Big data combines structured, unstructured, and semistructured data that is processed, analyzed and interpreted by corporates to extract actionable insights. Using these insights, businesses can improve their operations, product offerings, marketing strategies, etc., and make them more customer-oriented.
Big data is also used for several high-tech analytical projects, including machine learning, predictive modelling, etc. This allows the organizations to identify valuable information and use it in diverse ways.
Companies follow every behaviourism and preference of the consumers. This, in turn, helps them create an influential marketing platform and better business foundation.
Companies collect data like texts, phone calls, emails, media, searches, etc. This massive amount of data is beyond the processing capabilities of traditional systems.
Smartphones, IoT devices, social media apps, search engines, etc., create tons of data daily. For instance, search engines like Google track and store your preferences in your search history and leverage it to offer personalized results. All search engines function similarly, based on the recorded user data.
To be more specific, there are three Big data applications, including -
Social Data: As the name itself specifies, this data comes from every activity, including liking, uploading, and sharing, constantly occurring on social media platforms. Web searches have also become a great source of big data.
Machine Data: Several sensors in public places, such as satellites, road cameras, etc., are also commendable sources of Big Data.
Transactional Data: All online transactions on digital platforms are credible Big Data sources.
Other sources include black box data, stock exchange data, power grid data, transport data, and more.
Some significant examples of big data are medical records, media & entertainment records, BFSI data, internet logs, search engine history, meteorological data, etc. Other than that, locations are also used to provide the consumers a better view of the geographic information, traffic conditions, weather, and more.
The use of big data is essential as the companies further use these to provide improved customer service by creating personalized marketing platforms for the users. It is incorporated into the systems of the company and further enhances the revenues and profits of the businesses and gives them an upper hand in the competition.
For instance, if you search for a specific genre of music, more suggestions for artists will pop up on your "for you" page. Or, if you search for a type of shoes from a specific website, a similar kind of shoes or website will pop up in your suggestions. This is how Big Data is used to offer a personalized space for every user.
Certified data analytics help organizations understand the needs of their consumers. It can help companies understand which products are popular among customers and which aren't. This creates an advantage for the company as they can design targeted and customized ads for different buyer personas to boost profits and customer-brand engagement.
Not only online, but big data also offers a better understanding of offline space. For example, different branches of shopping markets will not offer the same kind of products. The offerings usually differ depending on the socio-cultural aspects and customer demographic of specific locations.
The characteristics of Big Data include the 5 Vs and more Vs being added. There are numerous algorithms and processes involved in Big Data Analytics.
Big data is compressed by a wide range of data types, including structured data, financial records or transactions, unstructured data (media logs, documents, etc.), and semistructured data (web searches, streaming data, etc.). This data is cleaned, processed, organized, and analyzed by big data analytics.
Certified data analytics is so huge in number that it has to be processed through artificial intelligence, where the system observes patterns in data and gives the input as soon as possible.
The three major Vs out of the 5 are -
Volume: To understand the consumer, there should be a large volume of structured data in several environments.
Velocity: A large amount of data should be processed, collected, analyzed, and interpreted at an incredible velocity.
Variety: There should be a wide variety in the data because, without these varieties, the pattern and behaviorism cannot be predicted.
The other two Vs include veracity and value.
During the analysis process, the data holds no individual value - it is not helping you make predictions to improve overall operations. However, after the analysis, you get business-ready actionable insights that you can implement in real-world scenarios. This is big data's value.
Veracity denotes the accuracy of the big data. Companies must collect data from credible and reliable sources as inaccurate data will deliver erroneous results. Hence, it is crucial to verify the data's authenticity.
Recently a few more Vs have been added to the list, including variability, validity, volatility, and visualization. The meanings are self-explanatory.
It is imperative to learn about Big Data, especially during a time when it is growing at a swift pace. The market size of Big Data is estimated to grow to $229.4 Million soon. Big Data skills are incredibly high in demand - anyone with big data skills can get the upper hand during job recruitment.
It allows more job opportunities for those who are capable of such skills. Learning Big data widens your horizon, and the more you know, the more you grow. Learning is challenging but enhances your analytical, statistics, problem-solving skills, and reasoning.
Data-driven decisions give companies a competitive advantage, allowing them to stay on top of the latest market trends. Hence, firms look for candidates who can leverage big data to fuel value-based decision-making. Naturally, Big Data training is essential for professionals dealing with analytics. You can enroll in online big data analytics courses to understand the use of big data and big data applications.
