#amazons3

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Amazon S3: Scalable Object Storage for Data Lakes

In today’s data-driven ecosystem, organizations need storage solutions that are secure, scalable, durable, and cost-efficient — and Amazon S3 continues to be a foundation for modern data architectures.

Amazon S3 provides highly scalable object storage that powers data lakes, analytics platforms, backup systems, machine learning workflows, and cloud-native applications across industries.

With strong durability, flexible storage classes, advanced security controls, and seamless integration with AWS analytics services, S3 enables businesses to manage and access massive volumes of data efficiently.

From big data analytics to enterprise-scale cloud storage, Amazon S3 helps organizations build reliable and future-ready data infrastructures.

Modern analytics begins with storage that is scalable, intelligent, and built for the cloud era.

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Getting Started with AWS

Getting Started with AWS

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Amazon Braket SDK Architecture And Components Explained

A comprehensive framework that abstracts the complexity of quantum hardware and simulators, Amazon Braket SDK is gradually becoming a major tool for quantum computing. It aims to give developers a consistent interface for using a variety of quantum resources and inspire creativity in the fast-growing field of quantum computing. Its multilayered construction.

SDK Architecture Amazon Braket

Abstraction for Comfort and Flexibility The SDK's core is its powerful device abstraction layer. This vital portion provides a single interface to Oxford Quantum Circuits, IonQ, Rigetti, and Xanadu quantum backends as well as simulators. This layer largely safeguards developers from understanding quantum processor details by turning user-defined quantum circuits into backend-specific instruction sets and protocols.

Quantum programs are portable and interoperable thanks to standardised quantum circuit representations and backend-specific adapters. Quantum computing is dynamic, therefore its modular architecture lets you add new backends without disrupting the core functionality. The main quantum development modules are: Braket.circuits: Hub Quantum Circuit The Braket SDK's main module, braket.circuits, offers comprehensive tools for building, altering, and refining quantum circuits. This module's DAG model of quantum circuits permits complicated optimisations like subexpression elimination and gate cancellation. Ability to construct bespoke gates and allow many quantum gate sets provides it versatility. Quantum computing frameworks like PennyLane and Qiskit allow developers to use current tools and knowledge. Compatible quantum computing platforms benefit from OpenQASM compliance. Braket.jobs: Management of Quantum Execution Braket.jobs controls quantum circuits on simulators and hardware. It tracks the Braket service's job submission process and receives results. This module is crucial for error handling, prioritisation, and job queue management. Developers can customise the execution environment by setting parameters like shots, random number seed, and experiment duration. The module supports synchronous and asynchronous execution, so developers can choose the right one. It also tracks resource use and cost to optimise quantum processes. Braket.devices: Hardware Optimisation and Access The braket.devices module is essential for accessing quantum processors and simulators. Developers can query qubit count, connection, and gate integrity. This module gives methods for selecting the optimum equipment for a task based on cost and performance. A device profile system that uniformly describes each device's capabilities allows the SDK to automatically optimise quantum circuits for the chosen device, enhancing efficiency and reducing errors. Device characterisation and calibration are also possible with the module, ensuring peak efficiency.

Amazon Braket SDK Parts

Smooth Amazon S3 Integration: The SDK's seamless interface with Amazon S3, a scalable and affordable storage alternative, is key to its architecture. Quantum circuits are usually saved in S3 as JSON files for easy sharing and version management. A persistent calculation record is established by saving job results in S3. The SDK's S3 APIs simplify data analysis and visualisation. The SDK can use AWS Lambda and Amazon SageMaker using this interface to construct more complex quantum applications. A Solid Error Mitigation Framework: Due to quantum hardware noise and defects, the Amazon Braket SDK includes a robust error mitigation system. This framework includes crucial error detection, correction, and noise characterisation algorithms. These procedures can be set up and implemented using SDK APIs, allowing developers to customise error mitigation. It helps developers improve their error mitigation strategy with tools to analyse approaches. As methods and algorithms become available, the framework will be updated. Security for Enterprises with AWS IAM Enterprise-grade security is possible with AWS IAM. The SDK's architecture relies on AWS IAM, making security crucial. IAM's fine-grained access control lets developers set policies that restrict quantum resource access to users and programs. Data in transit and at rest is encrypted by the SDK to prevent unauthorised access to sensitive quantum data. The SDK protects quantum data and meets enterprise clients' high security standards. Connects to AWS CloudTrail and GuardDuty for complete security monitoring and auditing. In conclusion

