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pallavinovel · 2 months ago

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Mastering AWS Certified Solutions Architect – Associate (SAA-C03)

Understanding the AWS Certified Solutions Architect

The AWS Certified Solutions Architect – Associate (SAA-C03) is a globally recognized certification that validates your ability to design secure, cost-effective, and scalable cloud solutions using AWS. It is ideal for IT professionals who want to build expertise in cloud architecture and best practices.

The AWS Solutions Architect Associate focuses on key areas such as AWS compute, storage, networking, security, and databases. Candidates learn how to design fault-tolerant architectures, optimize performance, and ensure high availability. Unlike other AWS certifications, this exam emphasizes real-world problem-solving with scenario-based questions.

Achieving this certification opens doors to roles like Cloud Architect, Solutions Engineer, and DevOps Specialist, making it a valuable asset for anyone pursuing a career in cloud computing.

Key Differences Between AWS SAA-C02 and SAA-C03

AWS replaced the SAA-C02 exam with SAA-C03 to align with the latest cloud technologies and best practices. Here are the main differences:

1. Increased Focus on Security & Best Practices

More emphasis on IAM policies, encryption techniques, and secure architecture design.

Expanded coverage of the AWS Well-Architected Framework for building reliable and efficient solutions.

2. Addition of New AWS Services

SAA-C03 includes AWS Graviton processors, AWS Global Accelerator, and Amazon FSx.

Greater focus on fault tolerance, high availability, and operational excellence.

3. More Real-World Scenario-Based Questions

The exam now tests practical problem-solving skills in designing cloud architectures.

Greater emphasis on cost optimization, performance efficiency, and scalability.

4. Exam Structure Remains the Same

Question format: Multiple-choice and multiple-response.

Time limit: No change in exam duration.

SAA-C03 ensures AWS professionals stay updated with the latest cloud advancements, making it essential for cloud architects.

AWS Services: Must-Know vs. Good-to-Know for SAA-C03

When preparing for the AWS Certified Solutions Architect – Associate (SAA-C03) exam, it's important to focus on critical services while also having a basic understanding of others. Here’s a breakdown:

Must-Know AWS Services (Core for the Exam)

These services are heavily tested and essential for designing scalable, secure, and cost-efficient architectures:

Compute: EC2, Lambda, Auto Scaling, Elastic Load Balancing (ELB)

Storage: S3, EBS, EFS, FSx

Networking: VPC, Route 53, CloudFront, AWS Global Accelerator

Databases: RDS, DynamoDB, Aurora, ElastiCache

Security & Identity: IAM, AWS Organizations, KMS, AWS Shield, AWS WAF

Monitoring & Management: CloudWatch, CloudTrail, AWS Config

Good-to-Know AWS Services (Helpful but Less Focused in the Exam)

These services may appear in some questions but aren’t the main focus:

Advanced Compute: AWS Outposts, EC2 Spot Instances, Batch

Big Data & Analytics: Athena, Glue, Kinesis, Redshift

Developer Tools: CodePipeline, CodeBuild, AWS SAM

Machine Learning & AI: SageMaker, Rekognition, Comprehend

Focusing on Must-Know services ensures better exam performance, while understanding Good-to-Know services helps in real-world AWS solutions.

Beyond Certification: Career Benefits of AWS Solutions Architect – Associate

Earning the AWS Certified Solutions Architect – Associate (SAA-C03) certification goes beyond just validating your skills—it boosts your career in cloud computing. It opens doors to roles like Cloud Architect, Solutions Engineer, and DevOps Specialist, with high demand across industries. AWS-certified professionals also enjoy higher salaries, often exceeding $120,000 annually, depending on experience and location. The certification enhances job security as more businesses migrate to the cloud. Additionally, it provides networking opportunities through AWS communities and events, helping professionals grow and advance. It also serves as a stepping stone to higher-level AWS certifications, ensuring continuous career progression in the cloud industry.

Read More: AWS Certified Solutions Architect

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softcrayons19 · 2 months ago

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Azure vs. AWS: A Detailed Comparison

Cloud computing has become the backbone of modern IT infrastructure, offering businesses scalability, security, and flexibility. Among the top cloud service providers, Microsoft Azure and Amazon Web Services (AWS) dominate the market, each bringing unique strengths. While AWS has held the position as a cloud pioneer, Azure has been gaining traction, especially among enterprises with existing Microsoft ecosystems. This article provides an in-depth comparison of Azure vs. AWS, covering aspects like database services, architecture, and data engineering capabilities to help businesses make an informed decision.

1. Market Presence and Adoption

AWS, launched in 2006, was the first major cloud provider and remains the market leader. It boasts a massive customer base, including startups, enterprises, and government organizations. Azure, introduced by Microsoft in 2010, has seen rapid growth, especially among enterprises leveraging Microsoft's ecosystem. Many companies using Microsoft products like Windows Server, SQL Server, and Office 365 find Azure a natural choice.

2. Cloud Architecture: Comparing Azure and AWS

Cloud architecture defines how cloud services integrate and support workloads. Both AWS and Azure provide robust cloud architectures but with different approaches.

AWS Cloud Architecture

AWS follows a modular approach, allowing users to pick and choose services based on their needs. It offers:

Amazon EC2 for scalable compute resources

Amazon VPC for network security and isolation

Amazon S3 for highly scalable object storage

AWS Lambda for serverless computing

Azure Cloud Architecture

Azure's architecture is designed to integrate seamlessly with Microsoft tools and services. It includes:

Azure Virtual Machines (VMs) for compute workloads

Azure Virtual Network (VNet) for networking and security

Azure Blob Storage for scalable object storage

Azure Functions for serverless computing

In terms of architecture, AWS provides more flexibility, while Azure ensures deep integration with enterprise IT environments.

3. Database Services: Azure SQL vs. AWS RDS

Database management is crucial for any cloud strategy. Both AWS and Azure offer extensive database solutions, but they cater to different needs.