Here are some ways to handle Big Data:
First, you must take professional big data training. You must stay updated on big data analytics applications and pursue a big data course to gain the necessary skills.
During the learning phase, it is crucial to set career goals to outline your progression graph clearly.
The next step is to understand big data's relevance and how to secure it. Since big data is highly volatile and dynamic, brands must know how to securely leverage the information without violating privacy mandates and making sensitive data vulnerable. Thus, data management and handling are pivotal. Remember that adapting to the new changes and trends is one of the biggest aspects of data management.
Learning algorithms and getting handsy with them will improve your handling of big data.
There is a vast distinction between traditional data and big data. At the very beginning, something that helps with the distinction is the 5 Vs.
Traditional data is generated per hour or daily, and different organizations, from smaller ones, maintain the collected structured data to bigger ones for business purposes. This data is processed and maintained by a centralized system. It is produced at a venture level, and its volume varies from gigabytes to terabytes.
On the other hand, big data is a more developed and higher version of traditional data. It can be structured, semi-structured, and unstructured and is processed outside the enterprise level. The range is from petabytes to zettabytes or exabytes. Its complex, massive nature makes it impossible to store it in a traditional system. Big data, unlike conventional, is generated every second.
Traditional reporting provides an analysis that is insufficient in today's fast-moving world. Hence Big Data applications act as an advantage because systems that are made generate high-quality data, which allows for better consumer behavior prediction with good sources and evidence to back up.
Here are some advantages of Big Data:
As digital footprint leaves behind patterns accumulated in big data, revealing a lot about a person's preferences. This opens up new opportunities for the organizations and helps improve efficiency, customer satisfaction, and horizons. It further opens up new avenues. For instance, shopping sites like Flipkart or Amazon use it to understand their customers' choices better, allowing them to put forward more personalized suggestions.
Big data gives organizations a better understanding of promoting products to a targeted audience during a specific time. This allows the companies to understand which customers would enjoy which campaigns, products, etc.
Big data helps in improving efficiency and provides better communication with the customers. The feedback will act as a way to enhance potential defects, allowing companies to cater to customer needs better.
Big data comes in handy in the healthcare industry as it understands a patient's medical history, allowing doctors to deliver targeted care and treatment. Big data apps can streamline the communication between healthcare professionals and patients, letting them access medical services promptly.
Big Data helps in the improvement of the customer service that is being provided by organizations and helps to create a personalized marketing space. This allows businesses to earn benefits, profits, and an advantage over the competition and helps them grow faster by making decisions that will help them work all their way up.
Big Data gives access to the companies and helps in refining and evolving in terms of marketing, advertising, and promotion, further enhancing the consumer's engagement with the company. For instance, Big Data is a major help in medical research as it helps identify the medical conditions very quickly and effectively.
Big Data modifies everyday living and helps find the best for an individual as it helps provide personalized suggestions. Most importantly, it helps the government identify a major emergency and helps prevent crimes or send help immediately in case of an emergency.
Big data works in an algorithm that is done through machine learning. There are rules, data, and models that will be set. After drawing it over the machine learning tools, computer programs, and constant reprogramming, the model will keep improving until it offers the final product that matches the desired output. This change happens due to continuous behavioural change.
For instance, if someone is buying tree ornaments, Big Data analytics will relate this to the fact that this pattern occurs t only during Christmas; hence, the sale of such ornaments is seasonal. So it would not be correct to suggest ornaments throughout the year, and the suggestions will be only applicable during that time of the year.
Effective ways of using big data:
The technology being flexible is extremely important to suit the customers' needs. To make it perfect, there will be a constant need for improvement.
Using all kinds of platforms will be necessary as the customers themselves won't stick to a specific one.
Capture all the information you can get without leaving behind anything, as each part of it will offer you some kind of information or the other. And after the accumulation of big data, you can truly understand the behavior.
Checking the authenticity of the information is very important as without authenticity it will produce a wrong idea which will bring the incorrect information earnt do any good in terms of profit.
And finally, putting it to use after a lot of consideration of the big data analytics.
Living in an era where even the smallest tools in the house are mechanical and deliver impactful services. For instance, you can order a house to switch on or off applications just by using your voice. The data generated will be further used in marketing strategies targeted at different user personas.
