Photography has become an increasingly popular hobby and profession in the United States. As the demand for visual content continues to rise, so too does the need for skilled photographers. In this article, we will explore some statistics on photographers in the United States, including their demographics, earnings, and employment trends.
According to data from the Bureau of Labor Statistics, there were 138,800 photographers employed in the United States as of May 2020. Of these, 66.3% were self-employed, while the remaining 33.7% worked for various businesses and organizations.
The demographics of photographers in the United States are diverse. In terms of gender, approximately 61.8% of photographers are male, while 38.2% are female. However, the gender distribution varies depending on the specific type of photography. For example, wedding and portrait photographers tend to be predominantly female, while sports and photojournalism photographers are more likely to be male.
Age is also a factor in the photography industry. According to data from Statista, the largest age group of photographers in the United States is those between the ages of 25 and 34, accounting for 33% of all photographers. The second-largest age group is those between 35 and 44 years old, accounting for 27% of photographers.
Here's a table summarizing some key statistics on photographers in the United States:
| Statistics | Data |
|---|---|
| Number of photographers | 138,800 (as of May 2020, according to BLS) |
| Self-employed | 66.3% of all photographers (according to BLS) |
| Gender distribution | 61.8% male, 38.2% female (according to BLS) |
| Median annual wage | $36,280 (as of May 2020, according to BLS) |
| Top 10% earnings | More than $78,760 per year (according to BLS) |
| Bottom 10% earnings | Less than $20,880 per year (according to BLS) |
| Average gross revenue | $228,000 (in 2019, according to Professional Photographers of America) |
| Employment projection | Decline of 4% from 2019 to 2029 (according to BLS) |
| Industry growth | Motion picture and video industries projected to grow 11% from 2019 to 2029 (according to BLS) |
Source: https://aziabe.com
Note that these statistics are subject to change over time as the photography industry evolves and new data becomes available.
The earning potential of photographers in the United States can vary greatly depending on factors such as experience, specialization, and location. According to data from the Bureau of Labor Statistics, the median annual wage for photographers in the United States was $36,280 as of May 2020.
The top 10% of photographers earned more than $78,760 per year, while the bottom 10% earned less than $20,880 per year. The highest-paying industries for photographers include motion picture and video industries, specialized design services, and advertising, public relations, and related services.
However, it's important to note that many photographers are self-employed and therefore their earnings can be highly variable. According to a survey conducted by Professional Photographers of America, the average gross revenue for a photography business in the United States was $228,000 in 2019.
The employment outlook for photographers in the United States is mixed. According to data from the Bureau of Labor Statistics, employment of photographers is projected to decline 4% from 2019 to 2029. This decline is largely due to the increasing availability of high-quality cameras and editing software, which have made it easier for non-professionals to take and edit their own photos.
However, there are still opportunities for photographers who specialize in certain areas. For example, employment of photographers in the motion picture and video industries is projected to grow 11% from 2019 to 2029, due to the increasing demand for visual content in these industries.
In addition, photographers who specialize in niche areas such as food, product, and real estate photography may also see job growth, as businesses and organizations continue to invest in high-quality visual content to showcase their products and services.
Photography is a diverse and dynamic industry in the United States. While the employment outlook for photographers as a whole may be declining, there are still opportunities for those who specialize in certain areas or who are able to adapt to changes in the industry. By staying up-to-date with the latest trends and technologies, photographers can continue to thrive in this exciting and ever-evolving field.
Data center relocation refers to the process of moving a company's data center from one location to another. This can involve physically moving servers, networking equipment, and other hardware, as well as migrating software applications and data from one environment to another.
Data center relocation is typically done for a variety of reasons, such as cost savings, improved performance, or a need for more space. It can be a complex and challenging process, as it requires careful planning and coordination to ensure that critical systems remain operational during the transition.
Some of the key steps involved in data center relocation may include:
Assessing the existing data center environment, including hardware, software, and networking infrastructure.
Developing a relocation plan that outlines the scope of the project, timelines, and budgets.
Identifying potential risks and challenges associated with the relocation, such as data loss or downtime.
Coordinating with vendors, contractors, and other stakeholders to ensure that the move is executed smoothly.
Testing systems and applications to ensure that they are functioning properly in the new environment.
Data center relocation is a complex process that requires careful planning and execution. However, it can offer significant benefits for companies that are looking to improve their IT infrastructure and reduce costs.
Planning a data center relocation involves a series of steps to ensure a smooth and successful transition. Here are some key steps to consider:
Start by defining the scope of the relocation project. This includes identifying the systems, equipment, and applications that will be moved, as well as any other factors that may impact the move, such as regulatory compliance requirements.
Perform a comprehensive assessment of your current infrastructure to identify any potential issues or challenges that may arise during the relocation. This includes evaluating your hardware, software, networking equipment, and other systems.
