#AmazonAurora

[govindhtech]

AWS AppSync API Allows Namespace Data Source Connectors

Amazon AppSync API

Amazon AppSync Events now supports channel namespace data source connections, enabling developers to construct more complicated real-time apps. This new functionality links channel namespace handlers to AWS Lambda functions, DynamoDB tables, Aurora databases, and other data sources. AWS AppSync Events allows complex, real-time programs with data validation, event transformation, and persistent event storage.

Developers may now utilise AppSync_JS batch tools to store events in DynamoDB or use Lambda functions to create complicated event processing processes. Integration enables complex interaction processes and reduces operational overhead and development time. Without integration code, events may now be automatically saved in a database.

Start with data source integrations.

Use AWS Management Console to connect data sources. I'll choose my Event API (or create one) in the console after browsing to AWS AppSync.

Direct DynamoDB event data persistence

Data sources can be integrated in several ways. The initial sample data source will be a DynamoDB table. DynamoDB needs a new table, thus create event-messages in the console. It just needs to build the table using the Partition Key id. It may choose Create table and accept default options before returning to AppSync in the console.

Return to the Event API setup in AppSync, choose Data Sources from the tabbed navigation panel, and click Create data source.

After identifying my data source, select Amazon DynamoDB from the drop-down. This shows DynamoDB configuration options.

After setting my data source, it may apply handler logic. A DynamoDB-persisted publish handler is shown here:

Use the Namespaces tabbed menu to add the handler code to a new default namespace. Clicking the default namespace's setup information brings up the Event handler creation option.

Clicking Create event handlers opens a dialogue window for Amazon. Set Code and DynamoDB data sources to publish.

Save the handler to test console tool integration. It wrote two events to DynamoDB using default parameters.

Error handling and security

The new data source connectors provide sophisticated error handling. You can return particular error messages for synchronous operations to Amazon CloudWatch to protect clients from viewing sensitive backend data. Lambda functions can offer specific validation logic for channel or message type access in authorisation circumstances.

Now available

AWS AppSync Events now provide data source integrations in all AWS regions. You may use these new features via the AWS AppSync GUI, CLI, or SDKs. Data source connectors only cost you for Lambda invocations, DynamoDB operations, and AppSync Events.

Amazon AppSync Events

Real-time events

Create compelling user experiences You may easily publish and subscribe to real-time data updates and events like live sports scores and statistics, group chat messages, price and inventory level changes, and location and schedule updates without setting up and maintaining WebSockets infrastructure.

Public/sub channels

Simplified Pub/sub

Developers can use AppSync Event APIs by naming them and defining their default authorisation mode and channel namespace(s). All done. After that, they can publish events to runtime-specified channels immediately.

Manage events

Edit and filter messages

Event handlers, which are optional, allow developers to run complex authorisation logic on publish or subscribe connection requests and change broadcast events.

[danielweasly]

Top 15 Database for Web Apps to Use in 2025

In 2025, the world of databases continues to evolve rapidly, offering a variety of powerful options to cater to different business needs. Among the top contenders are cloud-native databases like Amazon Aurora and Google BigQuery, which offer high scalability and low-latency performance. These cloud-based solutions are becoming the go-to for businesses that need to manage large-scale web applications and data warehousing. On the other hand, traditional relational databases like MySQL and PostgreSQL still hold strong, offering robust support for transactional systems and a wealth of developer tools. Additionally, NoSQL databases like MongoDB and Cassandra are increasingly popular for handling unstructured data, providing flexibility and speed in applications where scalability and fault tolerance are critical.

As companies continue to prioritize speed, reliability, and seamless integration, the database landscape of 2025 is filled with various solutions that cater to different use cases. Whether you are building a web app, a mobile application, or a data-intensive platform, choosing the right database is critical for ensuring optimal performance and scalability. The right choice ultimately depends on factors such as data structure, speed, and whether you require flexibility for handling big data. To explore more options for databases in 2025.

click here to know more: https://www.intelegain.com/top-15-database-for-web-apps-to-use-in-2025/

[akshay-09]

In this amazon aurora video you will learn what is rds and other database services, what is amazon aurora and it's concepts and how to launch an aurora cluster where you will learn how to move local database to amazon aurora.

