#aws no sql

[practicallogix]

AWS NoSQL: A Comprehensive Guide to Scalable and Flexible Data Management

As big data and cloud computing continue to evolve, traditional relational databases often fall short in meeting the demands of modern applications. AWS NoSQL databases offer a scalable, high-performance solution for managing unstructured and semi-structured data with efficiency. This blog provides an in-depth exploration of aws no sql databases, highlighting their key benefits, use cases, and best practices for implementation. 

An Overview of NoSQL on AWS 

Unlike traditional SQL databases, NoSQL databases are designed with flexible schemas, horizontal scalability, and high availability in mind. AWS offers a range of managed NoSQL database services tailored to diverse business needs. These services empower organizations to develop applications capable of processing massive amounts of data while minimizing operational complexity.

Key AWS NoSQL Database Services

1. Amazon DynamoDB

Amazon DynamoDB is a fully managed key-value and document database engineered for ultra-low latency and exceptional scalability. It offers features such as automatic scaling, in-memory caching, and multi-region replication, making it an excellent choice for high-traffic and mission-critical applications.

2. Amazon DocumentDB (with MongoDB Compatibility)

Amazon DocumentDB is a fully managed document database service that supports JSON-like document structures. It is particularly well-suited for applications requiring flexible and hierarchical data storage, such as content management systems and product catalogs.

3. Amazon ElastiCache

Amazon ElastiCache delivers in-memory data storage powered by Redis or Memcached. By reducing database query loads, it significantly enhances application performance and is widely used for caching frequently accessed data.

4. Amazon Neptune

Amazon Neptune is a fully managed graph database service optimized for applications that rely on relationship-based data modeling. It is ideal for use cases such as social networking, fraud detection, and recommendation engines.

5. Amazon Timestream

Amazon Timestream is a purpose-built time-series database designed for IoT applications, DevOps monitoring, and real-time analytics. It efficiently processes massive volumes of time-stamped data with integrated analytics capabilities.

Benefits of AWS NoSQL Databases

Scalability – AWS NoSQL databases are designed for horizontal scaling, ensuring high performance and availability as data volumes increase.

Flexibility – Schema-less architecture allows for dynamic and evolving data structures, making NoSQL databases ideal for agile development environments.

Performance – Optimized for high-throughput, low-latency read and write operations, ensuring rapid data access.

Managed Services – AWS handles database maintenance, backups, security, and scaling, reducing the operational workload for teams.

High Availability – Features such as multi-region replication and automatic failover ensure data availability and business continuity.

Use Cases of AWS NoSQL Databases

E-commerce – Flexible and scalable storage for product catalogs, user profiles, and shopping cart sessions.

Gaming – Real-time leaderboards, session storage, and in-game transactions requiring ultra-fast, low-latency access.

IoT & Analytics – Efficient solutions for large-scale data ingestion and time-series analytics.

Social Media & Networking – Powerful graph databases like Amazon Neptune for relationship-based queries and real-time interactions.

Best Practices for Implementing AWS NoSQL Solutions 

Select the Appropriate Database – Choose an AWS NoSQL service that aligns with your data model requirements and workload characteristics. 

Design for Efficient Data Partitioning – Create well-optimized partition keys in DynamoDB to ensure balanced data distribution and performance. 

Leverage Caching Solutions – Utilize Amazon ElastiCache to minimize database load and enhance response times for your applications. 

Implement Robust Security Measures – Apply AWS Identity and Access Management (IAM), encryption protocols, and VPC isolation to safeguard your data. 

Monitor and Scale Effectively – Use AWS CloudWatch for performance monitoring and take advantage of auto-scaling capabilities to manage workload fluctuations efficiently. 

Conclusion 

AWS NoSQL databases are a robust solution for modern, data-intensive applications. Whether your use case involves real-time analytics, large-scale storage, or high-speed data access, AWS NoSQL services offer the scalability, flexibility, and reliability required for success. By selecting the right database and adhering to best practices, organizations can build resilient, high-performing cloud-based applications with confidence.

[lutai256rokuta]

【個人開発】Google Cloud Shellの利用の仕方について

前回、クラウドIDEについてご説明（私の言い訳）をさせて��ただきました。 Googleアカウントにログイン Gmailアカウントにログインしましょう。 持っていない方は、作成しログインしてください！ ログイン情報を入力しましょう メールアドレスを入力して「次へ」を押下します。 Googleでサービスを検索します ログイン後、検索窓に【Cloud Shell…

[hubertdudek]

What’s New In Databricks? February 2025 Updates & Features Explained!

