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cloudolus · 5 months ago

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Amazon RDS Performance Insights | Monitor and Optimize Database Performance

Amazon RDS Performance Insights is an advanced monitoring tool that helps you analyze and optimize your database workload in Amazon RDS and Amazon Aurora. It provides real-time insights into database performance, making it easier to identify bottlenecks and improve efficiency without deep database expertise.

Key Features of Amazon RDS Performance Insights:

✅ Automated Performance Monitoring – Continuously collects and visualizes performance data to help you monitor database load. ✅ SQL Query Analysis – Identifies slow-running queries, so you can optimize them for better database efficiency. ✅ Database Load Metrics – Displays a simple Database Load (DB Load) graph, showing the active sessions consuming resources. ✅ Multi-Engine Support – Compatible with MySQL, PostgreSQL, SQL Server, MariaDB, and Amazon Aurora. ✅ Retention & Historical Analysis – Stores performance data for up to two years, allowing trend analysis and long-term optimization. ✅ Integration with AWS Services – Works seamlessly with Amazon CloudWatch, AWS Lambda, and other AWS monitoring tools.

How Amazon RDS Performance Insights Helps You:

🔹 Troubleshoot Performance Issues – Quickly diagnose and fix slow queries, high CPU usage, or locked transactions. 🔹 Optimize Database Scaling – Understand workload trends to scale your database efficiently. 🔹 Enhance Application Performance – Ensure your applications run smoothly by reducing database slowdowns. 🔹 Improve Cost Efficiency – Optimize resource utilization to prevent over-provisioning and reduce costs.

How to Enable Amazon RDS Performance Insights: 1️⃣ Navigate to AWS Management Console. 2️⃣ Select Amazon RDS and choose your database instance. 3️⃣ Click on Modify, then enable Performance Insights under Monitoring. 4️⃣ Choose the retention period (default 7 days, up to 2 years with paid plans). 5️⃣ Save changes and start analyzing real-time database performance!

Who Should Use Amazon RDS Performance Insights? 🔹 Database Administrators (DBAs) – To manage workload distribution and optimize database queries. 🔹 DevOps Engineers – To ensure smooth database operations for applications running on AWS. 🔹 Developers – To analyze slow queries and improve app performance. 🔹 Cloud Architects – To monitor resource utilization and plan database scaling effectively.

Amazon RDS Performance Insights simplifies database monitoring, making it easy to detect issues and optimize workloads for peak efficiency. Start leveraging it today to improve the performance and scalability of your AWS database infrastructure! 🚀

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damienthepious · 1 year ago

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watching @nanowrimo within a single hour:

make an awful, ill-conceived, sponsored post about "responsible"/"ethical" uses of ai in writing

immediately get ratio'd in a way i've never seen on tumblr with a small swarm of chastising-to-negative replies and no reblogs

start deleting replies

reply to their own post being like 'agree to disagree!!!' while saying that ai can TOTALLY be ethical because spellcheck exists!! (???) while in NO WAY responding to the criticisms of ai for its environmental impact OR the building of databases on material without author consent, ie, stolen material, OR the money laundering rampant in the industry

when called out on deleting replies, literally messaged me people who called them out to say "We don't have a problem with folks disagreeing with AI. It's the tone of the discourse." So. overtly stated tone policing.

get even MORE replies saying this is a Bad Look, and some reblogs now that people's replies are being deleted

DISABLE REBLOGS when people aren't saying what nano would prefer they say

im juust in literal awe of this fucking mess.

#what the fuck.#literally get better sponsors bestie<3<3<3 #elle babbles #nanowrimo #absolutely wank. what the fuck

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govindhtech · 8 months ago

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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);

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

#AuroraPostgreSQL #Database #AWS #SQL #PostgreSQL #AmazonRelationalDatabaseService #AmazonAurora #AmazonCloudWatch #News #Technews #Technologynews #Technology #Technologytrendes #govindhtech

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lucid-outsourcing-solutions · 11 months ago

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Managing ColdFusion Data with AWS DynamoDB: NoSQL Database Integration

#Managing ColdFusion Data with AWS DynamoDB: NoSQL Database Integration #Managing ColdFusion Data with AWS DynamoDB #Managing ColdFusion Data NoSQL Database Integration #ColdFusion Data with AWS DynamoDB NoSQL Database Integration #ColdFusion Data with AWS DynamoDB Database Integration

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simple-logic · 1 month ago

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Simple Logic's Expertise Transformed a Leading Enterprise's PostgreSQL System Performance!

