Big Data applications are no longer a thing of the future – they are here and are steadily gaining steam globally. In this blog, we will explore different types of Big Data technologies and how they are driving success across industries.
Introduction to Big Data
In the digital era, businesses generate and encounter large quantities of data on an everyday basis. “Big Data” is essentially a term used to describe this massive collection of data that exponentially increases with time. It is now imperative for companies to adopt smart data management systems if they want to extract relevant information from the vast and diverse stockpile.
According to Gartner, Big Data has the following characteristics:
- It is high-volume and high-velocity.
- Contains a huge variety of information assets.
- Requires cost-effective and innovative forms of processing.
- Enhances decision making in organisations.
Today, we are witnessing a new crop of big data companies that are utilising emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) to move beyond the conventional tools of management. Let us understand their reasons for doing so.
Demand for Big Data
Big Data Technologies refer to the software solutions that incorporate data mining, sharing, visualisation, etc. They embrace specific data frameworks, tools, and techniques used for sorting, examining, remodelling, analysing, and so on. In the internet age, having such capabilities can considerably improve business performance.
Based on their usage, big data technologies can be categorised into operational and analytical technologies. The former includes data generated by a firm on a daily basis, such as from online transactions, social media, etc. Online purchases from eCommerce platforms (Amazon, Flipkart, etc.), online ticket booking for flights and movies are some real-life examples. This data is further fed into analytical big data technologies to gain insights for critical decision-making. Complicated data from the domains of stock markets, weather forecasting, and medical-health records come under the purview of analytical technologies.
Modern-day data analytics companies require specialised staff for working on data-management tasks. According to a recent NASSCOM report, the current demand for qualified and technically adept professionals outstrips the supply of industry-ready talent. Nearly 140,000 people represent the “skill gap” in the broad Big Data space. This also highlights the underlying opportunities in equipping the IT workforce with the knowledge and practicalities of Big Data applications. IT professionals having a good grasp of data science can find lucrative employment in healthcare, the automotive industry, software development, and eCommerce, among many other spheres.
With this perspective, we have explained some leading technologies for you below. Read on to clarify your doubts and discover which areas you should consider for upskilling.
Top 10 Big Data Technologies in 2023
1. Artificial Intelligence
Artificial Intelligence (AI), along with augmented technologies like Machine Learning (ML) and Deep Learning, is spurring a shift not just in the IT landscape but across industries. It is an interdisciplinary branch of Computer Science and Engineering that deals with building human capabilities in machines.
The applications range from voice-based assistants and self-driving cars to accurate weather predictions and robotic surgeries. Moreover, AI and ML are powering business analytics in a way that the organisation can innovate to the next level. The greatest advantage lies in staying ahead of the competition by identifying potential problems that humans may overlook. It has, thus, become pertinent for software professionals and IT project managers to be aware of AI fundamentals.
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2. SQL-based Technologies
SQL stands for Structured Query language, a computer language used for structuring, manipulating, and managing the data stored in databases. Knowledge of SQL-based technologies like MySQL is a must for software development roles. As organisations grow beyond querying structured data from relational databases, practical skills in NoSQL databases arise to facilitate faster performance.
Within NoSQL, you can find a wider range of technologies that can be used for designing and developing modern applications. You can deliver specific methods for accumulating and retrieving data, which would be further deployed in real-time web apps and Big Data analytics software. MongoDB, Redis, and Cassandra are some of the most popular NoSQL databases in the market.
3. R Programming
R is open-source software that assists in statistical computing, visualisation, and communication via Eclipse-based environments. As a programming language, R offers an array of coding and pacing tools.
Data miners and statisticians mainly implement R for data analytics. It enables quality plotting, graphing, and reporting. Additionally, you can pair it with languages like C, C++, Python and Java, or integrate it with Hadoop and other database management systems.
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4. Data Lakes
Data Lakes are consolidated repositories of structured and unstructured data. During the process of accumulation, you can either save unstructured data as it is or execute different types of data analytics on it to transform it into structured data. In the latter case, you would need to utilise dashboards, data visualisation, real-time data analytics, etc. This would further increase the chances of gathering better business inferences.
Nowadays, AI-enabled platforms and microservices pre-integrate a lot of the capabilities required for data lake projects. Data analytics companies are also increasingly applying machine learning across new data sources of log files, social media, click-streams, and Internet of Things (IoT) devices.
Organisations that take advantage of these big data technologies can better respond to opportunities and advance their growth through active involvement and informed decisions.
5. Predictive Analytics
Predictive analytics is a sub-part of Big Data analytics that predicts future behaviour and events based on previous data. It is powered by the technologies like:
- Machine learning;
- Data modelling;
- Statistical and mathematical modelling.
Formulation of predictive models typically requires regression techniques and classification algorithms. Any firm deploying Big Data to forecast trends needs a high degree of precision. Therefore, software and IT professionals must know how to apply such models to explore and dig out relationships among various parameters. When done right, their skills and contributions can significantly minimise business risks.
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Hadoop is an open-source software framework that stores data in a distributed cluster. To do this, it uses the MapReduce programming model. Here are some hadoop important components that you should know about:
- YARN: Performs resource management tasks (for example, allocating to applications and scheduling jobs.)
- MapReduce: Allows data to be processed on top of the distributed storage system.
