May 27, 2022

Top 20 Amazon CloudSearch Interview Questions and Answers

 

Amazon CloudSearch is a managed service in the AWS Cloud that makes setting up, managing, and scaling a search solution for your website or application simple and cost-effective.

Amazon CloudSearch is available in 34 languages and includes popular search features including highlighting, autocomplete, and geographical search.

With Amazon CloudSearch, you can quickly add rich search capabilities to your website or application. You don't need to become a search expert or worry about hardware provisioning, setup, and maintenance. With a few clicks in the AWS Management Console, you can create a search domain and upload the data that you want to make searchable, and Amazon CloudSearch will automatically provision the required resources and deploy a highly tuned search index.


AWS(Amazon Web Services) Interview Questions and Answers


Ques. 1): How can you rapidly add rich search features to your website or application with Amazon CloudSearch?

Answer:

You won't have to learn how to search or bother about hardware provisioning, setup, or maintenance. You can build a search domain and upload the data you want to make searchable with a few clicks in the AWS Management Console, and Amazon CloudSearch will automatically supply the resources and deploy a finely tailored search index.


AWS Cloud Interview Questions and Answers 


Ques. 2): Is there a financial benefit to adopting the latest Amazon CloudSearch version?

Answer:

On each instance type, the current version of Amazon CloudSearch has enhanced index compression and support for bigger indexes. As a consequence, the new edition of Amazon CloudSearch is more efficient than the old one and can save you money.


AWS AppSync Interview Questions and Answers


Ques. 3): What is the definition of a search engine?

Answer:

A search engine allows you to rapidly locate the best matched results by searching enormous collections of largely textual data items (called documents). The most common type of search request is a few words of unstructured text, such as "matt damon movies." The best matched, or most relevant, items are generally listed first in the returning results (the ones that are most "about" the search words).

Documents can be fully unstructured or have various fields that can be searched separately if desired. For example, a movie search service may include documents with title, director, actor, description, and reviews fields. A search engine's results are usually proxies for the underlying content, such as URLs that point to specific web pages. The search service, on the other hand, may retrieve the actual contents of particular fields.


AWS Cloud9 Interview Questions and Answers


Ques. 4): How can I restrict access to my search domain for certain users?

Answer:

For the configuration service and all search domain services, Amazon CloudSearch enables IAM integration. You may give users complete access to Amazon CloudSearch, limit their access to select domains, and allow or disallow certain operations.


Amazon Athena Interview Questions and Answers


Ques. 5): What are the advantages of Amazon CloudSearch?

Answer:

Amazon CloudSearch is a fully managed search service that expands automatically to meet the volume of data and complexity of search queries in order to provide quick and accurate results. Customers may use Amazon CloudSearch to provide search functionality without having to worry about managing servers, traffic and data scalability, redundancy, or software packages. Users only pay for the resources they use at modest hourly rates. When compared to owning and administering your own search environment, Amazon CloudSearch can provide a much reduced total cost of ownership.


AWS RedShift Interview Questions and Answers 


Ques. 6): How can I figure out the instance type to use for my first setup?

Answer:

Start with the default settings of a single tiny search instance for datasets of less than 1 GB of data or less than one million 1 KB documents. Consider pre-warming the domain by specifying the preferred instance type for bigger data sets. Start with a big search instance for data sets up to 8 GB. Start with an extra big search instance for datasets between 8 and 16 GB. Start with a double extra big search instance for datasets between 16 and 32 GB.


AWS Cloud Practitioner Essentials Questions and Answers


Ques. 7): Is it possible to utilise Amazon CloudSearch in conjunction with a storage service?

Answer:

A storage service and a search service work together. Your documents must already be saved someplace for a search service to work, whether it's in files on a file system, data in Amazon S3, or records in an Amazon DynamoDB or Amazon RDS instance. The search service is a quick retrieval system that indexes those objects and makes them searchable with sub-second latency.


AWS EC2 Interview Questions and Answers 


Ques. 8): What is the purpose of the new Multi-AZ feature? Will there be any downtime if something goes wrong with my system?