AWS Database Services

AWS provides a wide range of managed database services, including:

Amazon RDS (Relational Database Service) – Supports MySQL, PostgreSQL, SQL Server, MariaDB, and Oracle.

Amazon Aurora – High-performance relational database compatible with MySQL and PostgreSQL.

Amazon DynamoDB – NoSQL database for low-latency applications.

Amazon Redshift – Data warehousing for big data analytics.

Azure Database Services

Azure offers strong database services, especially for Microsoft-centric workloads:

Azure SQL Database – Fully managed SQL database optimized for Microsoft applications.

Cosmos DB – Globally distributed, multi-model NoSQL database.

Azure Synapse Analytics – Enterprise-scale data warehousing.

Azure Database for PostgreSQL/MySQL/MariaDB – Open-source relational databases with managed services.

AWS provides a more mature and diverse database portfolio, while Azure stands out in SQL-based workloads and seamless Microsoft integration.

4. Data Engineering and Analytics: Which Cloud is Better?

Data engineering is a critical function that ensures efficient data processing, transformation, and storage. Both AWS and Azure offer data engineering tools, but their capabilities differ.

AWS Data Engineering Tools

AWS Glue – Serverless data integration service for ETL workloads.

Amazon Kinesis – Real-time data streaming.

AWS Data Pipeline – Orchestration of data workflows.

Amazon EMR (Elastic MapReduce) – Managed Hadoop, Spark, and Presto.

Azure Data Engineering Tools

Azure Data Factory – Cloud-based ETL and data integration.

Azure Stream Analytics – Real-time event processing.

Azure Databricks – Managed Apache Spark for big data processing.

Azure HDInsight – Fully managed Hadoop and Spark services.

Azure has an edge in data engineering for enterprises leveraging AI and machine learning via Azure Machine Learning and Databricks. AWS, however, excels in scalable and mature big data tools.

5. Pricing Models and Cost Efficiency

Cloud pricing is a major factor when selecting a provider. Both AWS and Azure offer pay-as-you-go pricing, reserved instances, and cost optimization tools.

AWS Pricing: Charges are based on compute, storage, data transfer, and additional services. AWS also offers AWS Savings Plans for cost reductions.

Azure Pricing: Azure provides cost-effective solutions for Microsoft-centric businesses. Azure Hybrid Benefit allows companies to use existing Windows Server and SQL Server licenses to save costs.

AWS generally provides more pricing transparency, while Azure offers better pricing for Microsoft users.

6. Security and Compliance

Security is a top priority in cloud computing, and both AWS and Azure provide strong security measures.

AWS Security: Uses AWS IAM (Identity and Access Management), AWS Shield (DDoS protection), and AWS Key Management Service.

Azure Security: Provides Azure Active Directory (AAD), Azure Security Center, and built-in compliance features for enterprises.

Both platforms meet industry standards like GDPR, HIPAA, and ISO 27001, making them secure choices for businesses.

7. Hybrid Cloud Capabilities

Enterprises increasingly prefer hybrid cloud strategies. Here, Azure has a significant advantage due to its Azure Arc and Azure Stack technologies that extend cloud services to on-premises environments.

AWS offers AWS Outposts, but it is not as deeply integrated as Azure’s hybrid solutions.

8. Which Cloud Should You Choose?

Choose AWS if:

You need a diverse range of cloud services.

You require highly scalable and mature cloud solutions.

Your business prioritizes flexibility and a global cloud footprint.

Choose Azure if:

Your business relies heavily on Microsoft products.

You need strong hybrid cloud capabilities.

Your focus is on SQL-based workloads and enterprise data engineering.

Conclusion

Both AWS and Azure are powerful cloud providers with unique strengths. AWS remains the leader in cloud services, flexibility, and scalability, while Azure is the go-to choice for enterprises using Microsoft’s ecosystem.

Ultimately, the right choice depends on your organization’s needs in terms of database management, cloud architecture, data engineering, and overall IT strategy. Companies looking for a seamless Microsoft integration should opt for Azure, while businesses seeking a highly scalable and service-rich cloud should consider AWS.

Regardless of your choice, both platforms provide the foundation for a strong, scalable, and secure cloud infrastructure in today’s data-driven world.

#AWS #aws course in noida #aws course #azure

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cloudastra1 · 6 months ago

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AWS Aurora vs RDS: An In-Depth Comparison

AWS Aurora vs. RDS

Amazon Web Services (AWS) offers a range of database solutions, among which Amazon Aurora and Amazon Relational Database Service (RDS) are prominent choices for relational database management. While both services cater to similar needs, they have distinct features, performance characteristics, and use cases. This comparison will help you understand the differences and make an informed decision based on your specific requirements.

What is Amazon RDS?

Amazon RDS is a managed database service that supports several database engines, including MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server. RDS simplifies the process of setting up, operating, and scaling a relational database in the cloud by automating tasks such as hardware provisioning, database setup, patching, and backups.

What is Amazon Aurora?

Amazon Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, combining the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. Aurora is designed to deliver high performance and reliability, with some advanced features that set it apart from standard RDS offerings.

Performance

Amazon RDS: Performance depends on the selected database engine and instance type. It provides good performance for typical workloads but may require manual tuning and optimization.

Amazon Aurora: Designed for high performance, Aurora can deliver up to five times the throughput of standard MySQL and up to three times the throughput of standard PostgreSQL databases. It achieves this through distributed, fault-tolerant, and self-healing storage that is decoupled from compute resources.

Scalability

Amazon RDS: Supports vertical scaling by upgrading the instance size and horizontal scaling through read replicas. However, the scaling process may involve downtime and requires careful planning.

Amazon Aurora: Offers seamless scalability with up to 15 low-latency read replicas, and it can automatically adjust the storage capacity without affecting database performance. Aurora’s architecture allows it to scale out and handle increased workloads more efficiently.

Availability and Durability

Amazon RDS: Provides high availability through Multi-AZ deployments, where a standby replica is maintained in a different Availability Zone. In case of a primary instance failure, RDS automatically performs a failover to the standby replica.