What’s New In Databricks? February 2025 Updates & Features Explained! #databricks #spark #dataengineering

Are you ready for the latest Databricks updates in February 2025? 🚀 This month brings game-changing features like SAP integration, Lakehouse Federation for Teradata, Databricks Clean Rooms, SQL Pipe, Serverless on Google Cloud, Predictive Optimization, and more!

✨ Explore Databricks AI insights and workflows—read more:   / databrickster  

🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬.    / @hubert_dudek  

🔗 Support Me Here! ☕Buy me a coffee: https://ko-fi.com/hubertdudek

🔗 Stay Connected With Me. Medium:   / databrickster  

==================

[ilracademy]

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[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

[thedbahub]

Navigating Cloud Databases: Azure Cosmos DB and AWS Aurora in Focus

When embarking on new software development projects, choosing the right database technology is pivotal. In the cloud-first world, Azure Cosmos DB and AWS Aurora stand out for their unique offerings. This article explores these databases through practical T-SQL code examples and applications, guiding you towards making an informed decision. Azure Cosmos DB, a globally distributed, multi-model…

[jnnctechnologies-blog]

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[ashratechnologiespvtltd]

Greetings from Ashra Technologies

we are hiring.....

[chappydev]

Future of LLMs (or, "AI", as it is improperly called)

Posted a thread on bluesky and wanted to share it and expand on it here. I'm tangentially connected to the industry as someone who has worked in game dev, but I know people who work at more enterprise focused companies like Microsoft, Oracle, etc. I'm a developer who is highly AI-critical, but I'm also aware of where it stands in the tech world and thus I think I can share my perspective. I am by no means an expert, mind you, so take it all with a grain of salt, but I think that since so many creatives and artists are on this platform, it would be of interest here. Or maybe I'm just rambling, idk.

LLM art models ("AI art") will eventually crash and burn. Even if they win their legal battles (which if they do win, it will only be at great cost), AI art is a bad word almost universally. Even more than that, the business model hemmoraghes money. Every time someone generates art, the company loses money -- it's a very high energy process, and there's simply no way to monetize it without charging like a thousand dollars per generation. It's environmentally awful, but it's also expensive, and the sheer cost will mean they won't last without somehow bringing energy costs down. Maybe this could be doable if they weren't also being sued from every angle, but they just don't have infinite money.

Companies that are investing in "ai research" to find a use for LLMs in their company will, after years of research, come up with nothing. They will blame their devs and lay them off. The devs, worth noting, aren't necessarily to blame. I know an AI developer at meta (LLM, really, because again AI is not real), and the morale of that team is at an all time low. Their entire job is explaining patiently to product managers that no, what you're asking for isn't possible, nothing you want me to make can exist, we do not need to pivot to LLMs. The product managers tell them to try anyway. They write an LLM. It is unable to do what was asked for. "Hm let's try again" the product manager says. This cannot go on forever, not even for Meta. Worst part is, the dev who was more or less trying to fight against this will get the blame, while the product manager moves on to the next thing. Think like how NFTs suddenly disappeared, but then every company moved to AI. It will be annoying and people will lose jobs, but not the people responsible.

ChatGPT will probably go away as something public facing as the OpenAI foundation continues to be mismanaged. However, while ChatGPT as something people use to like, write scripts and stuff, will become less frequent as the public facing chatGPT becomes unmaintainable, internal chatGPT based LLMs will continue to exist.

This is the only sort of LLM that actually has any real practical use case. Basically, companies like Oracle, Microsoft, Meta etc license an AI company's model, usually ChatGPT.They are given more or less a version of ChatGPT they can then customize and train on their own internal data. These internal LLMs are then used by developers and others to assist with work. Not in the "write this for me" kind of way but in the "Find me this data" kind of way, or asking it how a piece of code works. "How does X software that Oracle makes do Y function, take me to that function" and things like that. Also asking it to write SQL queries and RegExes. Everyone I talk to who uses these intrernal LLMs talks about how that's like, the biggest thign they ask it to do, lol.