Big data is something that contributes to the economy on a huge scale. In this era, executing personalized marketing strategies for individual customers is extremely important. This will enhance one's business goals and create an enriching customer experience.
It is vital to improve and restructure the business models based on big data analytics results. Big data helps generate smarter, targeted consumer services, improving company ROI. Companies use big data to form strategies and build better products.
There are four types of big data business models:
Data users need data to create strategies for their businesses to build a product suitable for the users.
Data Suppliers are those who provide and trade the data.
Delivery Networks are the businesses that advertise business models.
Data Facilitators are involved in data collection, analysis, and management.
Data science can be learned by anyone but is not ideal for everyone. If one considers a career shift to Big Data Analytics, there should be an understanding of your potential and subject. One can always opt for big data analytics courses to better understand the subject. Then they can choose a career path.
It is recommended to have a brief education about big data before even going for a course, as it will be money-consuming. So before you commit, get hold of books to assess if you are suitable for Big data analytics. You can also get guidance from industry experts and counselors to better understand your career prospects in this domain.
Learning Big data and taking big data courses would be worth the money whether you choose it as a career choice or not, as it will widen your knowledge which will be a bonus to your CV. Those associated with data science and machine learning can benefit from a big data course.
Big Data very soon is going to be the future of the world. In 5 years, the big data analytics global market is expected to have the fastest growth and is said to be expanding at 16-18%. As per sources, it has also been established that people will invest in big data, and the amount will be approximately $76 Billion, and 1.7 MB of data will be generated daily.
Adoption of big data will increase across industries, further increasing prospects and competitiveness. No wonder big data specialists are always in demand. However, as more companies start using big data to create personalized services, it will also increase the dependence on AI and ML tools.
Something that marks the difference between data and Big data is the Vs. Data is a set of variables acquired through machines, structured and unstructured, and could be created and analyzed with traditional software.
On the other hand, big data is a massive amount of unstructured data that is constantly accumulated at a rapid pace. Here, conventional technologies and software do not help. This big data is so advanced that it can derive information through some of the most complex data samples.
The biggest advantage of big data is understanding the pattern and finding the interdependence of different events in the world. Companies can use different algorithms to find solutions to business challenges and innovate.
All the data that is constantly getting generated from all types of sites or documents are getting stored in what is known as the data lake. A data warehouse can support only a specific type of data, but on the other hand, a data lake can support any data, and it is vastly based on each program. For example, big data Hadoop uses a distributed file system called "Hadoop distributed file system" to store big data.
This is because of the massive chunks of data being produced. Thus, it is broken down into smaller pieces in different machines. A duplicate copy is also created - so if one file gets corrupted or if one machine fails, you will always have a backup.
The lengthy task is broken down into several smaller tasks, so different machines take up each task and complete it simultaneously. Later these tasks are put together to get the results. This makes the process easy and fast, and this way of storing is known as parallel processing.
But several algorithms and systems are put work together to store big data. For instance, the data lake is connected with several other platforms like data warehouses. All this data that is getting accumulated is then organized, analyzed, and managed.
Then through data management, it gets processed depending on how it will be used. To generate and store Big Data, there are several minute details in the infrastructure that must be taken into consideration.
Several scenarios could involve data loss, including deletion, formatting, corruption, crashing, or failure. But advanced technologies have made it easier to retrieve it back almost immediately. Systems like data recovery software are built to make it easier to bring the data that has been lost. Cloud is an effective location from which data can be stored and retrieved.
Big data plays a huge role in our day-to-day life in a fast-changing life heavily packed with technology. It heavily impacts our lives, especially in a day when technology has grown so advanced.
Big data simplifies numerous aspects of our daily grind, giving us more time to work on avenues that thrive on human intervention. For instance, big data allows professionals to tailor-make products and marketing campaigns to expand customer outreach by automating user data tracking and analysis.
Big Data could be used to stop disasters by taking measures as early as possible. It allows predictions of disasters like hurricanes. Such valuable predicaments can help minimize losses and save lives.
The Big Data ecosystem is the understanding of the system components with tools that enable the capabilities of big data of storing and managing and use it for the advantages of several platforms.
The different processes in the big data ecosystem include:
Proposing a platform for big data
Harvesting for cloud storage
Analyzing the data using several applications and algorithms to get the maximum optimization
The big data ecosystem consists of:
Big Data Analytics
Data scientists are big data specialists and analytical experts with specialized skills in gathering, analyzing, and managing data. Their computer science, statistics, and mathematics expertise gives them a better hold in understanding and correctly using data.