The Amazon Braket SDK provides a customised, secure quantum computing framework. Abstraction of hardware difficulties, powerful circuit design and execution tools, integration with scalable AWS services, and prioritisation of security and error prevention lower the barrier to entry, allowing developers to fully explore quantum computing's possibilities.

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🔥 Learn Amazon S3 in Tamil – Cloud Storage Simplified 🇮🇳🧠

This is a full Tamil tutorial on Amazon S3, the popular cloud storage service from AWS. Understand cloud basics, S3 bucket creation, and file management, all in your own language!

🎯 Targeted for:

Tamil-speaking students

AWS beginners

Job seekers in cloud roles

📽️ Watch and follow step-by-step in Tamil!

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Working With EMR Notebooks AWS Using Jupyter Notebook

Working with AWS EMR Notebooks

Amazon EMR Notebooks, renamed EMR Studio Workspaces, simplify data processing cluster interaction. They use the popular open-source Jupyter Notebook or JupyterLab editors and are available from Amazon EMR. This may be more efficient than EMR cluster notebooks. Users with suitable IAM rights can open the editor in the console.

Notebook statuses

When and how to communicate with EMR Notebooks requires knowing their status. The numerous states you may encounter are listed below:

The notebook is being produced and connected to the cluster. Launching, stopping, removing, or changing the editor's cluster is currently impossible. It starts rapidly but can take longer if a cluster forms.

You can access the fully prepared notebook in the notebook editor. Stop or remove the notebook in this state. Stop the notebook before altering the cluster. A Ready notebook will shut down after a long inactivity.

The notebook has been produced, however cluster integration may require resource provisioning or additional steps. In this case, you can launch the notebook editor in local mode, but cluster-dependent code will fail.

Stopping: Laptop or cluster shutdown. Like the ‘Starting’ state, the editor cannot be opened, stopped, deleted, or clusters altered while stopping.

The laptop shut down successfully. You can delete the laptop, swap clusters, or restart it on the same cluster (assuming the cluster is still operating).

Notebook is being removed from console list. Even after the notebook entry is erased, Amazon S3 will charge for the notebook file (NotebookName.ipynb). To retrieve the latest status, reload the console's notebook list.

Working in Notebook Editor

The notebook editor starts when the notebook is Ready or Pending. You choose Open in JupyterLab or Jupyter after choosing the notebook from the list. This opens a new browser tab with the editor. After opening, select your programming language's kernel from the Kernel menu.

The console-accessible editor's ability to limit EMR notebooks to one user is critical. Opening an already-used notebook will result in an error. Amazon EMR produces a unique pre-signed URL for each session that is only valid for a short time, displaying security.

This URL should not be shared since recipients could inherit your rights and be at risk. IAM permissions policies and granting EMR Notebooks service role access to the Amazon S3 location are two strategies to control access.

Preserving Work

While editing, your notebook cells and output are automatically and occasionally saved to the Amazon S3 notebook file. When there are no modifications since the last save, the editor displays “autosaved,” and otherwise, “unsaved.” You can manually save the notebook by pressing CTRL+S or choosing Save and Checkpoint from File. Manual saves create a checkpoint file (NotebookName.ipynb) in the notebook's principal Amazon S3 folder's checkpoints folder. This site stores only the latest checkpoint.

Attached Cluster Change

Switching the cluster to which an EMR notebook is linked without affecting its content is useful. Only Stopped notebooks can accomplish this. The approach involves selecting the paused notebook, viewing its data, selecting the Change cluster, and then choosing an existing Hadoop, Spark, and Livy cluster or creating a new one. Finally, select the security group and click Change cluster and start laptop to confirm.

