Top Big Data Technologies – Trends in 2023

The numbers – $274.3 billion of global revenue and 440,000 related jobs in Big Data by the year 2022 – are telling that Big Data is not just another modern buzz-word, but also a big deal. Businesses elevate and enhance their performance when implementing Big Data technologies. They allow to collect, analyze, and predict many business processes.

This article will open you to the world of Big Data. Besides, you’ll know the key trends in 2023. Keep up to speed!

What Is Big Data? – 6 Key Features

Big Data is a huge volume of information, which is structured in different ways. These volumes are so extensive and complex that a traditional approach does not manage to operate with it. That is why the information can be only processed with high-load systems, i.e. machine learning ones or other advanced technologies.

6Vs of Big Data

1. Volume
2. Variety
3. Velocity
4. Value
5. Veracity
6. Variability

The first 3Vs highlight the key three features of Big Data: volume, variety, and velocity. They determine the ways the data is processed. These were the characteristics described back in 2001, presently, with the advanced progress, veracity, value, and variability also describe big data management. The volume data can be structured, semi-structured and
unstructured, which makes for its variety. Data can be processed at a different speed, which is velocity.

The last three notions demonstrate how modern businesses rely on Big Data and count on its power. That is, the value of data cannot be completely evaluated as its real impact and potential are not always completely discovered. However, when it is so, the companies using it cannot but produce high efficiency. Due to the constant analysis, they take one step further, they can predict many aspects starting with manufacturing costs and ending with customer behavior.

What is the veracity of Big Data and why does it matter especially in 2019? Data is everything now, it’s a new authority, the new power and the way businesses use it determines the success of set goals. For example, Facebook processes, analyzes and structures users’ data. The outcome shows a full picture of how a user perceives the world, categorizes the environment, sets priorities, hobbies, marital status and many more. What connection does it have with veracity? The example of Facebook data about users demonstrates what targeted and exact advertising can
be used. It also shows that Facebook’s big data is trustworthy and 100% reliable, it exactly reflects human personalities, the way people behave and think, the patterns of their behavior, the answers they seek, etc.

These huge sets of data must be compared somehow. This is where variability, or spread, or dispersion, comes handy. As we already know, the information is stored in sets. These sets are clustered in different ways, either spread out or close to one another. Variability enables to understand and then statistically prove how one cluster of data with its unique set of features and notions is different from another cluster.

Big Data is complex to process, it has a lot of characteristics that make it so hard to implement and proceed with. However, those who tackle doing it, get the best benefit.

The List of Big Data Technologies

Behind every approach and every solution in the business, there is a tool that enables specialists to drive a result. What technologies are emerging in the modern world and how are they contributing to business transformation? Let’s take a look. We divide big data-related technologies into two groups: Data Engineering and Data Analysis. Why them?

There are two ultimate goals of data application: its collection with infrastructure setting, proper formatting and generally processing it – it’s Data Engineering, and the actual act on the received information, getting insights into how the business works and make further decisions rely upon them – it’s the Data Analysis. The need to keep all the data arises and we’ve pointed out key data storage technologies as well.

Get acquainted with the most emerging and time-proven technologies of 2019.