Answer:

When you select the Multi-AZ option, Amazon CloudSearch instances in either zone may handle the full load in the case of a failure. If a service outage occurs or instances in one Availability Zone become unusable, Amazon CloudSearch redirects all traffic to the other Availability Zone. Without any administrator intervention or service disturbance, redundant instances are restored in a different Availability Zone.

Some in-flight searches may fail and must be performed again. Updates provided to the search domain are saved indefinitely and will not be lost if the server goes down.


AWS Lambda Interview Questions and Answers


Ques. 9): Is it possible to utilise Amazon CloudSearch with a database?

Answer:

Databases and search engines aren't mutually exclusive; in fact, they're frequently utilised together. If you already have a database with structured data, you might use a search engine to intelligently filter and rank the contents of the database using search terms as relevance criteria.

Both organised and unstructured data may be indexed and searched using a search service. Content can come from a variety of places, including database fields, files in various formats, web pages, and so on. A search service can allow custom result ranking as well as unique search capabilities like utilising facets for filtering that aren't accessible in databases, such as using facets for filtering.


AWS Cloud Security Interview Questions and Answers


Ques. 10): What is the maximum amount of data I can store on my search domain?

Answer:

The number of partitions you'll require is determined by your data and setup, therefore the most data you may upload is the set of data that results in 10 search partitions when your search configuration is applied. Your domain will cease accepting uploads if you reach your search partition limit unless you remove documents and re-index it.  


AWS Simple Storage Service (S3) Interview Questions and Answers 


Ques. 11): What are the most recent instance types for CloudSearch?

Answer:

To replace the earlier CloudSearch instance types, we announced new CloudSearch instance types in January 2021. Search.small, search.medium, search.large, search.xlarge, and search.2xlarge are the most recent CloudSearch instances, and they are one-to-one replacements for previous instances; for example, search.small replaces search.m1.small. The new instances are built on top of the current generation of EC2 instance types, resulting in improved availability and performance at the same price.


AWS Fargate Interview Questions and Answers 


Ques. 12): How does my search domain scale to suit the requirements of my application?

Answer:

Data and traffic scale in two dimensions in search domains. As your data volume rises, you'll need additional (or larger) Search instances to hold your indexed data, and your index will be divided amongst them. Each Search Partition must be replicated when your request volume or complexity grows, providing more CPU for that Search Partition. If your data requires three search partitions, for example, your search domain will have three search instances. When your traffic exceeds the capability of a single search instance, each partition is duplicated to offer extra CPU capacity, thereby expanding your search domain to three search instances. Additional copies, up to a maximum of 5, will be added to each search partition as traffic grows.


AWS SageMaker Interview Questions and Answers


Ques. 13): My domain hosts CloudSearch instances from the previous generation, such as search.m2.2xlarge. Is my domain going to be migrated?

Answer:

Yes, in later rounds of the migration, your domain will be transferred to corresponding new instances. Search.m2.2xlarge, for example, will be renamed to search.previousgeneration.2xlarge. These instances are the same price as the old instances, but they give improved domain stability.


AWS DynamoDB Interview Questions and Answers 


Ques. 14): What exactly is faceting?

Answer:

Faceting allows you to group your search results into refinements, which the user may then utilise to do more searches. For instance, if a user searches for "umbrellas," facets allow you to sort the results by price ranges like $0-$10, $10-$20, $20-$40, and so on. Result counts may also be incorporated in facets in Amazon CloudSearch, such that each refinement contains a count of the number of documents in that group. For instance, $0-$10 (4 things), $10-$20 (123 items), $20-$40 (57 items), and so on.


AWS Cloudwatch interview Questions and Answers


Ques. 15): What is the best way to change our domains to reflect the new instances?

Answer:

Your domain will be effortlessly moved to the new instances. You are not required to take any action. Amazon will execute this migration in stages over the following few weeks, starting with domains that are using the CloudSearch 2013 version. Once your domain has been upgraded to the new instance types, you will receive a message in the console. Any new domains you establish will start using the new instances immediately.