Delete Notebooks and Files

The Amazon EMR interface lets you remove an EMR notebook from your list. Importantly, this approach does not delete Amazon S3 notebook files. These S3 data continue to accrue storage fees.

To remove the notebook entry and files, delete the notebook from the console and note its Amazon S3 location (in the notebook details). The AWS CLI or Amazon S3 interface must be used to manually remove the folder and its contents from the S3 location. An example CLI command removes the notebook directory and its contents.

Share and Use Notebook Files

Every EMR notebook has a NotebookName.ipynb file in Amazon S3. If it works with EMR Notebook Jupyter Notebook, you can open a notebook file as an EMR notebook. Saving the.ipynb file locally and uploading it to Jupyter or JupyterLab makes using a notebook file from another user straightforward. This method can recover a console-erased notebook or work with publicly published Jupyter notebooks if you have the file.

A new EMR notebook can be created by replacing the S3 notebook file. Stop all running EMR notebooks and close any open editor sessions.

Create a new EMR notebook with the precise name you want for the new file, record its S3 location and Notebook ID, stop it, and.Using the AWS CLI, copy and change the ipynb file at that S3 location, making sure the file name matches the notebook's name. This technique is shown using an AWS CLI command.

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How To Create EMR Notebook In Amazon EMR Studio

How to Make EMR Notebook?

Amazon Web Services (AWS) has incorporated Amazon EMR Notebooks into Amazon EMR Studio Workspaces on the new Amazon EMR interface. Integration aims to provide a single environment for notebook creation and massive data processing. However, the new console's “Create Workspace” button usually creates notebooks.

Users must visit the Amazon EMR console at the supplied web URL and complete the previous console's procedures to create an EMR notebook. Users usually select “Notebooks” and “Create notebook” from this interface.

When creating a Notebook, users choose a name and a description. The next critical step is connecting the notebook to an Amazon EMR cluster to run the code.

There are two basic ways users associate clusters:

Select an existing cluster

If an appropriate EMR cluster is already operating, users can click “Choose,” select it from a list, and click “Choose cluster” to confirm. EMR Notebooks have cluster requirements, per documentation. These prerequisites, EMR release versions, and security problems are detailed in specialised sections.

Create a cluster

Users can also “Create a cluster” to have Amazon EMR create a laptop-specific cluster. This method lets users name their clusters. This workflow defaults to the latest supported EMR release version and essential apps like Hadoop, Spark, and Livy, however some configuration variables, such as the Release version and pre-selected apps, may not be modifiable.

Users can customise instance parameters by selecting EC2 Instance and entering the appropriate number of instances. A primary node and core nodes are identified. The instance type determines the maximum number of notebooks that can connect to the cluster, subject to constraints.

The EC2 instance profile and EMR role, which users can choose custom or default roles for, are also defined during cluster setup. Links to more information about these service roles are supplied. An EC2 key pair for cluster instance SSH connections can also be chosen.

Amazon EMR versions 5.30.0 and 6.1.0 and later allow optional but helpful auto-termination. For inactivity, users can click the box to shut down the cluster automatically. Users can specify security groups for the primary instance and notebook client instance, use default security groups, or use custom ones from the cluster's VPC.

Cluster settings and notebook-specific configuration are part of notebook creation. Choose a custom or default AWS Service Role for the notebook client instance. The Amazon S3 Notebook location will store the notebook file. If no bucket or folder exists, Amazon EMR can create one, or users can choose their own. A folder with the Notebook ID and NotebookName and.ipynb extension is created in the S3 location to store the notebook file.

If an encrypted Amazon S3 location is used, the Service role for EMR Notebooks (EMR_Notebooks_DefaultRole) must be set up as a key user for the AWS KMS key used for encryption. To add key users to key policies, see AWS KMS documentation and support pages.

Users can link a Git-based repository to a notebook in Amazon EMR. After selecting “Git repository” and “Choose repository”, pick from the list.

