#API monitoring

[uniskytechnologies]

Why big enterprises prefer outsourcing synthetic website monitoring services instead of in house monitoring tools?

Big enterprises often prefer outsourcing synthetic website monitoring services instead of using in-house monitoring tools for several reasons.

First, outsourcing synthetic website monitoring services can provide access to a wider range of expertise and experience. Monitoring companies specialize in providing these services and often have a team of experts with a deep understanding of website performance, as well as experience with various industries and technologies. This can help ensure that the monitoring is thorough and accurate, and that any issues are identified and resolved quickly.

Second, outsourcing synthetic website monitoring services can be more cost-effective than using in-house tools. Monitoring companies can provide services on a pay-as-you-go basis, which can help businesses save on costs associated with purchasing, maintaining, and updating expensive in-house tools. Additionally, outsourcing can help businesses avoid the costs associated with hiring and training additional staff to manage and maintain in-house monitoring tools.

Third, outsourcing synthetic website monitoring services can provide businesses with more flexibility. Monitoring companies can often provide services that are tailored to meet the specific needs of a business, and can scale up or down as needed. This can be especially important for big enterprises, which may have complex, multi-faceted websites with a large number of visitors and transactions.

Finally, outsourcing synthetic website monitoring services can also help businesses stay compliant with industry regulations. Monitoring companies can provide services that are compliant with regulations such as the Payment Card Industry Data Security Standard (PCI DSS) which can help businesses avoid potential penalties and fines.

In summary, big enterprises prefer outsourcing synthetic website monitoring service s over in-house monitoring tools because it provides access to expertise and experience, cost-effectiveness, flexibility and compliance with industry regulations. By outsourcing these services, businesses can ensure that their websites are performing optimally and that any issues are identified and resolved quickly.

Synthetic website monitoring,

website performance monitoring,

application performance monitoring,

cloud monitoring,

network monitoring,

real user monitoring,

remote monitoring,

API monitoring,

automated testing,

monitoring tools

[sitting-on-me-bum]

Western Honeybee (Apis mellifera)

Honeybees go the extra mile for some pollen.

USGS Bee Inventory and Monitoring Lab

[foodspark-scraper]

Get Your Hands on Ubereats Data: A Beginner's Guide to Web Scraping

Are you looking to scrape data from the Ubereats food delivery website? In this comprehensive guide, we'll walk you through the process of web scraping, from selecting the right tools to extracting data and storing it in a usable format. Whether you're an analyst or a data enthusiast, this guide will help you get started with web scraping and explore the wealth of data available on Ubereats.

[3idatascraping]

Flight Price Monitoring Services | Scrape Airline Data

We Provide Flight Price Monitoring Services in USA, UK, Singapore, Italy, Canada, Spain and Australia and Extract or Scrape Airline Data from Online Airline / flight website and Mobile App like Booking, kayak, agoda.com, makemytrip, tripadvisor and Others.

[techdirectarchive]

How to Configure and Run Performance Tests in Postman

APIs is the backbone of many businesses today and the quality and reliability of these APIs can have a great impact on how customers feel about a product. If you want to get great feedback from your customers then your APIs must meet the expected functionality and can handle the expected load through the traffic hitting the endpoints. That is the reason every business or organization carries out…

[retailgators]

How do you extract data by building web scrapers from eCommerce sites?

Web scrapers are tools commonly used to get information from websites. Building one requires programming skills, but it’s not as complicated as you think. The success of using a web scraper for eCommerce data gathering depends on more than just the scraper itself.

What Do You Mean By Web Scraping In The E-Commerce Industry?

Web scraping in the e-commerce industry is the automated process of extracting data from online store websites related to the retail industry. This data can cover product details, pricing details, customer feedback, the number of items in stock, and any other data businesses find essential to their work.

Visit Us :

[thedbahub]

Optimizing SQL Server Performance: Tackling High Page Splits

Diving into the world of SQL Server management, one stumbling block you might encounter is the vexing issue of high page splits. These splits happen when there’s simply no room left on a data page for new information, forcing SQL Server to divide the data across two pages. This can crank up I/O operations and lead to fragmentation, which, frankly, is a performance nightmare. This guide aims to…

[beeingapis]

"Every Flap You Take, Every Swarm You Make"

[uniskytechnologies]

Synthetic vs Real User Monitoring: Which is Right for Your Website?

Website monitoring is an essential aspect of ensuring that your website is performing well and providing a positive user experience. Two common methods of website monitoring are synthetic monitoring and real user monitoring. While both methods have their own advantages, understanding the differences between them can help you decide which one is best for your website.