Develop a detailed relocation plan that outlines the scope of the project, timelines, budgets, and resources required to execute the move successfully. Be sure to include contingency plans for unforeseen issues that may arise during the move.
Identify a suitable new location for your data center based on factors such as cost, accessibility, and available infrastructure. Be sure to evaluate the new location's power, cooling, and networking capabilities to ensure they can support your needs.
Coordinate with vendors, service providers, and other stakeholders to ensure that the move is executed smoothly. This includes coordinating with moving companies, contractors, and suppliers to ensure that all equipment is safely transported and installed at the new location.
Once the relocation is complete, thoroughly test and validate all systems and applications to ensure they are functioning properly in the new environment. This includes performing load testing, security testing, and other types of testing as necessary.
Planning a data center relocation requires careful planning and execution to ensure a successful transition. Be sure to work with experienced vendors and service providers to help you navigate the complex process of moving your data center to a new location.
The cost of a data center relocation can vary widely depending on a number of factors, such as the size and complexity of the data center, the distance of the move, and the amount of equipment and infrastructure that needs to be transported. Here are some of the key cost factors to consider when planning a data center relocation:
The cost of moving equipment and infrastructure, such as servers, storage arrays, networking equipment, and cabling, can be a significant cost factor in a data center relocation.
The cost of labor, including technicians, engineers, and project managers, is a major cost factor in any data center relocation. This includes the cost of planning, executing, and testing the move.
The cost of downtime and business interruption during the relocation can be significant. This includes lost revenue, productivity, and customer confidence.
The cost of transporting equipment and infrastructure to the new location, including trucking, shipping, and handling, can be a significant cost factor.
If you are moving to a new location that requires new infrastructure or facilities, such as additional power or cooling capacity, these costs should be factored into your relocation budget.
If your industry is heavily regulated, compliance costs may be a factor in your relocation budget. For example, you may need to comply with specific regulations around data privacy and security.
Overall, the cost of a data center relocation can be significant, but it is important to weigh the costs against the benefits of a new location, such as improved performance, scalability, and cost savings. It is also important to work with experienced vendors and service providers to help you plan and execute a successful data center relocation.
Relocating a data center can be a complex and challenging process, and there are several common problems that organizations may encounter during the relocation process. Here are some of the most typical problems with data center relocation:
Data loss or corruption: Data loss or corruption is another significant risk during a data center relocation. Data can be lost or corrupted during the move due to hardware failure, network disruptions, or other issues, which can result in significant data loss and potential legal or financial liabilities.
Equipment damage: Equipment damage can occur during the relocation process due to mishandling, transportation, or other issues. Damaged equipment can result in additional costs to repair or replace the damaged hardware.
Network and connectivity issues: Network and connectivity issues can arise during the relocation process due to changes in network topology, configuration errors, or other issues. These issues can impact application performance and cause disruptions to the organization's operations.
Overall, data center relocation requires careful planning, execution, and risk management to ensure a successful transition. Organizations should work with experienced vendors and service providers to help them navigate the complex process of relocating a data center and minimize the risks associated with the move.
Here is a table with some companies that provide data center relocation services:
| Company Name | Description |
|---|---|
| IBM | IBM provides data center relocation services, including planning, migration, and post-move support. |
| Dell Technologies | Dell Technologies offers data center relocation services that include site analysis, planning, and project management. |
| Hitachi Vantara | Hitachi Vantara provides data center relocation services, including equipment transport, installation, and configuration. |
| Schneider Electric | Schneider Electric offers data center relocation services that include project management, equipment handling, and testing. |
| Rackspace | Rackspace offers data center relocation services, including pre-migration planning, execution, and post-migration support. |
| CenturyLink | CenturyLink provides data center relocation services that include planning, execution, and testing, and post-move support. |
| Vertiv | Vertiv offers data center relocation services that include project management, equipment handling, and installation. |
| Comarch | Comarch provides data center relocation services, including equipment transport, installation, and configuration. |
| Data Dynamics Global | Data Dynamics Global offers data center relocation services that include project management, migration, and testing. |
| ServerCentral Turing Group | ServerCentral Turing Group provides data center relocation services that include planning, execution, and post-move support. |
Note that this list is not exhaustive, and there may be other companies that provide data center relocation services. It is important to carefully evaluate each provider's services, experience, and pricing to determine the best fit for your organization's needs.
Business payment data refers to information related to the financial transactions that occur between businesses and their suppliers, vendors, and other business partners. This data typically includes information such as payment amounts, payment dates, invoice numbers, and payment methods. Business payment data is often stored in accounting systems and can be used for a variety of purposes, such as financial reporting, budgeting, and forecasting.
Analyzing business payment data can provide insights into a company's financial health and help identify areas where cost savings can be realized. For example, analyzing payment data may reveal opportunities to negotiate better payment terms with suppliers or identify areas where operational efficiencies can be improved. Business payment data can also be used to detect fraudulent activity, such as double payments or unauthorized transactions.