Top 10 Big Data Technologies

1. ETL Tools or Extract Transform Load (ETL) include Talend, Informatica and serve to move data from disparate sources, move it between systems and cleanse it so that it is ready for
   analysis.
2. Apache Kafka is a high-load messaging system that helps to process huge volumes of data with a rich and flexible ecosystem.
3. Python is the language that performs similar functions to SQL. Many engineers, including our specialists who are skilled at Python development, nowadays refuse from using ETL languages to go through ETL tasks, but prefer Python for them, as it has proved to be more flexible and scalable.
4. Distributed file systems is a modern technology used to store data on more than one node. The information is fully controlled by systems, shared and accessed with ease.
5. NoSQL databases use in-memory and columnar storage technologies. Whereas the above-mentioned technologies provide data storage in a structured way, NoSQL databases specialize in processing unstructured data at a high-performance speed.
6. In-memory databases, known as RAM, is an innovative solution to store data in memory rather than on a hard drive. The technology allows to speed up data performance
7. Apache Spark and Hadoop. Apache Spark, originally coming from the Hadoop ecosystem, is now an engine that processes data at a fast speed. It includes graph processing, SQL support, and capabilities for machine learning.
8. Graph analytics is an exciting stage in big data analytics. It has especially become contributing to social networking and, thus, marketing research. Graph analysis can be run together with Apache Spark GraphX or IBM Graph.
9. Machine learning takes data analysis to a whole new level: learning from analyzed data and predicting future patterns for a particular topic. ML is based on statistical and predictive
   analysis.
10. Stream analytics is an unusual way of processing data, which is called data in motion, at a high speed. The analysis is focused on the speed and a stream of data is not stored. CEP technology, Complex Event Processing, allows to analyze streams of data in action, i.e. when events take place.

10 Areas of Big Data Expertise

What steps should be taken to make the right decisions and which specialists are required for that?

1. Data acquisition and engineering. Like anything in this world begins with something, data engineering and data warehousing are considered to be fundamental in applying Big Data
   technologies. At this stage, experts are in charge of collecting relevant data, putting it in the so-called warehouses and building a convenient infrastructure for further analysis. The specialists needed for this area are data analysts, data engineers, database developers. They must have a great command of data collecting and validating techniques.
2. Mastering data. The next stage of data deployment into business, i.e. interpreting business needs into Big Data solutions, is taken by data scientists, business analysts or statisticians. Their main goal is to transform data into structured and usable sources of information for the analysis, unleash patterns, categories and predict models for further use.
3. Cloud and distributed computing. These two types of computing refer to data distribution between different sources, which must create a single, synchronized and flawless communication through networks, servers or clouds, an ecosystem of data. The specialists – cloud architects, platform engineers – are responsible for choosing the right applications that will run flawlessly with integrated databases.
4. Database governance. Database analysts, database administrators take care of designing, integration and keeping databases while ensuring successful maintenance and running of
   systems under frequent transactions for specific business goals. They know exactly what information needs to be analyzed and what insights can improve business KPIs.
5. Business intelligence and analytics. No business will ever benefit from implementing Big Data unless it’s highly adjusted to their business needs. BI engineers, developers, analysts, and data strategists ensure that their analytics tools are kept up with business goals and fulfill them 100%.

Other possible areas of Big Data coincide with the next stages of its implementation into business:

1. Machine learning / cognitive computing development deals with building data pipelines, A/B – with testing, and benchmark.
2. Data visualization and presentation are provided by data viz engineers and developers, who are in charge of not only a graphic presentation of data flow but also introducing business and customer solutions.
3. Operations-related data analytics is about planning, focusing and dealing with opportunities that data can contribute to specific aspects of business, whether it is logistics or human
   resources.
4. Market-related data analytics is concentrated on external data that can contribute to the marketing and sales strategy performance of the company, interaction with customers and new
   market opportunities.
5. Sector-specific data analytics deals with expertise in specific domains, like Healthcare, E-commerce, Insurance, etc. Non-techies that have solid experience in one sphere might be very
   useful when it comes to data analytics applied in a specific field.

All in all, any Big Data implementation needs a certain stack of specialists with a profound understanding of both business and data management. Every stage of the Big Data application requires different specialists since work with data must be approached carefully and done right at the very first pipeline.

Modern Business Capabilities of Big Data

The possibilities to use big data are countless, they’re expanding every year so that it becomes challenging to follow all updates. What remains stable is the tendency of every business, especially enterprise, what remains stable is the tendency of most businesses, especially large enterprises, to use effective tools to elevate their performance.

Modern challenges that Big Data technologies can solve include:

• Failure analysis in real-time and further prediction or implementing stop-factors to prevent them;
• Processing customer habits, including buying ones, and together with machine learning generating decisions based on them;
• Risk management due to having processed risk cases;
• Fraudulent behavior detection and prevention of it.