Their works include forming information from complex, unorganized, unstructured data derived from sources such as emails, media, documents, and many more, including social apps. They are offered the crucial job of cleaning, processing, storing, and forming the best form of data. These data are ultimately what is used for the development of a country in terms of business.
The demand for data scientists has risen the most, and they are high in demand. Those accustomed to the big data course get more priority in terms of job applications.
Python is a programming language used in web and desktop data science applications that is taken by choice. It is recommended and needed for working science and machine learning to bring out the best project outcome. According to sources, Python has been and still is one of the most famous and fastest-growing projects due to its focus on simplicity and readability.
Not only that, it's an easier way of learning that makes it usually highly convenient for beginners. Python provides the learners with the advantage of accomplishing the tasks with fewer lines of code which makes it different from other older languages.
Reasons Python is a popular program:
Due to its open-source software nature, Python is easily accessible.
It is a highly interpretable programming language, and anyone mastering it can handle huge data volumes.
The code errors are easily detectable and detailed, making them easier to fix.
It is easy to write in the Python programming language.
It offers top-notch speed, a straightforward design goal, choices of libraries, and high-end visualization and graphics.
It helps in performing repetitive tasks and data manipulation.
The eCommerce sector uses big data to modify the customers' engagement by offering customized and personalized suggestions and increasing the visibility of the products' information. It analyzes a customer's preferences, and after a lot of tailoring, it prepares and enables a better understanding.
For instance, after buying clothes from a site, your clothes' size will be registered as the data after a certain point. So that the next time you purchase something, they will automatically recommend the size. It will try to prevent any errors or potential to ruin a customer's purchase and instead will help suggest the right page for the right customer.
Big data allows retailers to understand the requirements of their customers and form a bridge of an excellent seller-buyer relationship. Big data helps eCommerce companies understand the needs of different buyer personas. Consequently, they can design customized products and customer support plans.
Cost treatments have seen rapid growth in the past few years and have made it hard for those who are not at a great point financially. Big Data has the potential to reduce these costs and streamline healthcare delivery in myriad ways. For instance, there have been times it is difficult to go out and get yourself checked in a time when you are seriously ill. You can connect with your physician via a big data healthcare app and get the necessary treatment done remotely.
Big data in healthcare can offer customized recommendations for each individual after considering their medical history and requirements. Big data apps can also alert patients during medical emergencies, such as substantial fluctuations in their vital stats, letting them take charge of their health. You can leverage big data apps to track health issues, medicine assumptions, weight, etc. This way, big data can eliminate the need for physical visits for every minor issue.
Big data can provide compelling and more accurate decision-making insights into banking and financial institutions. The digital landscape offers a platform for easy transactions. Big data applications arrange revolutionize business strategies and help an individual business flourish.
Also, it makes a great analysis of stock prices and products depending on the stock market trends. It analyses and generates appropriate data to make a more thoughtful decision. It enables fraud detection, thereby eliminating security risks. Big data arranges safer and secure banking from the luxury of your home, and your information remains safe.
It prevents the information from getting stolen as it manages to make an accurate risk analysis. Financial firms have the leverage to use the data in generating and delivering personalized and more efficient customer service. It also speeds up the process and offers improved purchase management. Big data helps in advertising and promoting these firms.
Big data makes it efficient in terms of transportation and logistics. As per the source, there has been a rise of 91.6% in the investment in big data by transportation and logistics companies. The benefits of big data include easier delivery tracking.
Every step during the tracking is visible to the parties on both the receiving and sending ends. The logistics process involves moving products. So, there's always a risk of products getting destroyed and lost. Big data allows maximum optimization of sensors that can give information about the products' whereabouts. It also reduces cost expenses and helps in delivering goods of good quality.
In terms of transportation, big data helps predict certain events like weather forecasts, holidays, and the busiest times of the year and helps analyze the pick time when there might be a need to increase customer services. For instance, during Christmas, there has been a rise in travel, so the demand for transportation increases. Knowing about the growth beforehand allows transportation companies to take measures earlier.
Big data also helps the customers get a more personalized recommendation and shows all types of contrasting products from which the customers can choose. It assists these customers in choosing the most convenient geographic routes out of several options.
