Complex and voluminous sets of different information represent Big Data.
But in their raw form, they do not make any sense, and to extract valuable information from them, they must go through the analytic process.
This helps identify trends, market preferences, and patterns of customer behaviors in the data, influencing more effective decisions concerning the business.
The complexity of big data urges new analytical tools such as machine learning, predictive and streaming analytics, and such techniques as database and cluster analysis.
In this article, we will analyze the benefits of big data analytics, how to hire a Cisco Spark developer, and what is the role of different engineers in big data analytics.
Useful Information About Big Data Analysis
Big data can be defined by four Vs:
- Volume for large amounts of data
- Velocity for frequency of incoming data
- Variety for many different forms of data, unstructured and structured
- Veracity for the credibility of the data
Among the advantages of solutions for the analysis of big data are the following:
1- Visibility
A robust big data analytics platform enables users to discover invisible trends and patterns in large and complex datasets that facilitate faster identification of strategic opportunities and threats.
2- A unified view of business
With a unified data architecture, companies are provided with a large, consistent, and comprehensive data window that improves decision making and enables users to work with the most accurate and timely information.
3- The fastest time for action
Big data analytics improves decision-making productivity by enabling anyone in the company to anticipate situations and opportunities, ask relevant and timely questions, and receive answers that lead to decisive action.
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There Are Four Main Methods Of Big Data Analysis:
1- Descriptive analytics answers the question “What happened?” Analyzes real-time data and historical data. The main goal is to determine the reasons and patterns of success or failure in a particular area to use this data for the most effective models. Descriptive analytics use essential math functions. A typical example is case studies or web statistics data that a company receives through Google Analytics.
2- Predictive analytics helps predict the most likely course of events based on the available data. To do this, use ready-made templates based on any objects or phenomena with a similar set of characteristics. Using predictive (or predictive, predictive) analytics, you can, for example, calculate the collapse or change in prices in the stock market. Or evaluate the potential borrower’s ability to repay the loan.
3- Prescriptive analytics – the next level compared to predictive. With the help of Big Data and modern technologies, it is possible to identify problem points in business or any other activity and calculate in which scenario they can be avoided in the future.
4- Diagnostic analytics – uses data to analyze the reasons for what happened. This helps to identify anomalies and random connections between events and actions.
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Top 5 Solutions Used In Big Data Analytics
Many tools are used for the qualitative analysis of big data. Reporting, visualization, automation – these are just a few of the inherent features of good tools. Among the most popular solutions are:
1- R
R is known as a statistical language created for statisticians. Like Python, R also requires some coding and programming capabilities from the user to work. R’s most outstanding achievement is the robust statistical capabilities, which have made it widely used in both universities and government organizations. Reporting and visualization are one of the best R features as it has highly advanced and complex libraries like ggplot and plotly that can create beautiful and complex graphs. In addition, R allows the user to easily create dashboards with R shiny, making it a unique tool with a simple learning curve.
2- Power BI
Power BI is designed for big data analytics. The program has established itself as a fast payback product in companies with complex CRM and multiple advertising and sales channels. Executives use this tool to monitor the performance of the enterprise dynamically, and analysts use this tool to predict new opportunities and risk points.
3- NodeXL
NodeXL is software for data analysis, dependency, and network visualization. It can visualize its network as required, analyze social networks, identify critical subscribers; c also analyze content for suitable words, hashtags, and URLs embedded in messages, tweets, etc. Among the functionality of this software is also importing datasets from various sources and automation of reporting.
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4- Hadoop
Hadoop is powered by MapReduce technology. This open-sourced tool allows a user to access and process great amounts of both structured and unstructured data. It is widely recognized as a cost-effective and highly efficient solution for working with large amounts of data because it can operate on a cluster of machines with no additional financial transaction costs.