Overall, business payment data is an important component of financial management and can provide valuable insights into a company's financial performance and operational efficiency.
Business payment data can be used in a variety of ways to support financial management and decision-making. Here are some examples of how businesses can use payment data:
Payment data can be used to monitor and manage cash flow, by tracking incoming and outgoing payments and projecting future cash flows based on historical data.
Payment data can be used to generate financial reports, such as income statements and balance sheets, to help businesses understand their financial performance and make informed decisions.
Payment data can be used to develop budgets and financial forecasts, by analyzing historical payment data and projecting future income and expenses.
Payment data can be used to monitor vendor performance, by tracking payment history and identifying vendors who consistently deliver quality goods and services.
Payment data can be used to detect fraudulent activity, such as duplicate payments or unauthorized transactions.
Payment data can be used to identify areas where operational efficiency can be improved, such as by automating payment processing or negotiating better payment terms with suppliers.
Payment data analytics refers to the process of analyzing payment data in order to extract insights and make data-driven decisions related to financial management. This can involve using statistical and analytical methods to identify patterns and trends in payment data, as well as using data visualization techniques to present the data in a clear and understandable format.
Payment data analytics can be used for a variety of purposes, such as:
Fraud detection: Payment data analytics can be used to identify suspicious transactions or patterns of activity that may indicate fraudulent activity.
Financial forecasting: By analyzing historical payment data, companies can develop forecasts and projections for future cash flow and financial performance.
Vendor management: Payment data analytics can be used to evaluate the performance of suppliers and vendors, such as identifying which vendors are consistently paid on time and which may require additional attention or renegotiation of terms.
Expense management: Payment data analytics can be used to identify areas where expenses can be reduced or where process improvements can be made to increase efficiency and reduce costs.
Compliance monitoring: Payment data analytics can be used to monitor compliance with internal policies and external regulations related to financial management and payments.
In general, payment data analytics can provide valuable insights into a company's financial performance and help inform decision-making related to financial management and strategy.
Here's an example of a table with business payment data:
| Date | Vendor | Invoice # | Payment Amount | Payment Method |
|---|---|---|---|---|
| 01/15/2022 | Acme Supplies | INV-123 | $2,500 | ACH |
| 02/01/2022 | XYZ Consulting | INV-456 | $3,750 | Check |
| 02/15/2022 | Beta Services | INV-789 | $4,200 | Credit Card |
| 03/01/2022 | Acme Supplies | INV-234 | $2,750 | ACH |
| 03/15/2022 | Alpha Corporation | INV-567 | $6,500 | Wire Transfer |
| 04/01/2022 | XYZ Consulting | INV-890 | $4,500 | Check |
| 04/15/2022 | Beta Services | INV-123 | $3,800 | Credit Card |
In this example, the table includes columns for the date of the payment, the vendor who received the payment, the invoice number associated with the payment, the payment amount, and the payment method used. This data can be used to track expenses, monitor vendor performance, and generate financial reports, among other purposes.
A data warehouse for traffic violations is a centralized repository that stores information related to traffic violations. The purpose of a data warehouse is to provide a comprehensive and integrated view of data from multiple sources, making it easier for analysts and decision-makers to access, query, and analyze data.
In the context of traffic violations, a data warehouse might contain data such as the date, time, and location of the violation, the type of violation, the license plate number of the vehicle, the driver's name and contact information, the issuing officer's name and badge number, and the status of the violation (e.g., whether it has been paid or is outstanding).
This data can be used by law enforcement agencies to identify patterns of violations, monitor trends over time, and make data-driven decisions about enforcement and education efforts. It can also be used by researchers and policymakers to study the effectiveness of traffic safety policies and interventions.
Web traffic violations refer to activities or behaviors on the internet that violate laws, regulations, or acceptable use policies. These violations can include:
Copyright infringement: Using or distributing copyrighted material without permission or proper attribution.
Hacking: Gaining unauthorized access to computer systems or networks.
Malware distribution: Distributing malicious software such as viruses, worms, or Trojan horses.
Phishing: Attempting to steal sensitive information, such as usernames and passwords, by posing as a trustworthy entity in an electronic communication.
Spamming: Sending unsolicited and unwanted email messages to a large number of recipients.
Cyberbullying: Using electronic communication to harass or intimidate another person.
Online fraud: Using the internet to deceive others for personal gain.
These are just a few examples of web traffic violations. The specific laws and regulations governing internet use vary by country and jurisdiction.
There are several types of data warehouses that can be used to store and manage data.
Enterprise data warehouse (EDW): This is a large, centralized data warehouse that stores data from all areas of an organization. It is designed to support enterprise-wide reporting and analysis.
Operational data store (ODS): This is a type of data warehouse that stores real-time or near-real-time data from operational systems, such as transactional databases. It is designed to support operational reporting and analysis.
Data mart: This is a smaller, more specialized data warehouse that focuses on a specific business area, such as finance or marketing. It is designed to support departmental reporting and analysis.