Synthetic monitoring involves simulating the actions of a user on your website, such as clicking links and filling out forms. This method allows you to detect any issues or errors that may not be visible to real users. It also allows you to proactively identify and fix problems before they impact the user experience.

Real user monitoring, on the other hand, involves collecting data on how real users interact with your website. This includes information on load times, browser and device types, and geographic location. This data can be used to optimise website performance and improve the user experience.

One of the main advantages of synthetic monitoring is that it can be scheduled to run at specific intervals, allowing you to identify issues on a regular basis. This can be especially useful for detecting problems that only occur at certain times of the day or week. On the other hand, real user monitoring provides a more accurate picture of how users are actually interacting with your website, providing valuable insights into user behaviour and website performance.

Another advantage of synthetic monitoring is that it can be used to simulate different user scenarios, such as testing the performance of your website on different devices or browsers. Real user monitoring, in contrast, can only provide data on the actual users that are visiting your website.

In conclusion, both synthetic and real user monitoring have their own advantages and can be used to improve website performance . Synthetic monitoring can be used to detect issues proactively and schedule tests regularly, while real user monitoring can provide valuable insights into user behaviour and website performance. The best approach for your website will depend on your specific needs and goals. It may be beneficial to use both methods in combination to get a complete picture of your website performance. Unisky Technologies synthetic monitoring is one of the best available in the market.

Synthetic website monitoring,

website performance monitoring,

application performance monitoring,

cloud monitoring,

network monitoring,

real user monitoring,

remote monitoring,

API monitoring,

automated testing,

monitoring tools

[govindhtech]

NVIDIA GPUs monitoring issues on compute engine

For the requisite computational horsepower, businesses that use AI and ML for applications like product recommendations, scientific computing, and gaming often turn to NVIDIA GPUs on Google Cloud. They must keep an eye on the GPU performance indicators in order to comprehend the nature of their workload and streamline the ML development process. We’re happy to report that Ops Agent now gathers metrics from NVIDIA GPUs on Compute Engine VMs to assist.

The telemetry tool for Compute Engine that Google recommends for monitoring VM instances is called Cloud Ops Agent. You can now get better insight into your NVIDIA GPUs and accelerated workloads thanks to key metrics from the NVIDIA Management Library (NVML) and sophisticated profiling data from the NVIDIA Data Center GPU Manager (DCGM).

Ops Agent allows you to:

Utilize dashboards that come pre-built with GPU analytics to see how your GPU fleet is doing.

Identify underused GPUs and combine workloads to save expenses.

Plan scaling by observing patterns to determine when to increase GPU capacity or modernize current GPUs.

Determine which ML models on the GPU are using the most memory and CPU resources.

To locate GPU performance problems and bottlenecks, use the DCGM profiling measures.

Keep an eye on your GPU stats.

Get important GPU parameters straight now

The nvidia-smi command, which offers a summary of all GPU devices and the processes operating on them, is undoubtedly recognizable to those who utilize NVIDIA GPUs. Without further settings, Ops Agent may gather such crucial metrics by using the same underlying API in NVML. This comprises measurements for:

Utilizing GPU

memory use on the GPU

CPU and GPU memory usage maximum

CPU and GPU lifetime usage

With DCGM, collect sophisticated GPU metrics

To manage and keep track of NVIDIA GPUs at scale, NVIDIA offers a set of tools called DCGM. It provides an API for high-level measurements for sophisticated profiling of various hardware parts, such as streaming processors, connections like NVLink, and more. With the Ops Agent DCGM integration, we have put up a list of these advanced metrics.

Monitor the condition of your GPUs

You can quickly query and see the gathered GPU metrics from Ops Agent using the other services in Google Cloud’s operations suite. To develop queries, make custom charts, and add them to dashboards, use our Metrics Explorer query builder or PromQL. Utilizing GPU data gathered from both GKE GPU nodes and Compute Engine GPU VMs, our NVIDIA GPU Monitoring dashboard offers a single point of access to your entire GPU fleet. For information on adding this dashboard to your project, see the documentation. The DCGM dashboard, which provides a concentrated view of the GPU profiling metrics, is immediately added to your project for the Cloud Monitoring DCGM integration once DCGM metrics collecting starts.

[foodspark-scraper]

Competitor Price Monitoring Services - Food Scraping Services

Competitor Price Monitoring Strategies

Price Optimization

If you want your restaurant to stay competitive, it’s crucial to analyze your competitors’ average menu prices. Foodspark offers a Competitor Price Monitoring service to help you with this task. By examining data from other restaurants and trends in menu prices, we can determine the best price for your menu. That will give you an edge in a constantly evolving industry and help you attract more customers, ultimately increasing profits.