[the-blockchain-news]

DataArt and IntellectEU Join to Bring DAML Protocol and Smart Contracts to R3's Corda

Open-source blockchain protocol DAML smart contracts are now available on R3's Corda after system integrators DataArt and IntellectEU teamed up to deliver native integrations that allow developers to run DAML applications on Corda Open Source and Corda Enterprise. DAML on Corda joins the power and simplicity of DAML for smart contracts and multi-party workflows with the flexibility and broad adoption of the built-for-business and popular Hyperledger Corda DLT platform. Distributed ledger technology platforms like Corda offer a powerful way to deploy distributed, multi-party applications across all kinds of business domains but the types of unique applications that run on these platforms present some unique challenges for developers. DAML is a language that operates at a higher level of abstraction with built-in concepts of parties, rights, and obligations. DAML programmers write statements concerned only with the distributed workflow, leaving the execution details to the DAML interpreter, and the verified distribution of correct state changes to the underlying DLT. Applications consequently can be written much faster, with fewer errors, than using general-purpose languages. Using DAML hides the complexities of programming for distributed systems. This ensures that DLT projects become more feasible, more likely to succeed, cheaper to implement, more reliable, and more extensible. by Richard Kastelein #amazonaurora #Corda #DAML #datart #Fabric #Hyperledger #IntellectEU Read the full article

[awscloud4u]

With solutions from @NTTDATAServices, you can now have re-hosted mainframe workload data stored in #AmazonAurora: https://t.co/5PUfWpgZte https://t.co/NLfyapBKVk (via Twitter http://twitter.com/awscloud/status/1043231394678403072)

[oom-killer]

2017/02/27-28,2017/03/01-05

*監視・解析ツールから読み解く！トラブル対応＆負荷対策 [#e8239388] https://speakerdeck.com/cygames/jian-shi-jie-xi-turukaradu-mijie-ku-toraburudui-ying-fu-he-dui-ce >P.14  Cygamesで一般的に使っているサーバー系ツール >・munin >・Kibana(ログ収集、表示) >・NewRelic(API毎の処理時間の総数やスループット、関数内詳細等調べられる) >・Mackerel >・XHProf(PHPをAPI毎に詳細に分析する事が出来る。各関数ごとの解析も可能) > >P.26  muninよく見る項目 >  WEB >    Apache accesses >    Load average >    CPU usage >    Memory usage > >  DB >    Load average >    MySQL Connections >    MySQL queries >    MySQL throughput >     >  Memcache >    Evictions >    Hit and misses >    Cached items

*グランブルールーファンタジーを支えるインフラの技術 [#n42eeaa9] https://speakerdeck.com/cygames/kuranhuruhuantasiwozhi-eruinhurafalseji-shu >[大規模ソーシャルゲーム] >- 登録ユーザ数：1400万人 >- 月刊PV数　　：300億 >- クエリ数　　：100万qps >- 秒間アクセス数：8万/秒 >- トラフィック：12Gbps(CDN除く) >- ログ容量　　：5TB/日 > >[構成とスタック] >- CDN(Akamai),L/B(L4) >- Nginx(静),Apache+mod_php(動),MySQL,twemproxy,MHA,Memcached,Redis >- DBのShardingはMHAクラスタ単位 >- Node.jsでWebSocket(双方向リアルタイム通信) >- Lua(高速スクリプト言語,並列分散処理得意) > >[オンプレ環境の要点] >- ネットワーク通信量 >- 安定したトラフィック >  -- 低レイテンシ >  -- バーストトラフィック、スパイクがない >- ハイパフォーマンス >  -- I/Oアクセラレータ(NVMe規格SSD,Fusion-IO) >  -- ネットワークIRQのマルチコアスケーリング > >[ログ活用] >- GoogleBigQuery >- Mackerel >- Kibana >- AmazonAurora >- DWH分析(Redshift,Netezza) > >[大規模環境の運用] >- 複数サーバのリスト作成 >  -- 複数サーバに並列にコマンド実行 >  -- Jenkins等のツールからコマンド実行 >- 故障機器の排除 >  -- デプロイ対象から排除、アプリ運用エンジニアと迅速な対応 >- 分様に必要な情報の管理 >  -- 解析ツールのインストール情報 >  -- ロードバランサへの組込状況 >- Elasticsearch にサーバ情報を登録 >  -- サーバ情報(構築/デプロイ状況ステータス) > >[その他] >- Rundeck(並列コマンド実行) >- Mackerel(監視ツール)