AWS Elastic Block Store (EBS) Interview Questions and Answers 


Ques. 16): What data types does Amazon CloudSearch support in its latest version?

Answer:

Amazon Text and literal text fields are supported by CloudSearch. Individual words that potentially serve as matches for queries are determined by processing text fields according to the language defined for the field. Literal fields, including case, are not processed and must match perfectly. In addition, CloudSearch supports the following numeric types: int, double, date, and latlon. Signed 64-bit integer values are stored in int fields. Floating point values of double width are stored in double fields. Date fields store dates in UTC (Universal Time) format, as defined by IETF RFC3339: yyyy-mm-ddT00:00:00Z. A location is kept as a latitude and longitude value pair in Latlon fields.


AWS Elastic Block Store (EBS) Interview Questions and Answers 


Ques. 17): Is it possible to use the console to access the latest version of Amazon CloudSearch?

Answer:

Yes. You may use the console to access the updated version of Amazon CloudSearch. You may choose the version of Amazon CloudSearch you wish to use when creating new search domains if you're an existing Amazon CloudSearch client with existing search domains. New clients will be automatically switched to the new version of Amazon CloudSearch, with no access to the 2011-01-01 version.


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Ques. 18): Is it possible to use Amazon CloudSearch with several AZs?

Answer:

Yes. Multi-AZ installations are supported by Amazon CloudSearch. When you choose the Multi-AZ option, Amazon CloudSearch creates and maintains additional instances in a second Availability Zone for your search domain to provide high availability. Updates are applied to both Availability Zones' instances automatically. In the case of a failure, search traffic is dispersed over all instances, and instances in either zone are capable of bearing the full load.


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Ques. 19): Is it necessary for my documents to be in a specific format?

Answer:

You must format your data in JSON or XML to make it searchable. A document represents each item you wish to be able to obtain as a search result. Every document includes a unique document ID as well as one or more fields containing the data you wish to search for and return in results. According to the index fields set for the domain, Amazon CloudSearch creates a search index from your document data. You submit modifications to add or remove documents from your index as your data changes.


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Ques. 20): What steps can you take to avoid 504 errors?

Answer:

Try switching to a bigger instance type if you're getting 504 problems or a lot of replication counts. If you're experiencing trouble using m3.large, for example, try m3.xlarge. If you're still getting 504 problems after pre-scaling, batch the data and lengthen the time between retries.


AWS Cloud Support Engineer Interview Question and Answers



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AWS Data Pipeline Interview Questions and Answers

 



May 22, 2022

Top 20 AWS Data Pipeline Interview Questions and Answers

 

AWS Data Pipeline is a web service that enables you to process and move data between AWS computing and storage services, as well as on-premises data sources, at predetermined intervals. You may use AWS Data Pipeline to frequently access your data, transform and analyse it at scale, and efficiently send the results to AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.


AWS Data Pipeline makes it simple to build fault-tolerant, repeatable, and highly available data processing workloads. You won't have to worry about resource availability, inter-task dependencies, retrying temporary failures or timeouts in individual tasks, or setting up a failure notification system. Data that was previously locked up in on-premises data silos can also be moved and processed using AWS Data Pipeline.


AWS(Amazon Web Services) Interview Questions and Answers


Ques. 1): What is a pipeline, exactly?

Answer:

A pipeline is an AWS Data Pipeline resource that defines the chain of data sources, destinations, and preset or custom data processing activities that are necessary to run your business logic.


AWS Cloud Interview Questions and Answers


Ques. 2): What can I accomplish using Amazon Web Services Data Pipeline?

Answer:

You can quickly and simply construct pipelines using AWS Data Pipeline, which eliminates the development and maintenance effort necessary to manage your daily data operations, allowing you to focus on creating insights from that data. Simply configure your data pipeline's data sources, timetable, and processing tasks. AWS Data Pipeline manages the execution and monitoring of your processing tasks on a fault-tolerant, highly reliable infrastructure. AWS Data Pipeline also has built-in activities for typical tasks like moving data between Amazon S3 and Amazon RDS and executing a query on Amazon S3 log data to make your development process even easier.