Virtual data warehouse: This is a type of data warehouse that does not store data in a physical location. Instead, it provides a virtual view of data from multiple sources, such as transactional databases and data marts.
Federated data warehouse: This is a type of data warehouse that combines data from multiple sources, but the data remains in its original location. It provides a unified view of data without requiring data to be moved to a central location.
Cloud data warehouse: This is a type of data warehouse that is hosted in the cloud. It can be accessed and managed remotely and can provide scalability and flexibility for organizations that need to store and manage large amounts of data.
Each type of data warehouse has its own strengths and weaknesses, and the choice of which type to use will depend on the specific needs of the organization.
The five key components of a data warehouse are:
Data Sources: These are the systems or databases from which the data is extracted and transformed into the format that can be loaded into the data warehouse. Examples of data sources include operational databases, spreadsheets, and external data sources.
ETL Processes: ETL stands for Extract, Transform, and Load. This process involves extracting data from the data sources, transforming it into a consistent format, and loading it into the data warehouse. ETL processes are used to ensure data quality and consistency in the data warehouse.
Data Storage: This component of a data warehouse refers to the storage of the data in the warehouse. Typically, data is stored in a structured format, such as in tables, columns, and rows. The data should be organized in a way that makes it easy to query and analyze.
Metadata: Metadata is data about data. In a data warehouse, metadata describes the data in the warehouse, including its source, structure, and format. Metadata is important for ensuring the accuracy and consistency of the data in the warehouse.
Query and Analysis Tools: These are the tools used to query and analyze the data in the warehouse. These tools can range from simple ad-hoc reporting tools to complex analytics platforms that allow for predictive modeling and advanced analytics.
Together, these components form the foundation of a data warehouse, allowing organizations to store, manage, and analyze large amounts of data to gain insights and make data-driven decisions.
The analysis of traffic violation data can be performed using various techniques and tools. Here are a few steps that may be involved in analyzing traffic violation data:
Raw data from traffic violation tickets may require cleaning and preparation before analysis. This process may involve removing duplicate records, standardizing data fields, and correcting errors.
Data visualization techniques, such as charts, graphs, and maps, can be used to help identify patterns and trends in traffic violation data. For example, a map can be used to show the geographic distribution of violations, while a bar chart can be used to show the most common types of violations.
Statistical analysis techniques can be used to uncover correlations and relationships in the data. For example, regression analysis can be used to determine the factors that are most strongly associated with different types of violations.
Predictive modeling techniques can be used to forecast future traffic violation rates based on historical data. These models can be used to identify areas where enforcement efforts should be focused.
Business intelligence tools, such as dashboards and scorecards, can be used to provide executives and decision-makers with a high-level overview of traffic violation trends and key performance indicators.
Overall, the goal of analyzing traffic violation data is to identify patterns and trends that can be used to improve traffic safety and reduce violations. The insights gained from this analysis can be used to develop targeted enforcement and education campaigns, as well as inform policy decisions related to traffic safety.
Car sharing means affordable prices, no maintenance costs and a focus on the environment. Here's how it works.
Many public authorities provide the possibility to use car sharing services. Their operation is rather rigid, in fact, most of them provide for pick-up and drop-off at specific meeting points in the city. As these are seen as complementary services to city public transport, they are not strictly for profit.
Other carsharing modes charge a monthly fee for the use of the car.
Other companies offer a more flexible way of using the car, where there are no fixed parking spaces and cars can be picked up and left anywhere within the area covered by the service. This car-sharing service is also called 'one way'.
If the previous lines have been helpful in understanding what car sharing is, it is important to clarify what differentiates this service from car pooling. The latter, in fact, takes place when 2 or more parties who find themselves having to travel the same stretch of road, choose to share a private car in order to reduce petrol and motorway costs.
Let us dive deep into how a typical car sharing software solution works. Interaction between the machine and the server takes place on a software level. Data packets are sent from the telematics hardware to the server and vice versa. During each "polling" of the machine, approximately the following information is received from the telematics equipment:
{
"success": true,
"total": "1",
"car": [
{
"date": "2019-03-01 12:00:19.306955",
"id": "124",
"id_category": "2",
"id_emi": "295",
"id_status": "9",
"vendor": "Kia Rio",
"marka": "",
"year": "",
"id_modification": "",
"vin": "WF0DXXGTB60052135",
"numberplate": "У111ЕУ799",
"color": "",
"descr": "",
"emi": "580855023425759",
"data_fuel": "50",
"data_voltage": "12.6400000000000000",
"fuel_by_can": "1",
"onoffdirect": "1",
"insurance_card": null,
"id_typefuel": "1",
"name_typefuel": "АИ-95",
"id_typetransmission": "1",
"name_typetransmission": "АКПП",
"is_agg_car": null,
"odometer": "22471",
"clat": "55.61313000",
"clng": "37.61698000",
"plat": "55.61321100",
"plng": "37.61700000",
"doors": [
"0"
],
"sensor": [
"0",
"0",
"0",
"0",
"0",
"0"
]
}
]
}
From this example response from the machine, we see that absolutely all information is transmitted about the current status of the vehicle: sensors, location, door statuses, etc. Where does this information come from and what kind of device transmits it?