*日清食品、40年使い続けたメインフレームを撤廃 http://itpro.nikkeibp.co.jp/atcl/column/14/346926/022700859/?rt=nocnt >削減に当たって、メインフレームで動作していたシステムのうち、 >受発注管理や在庫管理、財務会計などの主要システムは欧州SAPのERP >（統合基幹業務）パッケージへと移行した。

>業務上必要性の低いシステムを廃止する方針も徹底。

>こうして2017年1月までにメインフレームの約60件のシステムは全廃し、 >メインフレームを撤去した。それ以外のシステムも54件にまで減らした。 >約50人が所属する情報企画部はこれまで慢性的な残業に追われていたが、 >極端な長時間残業者はゼロになり、総残業時間も前年比2～3割減で推移 >しているという。

*AWS S3の長時間サービス停止の原因はエンジニアの入力ミス http://www.itmedia.co.jp/enterprise/articles/1703/03/news061.html >2月28日にクラウドストレージサービス「S3」の北バージニアリージョン >（US-EAST-1）で起きた大規模なサービス停止の原因と対策を発表した。

>同日の午前9時37分、S3の課金システムのデバッグ中、S3のサブシステム >用の少数のサーバの接続を解除しようとした際、コマンドの入力を誤り、 >意図したよりも多数のサーバを解除してしまった。その中の2つのサーバ >が、同リージョン内のすべてのS3オブジェクトのメタデータと位置情報を >管理するインデックスサブシステムと、運営にとって重要な配置用サブ >システムだったため問題が大きくなった。

>今回の事故を受け、サーバ接続解除だけでなく、システム変更に関わる >すべての入力でミスを防ぐようツールを変更した。また、S3の主要サブ >システムのリカバリ時間を短縮するための改善も行った。 > >　また、AWSのService Health Dashboard（SHD）もこの問題の影響で >正しく表示できなかった（その間公式Twitterアカウントで進捗をツイ >ートしていた）ため、SHDのコンソールを複数のAWSリージョンで稼働 >するよう変更した。

*「Raspberry Pi Zero W」、Wi-FiとBluetoothサポートで10ドル http://www.itmedia.co.jp/enterprise/articles/1703/01/news079.html >ネットワークに対応した他のスペックは、2015年11月に5ドルで発売し >たZeroと同じ。

*サーバのセキュリティは大丈夫？Walti.ioで始める、さくらのVPS脆弱性診断。診断後の具体的な対策もご紹介！ http://knowledge.sakura.ad.jp/knowledge/7725/ >セキュリティ診断サービス Walti.io は、「日本のサーバのセキュリティ >水準を上げていこう！」とサーバサイドのセキュリティスキャンを身近に >するための活動の一環として、株式会社ウォルティが開発したサービスです。

>１回５円～という安さで、あなたのWebのセキュリティチェックを >サポートします

*IDCFクラウドでセカンダリIPを取得する方法 http://blog.idcf.jp/entry/secondaryip >パターン1 (手順1)　セカンダリIP経由でインターネットと通信する場合 >パターン2 (手順2)　セカンダリIP経由でインターネットと通信しない場合