Finally, notebook users can add Tags as key-value pairs. The documentation includes an Important Note about a default tag with the key creatorUserID and the value set to the user's IAM user ID. Users should not change or delete this tag, which is automatically applied for access control, because IAM policies can use it. After configuring all options, clicking “Create Notebook” finishes notebook creation.

Users should note that these instructions are for the old console, while the new console now uses EMR Notebooks as EMR Studio Workspaces. To access existing notebooks as Workspaces or create new ones using the “Create Workspace” option in the new UI, EMR Notebooks users need extra IAM role rights. Users should not change or delete the notebook's default access control tag, which contains the creator's user ID. No notebooks can be created with the Amazon EMR API or CLI.

The thorough construction instructions in some current literature match the console interface, however this transition symbolises AWS's intention to centralise notebook creation in EMR Studio.

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How to set up an EMR studio in AWS? Standards for EMR Studio

To ensure users can access and use the environment properly, Amazon EMR Studio setup involves many steps. Once you meet prerequisites, the process begins.

Setting up an EMR studio

Setup requirements for EMR Studio Before setting up, you need:

An AWS account

Establishing and running an EMR Studio.

A dedicated Amazon S3 bucket for EMR Studio notebook and workspace backups.

Five subnets and an Amazon VPC are recommended for Git repositories and connecting to Amazon EMR on EC2 or EKS clusters. EMR Studio works with EMR Serverless without VPC.

Setup steps Setup often involves these steps:

Choose an Authentication Mode: Choose IAM Identity Centre or IAM for your studio. User and permission management is affected by this decision. AWS IAM authenticates and IAM Identity Centre stores identities. Like IAM authentication or federation, IAM mode is compatible with many identity providers and straightforward to set up for identity management. IAM Identity Centre mode simplifies user and group assignment for Amazon EMR and AWS beginners. SAML 2.0 and Microsoft Active Directory integration simplifies multi-account federation.

Create the EMR Studio Service Role: An EMR Studio needs an IAM service role to create a secure network channel between Workspaces and clusters, store notebook files in Amazon S3, and access AWS Secrets Manager for Git repositories. This service role should describe all Amazon S3 notebook storage and AWS Secrets Manager Git repository access rights.

This role requires a trust policy from AWS to allow elasticmapreduce.amazonaws.com to play:AWS:SourceArn and SourceAccount settings for confused deputy prevention. After trust policy creation, you link an IAM permissions policy to the role. This policy must include permissions for Amazon EC2 tag-based access control and specific S3 read/write operations for your assigned S3 bucket. If your S3 bucket is encrypted, you need AWS KMS permissions. Some policy claims concerning tagging network interfaces and default security groups must remain unaltered for the service role to work.

Set EMR Studio user permissions: Set up user access policies to fine-tune Studio user access.

Create an EMR Studio user role to leverage IAM Identity Centre authentication. Sts:SetContext and AssumeRole allow elasticmapreduce.amazonaws.com to assume this role's trust relationship policy. You assign EMR Studio session policies to this user role before assigning users. Session policies provide Studio users fine-grained rights like creating new EMR clusters. The final permissions of a user depend on their session policy and EMR Studio user role. If a person belongs to multiple Studio groups, their permissions are a mix of group policies.

IAM authentication mode grants studio access via ABAC and IAM permissions policies. Allowing elasticmapreduce:CreateStudioPresignedUrl in a user's IAM permissions policy lets you use ARN or ABAC tags to limit the user to a Studio.

You specify one or more IAM permissions policies to describe user behaviours regardless of authentication mode. Workspace creation, cluster attachment and detachment, Git repository management, and cluster formation are basic, intermediate, and advanced rules with different authority. Clusters set data access control rights, not Studio user permissions.

(Optional) Create custom security groups to handle EMR Studio network traffic. If no custom security groups are selected, Studio uses defaults. When using custom security groups, specify a Workspace security group for outgoing access to clusters and Git repositories and an engine security group for inbound access.