Amazon Aurora: Enhances availability with six-way replication across three Availability Zones and automated failover mechanisms. Aurora’s storage is designed to be self-healing, with continuous backups to Amazon S3 and automatic repair of corrupted data blocks.

Cost

Amazon RDS: Generally more cost-effective for smaller, less demanding workloads. Pricing depends on the chosen database engine, instance type, and storage requirements.

Amazon Aurora: Slightly more expensive than RDS due to its advanced features and higher performance capabilities. However, it can be more cost-efficient for large-scale, high-traffic applications due to its performance and scaling advantages.

Maintenance and Management

Amazon RDS: Offers automated backups, patching, and minor version upgrades. Users can manage various configuration settings and maintenance windows, but they must handle some aspects of database optimization.

Amazon Aurora: Simplifies maintenance with continuous backups, automated patching, and seamless version upgrades. Aurora also provides advanced monitoring and diagnostics through Amazon CloudWatch and Performance Insights.

Use Cases

Amazon RDS: Suitable for a wide range of applications, including small to medium-sized web applications, development and testing environments, and enterprise applications that do not require extreme performance or scalability.

Amazon Aurora: Ideal for mission-critical applications that demand high performance, scalability, and availability, such as e-commerce platforms, financial systems, and large-scale enterprise applications. Aurora is also a good choice for organizations looking to migrate from commercial databases to a more cost-effective cloud-native solution.

Conclusion

Amazon Aurora vs Amazon RDS both offer robust, managed database solutions in the AWS ecosystem. RDS provides flexibility with multiple database engines and is well-suited for typical workloads and smaller applications. Aurora, on the other hand, excels in performance, scalability, and availability, making it the preferred choice for demanding and large-scale applications. Choosing between RDS and Aurora depends on your specific needs, performance requirements, and budget considerations.

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digitalcreativecreator · 11 months ago

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Pick Your Perfect Match: Aurora vs RDS - A Guide to AWS Database Solutions

Now that Database-as-a-service (DBaaS) is in high demand, there are multiple questions regarding AWS services that cannot always be answered easily: When should I use Aurora and when should I use RDS MySQL? What are the major differences in Aurora as well as RDS? What should I consider when deciding which one to choose?

The blog below we'll address all of these crucial questions and bring an overview of the two database options, Aurora vs RDS.

Understanding DBaaS

DBaaS cloud services permit users to access databases without configuring physical hardware infrastructure, or installing software. However, when figuring out which option perfect for an organization, diverse factors should be taken into account. They could include efficiency, operational costs, high availability and capacity planning, management, security, scalability, monitoring and more.

There are instances when, even though the work load and operational demands appear to perfect match to one solution however, there are other factors that could cause blockages (or at least require specific handling).

Understanding DBaaS

What we need to compare are those of the MySQL and Aurora database engines that are offered through Amazon RDS.

Download our ebook, “Enterprise Guide to Cloud Databases” to benefit you make better informed choices and avoid costly errors when you design and implement your strategy for cloud.

What is Amazon Aurora?

Amazon Aurora is a proprietary cloud-native, fully-managed relational database service created through Amazon Web Services (AWS). It supports MySQL and PostgreSQL and its automatic backup and replication capabilities, it is built to offer high performance as well as scalability and availability to support the requirements of critical applications.

Aurora Features

High Performance and Scalability

Amazon Aurora has gained widespread praise for its remarkable performance and scalability. This makes it a perfect solution to handle the demands of high-demand tasks. It efficiently handles the write and read operations, optimizes access to data and reduces contention which payoff in rapid throughput and low delay for you to assure that applications run to their desirable.

Aurora offers a range of options for scaling, such as the ability the addition of up 15 read replicas within one database cluster and the auto-scaling to read replications the development of read replicas across regions for disaster recovery, and enhanced read performance across different geographical locations, and auto-scaling for storage that can handle growing data without needing continuous monitoring.

Support for MySQL as well as PostgreSQL

Aurora provides seamless compatibility to MySQL and PostgreSQL that allows users and DBAs to use their database abilities and make use of the latest capabilities and improvements.

If you have applications developed using MySQL or PostgreSQL moving to Aurora is an easy process with minimal code modifications, because it works with the same protocols, tools and drivers.

Automated Backups and Point-in-Time Recovery

Aurora offers automated backup and point-in-time recovery that simplifies the management of backups and protecting data. Backups that are continuous and incremental are created automatically and then stored in Amazon S3, and data retention times can be set to satisfy compliance requirements.

The point-in-time recovery (PITR) feature enables the restoration of a database to a specific time within the set retention period, making it easier to roll the application back to a specific state or recover from accidental/purposeful data corruption.

Automated features lessen the workload on DBAs as well as organizations with their efforts to protect data by easing backups of databases and recovery.

Multi-Availability Zone (AZ) Deployment

Aurora’s multi-availability zone (AZ) deployment provides remarkably high reliability and resilience to faults by automatically replicating information across numerous accessibility zones together it’s distributed storage system to remove single point of failure. The constant synchronization between replica and primary storage ensures continuous redundancy. In the event of an interruption occurs within the main, Aurora seamlessly switches to the replica using automated failover to ensure continuous availability.

What is Amazon RDS?

Amazon Relational Database Service (Amazon RDS) is a cloud-hosted database service that offers diverse database options to pick from, such as Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and Microsoft SQL Server.

RDS Features

Managed Database Service

Amazon RDS is a fully-managed database service that is provided by AWS and offers a simple approach to manage and maintain relational databases hosted in the cloud. AWS manages the essential administrative tasks such as database configuration, setup backups, monitoring and scaling. It makes it simpler for companies to manage their complex databases.

By delegating these administrative duties by delegating these administrative tasks to AWS, DBAs, and developers are no longer required to devote time to tedious tasks such as software installation and hardware provisioning, giving them time to focus on more business-oriented processes while also reducing expenses.