>パターンによって手順が異なる理由は、IDCFクラウドのネットワーク仕様 >です。

*シャーディングされたシステムをAuroraに集約してリソースの消費を削減 https://aws.amazon.com/jp/blogs/news/reduce-resource-consumption-by-consolidating-your-sharded-system-into-aurora/ >Amazon Auroraの登場によりスケールアップという選択肢が戻ってきたの >です。Amazon Auroraは非常にスケールし、MySQL互換のマネージドデータ >ベースサービスです。Auroraは2 vCPU/4 GiBメモリというスペックのイン >スタンスから、32 vCPU/244 GiBメモリ搭載のインスタンスまでを提供して >います。Amazon Auroraは需要に応じてストレージが10 GBから64 TBまで >自動的に増加します。また、将来のリードキャパシティの増加に備えて >15インスタンスまで低遅延のリードレプリカを3つのアベイラビリティー >ゾーンに配置することが可能です。この構成の優れている点は、ストレ >ージがリードレプリカ間でシャーディングされている点です。

[nageshpolu-blog]

via Twitter @nageshpolu

[danielweasly]

Top 15 Database for Web Apps to Use in 2025

As the demand for web applications continues to grow, choosing the right database is crucial for ensuring optimal performance, scalability, and security. In 2025, the landscape of databases has evolved to support diverse web app needs, ranging from traditional relational databases to cutting-edge NoSQL solutions. Top contenders like PostgreSQL and MySQL remain popular for structured data and transactional support, while NoSQL options like MongoDB, CouchDB, and Cassandra are gaining traction for handling large volumes of unstructured or semi-structured data. Cloud-based databases such as Amazon Aurora and Google Cloud Firestore are also making waves, offering scalability, high availability, and ease of use for modern web applications.

The need for real-time data processing and analytics is another driving force behind the rise of databases like Redis and Apache Kafka, which excel in speed and event-driven architectures. Newer and innovative solutions such as FaunaDB, a globally distributed database, are also gaining attention for their serverless nature and seamless integration. As developers continue to look for solutions that provide flexibility, scalability, and performance, the right choice of database can significantly impact the success of a web app. To explore more about the best databases for web apps in 2025.

click here to know more: https://www.intelegain.com/top-15-database-for-web-apps-to-use-in-2025/

[govindhtech]

Aurora PostgreSQL Limitless Database: Unlimited Data Growth

Aurora PostgreSQL Limitless Database

The new serverless horizontal scaling (sharding) feature of Aurora PostgreSQL Limitless Database, is now generally available.

With Aurora PostgreSQL Limitless Database, you may distribute a database workload across several Aurora writer instances while still being able to utilize it as a single database, allowing you to extend beyond the current Aurora restrictions for write throughput and storage.

During the AWS re:Invent 2023 preview of Aurora PostgreSQL Limitless Database, to described how it employs a two-layer architecture made up of several database nodes in a DB shard group, which can be either routers or shards to grow according to the demand.Image Credit To AWS

Routers: Nodes known as routers receive SQL connections from clients, transmit SQL commands to shards, keep the system consistent, and provide clients with the results.

Shards: Routers can query nodes that hold a fraction of tables and complete copies of data.

Your data will be listed in three different table types: sharded, reference, and standard.

Sharded tables: These tables are dispersed among several shards. Based on the values of specific table columns known as shard keys, data is divided among the shards. They are helpful for scaling your application’s biggest, most I/O-intensive tables.

Reference tables: These tables eliminate needless data travel by copying all of the data on each shard, allowing join queries to operate more quickly. For reference data that is rarely altered, such product catalogs and zip codes, they are widely utilized.

Standard tables: These are comparable to standard PostgreSQL tables in Aurora. To speed up join queries by removing needless data travel, standard tables are grouped together on a single shard. From normal tables, sharded and reference tables can be produced.

Massive volumes of data can be loaded into the Aurora PostgreSQL Limitless Database and queried using conventional PostgreSQL queries after the DB shard group and your sharded and reference tables have been formed.

Getting started with the Aurora PostgreSQL Limitless Database

An Aurora PostgreSQL Limitless Database DB cluster can be created, a DB shard group added to the cluster, and your data queried via the AWS Management Console and AWS Command Line Interface (AWS CLI).