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Ques. 3): How do I install a Task Runner on my on-premise hosts?

Answer:

You can install the Task Runner package on your on-premise hosts using the following steps:

Download the AWS Task Runner package.

Create a configuration file that includes your AWS credentials.

Start the Task Runner agent via the following command:

java -jar TaskRunner-1.0.jar --config ~/credentials.json --workerGroup=[myWorkerGroup]

Set the activity to execute on [myWorkerGroup] when defining it so that it may be dispatched to the previously installed hosts.


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Ques. 4): What resources are used to carry out activities?

Answer:

AWS Data Pipeline actions are carried out on your own computing resources. AWS Data Pipeline–managed and self-managed computing resources are the two categories. AWS Data Pipeline–managed resources are Amazon EMR clusters or Amazon EC2 instances that are launched only when they're needed by the AWS Data Pipeline service. You can manage resources that run longer and can be any resource that can execute the AWS Data Pipeline Java-based Task Runner (on-premise hardware, a customer-managed Amazon EC2 instance, etc.).


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Ques. 5): Is it possible for me to run activities on on-premise or managed AWS resources?

Answer:

Yes. AWS Data Pipeline provides a Task Runner package that may be deployed on your on-premise hosts to enable performing operations utilising on-premise resources. This package polls the AWS Data Pipeline service for work to be done on a regular basis. AWS Data Pipeline will issue the proper command to the Task Runner when it's time to conduct a certain action on your on-premise resources, such as executing a DB stored procedure or a database dump. You may assign many Task Runners to poll for a specific job to guarantee that your pipeline operations are highly available. If one Task Runner is unavailable, the others will simply take up its duties.


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Ques. 6): Is it possible to manually restart unsuccessful activities?

Answer:

Yes. By changing the status of a group of completed or unsuccessful actions to SCHEDULED, you can restart them. This may be done using the UI's Rerun button or by changing their status via the command line or API. This will trigger a re-check of all activity dependencies, as well as the execution of further activity attempts. Following successive failures, the Activity will attempt the same number of retries as before.


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Ques. 7): What happens if an activity doesn't go as planned?

Answer:

If all of an activity's activity attempts fail, the activity fails. An activity retries three times by default before failing completely. The number of automated retries can be increased to ten, but the technology does not enable endless retries. After an activity's tries have been exhausted, it will trigger any preset onFailure alarms and will not attempt to run again until you explicitly issue a rerun command using the CLI, API, or console button.


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Ques. 8): What is a schedule, exactly?

Answer:

Schedules specify when your pipeline actions take place and how often the service expects your data to be provided. Every schedule must specify a start date and a frequency, such as every day at 3 p.m. beginning January 1, 2013. The AWS Data Pipeline service does not execute any actions after the end date specified in the schedule. When you link a timetable to an activity, the activity runs on that schedule. You notify the AWS Data Pipeline service that you want the data to be updated on that schedule when you connect a schedule with a data source. For example, if you define an Amazon S3 data source with an hourly schedule, the service expects that the data source contains new files every hour.


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Ques. 9): What is a data node, exactly?

Answer:

A data node is a visual representation of your company's information. A data node, for example, can point to a specific Amazon S3 route. AWS Data Pipeline has an expression language that makes it simple to refer to data that is created often. For example, you may specify s3:/example-bucket/my-logs/logdata-#scheduledStartTime('YYYY-MM-dd-HH').tgz as your Amazon S3 data format.


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Ques. 10): Does Data Pipeline supply any standard Activities?

Answer:

Yes, AWS Data Pipeline provides built-in support for the following activities:

CopyActivity: This activity can copy data between Amazon S3 and JDBC data sources, or run a SQL query and copy its output into Amazon S3.

HiveActivity: This activity allows you to execute Hive queries easily.

EMRActivity: This activity allows you to run arbitrary Amazon EMR jobs.