There are several people eager to learn about big data on the side while doing another job or pursuing another major. Also, some professionals want to pursue big data courses to upskill while maintaining a work-life balance. A big data course online makes it more convenient for them than going to campus.
Here are some advantages of an online big data course:
Personal attention: Something not found in an offline course is personalized guidance. The teacher's attention is often divided in the classroom, and individuals do not get the necessary guidance. On the other hand, if it is online, the course will be more customized, and each student's level of competence is considered.
Convenient: As established before, it is more convenient regarding time, travel, and even expenses. Going to the campuses will add to the traveling expenses, and online courses are devoid of such costs.
Attaining more than one course at once: For those who want to keep their other options open, online courses become the most helpful as they can do two courses side by side.
Interactive: Some learners find it easier to respond and interact when not in front of a huge group. Online courses could slowly enhance their public speaking as they learn and be comfortable in front of just their mentor.
Location: Online courses allow you to take the course from any part of the planet possible and, as a bonus, allow you to meet new personalities from different parts of the world. This will also give better knowledge from all sorts of personalities working in different fields and make the learner come in contact with various mindsets
Big Data Course Syllabus includes
Big data training has seen the face of an accelerating demand worldwide as it is believed it is the present and the future of the world. The benefits that are being provided by big data are very much needed in today's competition to be at the top. India is also one of the countries whose future will be Big Data, and companies are investing in it. But to fulfil the demand, there is a need for more big data specialists; hence, the courses become handy.
Here are the reasons why there soon will be an increase in demand for big data analytics courses in India:
Analytical professionals are in high demand. Several jobs offer high-end positions for those who specialize in big data. The reason is quite apparent: every company would like to have the upper hand in the competition, and big data provides that. Hence this demand will result in more and more people interested in big data.
Salary aspects are another major reason for the demand. There will be companies that will offer a better salary to those with knowledge of big data. In contrast to the other jobs related to data, people would want to learn about big data to get a higher position if this provides more salary.
Big Data has become one of the top priorities for several well-known companies, and it has been made compulsory to specialize in that to get a position in the company. It is now being used almost everywhere, so it is only relevant to use it for competition by big well-known firms.
Competition in the international market also plays a big role. At the international level, top-notch companies will use big data. So, to pursue one's business at the international level, the use of big data becomes compulsory.
All around the world, the salaries of Big Data Specialists vary from country to country and their economy.
As per sources, the chart below gives an introductory account of the average salary of a big data specialist overseas.
Salary approximately (per year)
The major factors on which salary of a Big Data specialist abroad depends on
Talent and Skills
Companies give utmost importance to skills. The two words are interlinked as your talents are the key to your skills, and it helps in shaping them.
The knack for the subject will automatically bring your thirst for knowledge. Knowledge is never-ending, so the more you learn, the more you know, and there will be space for more.
Experiences help make better decisions and not repeat the same mistakes, resulting in a better outcome.
Similar to knowledge, the practice has no end, and the more practice, the better your skills. The talent and skills are there, but practicing sharpens those and gives you a better version of them every moment.
Average Salary Hike
Restaurant finder is a basic application that finds restaurants on the basis of their names and shows their details such as timings and menu.
True Value Seller is a static car selling and reselling website. It showcases different promotions and has a ‘Contact Us’ component.
Mobile Cart is a simple frontend application which allows authorised users to add different mobile phones and their respective information on a website which can then be viewed by different users.
The project aims to create a Phone Directory application which allows a user to add subscribers to a service by entering the subscriber’s name and phone number; and delete the subscriber if necessary.
With this application, which is named BookMyMovie, users can browse upcoming and released movies;
HireWheels is a car rental service application.
Big data is unstructured, semistructured, and structured data that gets generated and processed every second someone uses a smartphone and invests their time in things like messages, media, phone calls, social apps, etc. Organizations then analyze and manage this data to offer better performances and services to their customers. It allows the creation of a more individualistic platform for every targeted audience.
The five Vs that big data consist of are -
Volume: The amount of the data that is getting accumulated
Velocity: At what speed, a certain amount of data is getting accumulated
Variety: The variety in the data, including structured, unstructured, and semistructured data
Veracity: The accuracy of the data
Value: The part of the data that brings profit to the businesses
Hadoop is one of the programs, and it and Big data go hand in hand as they almost are equivalent to each other. Hadoop is a big data specialized operation that has been recently on the rise and moving forward towards massive popularity.