This still has some ethical problems. It's bad for the enivronment, but it's not being done in some datacenter in god knows where and vampiring off of a power grid -- it's running on the existing servers of these companies. Their power costs will go up, contributing to global warming, but it's profitable and actually useful, so companies won't care and only do token things like carbon credits or whatever. Still, it will be less of an impact than now, so there's something. As for training on internal data, I personally don't find this unethical, not in the same way as training off of external data. Training a language model to understand a C++ project and then asking it for help with that project is not quite the same thing as asking a bot that has scanned all of GitHub against the consent of developers and asking it to write an entire project for me, you know? It will still sometimes hallucinate and give bad results, but nowhere near as badly as the massive, public bots do since it's so specialized.

The only one I'm actually unsure and worried about is voice acting models, aka AI voices. It gets far less pushback than AI art (it should get more, but it's not as caustic to a brand as AI art is. I have seen people willing to overlook an AI voice in a youtube video, but will have negative feelings on AI art), as the public is less educated on voice acting as a profession. This has all the same ethical problems that AI art has, but I do not know if it has the same legal problems. It seems legally unclear who owns a voice when they voice act for a company; obviously, if a third party trains on your voice from a product you worked on, that company can sue them, but can you directly? If you own the work, then yes, you definitely can, but if you did a role for Disney and Disney then trains off of that... this is morally horrible, but legally, without stricter laws and contracts, they can get away with it.

In short, AI art does not make money outside of venture capital so it will not last forever. ChatGPT's main income source is selling specialized LLMs to companies, so the public facing ChatGPT is mostly like, a showcase product. As OpenAI the company continues to deathspiral, I see the company shutting down, and new companies (with some of the same people) popping up and pivoting to exclusively catering to enterprises as an enterprise solution. LLM models will become like, idk, SQL servers or whatever. Something the general public doesn't interact with directly but is everywhere in the industry. This will still have environmental implications, but LLMs are actually good at this, and the data theft problem disappears in most cases.

Again, this is just my general feeling, based on things I've heard from people in enterprise software or working on LLMs (often not because they signed up for it, but because the company is pivoting to it so i guess I write shitty LLMs now). I think artists will eventually be safe from AI but only after immense damages, I think writers will be similarly safe, but I'm worried for voice acting.

[practicallogix]

Unleashing Scalability: Navigating the AWS NoSQL Landscape for Optimal Data Management

In the rapidly expanding domain of cloud computing, AWS (Amazon Web Services) has emerged as a prominent force, offering a diverse range of services to cater to varying business requirements. When it comes to efficiently handling extensive volumes of data with flexibility and speed, AWS NoSQL databases stand out as pivotal players in the data management landscape. This blog post delves into the significance of AWS NoSQL databases, their role in scalability, and how businesses can effectively navigate this landscape to achieve optimal data management.

The AWS NoSQL Landscape

AWS offers a range of NoSQL database services, each tailored to specific use cases and preferences. The two prominent offerings are Amazon DynamoDB and Amazon DocumentDB, catering to different data models and scaling requirements.

Amazon DynamoDB: DynamoDB is a fully-managed NoSQL database service designed for seamless scalability and low-latency performance. It supports both document and key-value data models, making it a versatile choice for a wide range of applications. Its ability to scale horizontally with automatic partitioning ensures that applications can handle varying workloads effortlessly.

Amazon DocumentDB: DocumentDB is a fully-managed MongoDB-compatible database service. It is purpose-built for applications that require the flexibility of a document database along with the robustness and scalability of AWS. DocumentDB is designed to provide the performance, scalability, and availability needed for modern, high-traffic applications.

Scaling with AWS NoSQL

Horizontal Scalability: One of the key advantages of AWS NoSQL databases is their ability to scale horizontally. As data volumes increase, these databases can effortlessly expand by adding more servers or nodes to the cluster. This enables applications to handle growing workloads without compromising performance.

Managed Infrastructure: AWS NoSQL databases alleviate businesses from the responsibility of managing infrastructure. With fully-managed services like DynamoDB and DocumentDB, AWS takes care of the underlying infrastructure, allowing developers to focus on building and optimizing their applications.

Auto-Scaling: AWS NoSQL databases provide auto-scaling capabilities, enabling the system to dynamically adjust its capacity based on demand. This ensures that applications can efficiently handle fluctuating workloads without manual intervention.