Market Insights

Our restaurant data analytics can help you stay ahead by providing valuable insights into your competitors’ pricing trends. By collecting and analyzing data, we can give you a deep understanding of customer preferences, emerging trends, and regional variations in menu pricing. With this knowledge, you can make informed decisions and cater to evolving consumer tastes to stay ahead.

Competitive Advantage

To stay ahead in the restaurant industry, you must monitor your competitors’ charges and adjust your prices accordingly. Our solution can help you by monitoring your competitors’ pricing strategies and allowing you to adjust your expenses in real-time. That will help you find opportunities to offer special deals or menu items to make you stand out and attract more customers.

Price Gap Tracking

Knowing how your menu prices compare to your competitors is essential to improve your restaurant’s profitability. That is called price gap tracking. Using our tracking system, you can quickly identify the price differences between restaurant and your competitors for the same or similar menu items. This information can help you find opportunities to increase your prices while maintaining quality or offering lower costs. Our system allows you to keep a close eye on price gaps in your industry and identify areas where your expenses are below or above the average menu prices. By adjusting your pricing strategy accordingly, you can capture more market share and increase your profits.

Menu Mapping and SKU

Use our menu and SKU mapping features to guarantee that your products meet customer expectations. Find out which items are popular and which ones may need some changes. Stay adaptable and responsive to shifting preferences to keep your menu attractive and competitive.

Price Positioning

It’s essential to consider your target audience and desired brand image to effectively position your restaurant’s prices within the market. Competitor data can help you strategically set your prices as budget-friendly, mid-range, or premium. Foodspark Competitor Price Monitoring provides data-driven insights to optimize your pricing within your market segment. That helps you stay competitive while maximizing revenue and profit margins.

Competitor Price Index (CPI)

The Competitor Price Index (CPI) measures how your restaurant’s prices compare to competitors. We calculate CPI for you by averaging the prices of similar menu items across multiple competitors. If your CPI is above 100, your prices are higher than your competitors. If it’s below 100, your prices are lower.

Benefits of Competitor Price Monitoring Services

Price Optimization

By continuous monitoring your competitor’s prices, you can adjust your own pricing policies, to remain competitive while maximizing your profit margins.

Dynamic Pricing

Real-time data on competitor’s prices enable to implement dynamic pricing strategies, allowing you to adjust your prices based on market demand and competitive conditions.

Market Positioning

Understanding how your prices compare to those of your competitors helps you position your brand effectively within the market.

Customer Insights

Analyzing customer pricing data can reveal customer behavior and preferences, allowing you to tailor your pricing and marketing strategies accordingly.

Brand Reputation Management

Consistently competitive pricing can enhance your brand’s reputation and make your product more appealing to customers.

Content Source: https://www.foodspark.io/competitor-price-monitoring/

[adifferenttopic]

[jcmarchi]

Drasi by Microsoft: A New Approach to Tracking Rapid Data Changes

New Post has been published on https://thedigitalinsider.com/drasi-by-microsoft-a-new-approach-to-tracking-rapid-data-changes/

Drasi by Microsoft: A New Approach to Tracking Rapid Data Changes

Imagine managing a financial portfolio where every millisecond counts. A split-second delay could mean a missed profit or a sudden loss. Today, businesses in every sector rely on real-time insights. Finance, healthcare, retail, and cybersecurity, all need to react instantly to changes, whether it is an alert, a patient update, or a shift in inventory. But traditional data processing cannot keep up. These systems often delay responses, costing time and missed opportunities.

That is where Drasi by Microsoft comes in. Designed to track and react to data changes as they happen, Drasi operates continuously. Unlike batch-processing systems, it does not wait for intervals to process information. Drasi empowers businesses with the real-time responsiveness they need to stay ahead of the competitors.

Understanding Drasi

Drasi is an advanced event-driven architecture powered by Artificial Intelligence (AI) and designed to handle real-time data changes. Traditional data systems often rely on batch processing, where data is collected and analyzed at set intervals. This approach can cause delays, which can be costly for industries that depend on quick responses. Drasi changes the game by using AI to track data continuously and react instantly. This enables organizations to make decisions as events happen instead of waiting for the next processing cycle.

A core feature of Drasi is its AI-driven continuous query processing. Unlike traditional queries that run on a schedule, continuous queries operate non-stop, allowing Drasi to monitor data flows in real time. This means even the smallest data change is captured immediately, giving companies a valuable advantage in responding quickly. Drasi’s machine learning capabilities help it integrate smoothly with various data sources, including IoT devices, databases, social media, and cloud services. This broad compatibility provides a complete view of data, helping companies identify patterns, detect anomalies, and automate responses effectively.