Multiple Database Engine Options

Amazon RDS supports various database engine options, such as MySQL, PostgreSQL, Oracle and SQL Server. This gives organizations the freedom to select the appropriate engine for their particular needs. With these choices, Amazon RDS empowers developers to adapt your database architecture to meet the unique requirements of their apps performance requirements, performance expectations, and compliance requirements, while ensuring that the database is compatible and efficient across all businesses.

Offering a simple method of migrating databases that are already in use, RDS allows for a variety of migration options that include imports of backup data from existing backups, and using AWS Database Migration Services (DMS) to enable real-time data migration. This flexibility lets businesses effortlessly move their databases into the AWS cloud without causing significant disruptions.

Automated Backups and Point-in-Time Recovery

Amazon RDS offers an automated backup feature to ensure the integrity of data and offers reliable protection for data. It takes regular backups, and captures small changes from the previous backup without affecting the performance. Users can choose the time frame for these backups. This allows the recovery of historical data in the event an accidental loss of data or corruption. Point-in-time recovery (PITR) permits users to restore the database at any time within the specified time. This is a great feature in reverting back to a prior state, or to repair damage caused by data or other occurrences.

Its RDS automatic backup as well as PITR features ensure that data is not lost and protect against system failures, providing the highest level of availability and performance, while making backup management easier for developers as well as DBAs.

Scalability and Elasticity

Amazon RDS offers several scalability options to allow organizations to adjust resources to accommodate changing applications and workload requirements. Vertical scaling permits for an increase in compute and memory capacity by upgrading to higher-end instances that are perfect for handling large demand for processing or traffic and horizontal scaling entails creating read replicas that distribute the workload across different instances, increasing the read scalability of applications that are heavy on reading.

RDS additionally simplifies the process of automatically scaling depending on demand for workloads by adding or subtracting replicas in order to efficiently divide read requests and decrease cost during periods of low demand. It also allows auto-scaling of storage and compute resources, adjusting capacity dynamically in accordance with the chosen thresholds for utilization to improve performance and decrease cost.

The ability to alter resources in response to changing demands gives organizations the capability to react quickly to fluctuations in demand without having to manually intervene — while still optimizing performance and decreasing costs.

Examining the similarities between Aurora vs RDS

If you compare Amazon Aurora and Amazon RDS It is clear that both provide advantages in time-saving administration of systems. Both options let you get a pre-configured system ready to run your apps. Particularly, in the absence of special database admins (DBAs), Amazon RDS offers a wide range of flexibility for different processes, such as backups and upgrades.

Amazon Aurora and Amazon RDS both Amazon Aurora and Amazon RDS offer continuous updates as well as patches that are applied by Amazon without interruption. You can set maintenance windows that allow automated patching to take place within these time frames. Furthermore, data is constantly stored on Amazon S3 in real-time, protecting your data without visible effect on performance. This means that there is no necessity for complex or scripted backup processes and defined backup windows.

Although these shared features provide significant benefits, it’s crucial to take into consideration potential issues like vendor lock-in, and the potential issues that result from enforced updates as well as client-side optimizations.

Aurora RDS RDS The key differences

In this article we will examine the distinct features and characteristics in Amazon Aurora along with Amazon RDS in addition to shedding light on their efficiency, scalability and pricing strategies, and so on.

Amazon Aurora is an open-source, relational closed-source database engine that comes and all the implications that it brings.

The RDS MySQL can be 5.5, 5.6, and 5.7 compatible, and provides the choice to select between minor versions. Although RDS MySQL supports numerous storage engines with different capabilities but not all are designed for recovery from crashes and long-term data protection. It was until recently an inconvenient fact to the extent that Aurora wasn’t compatible only with MySQL 5.6 however, the software is compatible now with MySQL 5.6 and 5.7 too.

In most instances, no major application modifications are needed to either of the products. Be aware that some MySQL features, such as those of the MyISAM storage engine aren’t available in Amazon Aurora. The migration to RDS is possible with the comprinno program.

For RDS products Shell access to the operating system in question is blocked, and access for MySQL user accounts that have access to the “SUPER” privilege isn’t allowed. To manage MySQL parameters or control users Amazon RDS provides specific parameters, APIs and other procedures for the system that are utilized. If you are looking to allow Amazon RDS remote access, this article can benefit to do it.

Considerations regarding performance

For instance, because of the requirement for disabling in the case of InnoDB changes buffer in Aurora (this is among the key components for this distributed storage system) and the fact that updates to secondary indexes need to be write-through, there’s an enormous performance hit when heavy writes which update the secondary indexes is performed. This is due to the method MySQL depends upon the buffer to delay and combine second index update. If your application has frequent updates to tables that have primary indexes Aurora speed may prove low. As you might have seen, AWS claims that the query_cache feature is a viable option and does not have issues with scalability. Personally, I’ve never had any issues with query_cache and the feature is able to greatly rise the overall performance.

In any event it is important to be aware that performance varies based on the schema’s design. When deciding to move, performance must be compared against the specific workload of your application. Conducting thorough tests will become the topic of a subsequent blog article.

Capacity Planning

In terms of storage under the hood Another factor to take into account is Aurora storage, there is no requirement for capacity planning. Aurora storage will grow automatically by a minimum of 10GB to 64 TiB in increments of 10GB without affecting the performance of databases. The limit on table size is only limited in relation to the volume of Aurora database cluster, which can reach an maximum capacity size of 64 Tebibytes (TiB). Therefore, the maximum size of a table for a table within the Aurora database will be 64 TiB. For RDS MySQL the maximum allocated storage limit limits the table’s size to a maximum that is 16TB when with InnoDB tablespaces that are file-per-table.

In the case of RDS MySQL, there has recently been added a brand-new function, known as storage autoscaling. Once you have created your instance you are able to enable this option which is somewhat similar to Aurora’s features. Aurora provides. More details are available here..

In August 2018. Aurora offers a second opportunity that does not need provisioned capacity. It’s Aurora Serverless.

“Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora (MySQL-compatible and PostgreSQL-compatible editions), where the database will automatically start up, shut down, and scale capacity up or down based on your application’s needs. It allows you to manage your database on the cloud, without having to manage all instances of your database. It’s an easy, affordable feature for occasional, irregular or unpredictably heavy work. Manually managing the database’s capacity can consume time and could result in inefficient utilization of the database’s resources. With Aurora Serverless It is as easy as create an endpoint for your database, indicate the desired capacity range, then connect your applications. The cost is per second basis for the capacity of your database that you utilize as long as the database is running and you can switch between serverless and standard configurations by a few clicks from the Management Console for Amazon RDS.”

#clouds #awscloud

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ericvanderburg · 1 year ago

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Aurora vs. RDS: How To Choose the Right AWS Database for 2024

http://securitytc.com/T2fGT4

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get-office · 4 years ago

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Difference Between Microsoft Azure vs Amazon AWS

What is Azure?

• Azure is viewed as both a Platform as a Service (PaaS) and an Infrastructure as a Service (IaaS) offering.

• Azure may be a uniquely powerful offering due to its builder. Few companies have A level of infrastructure support adequate to Microsoft.

Visit Office.com/setup to know more

What is AWS?

• AWS, like Amazon itself, features a vast toolset that's growing at an exponential rate.

• It's been within the cloud computing marketplace for quite 10 years, which suggests that AWS is that the frontrunner and has been for a few times.

• AWS offering services are categorized as Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS).

Microsoft Azure vs Amazon AWS Features and Services

Let's start with the basics.

In terms of basic capabilities, AWS and Azure are pretty similar. They share all of the common elements of public cloud services: self-service, security, instant provisioning, auto-scaling, compliance, and identity management. However, between the 2, AWS offers the best depth, with 140 services across computing, database, analytics, storage, mobile, and developer tools. confine mind, however, that they need a start on everyone else since they have been around the longest. That said, Azure is additionally strong on the features and services front and features a parent company that has the resources to carry their own against Amazon.

Storage

Successful cloud deployment relies on sufficient storage to urge the work done. Fortunately, this is often a neighborhood where Azure and AWS are equally strong. AWS's storage relies on machine instances, which are virtual machines hosted on AWS infrastructure. Storage is tied to individual instances--temporary storage is allocated once per instance and destroyed when an instance is terminated. you'll also get block storage attached to an instance, almost like a tough drive. If you would like object storage, you'll catch on through S3, and if you would like data archiving, you'll catch on through Glacier. Azure, on the opposite hand, offers temporary storage through D drive and block storage through Page Blobs for VMs, with Block Blobs and Files doubling as object storage. Like AWS, it supports relational databases, Big Data, and NoSQL through Azure Table and HDInsight. Azure offers two classes of storage: Hot and funky. Cool storage is a smaller amount expensive, but you'll incur additional read and write costs. For AWS, there's S3 Standard and S3 Standard-Infrequent Access. Both have unlimited allowed objects, but AWS has an object size limit of 5 TB, while Azure features a size limit of 4.75 TB.

Computing Power

One front for comparison is computing power, which may be a standard requirement for any IT team. If you are going to take a position in cloud services, you would like cloud services with enough horsepower to stay up together with your office's demands on a day-to-day basis (and during high-traffic periods). The primary issue here is scalability. AWS uses elastic cloud computing (EC2), which is when the available resource footprint can grow or shrink on demand using cloud computing, with an area cluster providing only a part of the resource pool available to all or any jobs. AWS EC2 users can configure their own virtual machines (VMs), choose pre-configured machine images (MIs), or customize as. Users have the liberty to settle on the dimensions, power, memory capacity, and number of VMs they want to use. Azure users, on the opposite hand, chose a virtual hard disc (VHD) to make a VM. this will be pre-configured by Microsoft, the user, or a separate third party. It relies on virtual scale sets for scalability purposes. The key difference is that EC2 is often tailored to a variety of options, while Azure VMs pair with other tools to assist deploy applications on the cloud.

Databases

Regardless of whether you would like an electronic database or a NoSQL offering, both AWS and Azure have robust database offerings.

Amazon's electronic database service (RDS) supports six popular database engines:

1. Amazon Aurora

2. MariaDB

3. Microsoft SQL

4. MySQL

5. Oracle

6. PostgreSQL

Azure's SQL database, on the opposite hand, is predicated solely on Microsoft SQL.

Both systems work perfectly with NoSQL and relational databases. They're highly available, durable, and offer easy, automatic replication.

AWS has more instance types you'll provision, but Azure's interface and tooling are delightfully user-friendly, making it easy to perform various database operations.

This was all about Microsoft Azure vs Amazon AWS. We differentiate these two things to understand you very well. For more help visit Office.com/setup.

#microsoft azure cloud #amazon aws #Microsoft Azure vs Amazon AWS

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vishnu0253 · 3 years ago

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The Key Differences Between Google Vs AWS Vs Azure Cloud

Cloud has surprised the world lately, particularly during the pandemic. It enabled associations to upset the manner in which they work, reclassify their plans of action, and reconsider their contributions. All while turning out to be more expense effective, light-footed, and creative. Civility to all the three top Cloud Service Providers (CSPs), in particular Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure Cloud. Notwithstanding, there’s dependably a discussion between Google Vs AWS Vs Azure Cloud despite everything, there’s a ton of disarray when you need to obtain cloud administrations from both of them.

Amazon, Google, and Microsoft have prepared for organizations to relocate their jobs to the cloud without any problem. Inside no time, these three tech goliaths ruled the public cloud environment by giving adaptable, dependable, and secure cloud administrations. Presently, there is a furious three-way fight among aws consulting, GCP, and Azure to stand out. They are exceeding everyone’s expectations by giving out an extensive variety of distributed computing, stockpiling, and systems administration administrations. There’s not a day that goes by without one of these three top cloud specialist organizations carrying out new highlights and updates. This high rivalry between Google Cloud, AWS, and Azure is making it extreme for organizations to pick their cloud stage.