An ordinary user to use the carsharing service uses a special mobile app, issued by a particular operator, to access the car fleet. However, we often hear about fake accounts in the news.
The principle of registering fake accounts is simple! The data required for registration (passport, driving licence) is taken from various sources by the attackers. Sometimes the "victims" transmit their data themselves. Having received the data, the fraudsters register an account, the carshare operator's security service approves the account and sends it for sale. Driving under such an account is a criminal offence. Security services actively monitor almost every ride in progress.
The second way cybercriminals use to register an account is by exploiting vulnerabilities in the software. For example, relatively recently, a carshare operator's user database was stolen. And just the other day – in order to access the account of one of the carsharing operators, it was not even necessary to enter a confirmation code, which comes via SMS. The operator's server itself sent it to the client in response to the request:
{
"success": 1,
"return": {
"veryfy_code": 3634,
"sms_id": "201907-1000009"
}
}
By entering the code from the "veryfy_code" field we were able to log into the victim's account, knowing only the telephone number.
How to register a company in the BVI: requirements for companies and stages of the procedure.
Registering an offshore company is a relatively simple and at the same time effective way to optimize your business. One of the most popular offshore jurisdictions is the British Virgin Islands (BVI), or BVI.
The British Virgin Islands (BVI) is a British dependency on the eastern part of the Virgin Islands archipelago in the Caribbean Sea and is one of the most popular offshore jurisdictions. Legally, this part of the islands belongs to Great Britain, which makes BVI a stable and economically reliable territory. The number of companies registered here has already exceeded one million. We can say that BVI is a reference offshore zone, the most prestigious and respectable tax haven of the planet, and it is also one of the oldest.
Offshore financial services occupy a significant part of the BVI economy. As for the tax system of the country, it is quite typical for an offshore zone: there is no income tax for companies and individuals, no tax on capital gains. There is a payroll tax (10% for small businesses, 14% for others), import duties, postage, license fees, as well as company registration fees.
Thus, if you open offshore company in Virgin Islands you get preferential taxation, business-friendly legal system, based on the standards of English legislation and allows companies registered here to cooperate with financial institutions in any country, the nonobligatory accounting and, finally, the minimum regulatory requirements for companies.
Today the BVI offers the following types of companies:
From bureaucratic point of view registration of business in BVI is simple. One shareholder and one director is enough, and it can be the same person. Accounting records of the company can be kept in any country, as there are no requirements for its form and mandatory submission. It is interesting that there are separate requirements for the name of the company – it must not cause associations with the UK, the government, ministries and departments. If the company is engaged in banking, insurance or trust activities, it must have the appropriate license.
BVI offshore companies are regulated by the British Virgin Islands Business Companies Act. Thus, according to the legislation, for the offshore companies registered in the BVI there is no currency control, the income tax rate is zero. Also there is no sales tax, value added tax, no tax on dividends and so on. Management only has to pay an annual license fee (from $300 to $1,000 – the exact amount depends on the size of the share capital).
Note that in 2016 the rules of doing business in this country have undergone significant changes. This is due to the entry into force of the 2004 amendments to the previously mentioned Act (The BVI Business Companies (Amendment) Act 2016 (no. 19 of 2015 and no. 2 of 2016) aimed at increasing the transparency of the jurisdiction's financial system. As of April 1, 2016, all companies registered here are required to file data on the directors and beneficiaries with the registration authority in the BVI. In addition, they must keep for five years documentation containing a description and confirmation of financial transactions (this could be, for example, a statement of income, expenses, assets and liabilities). The documents must be available for inspection by the local authorities. Violation of the rules on keeping financial documents is punishable by a fine of $75,000.
Important! Entering information on the directors and beneficiaries into the Public Registry incurs an additional cost to the company of $50 per registry filing. There is a fine of $100 or more for late filing.
Among other things, the Organization for Economic Cooperation and Development Convention providing for the interstate exchange of tax information entered into force in the BVI on March 1, 2014. Also, the BVI are parties to a multilateral agreement on the automatic exchange of financial information.
The growth in popularity of Artificial Intelligence tools such as ChatGPT, the software that has been talked about for weeks and in which Microsft is investing heavily, is broadening the debate to the world of information as well.
This technology in some cases may simplify some tasks, in others it may increase the risk of fake breaking news.
Researchers at Newsguard Technologies, put the tool to the test on sample of 100 already known hoaxes: for 80 of these ChatGPT generated false narratives. Meanwhile, the website Cnet has discontinued the use of artificial intelligence for articles. While at a U.S. university ChatGpt passed a law school exam, demonstrating how these tools offer possibilities yet to be understood and managed.