These and many more challenges have been solved by Big Data for a while now. However, to avoid this vague idea of what this arising technology is actually solving, let’s showcase some of its features and how they are used in specific activities.

Product Development

As the customer has become smarter and more demanding, companies are fighting for brand loyalty. Basically, companies need data that shows preferences, expectations, and needs of customers regarding their products or services. When they have it on their hands, they can focus on producing only those services which will be in high demand among their targeted audience.

What’s the algorithm? They take classified behavior models and the key features of the previous product and compare it all to the present features. With the comparison approach, they can predict the behavior pattern and reaction of a customer to the new product and decide whether it’s worthwhile.

Customer Experience

Customers are drivers of any industry, and companies are using all possible methods to win them over. Big Data is one of the most effective ways to determine the buyer persona, determine its characteristics, preferences, real attitude towards a product or service. This technology provides the information gathered from different resources, including social media and website analytics, and contributes to improving the communication between a brand and a customer. The brand acts more confident in targeting its audience by delivering the demanded interaction and service.

Predictive Maintenance

Imagine that there is a huge manufacturer of automobile parts. Every second thousand of them are made, the production cycle is running flawlessly. However, issues occur even with the most modern equipment. The task for Big Data is to store memory of all those breaches. For instance, it keeps the temperature variations, error notifications, log entries, all changes or edits, etc. Based on the information, Big Data technologies helps to classify the issues while correlating factors and outcomes, thus, predicting the potential threats.

Security Maintenance

Possessing large volumes of data seems appealing to many hackers because again data is a new power. The large expert teams are working on breaking into the systems and distracting useful information. However, big data itself cannot exist without top-notch professionals, which means that security fraud attacks are repulsed. The reporting is provided right away and uses patterns to prevent all possible threats in the future.

Machine Learning and Innovations

Big Data is one of the reasons why everyone is on machine learning these days, since data that it provides, enables machines to be taught rather than programmed. Enterprises collect data from various sources and try to use it in the most efficient way. For instance, big data allows to investigate up-to-date trends in the domain and build up further product or marketing decisions. In this way, companies stay close to their customers, understand their demands, abide by game rules and get the highest revenues.

A big data list of opportunities for business enhancement is endless. It affects every organization, every industry it’s implemented in. See how each domain can elevate its performance due to the wise use of information.

The Big Data Impact on Top Business Domains

Huge piles of documents, patient records, prescriptions, and other information are being squeezed out by digital storage. With Big Data possibilities, now we can analyze and further predict diseases, epidemics, find cures. Doctors do not focus on the treatment of disease symptoms without a deep understanding of the causes that lead to them. With the data analysis, they are able to spot all the factors causing the disease and see the warning signs for the potential ones. Let’s take a close look at a few of the numerous possibilities that top-notch healthcare vendors have already implemented.

• Health monitoring with IoT. Patients get more involved in the healthcare system. With IoT devices and data management technologies, they can monitor their heart rates, daily physical activity, sleep quality, number of burnt calories, etc. Poor indicators very often lead to healthcare awareness and lifestyle improvement.
• Disease treatment. One of the latest big data breakthroughs is a contribution to a cancer cure. It now allows researchers to link analysis records with patient treatment ones, spot the correlation, check on how an organism responds to the medicine, etc. The data is used to improve cures from cancer and also get a better picture of the whole process. The decrease in emergency room visits. Healthcare institutions have concluded that they spend way too much money and time on the patients who get to the hospital because of emergency cases. Doctors have to figure out what’s happened, let patients go through endless analyses, investigate the cause over and over again. Instead, there could have been a single base with all patient’s records, including diagnoses and analyses.

Banking is another sphere where it is necessary to deal with huge amounts of data in the right way, as the customer’s privacy and finances are at stake. Along with big data roles to manage this influx of information, it always allows companies to do one step ahead: minimize fraud or data attack risks, provide analytics of client behavior and expectations, research competitor activity and much more.