Establish a Cluster of Aurora PostgreSQL Limitless Databases

Choose Create database when the Amazon Relational Database Service (Amazon RDS) console is open. Select Aurora PostgreSQL with Limitless Database (Compatible with PostgreSQL 16.4) and Aurora (PostgreSQL Compatible) from the engine choices.Image Credit To AWS

Enter a name for your DB shard group and the minimum and maximum capacity values for all routers and shards as determined by Aurora Capacity Units (ACUs) for the Aurora PostgreSQL Limitless Database. This maximum capacity determines how many routers and shards are initially present in a DB shard group. A node’s capacity is increased by Aurora PostgreSQL Limitless Database when its present utilization is insufficient to manage the load. When the node’s capacity is greater than what is required, it reduces it to a lower level.Image Credit To AWS

There are three options for DB shard group deployment: no compute redundancy, one compute standby in a different Availability Zone, or two compute standbys in one Availability Zone.

You can select Create database and adjust the remaining DB parameters as you see fit. The DB shard group appears on the Databases page when it has been formed.Image Credit To AWS

In addition to changing the capacity, splitting a shard, or adding a router, you can connect, restart, or remove a DB shard group.

Construct Limitless Database tables in Aurora PostgreSQL

As previously mentioned, the Aurora PostgreSQL Limitless Database contains three different types of tables: standard, reference, and sharded. You can make new sharded and reference tables or convert existing standard tables to sharded or reference tables for distribution or replication.

By specifying the table construction mode, you can use variables to create reference and sharded tables. Until a new mode is chosen, the tables you create will use this mode. The examples that follow demonstrate how to create reference and sharded tables using these variables.

For instance, make a sharded table called items and use the item_id and item_cat columns to build a shard key.SET rds_aurora.limitless_create_table_mode='sharded'; SET rds_aurora.limitless_create_table_shard_key='{"item_id", "item_cat"}'; CREATE TABLE items(item_id int, item_cat varchar, val int, item text);

Next, construct a sharded table called item_description and collocate it with the items table. The shard key should be made out of the item_id and item_cat columns.SET rds_aurora.limitless_create_table_collocate_with='items'; CREATE TABLE item_description(item_id int, item_cat varchar, color_id int, ...);

Using the rds_aurora.limitless_tables view, you may obtain information on Limitless Database tables, including how they are classified.SET rds_aurora.limitless_create_table_mode='reference'; CREATE TABLE colors(color_id int primary key, color varchar);

It is possible to transform normal tables into reference or sharded tables. The source standard table is removed after the data has been transferred from the standard table to the distributed table during the conversion. For additional information, see the Amazon Aurora User Guide’s Converting Standard Tables to Limitless Tables section.postgres_limitless=> SELECT * FROM rds_aurora.limitless_tables; table_gid | local_oid | schema_name | table_name | table_status | table_type | distribution_key -----------+-----------+-------------+-------------+--------------+-------------+------------------ 1 | 18797 | public | items | active | sharded | HASH (item_id, item_cat) 2 | 18641 | public | colors | active | reference | (2 rows)

Run queries on tables in the Aurora PostgreSQL Limitless Database

The Aurora PostgreSQL Limitless Database supports PostgreSQL query syntax. With PostgreSQL, you can use psql or any other connection tool to query your limitless database. You can use the COPY command or the data loading program to import data into Aurora Limitless Database tables prior to querying them.

Connect to the cluster endpoint, as indicated in Connecting to your Aurora Limitless Database DB cluster, in order to execute queries. The router to which the client submits the query and shards where the data is stored is where all PostgreSQL SELECT queries are executed.

Two querying techniques are used by Aurora PostgreSQL Limitless Database to accomplish a high degree of parallel processing:

Single-shard queries and distributed queries. The database identifies whether your query is single-shard or distributed and handles it appropriately.

Single-shard queries: All of the data required for the query is stored on a single shard in a single-shard query. One shard can handle the entire process, including any created result set. The router’s query planner forwards the complete SQL query to the appropriate shard when it comes across a query such as this.

Distributed query: A query that is executed over many shards and a router. One of the routers gets the request. The distributed transaction, which is transmitted to the participating shards, is created and managed by the router. With the router-provided context, the shards generate a local transaction and execute the query.