Challenges: 🚨 Frequent FDW process failures, impacting system reliability 📉 Resource crunch due to server limitations 🕒 Delays causing customer dissatisfaction and operational inefficiencies

Our Solution: 🌐 Migrated the database to AWS for enhanced scalability and reliability 🔗 Optimized PostgreSQL Foreign Data Wrapper (FDW) connectivity for seamless functionality ⚡ Resolved resource management issues with a robust new server

The Results: ☁️ Successful FDW configuration in the AWS Cloud 💨 Improved database performance and faster processing speeds 📊 Enhanced resource management and system reliability

Transform your business operations with Simple Logic’s cutting-edge database solutions! 🚀

💻 Explore insights on the latest in #technology on our Blog Page 👉 https://simplelogic-it.com/blogs/

🚀 Ready for your next career move? Check out our #careers page for exciting opportunities 👉 https://simplelogic-it.com/careers/

👉 Contact us here: https://simplelogic-it.com/contact-us/

#DatabaseOptimization #PostgreSQLSolutions #AWS #Cloud #AWSCloud #Data #Database #DatabasePerformance #ResourceManagement #SystemReliability #PostgreSQL #SimpleLogicIT #MakingITSimple #MakeITSimple #SimpleLogic #ITServices #ITConsulting

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practicallogix · 8 months ago

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Aws Nosql Database| DynamoDB: Practical Logix

AWS NoSQL databases deliver scalable and high-performance solutions for managing large and diverse data types. With managed services such as Amazon DynamoDB, AWS offers low-latency, serverless options that facilitate automatic scaling and global replication. These databases are particularly suited for applications requiring flexibility, ensuring secure and rapid access to unstructured or semi-structured data, thereby enhancing the performance of modern applications.

#aws nosql database

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jcmarchi · 8 months ago

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Dhiren Bhatia, Co-Founder & CEO of Inventive AI – Interiew Series

New Post has been published on https://thedigitalinsider.com/dhiren-bhatia-co-founder-ceo-of-inventive-ai-interiew-series/

Dhiren Bhatia, Co-Founder & CEO of Inventive AI – Interiew Series

Dhiren Bhatia is the Co-Founder & CEO of Inventive AI, an AI-powered RFP and questionnaire response management platform.

RFP stands for Request for Proposal, a formal document issued by organizations to invite vendors or service providers to submit proposals for specific projects or services. The RFP outlines the project requirements, objectives, and evaluation criteria, allowing qualified vendors to submit detailed bids on how they plan to meet the organization’s needs.

Inventive AI is an AI-powered platform designed to streamline and optimize the response process for RFPs and questionnaires. By automating tasks such as drafting responses, gathering relevant data, and customizing proposals for specific clients, Inventive AI significantly improves the efficiency of sales response workflows, driving over 70% efficiency gains for businesses. This allows companies to respond to RFPs faster, more accurately, and with greater consistency, ultimately enhancing their chances of winning more contracts.

What inspired the founding of Inventive AI, and how did your personal experiences with RFP workflows shape its mission?

After taking some time off following my last exit (selling Viewics to Roche), I realized I missed the excitement and challenges of building a startup. During my time at Roche, my teams were involved in numerous RFPs, and I consistently saw how difficult it was to craft strategic, efficient responses. This experience highlighted a clear opportunity, and I set out to explore it further. Through conversations and interviews with dozens of companies, I validated that this pain point was widespread, reinforcing my decision to dive back in and build a solution to address it.

What were the key pain points in the RFP process that you identified, and how does Inventive AI address those challenges?

The key pain points in the RFP process include:

Manual, time-consuming effort: The process can take days or even weeks of work due to the extensive manual input required.

Managing content and knowledge: It’s challenging to maintain and organize the knowledge base for crafting accurate and relevant responses.

Strategic responses: Responding effectively requires understanding the customer’s specific needs and considering the competition, making it difficult to tailor responses strategically.

Collaboration across teams: Gathering input from multiple subject matter experts and senior stakeholders can be cumbersome and lead to delays.

Compliance and risk management: Ensuring alignment with regulatory requirements, internal policies, and legal constraints adds complexity and potential risks.

Inventive AI addresses these challenges with a suite of proprietary AI-driven agents designed to automate and streamline key aspects of the process. By leveraging AI, the platform significantly reduces manual effort, organizes and optimizes content management, enhances strategic response generation, simplifies stakeholder collaboration, and ensures compliance and risk management—all in one integrated solution.

How does Inventive AI’s technology make RFP responses faster and more accurate compared to traditional methods?

Our founding team brings deep expertise in machine learning, particularly in language models. Gaurav Nemade, an early Product Manager at Google Brain, contributed to the development of LLMs, while Vishakh Hegde conducted AI research at Stanford University. Leveraging this expertise, we’ve developed a proprietary pipeline and suite of tools that deliver accurate, strategic responses within seconds, all grounded in our customers’ unique knowledge sources. This enables us to provide a solution that is not only fast but also highly tailored to each client’s needs.

What makes RFP management a critical area for automation, and how does Inventive AI tackle this?

RFP management is a critical area for automation because an RFP signifies a high level of interest and buying intent for a company’s products or services. Delivering a high-quality, strategic response is crucial for maximizing sales opportunities. This process demands accuracy, compliance, risk management, and competitive positioning, all of which can be time-consuming and prone to errors when done manually.