Create an EMR Studio using the AWS CLI or Amazon EMR console. The interface creates an EMR Serverless application and offers simple configurations for interactive or batch workloads. ‘Custom’ gives full control over settings. Custom parameters include studio name, S3 location, workspace count, IAM or IAM Identity Centre authentication, VPC, subnets, and security groups. IAM authentication for federated users can include an IdP login URL and RelayState parameter name.

You must select EMR Studio Service and User Roles for IAM Identity Centre authentication. For speedier sign-on, enable trusted identity propagation. The AWS CLI tool create-studio requires programmatic creation options based on authentication method.

After building an EMR Studio, you may assign users and groups. Approach depends on authentication mode.

In IAM authentication mode, user assignment and permissions may require your identity provider. Limiting Studio access with ARN or ABAC tags and configuring the user's IAM rights policy to allow CreateStudioPresignedUrl does this.

The AWS CLI or Amazon EMR administration console can handle IAM Identity Centre authentication mode users. The console lets you assign users or groups from the Identity Centre directory. The AWS CLI command create-studio-session-mapping requires the Studio ID, identity name, identity type (USER or GROUP), and ARN of the session policy to associate. At assignment, you set a session policy. Altering the session policy lets you adjust user permissions later.

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What are the benefits of Amazon EMR? Drawbacks of AWS EMR

Benefits of Amazon EMR

Amazon EMR has many benefits. These include AWS's flexibility and cost savings over on-premises resource development.

Cost-saving

Amazon EMR costs depend on instance type, number of Amazon EC2 instances, and cluster launch area. On-demand pricing is low, but Reserved or Spot Instances save much more. Spot instances can save up to a tenth of on-demand costs.

Note

Using Amazon S3, Kinesis, or DynamoDB with your EMR cluster incurs expenses irrespective of Amazon EMR usage.

Note

Set up Amazon S3 VPC endpoints when creating an Amazon EMR cluster in a private subnet. If your EMR cluster is on a private subnet without Amazon S3 VPC endpoints, you will be charged extra for S3 traffic NAT gates.

AWS integration

Amazon EMR integrates with other AWS services for cluster networking, storage, security, and more. The following list shows many examples of this integration:

Use Amazon EC2 for cluster nodes.

Amazon VPC creates the virtual network where your instances start.

Amazon S3 input/output data storage

Set alarms and monitor cluster performance with Amazon CloudWatch.

AWS IAM permissions setting

Audit service requests with AWS CloudTrail.

Cluster scheduling and launch with AWS Data Pipeline

AWS Lake Formation searches, categorises, and secures Amazon S3 data lakes.

Its deployment

The EC2 instances in your EMR cluster do the tasks you designate. When you launch your cluster, Amazon EMR configures instances using Spark or Apache Hadoop. Choose the instance size and type that best suits your cluster's processing needs: streaming data, low-latency queries, batch processing, or big data storage.

Amazon EMR cluster software setup has many options. For example, an Amazon EMR version can be loaded with Hive, Pig, Spark, and flexible frameworks like Hadoop. Installing a MapR distribution is another alternative. Since Amazon EMR runs on Amazon Linux, you can manually install software on your cluster using yum or the source code.

Flexibility and scalability

Amazon EMR lets you scale your cluster as your computing needs vary. Resizing your cluster lets you add instances during peak workloads and remove them to cut costs.

Amazon EMR supports multiple instance groups. This lets you employ Spot Instances in one group to perform jobs faster and cheaper and On-Demand Instances in another for guaranteed processing power. Multiple Spot Instance types might be mixed to take advantage of a better price.

Amazon EMR lets you use several file systems for input, output, and intermediate data. HDFS on your cluster's primary and core nodes can handle data you don't need to store beyond its lifecycle.

Amazon S3 can be used as a data layer for EMR File System applications to decouple computation and storage and store data outside of your cluster's lifespan. EMRFS lets you scale up or down to meet storage and processing needs independently. Amazon S3 lets you adjust storage and cluster size to meet growing processing needs.