Another key aspect of Drasi’s design is its intelligent reaction mechanism. Instead of simply alerting users to a data change, Drasi can immediately trigger pre-set responses and even use machine learning to improve these actions over time. For example, in finance, if Drasi detects an unusual market event, it can automatically send alerts, notify the right teams, or even make trades. This AI-powered, real-time functionality gives Drasi a clear advantage in industries where quick, adaptive responses make a difference.

By combining continuous AI-powered queries with rapid response capabilities, Drasi enables companies to act on data changes the moment they happen. This approach boosts efficiency, cuts down on delays, and reveals the full potential of real-time insights. With AI and machine learning built in, Drasi’s architecture offers businesses a powerful advantage in today’s fast-paced, data-driven world.

Why Drasi Matters for Real-Time Data

As data generation continues to grow rapidly, companies are under increasing pressure to process and respond to information as it becomes available. Traditional systems often face issues, such as latency, scalability, and integration, which limit their usefulness in real-time settings. This is especially critical in high-stakes sectors like finance, healthcare, and cybersecurity, where even brief delays can result in losses. Drasi addresses these challenges with an architecture designed to handle large amounts of data while maintaining speed, reliability, and adaptability.

In financial trading, for example, investment firms and banks depend on real-time data to make quick decisions. A split-second delay in processing stock prices can mean the difference between a profitable trade and a missed chance. Traditional systems that process data in intervals simply cannot keep up with the pace of modern markets. Drasi’s real-time processing capability allows financial institutions to respond instantly to market shifts, optimizing trading strategies.

Similarly, in a connected smart home, IoT sensors track everything from security to energy use. A traditional system may only check for updates every few minutes, potentially leaving the home vulnerable if an emergency occurs during that interval. Drasi enables constant monitoring and immediate responses, such as locking doors at the first sign of unusual activity, thereby enhancing security and efficiency.

Retail and e-commerce also benefit significantly from Drasi’s capabilities. E-commerce platforms rely on understanding customer behavior in real time. For instance, if a customer adds an item to their cart but doesn’t complete the purchase, Drasi can immediately detect this and trigger a personalized prompt, like a discount code, to encourage the sale. This ability to react to customer actions as they happen can lead to more sales and create a more engaging shopping experience. In each of these cases, Drasi fills a significant gap where traditional systems lack and thus empowers businesses to act on live data in ways previously out of reach.

Drasi’s Real-Time Data Processing Architecture

Drasi’s design is centred around an advanced, modular architecture, prioritizing scalability, speed, and real-time operation. Maily, it depends on continuous data ingestion, persistent monitoring, and automated response mechanisms to ensure immediate action on data changes.

When new data enters Drasi’s system, it follows a streamlined operational workflow. First, it ingests data from various sources, including IoT devices, APIs, cloud databases, and social media feeds. This flexibility enables Drasi to collect data from virtually any source, making it highly adaptable to different environments.

Once data is ingested, Drasi’s continuous queries immediately monitor the data for changes, filtering and analyzing it as soon as it arrives. These queries run perpetually, scanning for specific conditions or anomalies based on predefined parameters. Next, Drasi’s reaction system takes over, allowing for automatic responses to these changes. For instance, if Drasi detects a significant increase in website traffic due to a promotional campaign, it can automatically adjust server resources to accommodate the spike, preventing potential downtime.

Drasi’s operational workflow involves several key steps. Data is ingested from connected sources, ensuring real-time compatibility with devices and databases. Continuous queries then scan for predefined changes, eliminating delays associated with batch processing. Advanced algorithms process incoming data to provide meaningful insights immediately. Based on these data insights, Drasi can trigger predefined responses, such as notifications, alerts, or direct actions. Finally, Drasi’s real-time analytics transform data into actionable insights, empowering decision-makers to act immediately.

By offering this streamlined process, Drasi ensures that data is not only tracked but also acted upon instantly, enhancing a company’s ability to adapt to real-time conditions.

Benefits and Use Cases of Drasi

Drasi offers benefits far beyond typical data processing capabilities and provides real-time responsiveness essential for businesses that need instant data insights. One key advantage is its enhanced efficiency and performance. By processing data as it arrives, Drasi removes delays common in batch processing, leading to faster decision-making, improved productivity, and reduced downtime. For example, a logistics company can use Drasi to monitor delivery statuses and reroute vehicles in real time, optimizing operations to reduce delivery times and increase customer satisfaction.