Mindful of this sensitive test, we have brought to you this selective blog on Google Cloud versus AWS versus Azure to assist you with picking the one that best suits your business prerequisites.

Purplish blue Vs AWS Vs Google secure cloud computing: Choosing the Right Cloud Platform Before we dig into the definite examination of Azure, GCP, and AWS cloud, how about we see each cloud stage.

What is Amazon Web Services (AWS)? Amazon, the trailblazer of distributed computing, entered the market with its leader cloud offering Amazon Web Services (AWS) a long time back. From that point forward, the AWS cloud has been overwhelming the public cloud framework market with regards to the quantity of items as well as clients.

The Aws cloud offers in excess of 200 business cloud services items and answers for a large group of utilizations, advancements, and enterprises. A portion of the top highlighted contributions remembered for Amazon’s cloud administration are Amazon EC2, Amazon Aurora, Amazon Simple Storage Service (S3), Amazon RDS, Amazon DynamoDB, Amazon VPC, AWS Lambda, Amazon SageMaker, and Amazon Lightsail.

“Amazon’s cloud administration” has the most broad worldwide organization. Its server farms are spread across 84 Availability Zones inside 26 geographic districts around the world. This broad foundation brings the advantages of low dormancy, exceptionally excess, and profoundly effective systems administration. Also, Amazon is making arrangements for 24 greater Availability Zones and 8 Regions.

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sristhitheengineer · 3 years ago

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The Key Differences Between Google Vs AWS Vs Azure Cloud

Mindful of this sensitive test, we have brought to you this selective blog on Google Cloud versus AWS versus Azure to assist you with picking the one that best suits your business prerequisites.

Purplish blue Vs AWS Vs Google secure cloud computing: Choosing the Right Cloud Platform

Before we dig into the definite examination of Azure, GCP, and AWS cloud, how about we see each cloud stage.

What is Amazon Web Services (AWS)?

Amazon, the trailblazer of distributed computing, entered the market with its leader cloud offering Amazon Web Services (AWS) a long time back. From that point forward, the AWS cloud has been overwhelming the public cloud framework market with regards to the quantity of items as well as clients.

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globalmediacampaign · 4 years ago

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MySQL HeatWave: 1100x Faster than Aurora, 400x than RDS, 18x than Redshift at 1/3 the cost

HeatWave is designed to enable customers to run analytics on data which is stored in MySQL databases without the need for ETL. This service is built on an innovative, in-memory analytics engine which is architected for scalability and performance and is optimized for Oracle Cloud Infrastructure (OCI) Gen 2 hardware. This results in a very performant solution for SQL analytics at a fraction of the cost compared to other cloud services including AWS Aurora, Redshift, Google Big Query, RDS. The amount of acceleration an application would observe with HeatWave depends upon a number of factors like the datasize, queries, operators being used in the query, the selectivity of the predicates. For the purpose of comparing, we are considering the TPCH benchmark which has the queries well defined and the only variable is the data size and the system configuration. HeatWave is able handle all workloads with a single shape so that significantly simplifies the choice for the customer. 400x Query Acceleration for MySQL The first comparison we make is with MySQL database which is representative of MySQL running on various cloud platforms or various flavors of MySQL. For 400G datasize, using the same number of cores and the same amount of DRAM for MySQL, HeatWave accelerates performance by 400x times for analytics workloads like TPCH. Furthermore, there is no need to create any indexes with HeatWave. Figure 1. HeatWave accelerates MySQL queries by 400x 1100x Faster than Aurora, 3x cheaper The next comparison we show is with Amazon Aurora, which is Amazon’s premium database service. HeatWave offers dramatic improvement in performance for complex and analytic queries. For a 4TB TPC-H workload, MySQL HeatWave is 1100x faster than Amazon Aurora. Furthermore, there is no need to create indexes on the base table which takes over 5 days with Amazon Aurora compared to under 4 hours to load data in HeatWave. As a result, the data is available to query much sooner than with Aurora. Furthermore, the cost is less than 1/3 of Aurora. Figure 2. HeatWave is 1100x faster and less than 1/3 the cost of Aurora The performance improvement of MySQL Database Service with HeatWave over Aurora increases with the size of data. Figure 3. The performance advantage of HeatWave increases with data size vs. Amazon Aurora 17x Faster than Redshift, 3x Cheaper Next, we compare with Amazon Redshift which is designed for analytics and is offered in multiple shapes. Compared to the fastest shape (dc2.8xlarge), HeatWave is up to 3x faster and 1/3 the cost. For HeatWave, the cost includes both OLTP and OLAP capabilities while for Redshift the additional cost of the OLTP system and the cost of ETL from the OLTP database to Redshift is not included. Figure 4. HeatWave is 2.7x faster and 1/3 the cost of Amazon Redshift’s fastest shape. Compared to the cheaper shape of Redshift (RA3.4xLarge), HeatWave is up to 18x faster and 3% less expensive. Unlike Redshift, HeatWave is capable of running both OLTP and OLAP wokloads, without the need for ETL. With Redshift listed cost is only for OLAP, and additional costs are needed for the OLTP database. Figure 5. HeatWave is 17.7x faster and cheaper than Amazon Redshift’s cheaper shape Customers who use HeatWave will benefit from significantly better performance, eliminating the need for ETL, support for real-time analytics, reduced monthly cost and a single database for OLTP and OLAP. Conclusion HeatWave is a cloud native service which is exclusively available in Oracle cloud Infrastructure and provides compelling performance and cost for analytic workloads. Organizations using MySQL database for managing their enterprise data can now run analytic queries with HeatWave with significantly better performance, lower cost, not requiring ETL and support for real- time analytics in contrast to other database services like RDS, Google Big Query, Snowflake, Aurora and Redshift. The service can be deployed in a cloud only or in a hybrid environment, and it simplifies management for both transactional and analytic applications. We welcome you to try this service for free: https://www.oracle.com/cloud/free/ https://blogs.oracle.com/mysql/mysql-heatwave-faster-and-cheaper