Reliability

Amazon EMR monitors cluster nodes and shuts down and replaces instances as needed.

Amazon EMR lets you configure automated or manual cluster termination. Automatic cluster termination occurs after all procedures are complete. Transitory cluster. After processing, you can set up the cluster to continue running so you can manually stop it. You can also construct a cluster, use the installed apps, and manually terminate it. These clusters are “long-running clusters.”

Termination prevention can prevent processing errors from terminating cluster instances. With termination protection, you can retrieve data from instances before termination. Whether you activate your cluster by console, CLI, or API changes these features' default settings.

Security

Amazon EMR uses Amazon EC2 key pairs, IAM, and VPC to safeguard data and clusters.

IAM

Amazon EMR uses IAM for permissions. Person or group permissions are set by IAM policies. Users and groups can access resources and activities through policies.

The Amazon EMR service uses IAM roles, while instances use the EC2 instance profile. These roles allow the service and instances to access other AWS services for you. Amazon EMR and EC2 instance profiles have default roles. By default, roles use AWS managed policies generated when you launch an EMR cluster from the console and select default permissions. Additionally, the AWS CLI may construct default IAM roles. Custom service and instance profile roles can be created to govern rights outside of AWS.

Security groups

Amazon EMR employs security groups to control EC2 instance traffic. Amazon EMR shares a security group for your primary instance and core/task instances when your cluster is deployed. Amazon EMR creates security group rules to ensure cluster instance communication. Extra security groups can be added to your primary and core/task instances for more advanced restrictions.

Encryption

Amazon EMR enables optional server-side and client-side encryption using EMRFS to protect Amazon S3 data. After submission, Amazon S3 encrypts data server-side.

The EMRFS client on your EMR cluster encrypts and decrypts client-side encryption. AWS KMS or your key management system can handle client-side encryption root keys.

Amazon VPC

Amazon EMR launches clusters in Amazon VPCs. VPCs in AWS allow you to manage sophisticated network settings and access functionalities.

AWS CloudTrail

Amazon EMR and CloudTrail record AWS account requests. This data shows who accesses your cluster, when, and from what IP.

Amazon EC2 key pairs

A secure link between the primary node and your remote computer lets you monitor and communicate with your cluster. SSH or Kerberos can authenticate this connection. SSH requires an Amazon EC2 key pair.

Monitoring

Debug cluster issues like faults or failures utilising log files and Amazon EMR management interfaces. Amazon EMR can archive log files on Amazon S3 to save records and solve problems after your cluster ends. The Amazon EMR UI also has a task, job, and step-specific debugging tool for log files.

Amazon EMR connects to CloudWatch for cluster and job performance monitoring. Alarms can be set based on cluster idle state and storage use %.

Management interfaces

There are numerous Amazon EMR access methods:

The console provides a graphical interface for cluster launch and management. You may examine, debug, terminate, and describe clusters to launch via online forms. Amazon EMR is easiest to use via the console, requiring no scripting.

Installing the AWS Command Line Interface (AWS CLI) on your computer lets you connect to Amazon EMR and manage clusters. The broad AWS CLI includes Amazon EMR-specific commands. You can automate cluster administration and initialisation with scripts. If you prefer command line operations, utilise the AWS CLI.

SDK allows cluster creation and management for Amazon EMR calls. They enable cluster formation and management automation systems. This SDK is best for customising Amazon EMR. Amazon EMR supports Go, Java,.NET (C# and VB.NET), Node.js, PHP, Python, and Ruby SDKs.

A Web Service API lets you call a web service using JSON. A custom SDK that calls Amazon EMR is best done utilising the API.

Complexity:

EMR cluster setup and maintenance are more involved than with AWS Glue and require framework knowledge.

Learning curve

Setting up and optimising EMR clusters may require adjusting settings and parameters.

Possible Performance Issues:

Incorrect instance types or under-provisioned clusters might slow task execution and other performance.

Depends on AWS:

Due to its deep interaction with AWS infrastructure, EMR is less portable than on-premise solutions despite cloud flexibility.