As time goes by, it has become essential to use and analyze data efficiently. Thus, the concept of Big Data came into being, which is one of the most promising technologies of this decade. Today, Big Data has become the primary focus of technologists and data analysts. They collect Everything You Should Know about large amounts of data and form charts and reports for easy access to the data. Therefore, learning about Big Data in the field of technology has become very important.
No doubt, everyone wants to learn about Big Data tools and techniques. As this term is used freely by everyone without proper understanding of what it is and how it can help us. In this blog, we will discuss various aspects of Big Data in detail.
What is Big Data?
To learn about big data, it is overseas chinese in canada data important to understand the meaning of the term “big data”. The term “big data” may raise a question: how is it different from the word “data” we usually use. Data is the characters or symbols in their raw form that computers can store or transmit in the form of signals and record on media. However, raw data is worthless if it is not processed.
By definition, big data refers to large amounts of unstructured data generated by business processes. It is usually massive data from websites, transaction records, emails, etc.
Categories of Big Data
Big data can be well-organized, unorganized, or semi-organized. Based on the form of data stored, data can be divided into three forms:
1. Structured Data – Data that is accessed, processed and stored in a fixed format or form is called structured data. An example of this form of data is a table called “Students” which stores different fields for different students and the data is presented in the form of rows and columns.
2. Unstructured Data – Data without any structure or specific form is called unstructured data. It is difficult to process and manage unstructured data. Examples of unstructured data could be data sources containing images, text, videos, etc.
3. Semi-structured data – This type of data contains a combination of structured and unstructured data. It has a structured form but is not as clearly defined as a table. Examples include data in XML files.
Characteristics of Big Data
After understanding the basic concepts, it is time to study the characteristics of big data. Its main characteristics are defined by 5 “Vs”, which are volume, velocity, variety, veracity and value. Let’s understand what these terms refer to:
1. Volume – It refers to the huge volume of data, which determines the size of the data. The scale determines whether the data can be called “big” data.
2. Velocity – It refers to the speed at amazon sales decline and the search for reasons which data is generated. It reflects how quickly data is generated and processed for analysis.
3. Variety – It means the heterogeneous nature of the related data. Today, data comes in different types like photos, videos, emails, audios, etc.
4. Variability – This refers to the inconsistency of data, which affects the way we can effectively manage or process data.
5. Veracity – It involves the credibility and messiness of the data. Since big data exists in different forms, it becomes particularly important to control the accuracy and quality of the data.
The advantages of big data in business
With the rise of new digital fax database trends, there have been many changes in consumer behavior in the industry, generating massive amounts of data. That is why every company wants their employees to learn big data so that they can make use of this data. This will help them gain insights and information about consumers, thereby helping the company grow.
The question is, what factors make enterprises inclined to use big data? Here are some of the key benefits that big data provides to enterprises today:
1. Save time – Big data technologies like Hadoop help in quickly identifying data sources and analyzing them. This enables businesses to make decisions quickly and in a timely manner.
2. Cost Savings – Big data technology helps save costs by efficiently storing massive amounts of data. So, if you learn big data, it will help you demonstrate your cost-effective data management skills.
3. Customer Service – It helps in building better feedback systems and effectively evaluates customer feedback. It enables people to properly manage customer interactions both online and offline.
4. Consumer Insights – Big data analytics tools can highlight new consumer insights. This information can help in creating and developing new products for the market.
5. Relevance and Trustworthiness – Web analytics using big data helps in understanding relevant data. Customer monitoring using the latest technology is now more reliable and trustworthy.
6. Security – Big data technology is a reliable choice for data analysis in collaboration with high-tech partners and better infrastructure.
Learning from Big Data: Key Facts
There are some facts about big data that can help you better understand this technology. These facts cover relevant aspects that enterprises should consider when formulating strategies for implementing and adopting big data technologies.
1. Big data is everywhere
In this highly digital world, big data is everywhere. The Internet of Things (IoT) has given rise to new data sources. Every object is digital today, and with these objects, new data is constantly flowing into businesses. The massive amount of data we generate and acquire every day is big data. There is no industry that has not been affected by big data, so it becomes important to learn about big data. It is crucial for businesses to realize this and use this data to their advantage.
2. Big Data Culture
IT giants must understand that adopting big data technology is a cultural shift. To make the business data-driven, there will be strategic and operational changes. Only with this cultural adaptation can employees make better use of data. To learn big data technology, we need to be mentally prepared to work with large data sets.
3. The role of people in big data
People are the core element of implementing big data technology in an enterprise. Data management strategies can only be implemented if the people in the enterprise learn big data technology and are ready to formulate strategies based on it. Therefore, it is important for enterprise employees to learn big data skills.