Researchers at Newsguard Technologies have tried to build conspiracy theories by relying on artificial intelligence (Ai). In 80 percent of the cases ChatGPT generated false and misleading claims on current topics including Covid-19 and Ukraine. "The results," they explain, "confirm the fears and concerns expressed by OpenAi itself (the company that created ChatGPT, ed.) about how the tool could be used if it fell into the wrong hands. To the eyes of those unfamiliar with the topics covered in this report, the findings could easily seem legitimate and even authoritative." However, NewsGuard verified that ChatGPT "has safeguards in place to prevent the spread of some examples of misinformation. For some hoaxes, it took as many as five attempts to lead the chatbot to provide incorrect information."
Several newsrooms have been using automation for some time. The Associated Press uses artificial intelligence to produce sports stories based on data and models. Dow Jones, Bloomberg and Reuters to streamline news coverage of corporate earnings and the stock market. But now that artificial intelligence has become so advanced and accessible, notes the Axios website, it has become more difficult for newsrooms to draw the line between using AI and over-relying on the technology. The tencology site Cnet, for example, announced a few days ago that it was pausing experiments with AI after being accused of poor accuracy in some articles written with this very technology. Meanwhile, ChatGPT is being put to the test in several fields. At a U.S. law school, it passed law exams and the results were good enough that some professors said this system could lead to widespread cheating and even spell the end of traditional teaching methods.
"We need to be able to understand the complexities of the world we are entering: if used well, AI can do wonderful things, but if used incorrectly or cheated, it can generate great difficulties,"
– stresses Gudo Di Fraia, pro-rector of Milan's Iulm University, which just today inaugurated a new Artificial Intelligence laboratory.
Most small or medium-sized companies, especially in their early years of operation, may lack the skills or budget to adequately develop their IT infrastructure. This can lead to loss of competitiveness against competitors, wasted time due to lack of specialized personnel, and generally decreased productivity, diverting attention away from the actual business.
Most small or medium-sized companies, especially in their early years of operation, may not have the skills or budget to adequately develop their IT infrastructure.
This can lead to loss of competitiveness against competitors, wasted time due to lack of skilled personnel, and generally decreased productivity, diverting attention away from the actual business to intricate technological issues.
The solution to these problems may be represented for many companies by outsourcing particular tasks, specifically those involving the IT area. In a nutshell, we are referring to outsourcing.
In the digital revolution, as we said, many companies do not have the skills, time or experience to cover the entire field of information technology.
Disrupting business continuity for IT infrastructure problems is not acceptable. Delegating the IT department to specialized companies – and Out Staffing to find an IT Developer – means, in most cases, not having to worry about these issues.
Obviously, moving IT departments out of the company does not only mean faster problem solving: the benefits are countless and also and above all concern the intelligent allocation of company resources, both in terms of money and personnel. Let's look together at some of these benefits:
An outsourced IT department means, first and foremost, a living saving on some fixed costs: you will no longer need to make investments in infrastructure and specialized personnel since you will be using the know-how of your chosen business partner.
A properly functioning IT department does not, as a rule, need special maintenance: it may therefore happen that the staff in charge will be idle for some periods. This is not the case with outsourcing, since you pay for a service and not for each of its individual components.
By entering into an outsourcing contract with a specialized company, you can count on technicians who offer continuous IT support for any need or problem that should arise during the company's operations, even in different areas among them.
Companies offering outsourcing services usually invest heavily in training their staff and obtaining certifications for particular tools or technologies, so that they are always ready for market needs, even in the event of new legal requirements.
An outsourcing service follows the company as it grows: instead of facing unforeseen investments, simply upgrade the contract with additional services or new technology infrastructure.
A serious company offering IT outsourcing services almost certainly has protocols in place to thwart cyber attacks already in place: advanced firewalls, multi-factor authentication, and access control are key features when choosing an IT partner. Similarly, the use of hyperconverged facilities that enable prompt disaster recovery following malfunctions or extended attacks can zero out or at least dramatically reduce downtime.
These kinds of services are all the more important after the GDPR comes into effect: it is not tolerable for any company to lose sensitive internal or customer data, and not having an IT structure to protect it at an adequate level is absolutely not recommended. Again, IT outsourcing allows these risks to be nimbly resolved.
In the case of difficulties in managing specialized personnel and technological infrastructure, it is a good option to proceed with an evaluative analysis with a reliable partner to determine to what extent (total or partial) to outsource IT management to an external firm and what services to include: this will make it possible to define costs and particularities of the outsourcing contract from the outset.
In summary, for companies that have been operating for a short time or are not large enough to justify their own in-house IT department, outsourcing is a reliable tool for managing business IT aspects.
IHS Automotive predicts that by 2020, some 152 million "connected" cars will generate up to 30 terabytes of data daily. And the business that will be able to competently use this wealth, obviously, will be "on horseback." Let's talk about what information can be used and what is needed for this.