ShellCommandActivity: This activity allows you to run arbitrary Linux shell commands or programs.

 

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Ques. 11): Is it possible to employ numerous computing resources on the same pipeline?

Answer:

Yes, just construct numerous cluster objects in your definition file and use the runsOn attribute to associate the cluster to use for each activity. This enables pipelines to use a mix of AWS and on-premise resources, as well as a mix of instance types for their activities – for example, you might want to use a t1.micro to run a quick script cheaply, but later on the pipeline might have an Amazon EMR job that requires the power of a cluster of larger instances.


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Ques. 12): What is the best way to get started with AWS Data Pipeline?

Answer:

Simply navigate to the AWS Management Console and choose the AWS Data Pipeline option to get started with AWS Data Pipeline. You may then use a basic graphical editor to design a pipeline.


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Ques. 13): What is a precondition?

Answer:

A readiness check that may be coupled with a data source or action is known as a precondition. If a data source contains a precondition check, that check must pass before any operations that use the data source may begin. If an activity contains a precondition, the precondition check must pass before the activity may be executed. This is handy if you're performing a computationally intensive activity that shouldn't run unless certain requirements are satisfied.


AWS DynamoDB Interview Questions and Answers


Ques. 14): Does AWS Data Pipeline supply any standard preconditions?

Answer:

Yes, AWS Data Pipeline provides built-in support for the following preconditions:

DynamoDBDataExists: This precondition checks for the existence of data inside a DynamoDB table.

DynamoDBTableExists: This precondition checks for the existence of a DynamoDB table.

S3KeyExists: This precondition checks for the existence of a specific AmazonS3 path.

S3PrefixExists: This precondition checks for at least one file existing within a specific path.

ShellCommandPrecondition: This precondition runs an arbitrary script on your resources and checks that the script succeeds.


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Ques. 15): Will AWS Data Pipeline handle my computing resources and provide and terminate them for me?

Answer:

Yes, compute resources will be supplied when the first activity that utilises those resources for a planned time is ready to begin, and those instances will be terminated when the last activity that uses those resources has concluded successfully or failed.


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Ques. 16): What distinguishes AWS Data Pipeline from Amazon Simple Workflow Service?

Answer:

While both services allow you to track your execution, handle retries and errors, and conduct arbitrary operations, AWS Data Pipeline is designed to help you with the stages that are prevalent in most data-driven processes. For example, actions may be executed only once their input data fulfils certain readiness requirements, data can be readily copied between multiple data stores, and chained transformations can be scheduled. Because of this narrow emphasis, Data Pipeline process definitions may be generated quickly and without coding or programming skills.


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Ques. 17): What is an activity, exactly?

Answer:

As part of a pipeline, AWS Data Pipeline will initiate an activity on your behalf. EMR or Hive tasks, copies, SQL queries, and command-line scripts are all examples of activities.


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Ques. 18): Is it possible to create numerous schedules for distinct tasks inside a pipeline?

Answer:

Yes, just construct numerous schedule objects in your pipeline definition file and use the schedule field to connect the selected schedule with the appropriate activity. This enables you to create a pipeline in which log files are stored in Amazon S3 every hour, for example, to drive the production of an aggregate report once per day.


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Ques. 19): Is there a list of sample pipelines I can use to get a feel for AWS Data Pipeline?

Answer:

Yes, our documentation includes sample workflows. In addition, the console includes various pipeline templates to help you get started.


AWS Cloud Support Engineer Interview Question and Answers


Ques. 20): Is there a limit to how much I can fit into a single pipeline?

Answer:

Each pipeline you construct can have up to 100 items by default.

 

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May 16, 2022

Top 20 AWS Lake Formation Interview Questions and Answers

 

        AWS Lake Formation is a service that allows you to quickly create a secure data lake. A data lake is a centralised, controlled, and secure repository where you may keep all of your data, both raw and processed for analysis. A data lake allows you to mix multiple forms of analytics and break down data silos to acquire insights and make better business decisions.