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codeonedigest · 2 years ago

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Amazon Aurora Database Explained for AWS Cloud Developers

Full Video Link - https://youtube.com/shorts/4UD9t7-BzVM Hi, a new #video #tutorial on #amazonrds #aws #aurora #database #rds is published on #codeonedigest #youtube channel. @java @awscloud @AWSCloudIndia @YouTube #youtube @codeonedigest #cod

Amazon Aurora is a relational database management system (RDBMS) built for the cloud & gives you the performance, availability of commercial-grade databases at one-tenth the cost. Aurora database comes with MySQL & PostgreSQL compatibility. Amazon Aurora provides built-in security, continuous backups, serverless compute, up to 15 read replicas, automated multi-Region replication, and…

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cloudemind · 4 years ago

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Cách tính giá của Amazon RDS Postgres và Aurora Postgres

Có bài viết học luyện thi AWS mới nhất tại https://cloudemind.com/amazon-rds-postgres-vs-aurora-postgres-pricing/ - Cloudemind.com

Cách tính giá của Amazon RDS Postgres và Aurora Postgres

Bài này mình cùng tìm hiểu về cách tính giá của RDS và Aurora postgres, từ đó có lựa chọn phù hợp cho các workload chạy trên Postgres nhé.

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Have fun!

Xem thêm: https://cloudemind.com/amazon-rds-postgres-vs-aurora-postgres-pricing/

#aurora #aws #postgres #pricing #rds

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theresawelchy · 6 years ago

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Amazon Aurora: design considerations for high throughput cloud-native relational databases

Amazon Aurora: design considerations for high throughput cloud-native relational databases Verbitski et al., SIGMOD’17

Werner Vogels recently published a blog post describing Amazon Aurora as their fastest growing service ever. That post provides a high level overview of Aurora and then links to two SIGMOD papers for further details. Also of note is the recent announcement of Aurora serverless. So the plan for this week on The Morning Paper is to cover both of these Aurora papers and then look at Calvin, which underpins FaunaDB.

Say you’re AWS, and the task in hand is to take an existing relational database (MySQL) and retrofit it to work well in a cloud-native environment. Where do you start? What are the key design considerations and how can you accommodate them? These are the questions our first paper digs into. (Note that Aurora supports PostgreSQL as well these days).

Here’s the starting point:

In modern distributed cloud services, resilience and scalability are increasingly achieved by decoupling compute from storage and by replicating storage across multiple nodes. Doing so lets us handle operations such as replacing misbehaving or unreachable hosts, adding replicas, failing over from a writer to a replica, scaling the size of a database instance up or down, etc.

So we’re somehow going to take the backend of MySQL (InnoDB) and introduce a variant that sits on top of a distributed storage subsystem. Once we’ve done that, network I/O becomes the bottleneck, so we also need to rethink how chatty network communications are.

Then there are a few additional requirements for cloud databases:

SaaS vendors using cloud databases may have numerous customers of their own. Many of these vendors use a schema/database as the unit of tenancy (vs a single schema with tenancy defined on a per-row basis). “As a result, we see many customers with consolidated databases containing a large number of tables. Production instances of over 150,000 tables for small databases are quite common. This puts pressure on components that manage metadata like the dictionary cache.”

Customer traffic spikes can cause sudden demand, so the database must be able to handle many concurrent connections. “We have several customers that run at over 8000 connections per second.”

Frequent schema migrations for applications need to be supported (e.g. Rails DB migrations), so Aurora has an efficient online DDL implementation.

Updates to the database need to be made with zero downtime

The big picture for Aurora looks like this:

The database engine as a fork of “community” MySQL/InnoDB and diverges primarily in how InnoDB reads and writes data to disk.

There’s a new storage substrate (we’ll look at that next), which you can see in the bottom of the figure, isolated in its own storage VPC network. This is deployed on a cluster of EC2 VMs provisioned across at least 3 AZs in each region. The storage control plane uses Amazon DynamoDB for persistent storage of cluster and storage volume configuration, volume metadata, and S3 backup metadata. S3 itslef is used to store backups.

Amazon RDS is used for the control plane, including the RDS Host Manager (HM) for monitoring cluster health and determining when failover is required.

It’s nice to see Aurora built on many of the same foundational components that are available to us as end users of AWS too.

Durability at scale

The new durable, scalable storage layer is at the heart of Aurora.

If a database system does nothing else, it must satisfy the contract that data, once written, can be read. Not all systems do.

Storage nodes and disks can fail, and at large scale there’s a continuous low level background noise of node, disk, and network path failures. Quorum-based voting protocols can help with fault tolerance. With copies of a replicated data item, a read must obtain votes, and a write must obtain votes. Each write must be aware of the most recent write, which can be achieved by configuring . Reads must also be aware of the most recent write, which can be achieved by ensuring . A common approach is to set and .

We believe 2/3 quorums are inadequate [even when the three replicas are each in a different AZ]… in a large storage fleet, the background noise of failures implies that, at any given moment in time, some subset of disks or nodes may have failed and are being repaired. These failures may be spread independently across nodes in each of AZ A, B, and C. However, the failure of AZ C, due to a fire, roof failure, flood, etc., will break quorum for any of the replicas that concurrently have failures in AZ A or AZ B.

Aurora is designed to tolerate the loss of an entire AZ plus one additional node without losing data, and an entire AZ without losing the ability to write data. To achieve this data is replicated six ways across 3 AZs, with 2 copies in each AZ. Thus ; is set to 4, and is set to 3.

Given this foundation, we want to ensure that the probability of double faults is low. Past a certain point, reducing MTTF is hard. But if we can reduce MTTR then we can narrow the ‘unlucky’ window in which an additional fault will trigger a double fault scenario. To reduce MTTR, the database volume is partitioned into small (10GB) fixed size segments. Each segment is replicated 6-ways, and the replica set is called a Protection Group (PG).