Digital technologies are changing the world. Objects cease to be just things – they turn into information, media centers that have access to the Internet, unite in networks and acquire new opportunities. In the automotive industry, this is connected cars.
The success of work in this direction depends not so much on the characteristics of the modules installed in the machines, but on the services themselves that use this data, and analytical models that process and analyze what they have received, making conclusions and forecasts useful for business.
The car allows you to collect information about its location and instantaneous speed, as well as analyze the data of the self-diagnosis system via OBD2. Based only on this information from one car, it is already possible to draw a conclusion, for example, about the driving style of the driver or the mode of his movements (highway / city).
The analysis of such data "in bulk" is even more interesting. For example, by building a map of the movements of cars of a certain model, you can determine the target audience of this model and its “typical” habits. The horizon for the application of such information is wide enough. And business models for monetizing the collected unstructured data and the conclusions formed on the basis of their analysis can be very diverse.
Data on the preferred speed, periods and frequency of acceleration and braking allow you to determine the driving style of the car owner and the likelihood of an accident. This approach gives careful drivers the opportunity to get a discount, for example, on insurance. A similar system is already in use in a number of countries. Although, in general, the global volume of insurance premiums calculated using the analysis of telematics data is still small.
A similar story with loans – if a person drove carefully on a previous car, why not give him a loan for a new car at a reduced rate (the risks included by the bank in the interest rate will be slightly lower in this case).
The analysis of telematic data makes it possible to create a kind of "electronic navigator" who will advise which gas station is more convenient to call in, which route to prefer in order to save fuel, time and, ultimately, money. The service can also report maintenance in advance, and not only based on mileage data, but also on the basis of the analysis of service data for cars of the same configuration from owners with a similar operating mode.
Based on data on all cars of a certain brand that came off the same assembly line, it is possible to predict the remaining useful life of a car (RUL) and time to breakdown (TTF). And when comparing information about where and how the car was operated with visual inspection data, the reasons for some breakdowns are clear.
Theoretically, if a driver demonstrates the same driving style year after year, and then suddenly changes his habits, the system can detect an anomaly and signal this.
The anomaly can be caused by an emergency situation – theft, illness – or a completely ordinary thing – teaching children to drive or updating applications. A correct analysis of such anomalies will be possible only after analyzing huge amounts of data from a large number of cars, since it is necessary to identify patterns of behavior that would unambiguously indicate an emergency situation.
Information about where and how the car is operated, what difficulties arise in this case and how well certain components function is also of interest to “sellers”. After analyzing it, automakers will be able to identify "systemic problems" of the series or model and fix them in new versions. Dealers, on the basis of this data, will be able to plan the purchase of spare parts, bolt pattern or potential repairs.
In principle, such systems are already used by many dealers and are being tested by manufacturers. The latter are unlikely to spend a lot of time building processes – and, perhaps, we will soon see such solutions in commercial operation.
Another area of application of "big automotive data" is working with "post-warranty" customers. Detailed information about visitors by car will reveal patterns in their behavior, which, in turn, will give scope for developing ways to keep them.
Additional information about the car owner and his movements allows you to target ads aimed at the driver and his passengers. For example, if the collection of data from a large number of cars shows that mainly families with children pass by an advertising banner along the road (and you can find out by fixing the regular parking of the same cars near schools and other children's institutions), this will give an trump card in the hands of the advertising rental agency. Roughly speaking, ad targeting techniques that have long been used online will become available offline.
At the same time, it becomes possible to apply cross-marketing. Based on the client's previous interests, analyzed through the prism of information about his movements and driving style, dealers, gas station owners and other service providers will be able to create a personal package of offers from partner companies (shops, leisure centers, etc.).
All of the above becomes possible thanks to the analysis of already collected data. Imagine what opportunities will open up for the market if the car begins to "communicate" with surrounding objects (other cars and elements of the road network), responding to their actions or collecting information about the driver's reaction.
Everything written above is very cool in theory, but so far on the scale of the entire traffic is not available in practice. And there is a simple explanation for this.
The correct use of big data requires three components: a developed infrastructure, the willingness of industry representatives to innovate, and resources, including human resources, to turn all ideas into reality. Let's see how things are now.
Technically, everything is ready for the transition to the ideology of connected cars data. Everywhere there is a mobile network with access to the Internet. Data exchange standards have already been developed that provide relatively easy integration of devices that support them into the infrastructure of a potential system. There are ready-made and generally accepted solutions for analyzing and storing big data, such as Hadoop, Spark, Storm, and others, as well as large cloud services (Amazon RedShift, Azure DataLake, Azure HDInsight).
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It makes sense to talk about readiness for innovation in two planes: from the point of view of the market and from the side of ordinary motorists.