Below there are the most widely spread applications and trends of big data in finances and banking.

• There are business intelligence platforms that give profound insights into the best and worst selling products. The base, or data, for it is all company information maintained on a repository, namely transactions and customer website activity records, social media feeds, etc. Regulatory risk management is settled by big data and artificial intelligence applications that monitor all user’s financial actions, averts them from any money laundering and suspicious financial activity.
• Personalized offers. The dream of every bank is to give loans to trustworthy clients and engage such into cooperation. That’s where data comes handy: it analyzes previous client’s records and prevents banks from giving out money to a squanderer on a short-term basis with low return on interest. Instead, banks can focus on investors and encourage them with better and personalized conditions.

While the market is becoming competitive, it is of great significance to stay one step ahead and be ready for new challenges. Top Big Data technologies help in finding more agile solutions and diminish risks in businesses of producing goods in factories. Big Data cases in manufacturing are countless, they are generally focused on production quality enhancement, time and expenses efficiency, and flaws reduction.

• Optimization. As for manufacturing processes, Big Data is a perfect approach to reduce any inconsistency, capacity, or quality flaws in production. One of the brightest examples of business transformation is Biopharma Manufacturing Co. that included Big Data analytics into every process of vaccine production, saved up to $10 million, and left competitors behind due to vast optimizations.
• Failure prediction. Forbes counted that Big Data can prevent as much as 26% breakdowns and as much as 23% unscheduled downtime cases.
• Supply chain optimization. Enterprises globally are dealing with one and the same issue – they compile data from completely different resources, that is why many organizations are trying to use Big Data to get a 360-degree view of the client, predict their needs and demands and create a fully customizable solution. The right supply chain network is one of the most important directions that contribute to the customer’s satisfaction with the brand.

Data in education can be divided into two big groups which are operated differently. The first set includes educational materials itself with books, whitepapers, interactive assignments, etc.; and the second – students’ records, blogs, forums and so on. Both of them are contributing to the onslaught of information.

Some of the examples of how big data tackles these challenges in education:

• Enhanced student performance. One of the key reasons to integrate Big Data into the studies is to analyze students’ behavior, their activity during the classes, their cognitive characteristics and adapt them to the educational challenges.

• Improved grading. Grading has been improved in different ways: 1) all students’ records are stored and have an open access (mostly), 2) systems rather than teacher are involved in grading process, which makes it more credible and flawless; 3) decreased level of stress, as students do not have to worry about being biased.
• Online learning. Education has become not a privilege but a right and opportunity for every person. Millions of students enroll every year into the online courses of universities globally. The influx of information must be handled: complex infrastructures are built in order to keep the courses going while leaving a place for an individual approach, teacher-to-student, and student-to-student interaction.

This sector is the most vividly concentrated on building the tight and emotional interaction with them, increasing brand loyalty. As in other industries, retailers are striving to a complete understanding of their customer needs and pain points, behavioral patterns, and personalities.

• Shopping analysis. When a customer searches, scrolls down, saves in shopping baskets or puts items in favorite, it helps retailers to enhance UI and UX of their websites or selling platforms, sort out items in the order we are most likely to look for and like.
• Customer services. The digital era has changed the rules of any game, now companies are fighting for 5/5 ratings from their customers, learn how to deal with negative feedback and even turn them to improve their communication with clients.
• Keeping up with customer behavior. Customer patterns of behavior are changing all the time. 2018 was a turning point in digitalization across all industries. For instance, anyone can get a product or service in one tap. Customers want faster and more quality. For a retail industry it’s a must-have feature to fulfill demands ASAP.
• Online payments. Big Data has given so many insights into human psychology, one of which is their natural unwillingness to part with money. The more a paying process takes, the less engaged and satisfied the customer stays.