To configure the output from the EXPLAIN command for single-shard query examples, use the following arguments.postgres_limitless=> SET rds_aurora.limitless_explain_options = shard_plans, single_shard_optimization; SET postgres_limitless=> EXPLAIN SELECT * FROM items WHERE item_id = 25; QUERY PLAN -------------------------------------------------------------- Foreign Scan (cost=100.00..101.00 rows=100 width=0) Remote Plans from Shard postgres_s4: Index Scan using items_ts00287_id_idx on items_ts00287 items_fs00003 (cost=0.14..8.16 rows=1 width=15) Index Cond: (id = 25) Single Shard Optimized (5 rows)

You can add additional items with the names Book and Pen to the items table to demonstrate distributed queries.postgres_limitless=> INSERT INTO items(item_name)VALUES ('Book'),('Pen')

A distributed transaction on two shards is created as a result. The router passes the statement to the shards that possess Book and Pen after setting a snapshot time during the query execution. The client receives the outcome of the router’s coordination of an atomic commit across both shards.

The Aurora PostgreSQL Limitless Database has a function called distributed query tracing that may be used to track and correlate queries in PostgreSQL logs.

Important information

A few things you should be aware of about this functionality are as follows:

Compute: A DB shard group’s maximum capacity can be specified between 16 and 6144 ACUs, and each DB cluster can only have one DB shard group. Get in touch with us if you require more than 6144 ACUs. The maximum capacity you provide when creating a DB shard group determines the initial number of routers and shards. When you update a DB shard group’s maximum capacity, the number of routers and shards remains unchanged.

Storage: The only cluster storage configuration that Aurora PostgreSQL Limitless Database offers is Amazon Aurora I/O-Optimized DB. 128 TiB is the maximum capacity of each shard. For the entire DB shard group, reference tables can only be 32 TiB in size.

Monitoring: PostgreSQL’s vacuuming tool can help you free up storage space by cleaning up your data. Aurora PostgreSQL Limitless Database monitoring can be done with Amazon CloudWatch, Amazon CloudWatch Logs, or Performance Insights. For monitoring and diagnostics, you can also utilize the new statistics functions, views, and wait events for the Aurora PostgreSQL Limitless Database.

Available now

PostgreSQL 16.4 works with the AWS Aurora PostgreSQL Limitless Database. These regions are included: Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), US East (N. Virginia), US East (Ohio), and US West (Oregon). Try the Amazon RDS console’s Aurora PostgreSQL Limitless Database.

Read more on Govindhtech.com

[govindhtech]

What Is Amazon RDS? Amazon RDS Best Practices, Benefits

What is Amazon RDS?

The service for relational databases Designed to minimize total cost of ownership, Amazon Relational Database Service (Amazon RDS) is easy to use. Setting up, running, and scaling to meet demand is easy. Tasks related to provisioning, configuration, backup, and patching are among the undifferentiated database management tasks that are automated by Amazon RDS. With Amazon RDS, users can quickly establish a new database and personalize it to suit their needs using two deployment options and eight engines. Customers can optimize performance with features including AWS Graviton3-based instances, optimized writes and reads, and Multi-AZ with two readable standbys. They can also select from a variety of pricing choices to efficiently control expenses.

New developments with Amazon RDS

Aurora

Fully compatible with MySQL and PostgreSQL, Amazon Aurora offers unmatched performance and availability on a global scale for a tenth of the price of commercial databases. With features like Amazon Aurora Serverless, which can scale to hundreds of thousands of transactions in a split second, Amazon Aurora I/O-Optimized, which predicts prices, and zero-ETL integrations to Amazon Redshift, which provide near-real-time analytics on your transactional data, you can take advantage of improved capabilities that delight your users.

The Amazon RDS

When you utilize Amazon RDS, you may start using the same commercial and open source database software that you are accustomed to and trust, like MariaDB, PostgreSQL, MySQL, SQL Server, Oracle, and Db2. With support for AWS Graviton3-based instances, Amazon Elastic Block Store (Amazon EBS), and io2 Block Express storage, you can take advantage of the innovation of the AWS stack and eliminate the burden of undifferentiated administrative duties.