The market is theoretically ready. Already more than half of the cars sold in the world are connected. Visiongain believes that Big Data is one of the fastest growing market segments in the automotive industry. This indicates a great demand for big data analysis. At the same time, automakers, which have not yet taken the initiative, are being pushed by investors and shareholders.
However, the active movement towards Big Data is still hindered by a purely technical barrier: closed data exchange protocols inside the car do not allow you to easily and quickly collect all information from cars of all brands on the market. Perhaps the situation will be corrected by the appearance of a certain common standard, but for now this question is open.
It is difficult to judge the degree of readiness of the mass user now. Like any innovation, services based on Big Data analysis have their supporters and opponents. For example, fans of an aggressive driving style are unlikely to like the revision of the insurance calculation scheme. On forums and blogs, the very idea of collecting data from cars causes the same controversy as the analysis of user behavior by devices and the Google search engine: some like the new features, while others protest against “total surveillance” and tell horrors about the insecurity of the accumulated data arrays. But the flywheel is running.
Implementing Big Data analysis from scratch implies a large intellectual and financial investment. Alone, of course, not everyone will pull them, but, as in other markets, they may well be divided between interested parties. For example, we used this approach when creating Remoto: we took over research and development, and transferred the installation of equipment to car manufacturers. So the device becomes an additional option of the car, due to which users receive a number of necessary convenient functions.
With personnel capable of working effectively with Big Data, everything is somewhat more complicated, because globally this is a new market, to which the “right” approach has yet to be found. For several years now we have been forming our team, focusing on active specialists with a creative approach to work, and we are open for new contacts with people interested in this direction.
SQL Server Big Data Clusters provides a deployment of scalable SQL Server clusters, Spark, and HDFS containers running on Kubernetes. These components run in parallel, allowing you to read, write and process big data in Transact-SQL or Spark, so you can easily merge and analyse important relational data with voluminous big data.
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The Controller provides cluster management and security. It includes the control service, configuration repository, and other cluster-level services such as Kibana, Grafana, and Elastic Search.
The compute pool provides compute resources to the cluster. It contains nodes with a SQL Server pod on Linux. Pods in the compute pool are subdivided into SQL compute instances for specific processing tasks.
The data pool is used to store data. The data pool consists of one or more SQL Server pods on Linux. It is used to receive data from SQL queries or Spark jobs.
A media pool is formed from a pod pool of media consisting of SQL Server on Linux, Spark and HDFS. All storage nodes in the SQL Server big data cluster are part of the HDFS cluster.
Application Deployment allows you to deploy applications in SQL Server Big Data Clusters, providing interfaces for creating, running, and managing applications.
SQL Server Big Data Clusters provide high flexibility when working with big data. You can query external data sources, store big data in HDFS under SQL Server control, and query data from multiple external data sources through a cluster. The resulting data can be processed using artificial intelligence, machine learning, and other analytical techniques.
Use SQL Server big data clusters for the following tasks:
Ensure high availability for the main SQL Server instance and all databases using Always On availability group technology.
The following subsections contain more information about these scenarios.
With PolyBase, SQL Server Big Data Clusters can query external data sources without having to move or copy data. SQL Server 2019 (15.x) includes new connectors for data sources. For more information, see New PolyBase 2019 features.
SQL Server big data cluster includes a scalable HDFS media pool. It can be used to store big data that can come from multiple external sources. Once you store big data in HDFS in the big data cluster, you can analyse and query it and merge it with relational data.
SQL Server big data clusters allow you to perform artificial intelligence and machine learning tasks on data stored in HDFS data pools and media pools. You can use Spark as well as artificial intelligence-based tools built into SQL Server that use R, Python, Scala or Java.
Management and monitoring capabilities are implemented through a combination of command line tools, APIs, portals, and dynamic administrative views.
You can use Azure Data Studio to perform a variety of tasks in a big data cluster.
A data virtualization wizard that simplifies the process of creating external data sources (enabled with the Data Virtualization extension).
SQL Server Big Data Cluster is a cluster of Linux containers running Kubernetes.
Kubernetes is an open-source container orchestrator that provides scalable container deployments according to needs.
A Kubernetes cluster is a set of computers, also called nodes. One node is used to manage the cluster and is the main node. The other nodes are considered to be worker nodes. The Kubernetes master node is responsible for distributing the workload among the worker nodes and also for monitoring the health of the cluster.
The node runs the container applications. This can be either a physical computer or a virtual machine. A Kubernetes cluster can include nodes of both physical computers and virtual machines.
A Pod is an atomic unit of a Kubernetes deployment. A Pod is a logical group that consists of one or more containers and associated resources needed to run an application. Each pod runs on a node. In doing so, a node can execute in one or more pods. The Kubernetes master node automatically assigns existing pods to nodes in the cluster.
In SQL Server Big Data Clusters, the Kubernetes service is responsible for the state of the cluster. To perform this task, Kubernetes creates and configures cluster nodes, assigns existing pod modules to them, and monitors the health of the cluster.