A storage volume is a concatenated set of PGs, physically implemented using a large fleet of storage nodes that are provisioned as virtual hosts with attached SSDs using Amazon EC2… Segments are now our unit of independent background noise failure and repair.

Since a 10GB segment can be repaired in 10 seconds on a 10Gbps network link, it takes two such failures in the same 10 second window, plus a failure of an entire AZ not containing either of those two independent failures to lose a quorum. “At our observed failure rates, that’s sufficiently unlikely…”

This ability to tolerate failures leads to operational simplicity:

hotspot management can be addressed by marking one or more segments on a hot disk or node as bad, and the quorum will quickly be repaired by migrating it to some other (colder) node

OS and security patching can be handled like a brief unavailability event

Software upgrades to the storage fleet can be managed in a rolling fashion in the same way.

Combating write amplification

A six-way replicating storage subsystem is great for reliability, availability, and durability, but not so great for performance with MySQL as-is:

Unfortunately, this model results in untenable performance for a traditional database like MySQL that generates many different actual I/Os for each application write. The high I/O volume is amplified by replication.

With regular MySQL, there are lots of writes going on as shown in the figure below (see §3.1 in the paper for a description of all the individual parts).

Aurora takes a different approach:

In Aurora, the only writes that cross the network are redo log records. No pages are ever written from the database tier, not for background writes, not for checkpointing, and not for cache eviction. Instead, the log applicator is pushed to the storage tier where it can be used to generate database pages in background or on demand.

Using this approach, a benchmark with a 100GB data set showed that Aurora could complete 35x more transactions than a mirrored vanilla MySQL in a 30 minute test.

Using redo logs as the unit of replication means that crash recovery comes almost for free!

In Aurora, durable redo record application happens at the storage tier, continuously, asynchronously, and distributed across the fleet. Any read request for a data page may require some redo records to be applied if the page is not current. As a result, the process of crash recovery is spread across all normal foreground processing. Nothing is required at database startup.

Furthermore, whereas in a regular database more foreground requests also mean more background writes of pages and checkpointing, Aurora can reduce these activities under burst conditions. If a backlog does build up at the storage system then foreground activity can be throttled to prevent a long queue forming.

The complete IO picture looks like this:

Only steps 1 and 2 above are in the foreground path.

The distributed log

Each log record has an associated Log Sequence Number (LSN) – a monotonically increasing value generated by the database. Storage nodes gossip with other members of their protection group to fill holes in their logs. The storage service maintains a watermark called the VCL (Volume Complete LSN), which is the highest LSN for which it can guarantee availablity of all prior records. The database can also define consistency points through consistency point LSNs (CPLs). A consistency point is always less than the VCL, and defines a durable consistency checkpoint. The most recent consistency point is called the VDL (Volume Durable LSN). This is what we’ll roll back to on recovery.

The database and storage subsystem interact as follows:

Each database-level transaction is broken up into multiple mini-transactions (MTRs) that are ordered and must be performed atomically

Each mini-transaction is composed of multiple contiguous log records

The final log record in a mini-transaction is a CPL

When writing, there is a constraint that no LSN be issued which is more than a configurable limit— the LSN allocation limit— ahead of the current VDL. The limit is currently set to 10 million. It creates a natural form of back-pressure to throttle incoming writes if the storage or network cannot keep up.

Reads are served from pages in a buffer cache and only result in storage I/O requests on a cache miss. The database establishes a read point: the VDL at the time the request was issued. Any storage node that is complete with respect to the read point can be used to serve the request. Pages are reconstructed using the same log application code.

A single writer and up to 15 read replicas can all mount a single shared storage volume. As a result, read replicas add no additional costs in terms of consumed storage or disk write operations.

Aurora in action

The evaluation in section 6 of the paper demonstrates the following:

Aurora can scale linearly with instance sizes, and on the highest instance size can sustain 5x the writes per second of vanilla MySQL.

Throughput in Aurora significantly exceeds MySQL, even with larger data sizes and out-of-cache working sets:

Throughput in Aurora scales with the number of client connections:

The lag in an Aurora read replica is significantly lower than that of a MySQL replica, even with more intense workloads:

Aurora outperforms MySQL on workloads with hot row contention:

Customers migrating to Aurora see lower latency and practical elimination of replica lag (e.g, from 12 minutes to 20ms).

the morning paper published first on the morning paper

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sristhitheengineer · 3 years ago

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The Key Differences Between Google Vs AWS Vs Azure Cloud

Mindful of this sensitive test, we have brought to you this selective blog on Google Cloud versus AWS versus Azure to assist you with picking the one that best suits your business prerequisites.

Purplish blue Vs AWS Vs Google secure cloud computing: Choosing the Right Cloud Platform

Before we dig into the definite examination of Azure, GCP, and AWS cloud, how about we see each cloud stage.

What is Amazon Web Services (AWS)?

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sristhitheengineer · 3 years ago

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The Key Differences Between Google Vs AWS Vs Azure Cloud

Amazon, Google, and Microsoft have prepared for organizations to relocate their jobs to the cloud without any problem. In no time, these three tech goliaths ruled the public cloud environment by giving adaptable, dependable, and secure cloud administrations. Presently, there is a furious three-way fight among aws consulting, GCP, and Azure to stand out. They are exceeding everyone’s expectations by giving out an extensive variety of distributed computing, stockpiling, and systems administration administrations. There’s not a day that goes by without one of these three top cloud specialist organizations carrying out new highlights and updates. This high rivalry between Google Cloud, AWS, and Azure is making it extreme for organizations to pick their cloud stage.

Mindful of this sensitive test, we have brought to you this selective blog on Google Cloud versus AWS versus Azure to assist you with picking the one that best suits your business prerequisites.

Purplish blue Vs AWS Vs Google secure cloud computing: Choosing the Right Cloud Platform

Before we dig into the definite examination of Azure, GCP, and AWS cloud, how about we see each cloud stage.

What is Amazon Web Services (AWS)?

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