Creating applications for generative AI

The performance of your generative AI applications can be enhanced by using Amazon Relational Database Service (Amazon RDS) for PostgreSQL and Amazon Aurora PostgreSQL-Compatible Edition. You can accomplish 20 times better queries per second than with pgvector_IVFFLAT when you combine the capabilities of Amazon Aurora Optimized Reads and pgvector_hnsw.

ML and analytics with integrations at zero ETL

With zero-ETL integrations, the laborious task of creating and overseeing ETL pipelines from live databases to data warehouses is eliminated. In order to achieve desired business objectives, zero-ETL enables clients to access their transactional data in almost real-time for analytics and machine learning (ML) purposes.

Possibilities for deployment

Deployment choices are flexible with Amazon RDS. Applications that need to customize the underlying database environment and operating system can benefit from the managed experience offered by Amazon Relational Database Service (Amazon RDS) Custom. Fully managed database instances can be set up in your on-premises environments using Amazon Relational Database Service (Amazon RDS) on AWS Outposts.

Use Cases

Construct mobile and web applications

Provide high availability, throughput, and storage scalability to accommodate expanding apps. Utilize adaptable pay-per-use pricing to accommodate different application usage trends.

Use managed databases instead

Instead of worrying about self-managing your databases, which may be costly, time-consuming, and complex, experiment and create new apps with Amazon RDS.

Become independent of legacy databases

By switching to Aurora, you can get rid of costly, punitive, commercial databases. When you switch to Aurora, you can obtain commercial databases’ scalability, performance, and availability for a tenth of the price.

Amazon RDS advantages

One type of managed database service is Amazon RDS. It is in charge of the majority of management duties. Amazon RDS allows you to concentrate on your application and users by removing time-consuming manual procedures.

The following are the main benefits that Amazon RDS offers over partially managed database deployments:

You can utilize database engines like IBM Db2, MariaDB, Microsoft SQL Server, MySQL, Oracle Database, and PostgreSQL, which you are already familiar with.

Backups, software patching, automatic failure detection, and recovery are all handled by Amazon RDS.

You have the option to manually create backup snapshots or enable automated backups. These backups can be used to restore a database. The Amazon RDS restoration procedure is dependable and effective.

With a primary database instance and a synchronous backup database instance that you may switch to in case of issues, you can achieve high availability. To improve read scaling, you may also employ read replicas.

In addition to the security features included in your database package, you may manage access by defining users and permissions using AWS Identity and Access Management (IAM). Putting your databases in a virtual private cloud (VPC) can also help protect them.

Read more on Govindhtech.com

[govindhtech]

Galxe Quest Saves With Google Cloud AlloyDB For PostgreSQL

Galxe Quest

The best platform for creating web3 communities is called Galxe Quest. Galxe connects projects with millions of consumers through reward-based loyalty programs while providing a straightforward, no-code solution. Optimism, Arbitrum, Base, and other prominent players in the market trust Galxe as their portal to the largest onchain distribution network on web3.

Create the community of your dreams

Accurate User Acquisition

Turn on autopilot mode for your product marketing and user growth, from on-chain KPIs to off-chain engagements.

Simplified Onboarding of Users

ZK technology is used to encrypt and store your identification data in off-chain vaults so that Galxe never has access to unencrypted data.

More People, Fewer Robots

Your information is private to you alone, and you have complete control over it. As you see appropriate, choose when to provide or deny access to third parties.

Boost Recognition of Your Brand

Your universal web3 identification is Galxe Passport, which removes the need for numerous sign-ups and verifications for various services and apps.

Recognize Faithful Users

Your information is private to you alone, and you have complete control over it. As you see appropriate, choose when to provide or deny access to third parties.

Boost User Retention

Your universal web3 identification is Galxe Passport, which removes the need for numerous sign-ups and verifications for various services and apps.

Plug-and-play, no code solution

Select one of the many Quest templates and easily customize with a few clicks.

Provide Your Own Information

Add your own Google Sheets, CSV files, and API connections to the BYOD (Bring Your Own Data) experience.

Chain-wide Imprint

To automatically track on-chain footprint, connect an API or subgraph.

Twitter Interaction

Keep tabs on interactions like following, liking, retweeting, and attending Twitter Space.