Global Distribution: For businesses with a global presence, AWS NoSQL databases offer options for global distribution. This allows data to be replicated and stored in multiple geographic locations, enhancing data access speed and providing high availability.

Navigating the AWS NoSQL Landscape

Understanding Data Models: Select the appropriate AWS NoSQL database that aligns with your application's data model requirements. DynamoDB is well-suited for key-value and document data, while DocumentDB is a suitable choice for those familiar with MongoDB's document data model.

Scalability Planning: Evaluate your scalability needs and choose the suitable AWS NoSQL service accordingly. Whether it's the seamless horizontal scaling of DynamoDB or the compatibility of DocumentDB, comprehending your growth patterns is of utmost importance.

Optimizing Performance: Utilize the performance optimization features offered by AWS NoSQL databases. This may include fine-tuning read and write capacities, leveraging indexes, and optimizing queries for efficient data retrieval.

Security Considerations: Implement robust security measures by configuring access controls, encryption, and monitoring features. AWS NoSQL databases provide a range of security features to safeguard sensitive data and ensure compliance with regulatory requirements.

Conclusion

Effectively navigating the aws no sql landscape is a strategic imperative for businesses aiming to optimize data management and achieve scalability. The selection between Amazon DynamoDB and Amazon DocumentDB relies on specific use cases and preferences. By leveraging the capabilities of AWS NoSQL databases, businesses can unlock scalability, ensure high performance, and streamline data management in the dynamic and competitive cloud era. Embrace scalability, harness the power of AWS NoSQL, and propel your data management strategy to new heights with confidence.

[lutai256rokuta]

【個人開発】AWS Cloud9からGoogle Cloud Shellへの移行：開発者向け代替選択肢

当ブログでは、実際に動くサービスを自分で作成してみたい。という方に向けて、自作のアプリケーションの作成からデプロイ（本番リリース）をご案内するつもりでした。 しかし、そこで困っているのが開発環境です。 この記事を見ていただいている方は、それぞれ環境が異なります。 OSひとつとっても、Windowsの方もいれば、Macの人もいるはず。 そのため、ローカルで開発環境を整備する。というところが、環境によって異なるため、どうしても一つの手順でまとめることが難しいのです。。。 以前こう言った記事を投稿したのですが、私の環境と差異があると通用しなくなってしまうのは、イケていないなと。。。 そこで考えついたのが、クラウドベースのIDEを利用することでした。 今回は、クラウドサービスで展開されているWebIDEをご紹介しながら、簡単な環境構築についてご紹介したいと思います。 クラウドID…

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[capitangoosie]

god awful illustration for sql

[govindhtech]

Amazon DynamoDB: A Complete Guide To NoSQL Databases

Amazon DynamoDB fully managed, serverless, NoSQL database has single-digit millisecond speed at any scale.

What is Amazon DynamoDB?

You can create contemporary applications at any size with DynamoDB, a serverless NoSQL database service. Amazon DynamoDB is a serverless database that scales to zero, has no cold starts, no version upgrades, no maintenance periods, no patching, and no downtime maintenance. You just pay for what you use. A wide range of security controls and compliance criteria are available with DynamoDB. DynamoDB Global Tables is a multi-region, multi-active database with a 99.999% availability SLA and enhanced resilience for globally dispersed applications. Point-in-time recovery, automated backups, and other features support DynamoDB dependability. You may create serverless event-driven applications with Amazon DynamoDB streams.

Use cases

Create software programs

Create internet-scale apps that enable caches and user-content metadata, which call for high concurrency and connections to handle millions of requests per second and millions of users.

Establish media metadata repositories

Reduce latency with multi-Region replication between AWS Regions and scale throughput and concurrency for media and entertainment workloads including interactive content and real-time video streaming.

Provide flawless shopping experiences

When implementing workflow engines, customer profiles, inventory tracking, and shopping carts, use design patterns. Amazon DynamoDB can process millions of queries per second and enables events with extraordinary scale and heavy traffic.

Large-scale gaming systems

With no operational overhead, concentrate on promoting innovation. Provide player information, session history, and leaderboards for millions of users at once when developing your gaming platform.

Amazon DynamoDB features

Serverless

You don’t have to provision any servers, patch, administer, install, maintain, or run any software when using Amazon DynamoDB. DynamoDB offers maintenance with no downtime. There are no maintenance windows or major, minor, or patch versions.