Big data training is important for getting a better job opportunity and contributing to a company's growth as it helps identify a customer's patterns which will result in profit. Hence, a certified data analytics course is recommended because extra knowledge never hurts; instead, it will land more job opportunities.
The analysis produced by big data enables businesses to create specific products. These products are highly dependent on the needs of each customer. This is possible because big data allows the company to follow the pattern of a customer, which helps them predict their preferences. It enhances the relationship between the retailer and the customers. This increases sales and benefits them in spreading good words about their business.
Artificial intelligence is rapidly taking over our surroundings, thanks to rapid adoption. Human errors are avoided when AI is used. Traditional data was easier to process and could be done by humans. But the complex, massive and high-velocity datasets that are big data cannot just be done traditionally, adding to the influence of AI's decision-making. Due to its size, it can only be processed and analyzed for information in high-tech AIs.
The distributed computing system is the only way to help store such an enormous amount of data. If there were no concept of a distributed computing system, organisations would be forced to use computers with individual memories or would need to build a computer with enormous memory space capable of processing an exhausting amount of data. It will not only cost way much to make such a device but would not be practical to practice. Hence, most companies and individuals would have been deprived of utilising Big data.
The laws regarding data mining are different in different countries. The countries where government states that any individual or organisation with lawful access to material protected by copyright has the authority to carry out analysis without asking for permission from the copyright owner. Hence, before trying to mine data, you must have a sound understanding of the respective country’s legislation.
A handful of reasons contribute to projects utilising the power of big data failing. Some main reasons include collecting the wrong data, not defining the problems they aim to solve, not having realistic goals and objectives on how to utilise the data, and lack of right talent for skilful data analysis.
No. The major similarities between data warehouses and Big data are that they hold enormous data, are useful for reporting and are managed with the help of electronic storage devices. However, both have some significant differences and purposes. The primary difference is that the big data solution is technology; however, data warehousing is an architecture. Hence, it is evident that none of them can replace each other.
Big data is undoubtedly one of the fastest growing fields and holds exciting global opportunities. Opting for a career in Big data can offer a handsome salary. The average salary of a Data Scientist is around $100,000, and there is also a wide range of job roles in the field to choose from.
Yes, coding is one of the top skills required for a Big Data analyst. A Big Data analyst needs to know coding in order to perform numerical and statistical analyses of huge data sets. Some of the programming languages in which you may invest your time and money would be Python, Java, R, and C++. If not all, you must learn Python, as it has a built-in feature of supporting data processing of unstructured types.
Companies and big organisations utilise distributed file systems to store the unstructured type of data. This file system further helps divide the large files into categorised data blocks. These data blocks are further distributed amongst the cluster nodes. The more similar blocks are formed, the number of nodes decreases. Organisations often use data lakes as well to store big data.
Big data is currently being used in different industries such as healthcare, education, gambling, environmental protection, etc. apart from them. Big data is also used in different government sectors and entertainment industries as well. Different companies in these industries use big data to identify patterns and trends from the explosion of data.
Numerous free data sets available on the internet can be used for personal analysis. Big companies usually prefer to collect their own data and curate unique data sets from them. However, you can also check free sources such as Socrata, Google Trends, Facebook Graph, etc., to find data sets.
Big data technologies that are high in demand in 2022 include Artificial Intelligence, Machine Learning, SQL-based technology, Apache Spark, and more.
Frameworks based on the MapReduce model provide the most scalability for big data storing and processing. An example of such a framework is Apache Hadoop. It is also an open-source framework which is why it is even more prevalent.
There are several databases to store big data. The most popular ones include:- AWS DynamoDB. Azure Cosmos DB is best suited for operational workloads, IoT, social media, etc. Best for operations management, eCommerce and gaming, and Amazon Document DB, best for storing user profiles, catalogues, and managing content.
Both data science and big data carry different objectives. If you plan to build a career utilising statistical and predictive analytics, then you should go for data science. However, if you wish to strengthen your skills in using Hadoop, R, and Tableau to curate BI reports, you should go for big data. On that note, data science jobs offer better salary packages and greater promotion opportunities.
A Synapse is a tool that multiple experts suggest as the best alternative to integrating PBI for very big data streaming. However, they also believe that the prices are significantly high compared to the size of data it streams.