Defining data sources and the access and security policies you want to apply is all it takes to create a data lake using Lake Formation. Lake Formation then assists you in gathering and cataloguing data from databases and object storage, moving it to your new Amazon Simple Storage Service (S3) data lake, cleaning and classifying your data with machine learning algorithms, and securing access to your sensitive data with granular controls at the column, row, and cell levels. Your users will have access to a centralised data catalogue that lists accessible datasets and how they should be used. They then leverage these datasets with Amazon Redshift, Amazon Athena, Amazon EMR for Apache Spark, and Amazon QuickSight, among other analytics and machine learning services. Lake Formation builds on the capabilities available in AWS Glue.

 

AWS(Amazon Web Services) Interview Questions and Answers

 

Ques. 1): Is there an API or a CLI available from Lake Formation?

Answer:

Yes. To incorporate Lake Formation capabilities into your bespoke apps, Lake Formation provides APIs and a CLI. You can also use Java and C++ SDKs to combine your own data engines with Lake Formation.

 

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Ques. 2): What is a data lake, exactly?

Answer:

A data lake is a scalable central store for both organised and unstructured data in huge numbers and types. Data lakes allow you to manage your data over its entire lifecycle. Ingestion and classifying data from various sources is the first stage in creating a data lake. Before analysis, the data is enriched, merged, and cleansed. Direct searches, visualisation, and machine learning make it simple to explore and evaluate data (ML). Traditional data warehouses are supplemented by data lakes, which offer greater flexibility, cost-effectiveness, and scalability for data acquisition, storage, transformation, and analysis. The typical issues of building and maintaining data warehouses, as well as constraints in the sorts of analysis that may be performed, can be overcome utilising data lakes.

 

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Ques. 3): What is the AWS Lake Formation Storage API, and why should I use it?

Answer:

The Lake Formation Storage API gives AWS services, ISV solutions, and application developers a single interface to read and write data in the data lake securely and reliably. To write data, the Storage API supports ACID (atomic, consistent, isolated, and durable) transactions, which allow you to reliably and consistently write data into Governed Tables, a new form of Amazon S3 table. You can query data in Governed Tables and ordinary S3 tables guarded with Lake Formation fine-grained permissions using the Storage API. Before sending the filtered results to the requesting application, the Storage API will automatically enforce permissions. Permissions for access are applied uniformly across a variety of services and tools.

 

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Ques. 4): What exactly is the AWS Lake Formation?

Answer:

Lake Formation is a data lake service that makes it simple to collect, clean, categorise, convert, and secure your data before making it available for analysis and machine learning. Lake Formation provides a central console from which you can discover data sources, set up transformation jobs to move data to an Amazon Simple Storage Service (S3) data lake, remove duplicates and match records, catalogue data for analytic tools, configure data access and security policies, and audit and control access to AWS analytic and machine learning services.

Lake Formation uses Zeppelin notebooks with Apache Spark to automatically control access to the registered data in Amazon S3 using AWS Glue, Amazon Athena, Amazon Redshift, Amazon QuickSight, and Amazon EMR to ensure compliance with your established regulations. Lake Formation configures the flows, centralises their orchestration, and allows you to monitor transformation operations that span AWS services. You may configure and maintain your data lake using Lake Formation instead of manually integrating numerous underlying AWS services.

 

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Ques. 5): Can I utilise Lake Formation with third-party business intelligence tools?

Answer:

Yes. You can connect to your AWS data sources using services like Athena or Redshift using third-party business applications like Tableau and Looker. The underlying data catalogue manages data access, so you can rest certain that access to your data is authorised and controlled regardless of whatever application you use.

 

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Ques. 6): How does Lake Formation de-duplicate my data?

Answer:

The FindMatches ML Transform from Lake Formation makes it simple to locate and link records that refer to the same thing but lack a valid identifier. Before FindMatches, data-matching problems were usually solved deterministically by constructing a large number of hand-tuned rules. Behind the scenes, FindMatches uses machine learning algorithms to learn how to match records according to each developer's business requirements. FindMatches selects records for you to categorise as matching or not matching, and then utilises machine learning to generate an ML Transform. You can then use this Transform to find matching records in your database, or you can ask FindMatches to provide you with more records to label in order to improve the accuracy of your ML Transform.