Real-time insights are another benefit. In industries like finance, healthcare, and retail, where information changes quickly, having live data is invaluable. Drasi’s ability to provide immediate insights enables organizations to make informed decisions on the spot. For example, a hospital using Drasi can monitor patient vitals in real time, supplying doctors with important updates that could make a difference in patient outcomes.

Furthermore, Drasi integrates with existing infrastructure and enables businesses to employ its capabilities without investing in costly system overhauls. A smart city project, for example, could use Drasi to integrate traffic data from multiple sources, providing real-time monitoring and management of traffic flows to reduce congestion effectively.

As an open-source tool, Drasi is also cost-effective, offering flexibility without locking businesses into expensive proprietary systems. Companies can customize and expand Drasi’s functionalities to suit their needs, making it an affordable solution for improving data management without a significant financial commitment.

The Bottom Line

In conclusion, Drasi redefines real-time data management, offering businesses an advantage in today’s fast-paced world. Its AI-driven, event-based architecture enables continuous monitoring, instant insights, and automatic responses, which are invaluable across industries.

By integrating with existing infrastructure and providing cost-effective, customizable solutions, Drasi empowers companies to make immediate, data-driven decisions that keep them competitive and adaptive. In an environment where every second matters, Drasi proves to be a powerful tool for real-time data processing.

Visit the Drasi website for information about how to get started, concepts, how to explainers, and more.

[mariacallous]

It’s April, and the US is experiencing a self-inflicted trade war and a constitutional crisis over immigration. It’s a lot. It’s even enough to make you forget about Elon Musk’s so-called Department of Government Efficiency for a while. You shouldn’t.

To state the obvious: DOGE is still out there, chipping away at the foundations of government infrastructure. Slightly less obvious, maybe, is that the DOGE project has recently entered a new phase. The culling of federal workers and contracts will continue, where there’s anything left to cull. But from here on out, it’s all about the data.

Few if any entities in the world have as much access to as much sensitive data as the United States. From the start, DOGE has wanted as much of it as it could grab, and through a series of resignations, firings, and court cases, has mostly gotten its way.

In many cases it’s still unclear what exactly DOGE engineers have done or intend to do with that data. Despite Elon Musk’s protestations to the contrary, DOGE is as opaque as Vantablack. But recent reporting from WIRED and elsewhere begins to fill in the picture: For DOGE, data is a tool. It’s also a weapon.

Start with the Internal Revenue Service, where DOGE associates put the agency’s best and brightest career engineers in a room with Palantir folks for a few days last week. Their mission, as WIRED previously reported, was to build a “mega API” that would make it easier to view previously compartmentalized data from across the IRS in one place.

In isolation that may not sound so alarming. But in theory, an API for all IRS data would make it possible for any agency—or any outside party with the right permissions, for that matter—to access the most personal, and valuable, data the US government holds about its citizens. The blurriness of DOGE’s mission begins to gain focus. Even more, since we know that the IRS is already sharing its data in unprecedented ways: A deal the agency recently signed with the Department of Homeland Security provides sensitive information about undocumented immigrants.

It’s black-mirror corporate synergy, putting taxpayer data in the service of President Donald Trump’s deportation crusade.

It also extends beyond the IRS. The Washington Post reported this week that DOGE representatives across government agencies—from the Department of Housing and Urban Development to the Social Security Administration—are putting data that is normally cordoned off in service of identifying undocumented immigrants. At the Department of Labor, as WIRED reported Friday, DOGE has gained access to sensitive data about immigrants and farm workers.

And that’s just the data that stays within the government itself. This week NPR reported that a whistleblower at the National Labor Relations Board claims that staffers observed spikes in data leaving the agency after DOGE got access to its systems, with destinations unknown. The whistleblower further claims that DOGE agents appeared to take steps to “cover their tracks,” switching off or evading the monitoring tools that keep tabs on who’s doing what inside computer systems. (An NLRB spokesperson denied to NPR that DOGE had access to the agency’s systems.)

What could that data be used for? Anything. Everything. A company facing a union complaint at the NLRB could, as NPR notes, get access to “damaging testimony, union leadership, legal strategies and internal data on competitors.” There’s no confirmation that it’s been used for those things—but more to the point, there’s also currently no way to know either way.

That’s true also of DOGE’s data aims more broadly. Right now, the target is immigration. But it has hooks into so many systems, access to so much data, interests so varied both within and without government, there are very few limits to how or where it might next be deployed.

The spotlight shines a little less brightly on Elon Musk these days, as more urgent calamities take the stage. But DOGE continues to work in the wings. It has tapped into the most valuable data in the world. The real work starts when it puts that to use.