For sure, we have only touched the tip of the iceberg as the possibilities to deploy Big Data technologies are countless. Even with certain challenges it may arise, businesses of different domains are
experiencing the benefits today from digital transformation solutions: increased brand recognition and customer loyalty, revenues increase and expenses decrease, optimization of all processes, better customer engagement, and, very importantly, the possibility to gain a bigger market share.

5 Big Data Trends in 2023

There are no limits for Big Data solutions, it is integrated into enterprises, companies and other organizations. In recent years, more new trends started appearing. However, it’s far more than existing technologies simplifying the business processes. BD itself has something to surprise us with this year.

1. All-in-one platforms. With Hadoop having had the prime of life, trying to be an all-in-one platform both for data processing and analysis, it has had its major downs. However, the idea to
   automate all processes while using a single system remains and will be developed during the next years.
2. IoT full integration. IoT Analytics shows that there are over 7 billion Internet-connected devices are used globally. They are all collecting priceless data which allows leaders to see
   far beyond the present state of things and implement game-changing solutions.
3. Augmented analytics. It is tightly connected with Machine Learning and Artificial Intelligence as it has merged possibilities of both to deliver even more creativity, development and
   depth into data analytics. Business analytics is benefiting already with this approach because it allows to automate more BD capabilities and generate insights better.
4. Data-as-a-service. The DaaS market is projected to reach $10.7 billion by 2023. It includes cloud-based tools used to collect, analyze, and manage data. With DaaS, companies can take advantage of big data without having to build their own data collection solutions or costly storage platforms. Using a DaaS provider is the most cost-effective and strategic way for many companies to manage their needs.
5. Data lakes and data lakehouses. Although immature, lake house technology can simplify data management, enhance security, and speed up data analysis. In terms of data lakes, Modor Intelligence predicts a CAGR of nearly 30% through 2026. The principle of “store now, analyze later” without restrictions on size, sources, and speed makes data lakes a popular trend.

Big Data is a direction that is still developing and will go through the considerable transformation in the nearest future. Silicon Valley is releasing BD technologies on an open-source basis which is
only accelerating the interest in innovations. It’s important to remember that these technologies go through major changes every year, and businesses should be prepared for constant changes.

There always should be data engineers who are keeping up with the progress and won’t let the business go down because of outdated technologies. Big Data in the modern world is synonymical to business breakthroughs, acceleration, and innovative solutions. And in the near future, it’s not only going to maintain its presence but also to evolve to unbelievable scales. Enterprises that have applied Big Data technologies are already one step ahead of their competitors and their success can only be beaten with the same approach and a talented pool.

Remember you can always rely on our experts to find the most beneficial solutions for your own business. If you have any questions about Big Data technologies or want to find out more details about our services, feel free to contact us!

FAQs

What does Big Data do?

Big Data sets help businesses make decisions based on the large amount of information they collect. For example, Big Data can show businesses their key customers, how they behave, and how they interact with the business. Big Data also often controls the supply chain of a business, defines performance standards, and assists in the HR process.

What are the Big Data analysis examples?

Examples of Big Data include customized marketing based on collected information about users, personalized health plans, improved cybersecurity protocols, predictive user behavior analytics, etc.

What are the Big Data trends in 2023?

Top big data technologies in 2023 that will benefit enterprises are the deep automation of the data value stream, the application of microservice architecture patterns to big data, and the growth of online data marketplaces. In general, top big data technologies are software utilities for analyzing, processing and extracting information from large data sets with a complex structure that cannot be processed by traditional data processing technologies. Top ten big data technologies in 2023 can be called the following:

1. NoSQL databases.
2. Data lakes.
3. Predictive analytics.
4. Prescriptive analytics.
5. Blockchain.
6. R programming.
7. Hadoop.
8. Tableau.
9. Kubernetes.
10. ElasticSearch.

Also, artificial intelligence can be added to this list of top 10 big data technologies, as it covers more and more areas and is gradually becoming not one of the technologies of big data, but a separate strong player in the technology market.