4. Demand for Big Data Engineers
There is already a shortage of big data engineers, and it is predicted that this shortage will increase. As enterprises rapidly adopt big data technologies, the demand for trained professionals has also emerged. In large companies, they want existing employees to learn and train big data technologies, and they also hire experts from outside.
5. Funding and investment in the field of big data
There has been a huge growth in the funding available in the field of Big Data. Many venture capital firms are investing in startups around the world. Governments are also investing R&D funds in this field. Therefore, if you study Big Data, there are countless opportunities in this field.
However, there are some problems when utilizing big data. Statistical work for data analysis should be done with caution as the data can be misleading. Misreading or misanalysis can give a false insight into the data, which can lead to wrong decisions.
Big data solutions come with high expenses and proper budget allocation is necessary to get the appropriate return on investment. Implementing these solutions requires adaptability. Existing systems should be properly connected with the latest systems to achieve efficient utilization.
Today, companies generally want their employees to learn big data technology because of the many benefits it can bring. This is not only about the amount of data collected by the company, but also about how the company uses this data to analyze and make decisions.
The hottest big data technologies
Companies are investing heavily in big data technologies, and the big data market continues to grow. Big data and analytics have now become mainstream in the information technology sector. The largest growth in investment is concentrated in the banking, insurance, investment services, and healthcare industries. The most commonly adopted technologies include data analytics and its application in risk management, fraud detection, and customer service. Here are some of the popular technologies:
1. Hadoop Ecosystem
Apache Hadoop is the most popular and widely used big data technology in the world. The number of products based on Hadoop is increasing, and there are many vendors supporting the Hadoop ecosystem. If you want to learn big data, starting with Hadoop is a good choice.
2. Apache Spark
Spark is another part of the Hadoop ecosystem that can be used independently everywhere. Spark is the processing engine for big data in Hadoop, and it is faster than Hadoop’s engine. Hadoop vendors also support products based on Spark.
3. NoSQL Databases
These are special databases designed for unstructured data usage and storage. Common databases are MongoDB, Cassandra, etc. They are known for their fast performance.
4. R software
R is an open source programming language specifically designed for statistical analysis. This software environment and language are popular among data scientists due to its user-friendly integrated development environment (IDE).
5. Predictive Analytics
This technology involves the use of data mining, modeling, and machine learning to predict future behavior or events. It has a wide range of applications in marketing, finance, credit scoring, fraud detection, and more.
6. Prescriptive Analysis
This part of data analysis helps to provide recommendations to the business on what and how it should do to achieve the desired results.
7. Data Lake
Enterprises are creating large repositories that collect data from different sources and store it in its natural state. These are data lakes. They allow enterprises to store data as they use it.
8. Artificial Intelligence
Artificial intelligence has become viable in the past few years. Data analytics, deep learning, and machine learning are now part of the AI landscape. The use of analytical tools in AI is inevitable, and its applications continue to grow.
9. Big Data Governance Solutions
Data governance has become extremely important due to today’s security issues. This covers processes such as data integrity, availability, and accessibility.
10. Big Data Security Solutions
As enterprises increasingly adopt big data, it becomes imperative to protect data repositories from hacker attacks and other threats. This has led to an increase in the demand for data security solutions.
11. Blockchain
It is the technology behind the Bitcoin digital currency and functions as a distributed database. The unique thing about blockchain is that once data is written to the database it cannot be deleted or altered.
Popular big data tools on the market
There are many tools available in the market today that are worth knowing. If you want to learn about big data, you should have a good understanding of big data tools. These tools are widely used in enterprises to achieve efficient data analysis for cost-effectiveness and time-saving purposes. They are as follows:
1. Hadoop. Apache Hadoop is the most popular tool and is often used interchangeably with Big Data itself. Hadoop is a Java-based open source software framework for distributed storage of large data sets on a cluster. It provides scalability for data sets and fault tolerance for hardware. Hadoop is the best tool for storing all kinds of data and handling concurrent tasks as it facilitates processing of structured and unstructured data.
2. Hive. Apache Hive is another popular big data tool that helps query and manage large data sets. It supports a query language for data modeling and interaction. Programmers to analyze data sets using tasks defined in Java and Python. It is only used to query structured data, but it reduces the complex programming of MapReduce for users.
3. Storm. Apache Storm is an open source tool for real-time data stream processing. It is a distributed fault-tolerant system with real-time computing capabilities. Storm uses parallel processing across a cluster of machines and is considered one of the easiest big data tools to use.
Why is a career in Big Data the best choice in the industry?