Inventive AI tackles this challenge by automating key aspects of RFP management through advanced AI technology. The platform ensures that responses are accurate, compliant with regulations, and strategically aligned with customer needs. By automating these tasks, Inventive AI not only improves the quality and consistency of responses but also allows companies to handle a higher volume of RFPs, expanding their ability to pursue more opportunities and ultimately increasing win rates.

Why are RFPs often overlooked in digital transformation, and how is Inventive AI changing this dynamic?

RFPs are often overlooked in digital transformation initiatives because they are seen as administrative or transactional tasks rather than strategic opportunities for innovation. Many companies focus their digital transformation efforts on customer-facing functions, internal operations, or product development, leaving procurement and sales processes, like RFP management, relatively untouched. This is partly because RFP processes are traditionally manual and highly customized, which can make them seem less suitable for automation or digital overhaul.

Additionally, the complexity and cross-functional nature of RFPs often lead companies to assume that automating or streamlining these processes would be too difficult or disruptive. As a result, the potential gains from improving RFP workflows—such as increased efficiency, better accuracy, faster turnaround times, and enhanced competitiveness—are frequently missed during digital transformation efforts. However, with the progress in AI and thereby solutions like Inventive AI, automating RFP management is not only feasible but can also provide significant strategic advantages.

Can you explain how the unified knowledge hub works, and how it integrates with various enterprise systems?

The Inventive AI Knowledge Hub functions as a centralized, AI-powered resource that acts like a subject matter expert (SME) with access to a company’s vast, distributed knowledge. In a typical enterprise, relevant content for responding to sales questionnaires and RFPs exists across multiple systems and departments, making it difficult to manually pull together accurate and strategic responses. A simple Q&A library of boilerplate responses is often insufficient for creating competitive, tailored proposals.

Inventive AI addresses this challenge by integrating with commonly used enterprise systems such as Salesforce, Hubspot, Seismic, Google Drive, SharePoint, OneDrive, and more. Our AI can automatically ingest and understand the context of the incoming RFP, retrieving the most relevant information from across these platforms to craft high-quality responses. Additionally, our AI agents—responsible for tasks such as competitive research, error checking, conflict resolution, and compliance and risk management—operate on this unified Knowledge Hub, ensuring consistency and accuracy by leveraging a complete, connected view of the company’s knowledge, rather than relying on siloed content.

What are the key features of Inventive AI that differentiate it from other RFP management tools on the market?

What differentiates Inventive AI from other RFP management tools is our focus on helping customers win RFPs, not just answer questions. While many tools simply provide a way to search for boilerplate responses within a static database, requiring the customer to constantly maintain and update it, Inventive AI goes far beyond that. Our platform dynamically leverages enterprise-wide knowledge and uses advanced AI to generate strategic, high-quality responses tailored to each RFP, significantly increasing win rates.

Our deep enterprise experience and AI expertise allow us to address both the business and technical aspects of the RFP process. We’ve built a system that not only delivers accurate responses but also effectively manages AI challenges like language model hallucinations, ensuring precision and relevance in every response. This level of response quality and strategic insight is unmatched in the market, making Inventive AI a true competitive advantage for RFP management.

How does the AI Content Manager ensure that only the most relevant and up-to-date information is used in RFP responses?

This is a great question and easier said than done. While it’s relatively straightforward to create a compelling demo using popular AI tools like OpenAI, Google, or AWS, our platform goes beyond simple solutions that are just not good enough for enterprise settings. Drawing from our AI research backgrounds at Google and Stanford Research, we’ve built a proprietary machine learning pipeline combined with a strategy of specialized AI agents. This allows us to ensure that only the most relevant and up-to-date content is used in RFP responses, continuously refining the accuracy of the information.

When the AI encounters uncertainty, it doesn’t make assumptions. Instead, it presents users with potential options and learns from the feedback provided. This iterative learning ensures that future responses are even more precise when similar questions arise, improving over time and ensuring that RFP responses stay relevant, current, and strategic.

What kind of productivity boosts have users seen by using Inventive AI’s suite of AI agents, and what specific tasks do these agents handle?

Users of Inventive AI’s suite of AI agents have seen significant productivity boosts, particularly in their ability to respond to a greater number of RFPs, which directly impacts top-line revenue. By generating more strategic, accurate, and tailored responses, companies have also experienced higher win rates. Customers report that they complete the RFP process 70% faster than before, allowing them to take on more opportunities without sacrificing quality.

Our AI agents handle a variety of critical tasks, such as conducting competitive analysis, brainstorming response ideas, and detecting stale or outdated content. They also identify conflicting information within responses, unearth multiple potential answers to RFP questions, and check for compliance with regulatory or internal guidelines. These automated capabilities enable teams to focus on higher-value activities, ensuring that responses are both efficient and strategically sound.

Thank you for the great interview, readers who wish to learn more should visit Inventive AI.

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