 

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Ques. 7): How does Lake Formation keep my information safe?

Answer:

Lake Formation safeguards your data by allowing you to define granular data access policies that protect your data regardless of which services are utilised to access it.

To use Lake Formation to consolidate data access policy restrictions, disable direct access to your Amazon S3 buckets so that Lake Formation handles all data access. Then, using Lake Formation, set up data protection and access controls that are enforced across all AWS services that access data in your lake. Users and roles can be configured, as well as the data that these roles have access to, down to the table and column level.

S3 server-side encryption is now supported by Lake Formation (SSE-S3, AES-265). Lake Formation additionally supports private endpoints in your Amazon VPC and logs all activity in AWS CloudTrail, ensuring network isolation and auditability.

 

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Ques. 8): What are Machine Learning Transforms?

Answer:

ML Transforms is a place where you can create and manage machine-learned transforms. These ML Transforms can be used in ordinary AWS Glue scripts once they've been constructed and trained. You choose an algorithm (for example, the FindMatches ML Transform), then input datasets and training samples, as well as tweaking parameters. These inputs are used by AWS Lake Formation to create an ML Transform that can be integrated into a standard ETL job workflow.

 

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Ques. 9): How can I turn an existing Amazon S3 table into a regulated table?

Answer:

You can convert existing Amazon S3–based tables in the AWS Glue Data Catalog to controlled tables by running the AWS Glue blueprint available on the AWS Labs Github page. Using the AWS SDK and CLI, you can also create a new governed table and edit the manifest information in Lake Formation. A list of S3 objects and related metadata indicate the current status of your table in the manifest information. You can also use AWS Glue ETL to read data from an existing table and construct a Governed Table duplicate of it. This allows you to migrate your applications and users to the Governed Table at your own pace.

 

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Ques. 10): How does Lake Formation relate to other AWS services?

Answer:

Lake Formation manages data access for registered data that is stored in Amazon S3 and manages query access from AWS Glue, Athena, Redshift, Amazon QuickSight, and EMR using Zeppelin notebooks with Apache Spark through a unified security model and permissions. Lake Formation can ingest data from S3, Amazon RDS databases, and AWS CloudTrail logs, understand their formats, and make data clean and able to be queried. Lake Formation configures the flows, centralizes their orchestration, and lets you monitor the jobs.

 

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Ques. 11): What other options do I have for getting data into AWS to utilise with Lake Formation?

Answer:

With AWS Snowball, AWS Snowball Edge, and AWS Snowmobile, you can transport petabytes to exabytes of data from your data centres to AWS utilising physical equipment. AWS Storage Gateway allows you to link your on-premises apps directly to AWS. You can use AWS Direct Connect to create a dedicated network link between your network and AWS, or Amazon S3 Transfer Acceleration to boost long-distance global data transfers using Amazon's internationally spread edge locations. Amazon Kinesis can also be used to import streaming data into S3. Lake Formation Data Importers can be configured to run ETL processes in the background and prepare data for analysis.

 

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Ques. 12): What is the relationship between Lake Formation and AWS Glue?

Answer:

With AWS Glue, Lake Formation shares infrastructure such as console controls, ETL code development and job monitoring, blueprints for creating data import workflows, the same data catalogue, and a serverless architecture. Although AWS Glue focuses on these operations, Lake Formation includes all of AWS Glue's functionality as well as extra capabilities for building, securing, and managing a data lake. For additional information, see the AWS Glue features page.

 

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Ques. 13): How does Lake Formation sanitise my data using machine learning?

Answer:

Lake Formation offers jobs that use machine learning methods to deduplicate and connect records. Select your source, choose a desired transform, and provide training data for the necessary changes to create ML Transforms. The ML Transforms can be run as part of your regular data movement procedures once they've been trained to your satisfaction.