You only pay for what you use using DynamoDB’s on-demand capacity mode, which offers pay-as-you-go pricing for read and write requests. With on-demand, DynamoDB maintains performance with no management and quickly scales up or down your tables to accommodate capacity. Additionally, when there is no traffic or cold starts at your table, it scales down to zero, saving you money on throughput.

Amazon DynamoDB NoSQL

NoSQL

DynamoDB is a NoSQL database that outperforms relational databases in performance, scalability, management, and customization. DynamoDB supports several use cases with document and key-value data types.

DynamoDB does not offer a JOIN operator, in contrast to relational databases. To cut down on database round trips and the amount of processing power required to respond to queries, advise you to denormalize your data model. DynamoDB is a NoSQL database that offers enterprise-grade applications excellent read consistency and ACID transactions.

Fully managed

DynamoDB is a fully managed database service that lets you focus on creating value to your clients. It handles hardware provisioning, security, backups, monitoring, high availability, setup, configurations, and more. This guarantees that a DynamoDB table is immediately prepared for production workloads upon creation. Without the need for updates or downtime, Amazon DynamoDB continuously enhances its functionality, security, performance, availability, and dependability.

Single-digit millisecond performance at any scale

DynamoDB was specifically designed to enhance relational databases’ scalability and speed, achieving single-digit millisecond performance at any scale. DynamoDB is designed for high-performance applications and offers APIs that promote effective database utilization in order to achieve this scale and performance. It leaves out aspects like JOIN operations that are ineffective and do not function well at scale. Whether you have 100 or 100 million users, DynamoDB consistently provides single-digit millisecond performance for your application.

What is a DynamoDB Database?

Few people outside of Amazon are aware of the precise nature of this database. Although the cloud-native database architecture is private and closed-source, there is a development version called DynamoDB Local that is utilized on developer laptops and is written in Java.

You don’t provision particular machines or allot fixed disk sizes when you set up DynamoDB on Amazon Web Services. Instead, you design the database according to the capacity that has been supplied, which includes the number of transactions and kilobytes of traffic that you want to accommodate per second. A service level of read capacity units (RCUs) and write capacity units (WCUs) is specified by users.

As previously mentioned, users often don’t call the Amazon DynamoDB API directly. Rather, their application will incorporate an Amazon Web Services, which will manage the back-end interactions with the server.

Denormalization of DynamoDB data modeling is required. Rethinking their data model is a difficult but manageable step for engineers accustomed to working with both SQL and NoSQL databases.

Read more on govindhtech.com

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Why Tableau is Essential in Data Science: Transforming Raw Data into Insights

Data science is all about turning raw data into valuable insights. But numbers and statistics alone don’t tell the full story—they need to be visualized to make sense. That’s where Tableau comes in.

Tableau is a powerful tool that helps data scientists, analysts, and businesses see and understand data better. It simplifies complex datasets, making them interactive and easy to interpret. But with so many tools available, why is Tableau a must-have for data science? Let’s explore.

1. The Importance of Data Visualization in Data Science

Imagine you’re working with millions of data points from customer purchases, social media interactions, or financial transactions. Analyzing raw numbers manually would be overwhelming.

That’s why visualization is crucial in data science:

Identifies trends and patterns – Instead of sifting through spreadsheets, you can quickly spot trends in a visual format.

Makes complex data understandable – Graphs, heatmaps, and dashboards simplify the interpretation of large datasets.

Enhances decision-making – Stakeholders can easily grasp insights and make data-driven decisions faster.

Saves time and effort – Instead of writing lengthy reports, an interactive dashboard tells the story in seconds.

Without tools like Tableau, data science would be limited to experts who can code and run statistical models. With Tableau, insights become accessible to everyone—from data scientists to business executives.

2. Why Tableau Stands Out in Data Science

A. User-Friendly and Requires No Coding

One of the biggest advantages of Tableau is its drag-and-drop interface. Unlike Python or R, which require programming skills, Tableau allows users to create visualizations without writing a single line of code.

Even if you’re a beginner, you can:

✅ Upload data from multiple sources

✅ Create interactive dashboards in minutes

✅ Share insights with teams easily

This no-code approach makes Tableau ideal for both technical and non-technical professionals in data science.