As we can see, today, big data technologies and tools are becoming more and more popular and developing rapidly, and IT engineers are more and more interested in learning big data. In a few years, there will be about 2.7 million jobs in the field of analytics and data science. Enterprises have begun to adopt these technologies at a rapid pace, which has also created a demand for related talents. In the coming period, big data career options will prove to be the best move in the market. Here are the reasons:
1. Strong demand
Big data analysis is the hottest job in the market today. Although the demand is huge, the relevant talents are scarce. Therefore, it is easier for engineers with relevant expertise to find a good job.
2. Good salary and benefits
If you learn big data, it will add big data expertise and skills to your resume, and you can get quite a high salary package. Today, big data jobs are considered to be one of the high-paying jobs. The competition for positions such as data engineers, data scientists, and data architects in the IT field is getting increasingly fierce. So, learning big data can get you the job you have been waiting for.
3. Opportunities from well-known companies
Multinational companies like SAP, IBM, Microsoft, Oracle, etc. are hiring big data professionals in large numbers. Experienced professional data scientists and experts can get excellent development opportunities in these big brand companies.
4. Multi-field and multi-industry
Big data analytics is becoming increasingly popular in many industries including healthcare, media, education, retail, manufacturing, etc. These industries now commonly utilize quick decisions and effective solutions, thus providing job opportunities in multiple fields.
5. New learning opportunities
The field of big data opens new doors for you to explore the potential of other fields such as marketing, finance, business intelligence (BI), etc. You can learn big data skills such as data mining, data visualization, data infrastructure, etc. to further enhance your professional capabilities.
Big Data Employment Trends
The big data market has grown tremendously in the past few years and is still growing rapidly. The job market in the field of big data will grow significantly in the next few years. This growth will be reflected in all big data related positions. So, if you choose to learn big data, you will have a lot of job opportunities to start a big data career. In 2020, the annual job demand for data engineers, data scientists, and data developers will increase by as much as 700,000 job postings.
The analytical skills considered most attractive include machine learning, MapReduce, Apache Pig, Hive, and Hadoop. Jobs with all of these technologies command high salaries. Data scientists and analytics professionals with Apache Hive, Pig, and Hadoop skills can command salaries as high as $100,000.
Across the data science and analytics (DSA) space, 59% of jobs are concentrated in the IT, finance and insurance, and professional services sectors. Finance and insurance account for 19% of jobs, followed by professional services at 18% and IT at 17%. Jobs that require machine learning, data science, and big data technology experts are the hardest to fill. This leads to extra effort on the part of recruiters and the need for training programs for existing talent.
The positions with the highest growth rates are senior analysts and data scientists. Data scientist and analyst positions are also the most difficult for employers to fill. Employers pay much more for these positions. About 39% of senior analyst and data scientist positions require candidates to have a doctorate or master’s degree to qualify for these demanding jobs. Experienced candidates can command higher than typical salary levels.
Job titles under the umbrella term “Big Data Professional”
“Big data professionals” is a general term used for all professionals who work with data science, data tools, and technologies. Due to the complexity of big data technology, these job roles can be confusing. Therefore, it is important to understand what each job or position title is and what the corresponding responsibilities are.
1. Data Engineer
Data Engineer is a common job title in the field of Big Data. They are responsible for the design and implementation of data infrastructure. They play a vital role in managing the Big Data ecosystem. Engineers need to focus on the Apache Hadoop ecosystem, the Spark ecosystem, and various databases.
2. Data management professionals
This is a key position similar to the role of a database administrator (DBA) in the information technology field. Data management professionals are responsible for managing structured and unstructured data and the corresponding supporting infrastructure. Experts in this role are essential for enterprises to establish big data infrastructure.
Key skills required for this position include Hadoop-related query languages such as Pig and Hive. Data management professionals need to have knowledge of non-relational databases (NoSQL), structured query language (SQL), relational databases, and Apache Spark and Hadoop.
3. Business Analyst
This is a position responsible for data analysis and data presentation. The duties of a business analyst include creating reports, dashboards, and business intelligence related work. This position also involves interacting with big data frameworks and databases. For a business analyst, it is important to have knowledge of BI packages and reporting solutions.
4. Data-driven professionals
Data-oriented professionals (true data scientists) have expertise in data and the tools used to analyze it. They need to understand statistics, data visualization, and aspects of programming languages such as R, SQL, Python, etc.
5. Machine Learning Practitioner/Researcher
These positions handle statistical analysis of data. They perform predictive analysis and use correlation tools to analyze existing data. Statistics is key to this position. Other skills include algebra, calculus, machine learning algorithms, and programming skills.
Summarize
The future of the information technology world and the tech market lies in big data technology. No industry can grow without utilizing big data tools and technologies. Not to mention, with the increasing demand for big data implementation and data analysis, the demand for relevant talents is also rising. Professionals can gain a lot in their careers if they learn big data technology. Therefore, big data is becoming a part of changing the world we live in today.