 

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Ques. 14): What is the relationship between Lake Formation and AWS IAM?

Answer:

Lake Formation works with IAM to automatically map authorised users and roles to data protection policies maintained in the data catalogue. You may also utilise Microsoft Active Directory or LDAP to federate into IAM utilising SAML thanks to the IAM integration.

 

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Ques. 15): How does Lake Formation assist me in locating data for my data lake?

Answer:

Lake Formation detects all AWS data sources to which it has access thanks to your AWS IAM policies. It scans Amazon S3, Amazon RDS, and AWS CloudTrail sources, identifying them as data that can be consumed into your data lake using blueprints. Without your permission, no data is ever moved or made accessible to analytic services. AWS Glue may also consume data from other AWS services, such as S3 and Amazon DynamoDB.

Lake Formation may also use JDBC connections to connect to your AWS databases as well as on-premises databases including Oracle, MySQL, Postgres, SQL Server, and MariaDB.

Lake Formation guarantees that all of your data is documented in a central data catalogue, allowing you to browse and query data that you have authorization to see and query from a single location. Permissions can be specified at the table and column level and are described in your data access policy.

You can add labels (including business attributes like data sensitivity) at the table or column level, as well as field-level comments, in addition to the properties automatically provided by the crawlers.

 

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Ques. 16): What types of issues does the FindMatches ML Transform address?

Answer:

FindMatches solves record linkage and data deduplication issues in general. When you're trying to find records in a database that are theoretically the same yet have separate records, deduplication is required. If duplicate entries can be identified by a unique key (for example, if products can be uniquely identified by a UPC Code), this problem is straightforward, but it gets exceedingly difficult when you have to execute a "fuzzy match."

Record linkage is essentially the same as data deduplication, however instead of deduplicating a single database, this phrase usually refers to a "fuzzy join" of two databases that don't share a unique key. Consider the difficulty of matching a large consumer database with a limited database of known fraudsters. Both record linkage and deduplication difficulties can be solved with FindMatches.

 

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Ques. 17): How does Lake Formation organize my data in a data lake?

Answer:

You can use one of the blueprints available in Lake Formation to ingest data into your data lake. Lake Formation creates Glue workflows that crawl source tables, extract the data, and load it to Amazon S3. In S3, Lake Formation organizes the data for you, setting up partitions and data formats for optimized performance and cost. For data already in S3, you can register those buckets with Lake Formation to manage them.

Lake Formation also crawls your data lake to maintain a data catalog and provides an intuitive user interface for you to search entities (by type, classification, attribute, or free-form text).

 

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Ques. 18): How does Lake Formation assist a data scientist or analyst in determining what data they have access to?

Answer:

Lake Formation guarantees that all of your data is defined in the data catalogue, providing you with a central area to browse and query the data that you have access to. Permissions can be specified at the table and column level and are described in your data access policy.

 

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Ques. 19): Why should I build my data lake with Lake Formation?

Answer:

Building, securing, and managing your AWS data lake is simple with Lake Formation. Lake Formation automatically configures underlying AWS security, storage, analysis, and machine learning services to meet with your centrally set access policies. You can also monitor your jobs, data transformation, and analytic workflows from a single console.

AWS Glue allows Lake Formation to handle data intake. Data is automatically categorised, and the central data catalogue stores pertinent data definitions, schema, and metadata. AWS Glue also cleans your data, removing duplicates and linking entries across datasets before converting it to one of several open data formats for storage in Amazon S3. You can create access restrictions, including table-and-column-level access controls, and enforce encryption for data at rest once your data is in your S3 data lake. You may then access your data lake using a range of AWS analytic and machine learning services. All access is controlled, monitored, and audited.

 

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Ques. 20): Can I utilise Lake Formation with my existing data catalogue or Hive Metastore?

Answer:

You can import your existing catalogue and metastore into the data catalogue using Lake Formation. To provide governed access to your data, Lake Formation requires your metadata to be stored in the data catalogue.

 

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