studio?

Answer:

To interact with other AWS services, each EMR studio requires permissions. Your administrators must build an EMR Studio service role using the specified policies to grant the essential access to your EMR Studios. They must also create a user role for EMR Studio that defines permissions at the Studio level. They may assign a session policy to a user or group when they add users and groups from AWS Single Sign-On (AWS SSO) to EMR Studio to apply fine-grained authorization constraints. Administrators may utilise session policies to fine-tune user rights without having to create several IAM roles. See Policies and Permissions in the AWS Identity and Access Management User Guide for further information on session policies.


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Ques. 13): What may EMR Notebooks be used for?

Answer:

EMR Notebooks make it simple to create Apache Spark apps and conduct interactive queries on your EMR cluster. Multiple users may build serverless notebooks straight from the interface, attach them to an existing shared EMR cluster, or provision a cluster and begin playing with Spark right away. Notebooks can be detached and reattached to new clusters. Notebooks are automatically saved to S3 buckets, and you may access them from the console to resume working. The libraries contained in the Anaconda repository are preconfigured in EMR Notebooks, allowing you to import and utilise them in your notebooks code to modify data and show results. Furthermore, EMR notebooks feature built-in Spark monitoring capabilities, allowing you to track the status of your Spark operations and debug code directly from the notebook.


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Ques. 14): Is Amazon EMR compatible with Amazon EC2 Spot, Reserved, and On-Demand Instances?

Answer:

Yes. On-Demand, Spot, and Reserved Instances are all supported by Amazon EMR.


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Ques. 15): What role do Availability Zones play in Amazon EMR?

 Answer:

All nodes for a cluster are launched in the same Amazon EC2 Availability Zone using Amazon EMR. Running a cluster in the same zone enhances work flow performance. By default, Amazon EMR runs your cluster in the Availability Zone with the greatest available resources. You can, however, define a different Availability Zone if necessary. You may also utilise On-Demand Capacity Reservations to optimise your allocation for the lowest-priced on-demand instances, best spot capacity, or lowest-priced on-demand instances.


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Ques. 16): What are node types in a cluster?

Answer:

There are three sorts of nodes in an Amazon EMR cluster:

master node : A master node supervises the cluster by executing software components that coordinate the distribution of data and tasks among the other nodes for processing. The master node keeps track of task progress and oversees the cluster's health. A master node is present in every cluster, and it is feasible to establish a single-node cluster using only the master node.

core node : A core node is a node that contains software components that conduct jobs and store data in your cluster's Hadoop Distributed File System (HDFS). At least one core node exists in multi-node clusters.

task node: A task node is a node that only performs tasks and does not store data in HDFS. Task nodes are not required.


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Ques. 17): Can Amazon EMR restore a cluster's master node if it goes down?

Answer:

Yes. You may set up an EMR cluster with three master nodes (version 5.23 or later) to offer high availability for applications like YARN Resource Manager, HDFS Name Node, Spark, Hive, and Ganglia. If the primary master node fails or important processes, such as Resource Manager or Name Node, crash, Amazon EMR immediately switches to a backup master node. You may run your long-lived EMR clusters without interruption since the master node is not a potential single point of failure. When a master node fails, Amazon EMR immediately replaces it with a new master node that has the same configuration and boot-strap activities.


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Ques. 18): What are the steps for configuring Hadoop settings for my cluster?

Answer:

For most workloads, the EMR default Hadoop setup is sufficient. However, depending on the memory and processing needs of your cluster, changing these values may be necessary. If your cluster activities are memory-intensive, for example, you may want to employ fewer tasks per core and limit the size of your job tracker heap. A pre-defined Bootstrap Action is offered to configure your cluster on starting in this case. For setup information and usage instructions, see the Developer's Guide's Configure Memory Intensive Bootstrap Action. You may also use an extra preset bootstrap action to tailor your cluster parameters to whatever value you like.


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Ques. 19): Is it possible to modify tags directly on Amazon EC2 instances?

Answer:

Yes, tags may be added or removed directly on Amazon EC2 instances in an Amazon EMR cluster. However, because Amazon EMR's tagging system does not immediately sync changes to a corresponding Amazon EC2 instance, we do not advocate doing so. To guarantee that the cluster and its associated Amazon EC2 instances have the necessary tags, we recommend using the Amazon EMR GUI, CLI, or API to add and delete tags for Amazon EMR clusters.


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Ques. 20): How does Amazon EMR operate with Amazon EKS?

Answer:

Amazon EMR requires you to register your EKS cluster. Then, using the CLI, SDK, or EMR Studio, send your Spark tasks to EMR. The Kubernetes scheduler on EKS is used by EMR to schedule Pods. EMR on EKS creates a container for each task you perform. The container includes an Amazon Linux 2 base image with security updates, as well as Apache Spark and its dependencies, as well as your application's particular needs. Each Job is contained within a pod. This container is downloaded and executed by the Pod. If the container's image has already been deployed to the node, the download is skipped and a cached image is utilised instead. Log or metric forwarders, for example, can be deployed as sidecar containers to the pod. When the job finishes, the Pod finishes as well. You may continue debug the task using Spark UI after it has finished.


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