B. Handles Large Datasets Efficiently

Data scientists often work with massive datasets—whether it’s financial transactions, customer behavior, or healthcare records. Traditional tools like Excel struggle with large volumes of data.

Tableau, on the other hand:

Can process millions of rows without slowing down

Optimizes performance using advanced data engine technology

Supports real-time data streaming for up-to-date analysis

This makes it a go-to tool for businesses that need fast, data-driven insights.

C. Connects with Multiple Data Sources

A major challenge in data science is bringing together data from different platforms. Tableau seamlessly integrates with a variety of sources, including:

Databases: MySQL, PostgreSQL, Microsoft SQL Server

Cloud platforms: AWS, Google BigQuery, Snowflake

Spreadsheets and APIs: Excel, Google Sheets, web-based data sources

This flexibility allows data scientists to combine datasets from multiple sources without needing complex SQL queries or scripts.

D. Real-Time Data Analysis

Industries like finance, healthcare, and e-commerce rely on real-time data to make quick decisions. Tableau’s live data connection allows users to:

Track stock market trends as they happen

Monitor website traffic and customer interactions in real time

Detect fraudulent transactions instantly

Instead of waiting for reports to be generated manually, Tableau delivers insights as events unfold.

E. Advanced Analytics Without Complexity

While Tableau is known for its visualizations, it also supports advanced analytics. You can:

Forecast trends based on historical data

Perform clustering and segmentation to identify patterns

Integrate with Python and R for machine learning and predictive modeling

This means data scientists can combine deep analytics with intuitive visualization, making Tableau a versatile tool.

3. How Tableau Helps Data Scientists in Real Life

Tableau has been adopted by the majority of industries to make data science more impactful and accessible. This is applied in the following real-life scenarios:

A. Analytics for Health Care

Tableau is deployed by hospitals and research institutions for the following purposes:

Monitor patient recovery rates and predict outbreaks of diseases

Analyze hospital occupancy and resource allocation

Identify trends in patient demographics and treatment results

B. Finance and Banking

Banks and investment firms rely on Tableau for the following purposes:

✅ Detect fraud by analyzing transaction patterns

✅ Track stock market fluctuations and make informed investment decisions

✅ Assess credit risk and loan performance

C. Marketing and Customer Insights

Companies use Tableau to:

✅ Track customer buying behavior and personalize recommendations

✅ Analyze social media engagement and campaign effectiveness

✅ Optimize ad spend by identifying high-performing channels

D. Retail and Supply Chain Management

Retailers leverage Tableau to:

✅ Forecast product demand and adjust inventory levels

✅ Identify regional sales trends and adjust marketing strategies

✅ Optimize supply chain logistics and reduce delivery delays

These applications show why Tableau is a must-have for data-driven decision-making.

4. Tableau vs. Other Data Visualization Tools

There are many visualization tools available, but Tableau consistently ranks as one of the best. Here’s why:

Tableau vs. Excel – Excel struggles with big data and lacks interactivity; Tableau handles large datasets effortlessly.

Tableau vs. Power BI – Power BI is great for Microsoft users, but Tableau offers more flexibility across different data sources.

Tableau vs. Python (Matplotlib, Seaborn) – Python libraries require coding skills, while Tableau simplifies visualization for all users.

This makes Tableau the go-to tool for both beginners and experienced professionals in data science.

5. Conclusion

Tableau has become an essential tool in data science because it simplifies data visualization, handles large datasets, and integrates seamlessly with various data sources. It enables professionals to analyze, interpret, and present data interactively, making insights accessible to everyone—from data scientists to business leaders.

If you’re looking to build a strong foundation in data science, learning Tableau is a smart career move. Many data science courses now include Tableau as a key skill, as companies increasingly demand professionals who can transform raw data into meaningful insights.

In a world where data is the driving force behind decision-making, Tableau ensures that the insights you uncover are not just accurate—but also clear, impactful, and easy to act upon.

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Migrating SQL Server On-Prem to the Cloud: A Guide to AWS, Azure, and Google Cloud

Taking your on-premises SQL Server databases to the cloud opens a world of benefits such as scalability, flexibility, and often, reduced costs. However, the journey requires meticulous planning and execution. We will delve into the migration process to three of the most sought-after cloud platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), providing you with…