5- Apache Spark
Apache Spark is a next-generation analytics tool where large amounts of data can be involved. This open-source data analysis tool has a large data structure. Also, it can integrate with Hadoop, making it an attractive option for those analytics firms that deal with large amounts of data. Spark developers can process data in real-time with Apache Spark. This is especially useful in analytics of social media, fraud detection, and others, where data transfer rates are incredibly high. Apache also allows you to work with numerous algorithms, especially machine learning ones, SQL queries, and more, to get stuck as one more big data platform.
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The more we know about a specific object or phenomenon, the more accurately we comprehend the essence and predict the future. By capturing and processing data streams from sensors, the Internet, and transactional operations, companies can reasonably expect the demand for products and emergency services can prevent man-made disasters. Here are some examples of how big data technologies are used:
- Healthcare. More knowledge about diseases, more treatment options, more information about medicines – all this allows us to fight infections that were considered incurable 40-50 years ago.
- Law enforcement agencies. Big data is used to predict crime spikes in different countries and to take deterrent measures where the situation calls for it.
- Public administration. Exploration and analysis of big data helps governments make decisions in areas such as health, employment, economic regulation, crime and security, emergency response;
- Industry. The implementation of Big Data tools helps to increase the transparency of industrial processes and introduce “predictive production”, which allows more accurately predicting the demand for products and, accordingly, planning the use of resources;
- Retail. The development of network and e-commerce is impossible to imagine without Big Data-based solutions – this is how stores personalize assortment and delivery.
- Internet of Things. Big Data and the Internet of Things are inextricably linked. Industrial and household appliances connected to the Internet of Things collect a vast amount of data, based on the analysis of which the operation of these devices is subsequently regulated;
- Real estate market. Developers use Big Data technologies to collect and analyze the entire array of information and then present the most exciting options to the user.
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Importance of Having a Dedicated Engineer To Work With The Data Analytics Tools
At the core of analytics is turning data into insightful actions that add value to the organization. Still, the rise of structured and unstructured data, also known as big data, has radically changed the function of analysts.
While big data has expanded the opportunities available to businesses, it has also created more challenges for collecting, storing, and accessing information. This groundbreaking shift presents significant new storage requirements and challenges for analysts. It also creates powerful opportunities for discovering and implementing new strategies to develop a competitive advantage.
Realizing these opportunities requires two things: the technological capacity to collect and store big data and the expertise of analysts to turn data into insight and ultimately value.
You need to hire dedicated development team to work with data analytics tools because they know how to make the most of them.
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How to Find and Hire Such a Specialist
There are several options for hiring an engineer: in-house, dedicated team model, freelancer. We will look at all three options and analyze which one is most suitable for different businesses.
- Freelancer
A freelance engineer should be entrusted with tasks that are not core to the company. This is because such specialists work quite often remotely, which means they do not have the opportunity to fully integrate into the team, except that it also consists entirely of freelancers. We recommend resorting to the services of freelancers for all types of business. However, take into account the geographic remoteness of this specialist.
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- Dedicated team model
The Dedicated Team Model is a financial engagement business model that is increasingly used in outsourcing and software testing. A dedicated team is a model of collaboration between customers and service providers. By signing an agreement to work on the “dedicated team” model, the client gets at his disposal specialists selected, taking into account the requirements for a specific product.
A dedicated team focuses entirely on the IT solution, while the client controls the workflow and can shift their focus to high-level tasks. The service provider is responsible for recruiting new talent and administering the team. Companies often choose DTM when they need to increase the size of the group within the company, but there is no time or opportunity to hire a Spark developer and train him.
- In-house engineer
Hiring a local development team means working with people who share the same culture and beliefs. This helps employees integrate effectively into the company, allowing them to add value to their work. The decision to hire a Cisco Spark developer as an in-house engineer is beneficial for large companies with a well-developed onboarding and corporate culture.
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Conclusion
With the advent of the Internet and the expansion of computer processing capabilities, the amount of data generated has increased dramatically. To handle this massive amount of data, commonly known as Big Data, several tools have been developed to address the ever-increasing variety, volume, and data speed. Engineers who can deal with different types of data are highly valued in companies. Companies always have the opportunity to choose the most appropriate kind of work with them.