- HIVE: Lets SQL-proficient professionals perform data analytics.
- PIG: Facilitates data transformation on top of Hadoop as a high-level scripting language.
- Flume: Imports unstructured data into the file system.
- Sqoop: Imports and exports structured data from relational databases.
- ZooKeeper: Assist in configuration management by synchronising distributed services in the Hadoop environment.
- Oozie: Binds different logical jobs to completely accomplish a particular task.
6. Apache Spark
Spark, a real-time data processing framework, is another must-know tool for aspiring software developers. It has built-in features for SQL, machine learning, graph processing, and streaming analytics. Some use cases include credit card fraud detection systems and eCommerce recommendation engines.
Also, it can be easily integrated with Hadoop to perform quick actions depending on business needs. Spark is believed to be faster than MapReduce in data processing, making it a favourite among data science professionals.
Speed is a top priority for enterprises looking to harness Big Data. They want solutions that can gather input from disparate sources, process it, and return insights and useful trends. The urgency and immediacy of the need have prompted interest in technologies like Streaming Analytics. With the rise of IoT, such applications are expected to grow even further. It is also likely that edge computing (systems that analyse data close to the source of creation and reduce network traffic) will witness higher demand in big data companies.
7. Prescriptive Analytics
Prescriptive analytics is concerned with guiding actions towards desired outcomes in a given situation. For example, it can help companies respond to market changes like the emergence of borderline products by suggesting possible courses of action. This way, it combines predictive and descriptive analysis.
Prescriptive analytics is one of the most sought-after Big Data technologies in 2023 as it goes above and beyond data monitoring. It emphasises customer satisfaction and operational efficiency, the two cornerstones of any 21st-century enterprise.
8. In-memory Database
It is crucial for data engineers to thoroughly understand database design and architecture. That said, it is equally important to keep up with the times and try out upcoming technologies. One example is In-memory Computing (IMC), where many computers spread across multiple locations share data processing tasks. Data can be accessed instantaneously and at any scale. Gartner estimates industry applications to exceed the $15 billion mark by the end of 2023.
We can already see IMC applications flourish in the healthcare, retail, and IoT sectors. Companies like e-Therapeutics are using it for network-driven drug discovery. Whereas online clothing companies like Zalando have been able to attain flexibility in managing increasing data volumes with the help of in-memory databases.
Blockchain is the primary technology behind cryptocurrencies like bitcoin. It uniquely captures structured data in a way that, once written, it can never be deleted or changed. This results in a highly secure ecosystem, which is perfect for Banking, Finance, Securities and Insurance (BFSI).
Apart from BFSI, blockchain applications are gaining prominence in social welfare sectors like education and healthcare. So, software professionals with advanced knowledge of database technologies have a wide range of options available.
With this, we have briefed you about some leading Big Data applications to look out for in 2023. At the current pace of technological advancement, the future scope looks expansive and promising.
Let us now understand how specialised higher education can help you in marking a mark in this field.
How to Upskill in Big Data?
The Advanced Certificate Programme in Big Data Programming by IIIT-Bangalore and upGrad offers a specialisation in Big Data to prepare the next generation of leaders in the global IT industry.
The 13-month course is delivered in an online format, giving much-needed flexibility to working professionals. It facilitates career support through job fairs, mock interviews, and industry mentorship sessions. You get exclusive access to interview questions from top recruiters, including Amazon, Google, and Microsoft. You can also earn additional certifications in Data Science, Data Structures and Algorithms. These credentials demonstrate your skills to prospective employers.
Study options, such as the one described above, are highly valued by entry-level IT professionals. Coders, project managers, data analysts, and software developers all can benefit from the hands-on and industry-oriented learning experience.
We hope this blog familiarised you with the salient Big Data technologies of 2023
and motivated you to chart your career path with a renewed outlook!
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What are the types of Big Data technologies?
Big Data technologies are of two types: Operational and Analytical. Operational Big Data technologies work on gigantic amounts of data on a daily basis. These include social media, transactions, and other useful information. Some of the operational Big Data technologies are Amazon, Flipkart, and Walmart. Analytics Big Data technologies revolve around Big Data technologies and are more complicated than operational ones. This category focuses on the real-time use of Big Data that is crucial for making business decisions. Weather forecasts and stock markets are examples of Analytical Big Data technologies.
What are the emerging Big Data technologies?
TensorFlow is emerging because of how robust and scalable it is. Moreover, it offers the right tools and technologies to deploy machine learning applications quickly. Docker is next and is one of the biggest tools of Big Data which makes continuous deployment and development of container applications swift. Developers use containers to stack their applications taking help from several libraries. Kubernetes counts as the next emerging Big Data technology since it is open-source. Automation, escalation, and execution can be easily performed on the Kubernetes platform. Blockchain is a Big Data technology that is extremely safe. Its secure environment makes it a considerable choice to opt for. Blockchain is now expanding to sectors like insurance, medical, finance, and banking.
What are the factors to keep in mind before switching to another position in Big Data?
Some of the important questions that you must ask yourself before taking up a career in data science is if you have a machine learning engineer in your team to tackle algorithms. Next, you must check if an SQL expert is present in the team or not. Figure out the possibilities of your commitment to work on one or multiple projects at once. It barely takes time for a single project to convert into a long-term deadline. Make your pick wisely.