Activities on Discord

Using the Galxe Discord Bot, you can keep tabs on active members, AMA attendees, and Discord roles.

Contributions on GitHub

Recognize the top developers who worked on your product.

Participation in Events

Geo-fenced QR codes facilitate the easy tracking of attendees at both offline and online events.

Status of DAO Voting

With a few clicks, import every voter from your Snapshot.org proposal.

Confirmed People

Your community is shielded from bothersome bots and sybil attacks by using sybil prevention credentials.

Web3 is made of community. Your community and contributors can be encouraged with a range of rewards, such as tokens, loyalty points, reputation, and accomplishments. Learn about other features that Galxe Quest provides.

The next wave of internet development based on blockchain technology is referred to as Web3. It seeks to safeguard self-sovereign identification information, facilitate transactional freedom, decentralize online ownership, and accomplish other goals. Web3, however, is a quickly growing industry with its own set of difficulties. For both developers and end users, there are significant entrance barriers and onboarding friction due to the lack, if not outright absence, of a uniform infrastructure and user experience.

In order to address these current issues, Galxe has created an ecosystem of goods with the goal of bringing the entire planet online. Galxe Quest, the biggest on-chain distribution platform for Web3, is essential to this ecosystem since it links Web3 communities and companies through gamified learn-to-earn opportunities. Galxe Identity Protocol, Google Cloud’s permissionless self-sovereign identity infrastructure, which standardizes the creation, validation, and distribution of on-chain credentials using Zero-Knowledge Proofs, is the foundation upon which this platform is based. Additionally, they introduced Gravity, a Layer-1 blockchain intended for widespread use that eliminates the technical difficulties associated with multichain interactions. This enables developers to leverage Galxe’s 26 million users to produce new Web3 products.

AlloyDB for PostgreSQL earns Google Cloud confidence

Galxe Quest, with more than 26 million active users, requires scalable solutions as well as reliable and substantial data access. A key component in scaling the platform to satisfy the needs of Google expanding user base has been AlloyDB for PostgreSQL.

Because AlloyDB is fully compatible with PostgreSQL and offers high performance for fast online transaction processing (OLTP) workloads, Google Cloud may transition to it with little difficulty and at a reduced cost. After switching to AlloyDB from their previous solution, Amazon Aurora for PostgreSQL, which had much higher costs owing to read and write operations, Google Cloud is able to reduce database expenditures by 40%.

Google Cloud has faith in AlloyDB because of its exceptional performance, almost little downtime, and adaptability. AlloyDB securely stores millions of on-chain and off-chain user data records, serving as their only source of truth. Google Cloud developers may now access detailed datasets to construct blockchain-based loyalty schemes.

Everything we require for faith in Web3

Galxe’s Web3 projects are powered by multiple Google Cloud services. Depending on the requirements of each job, Google Cloud employ Google Kubernetes Engine (GKE) for containerized workloads and AlloyDB and BigQuery for data analysis and storing. Setting up services that continually replicate data from Amazon Aurora to AlloyDB with the goal of minimizing downtime during the transfer was made simple by Google Cloud’s serverless Database transfer Service. Additionally, they used Datastream, a user-friendly change data capture tool that reads and copies AlloyDB data automatically to BigQuery for analysis.

Google Cloud’s network, with its worldwide coverage, low latency, and strong stability offered by premium service tiers, supports Google Cloud’s objective to onboard everyone on the Web 3. With an unmatched user experience, Google Cloud’s consumers worldwide may now easily utilize Galxe’s solutions to explore Web3. Galxe’s architecture also heavily relies on memorystore, which acts as a caching mechanism to manage the substantially higher volume of read operations than write operations in their situations.

Spearheading the upcoming Web3 innovation wave

Galxe collaboration with Google Cloud has yielded several advantages, including improved scalability and stability at a markedly reduced cost. Their ability to grow and support the global Web3 enterprises that are just getting started has shown how important Google Cloud is. Furthermore, AlloyDB’s machine-type scaling and nearly 0% downtime for maintenance windows provide for seamless, uninterrupted service and quick scalability for their clients, allowing them to innovate and expand without hindrance.

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