Table of Contents
We now live in a digital age where businesses generate and manage enormous amounts of data every day. This enormous collection of structured and unstructured data, which is growing exponentially with increased digitization, is referred to as “Big Data.” Traditional data processing software is unable to handle and extract useful information from data due to the sheer volume and complexity of Big Data. The good news is that there are several trustworthy big data technologies available to choose from as we anticipate 2024.
Reviewing and contrasting each Big Data technology’s features is crucial in order to select the best option. We will also go over the characteristics of the various big data technologies and the businesses that employ them. Let’s first gain a thorough understanding of big data technology before delving into these technologies.
What is Big Data Technology?
Big Data Technologies are described as software tools that are primarily made to process, analyze, and extract data from massive datasets with incredibly complex structures that can’t be handled by conventional data processing technologies.
The emergence of Big Data technologies started to close the gap between existing data technologies (like RDBMS, file systems, etc.) and the business needs for data, which are expanding quickly. These technologies essentially incorporate particular data frameworks, methods, tools, and techniques used for data archiving, examination, remodeling, analysis, and evaluation. Big Data Processing Technologies must be used to analyze this vast amount of real-time data in order to draw conclusions and make predictions that will help to lower future risks. In the internet age, such capabilities are becoming more and more crucial.
Enroll here To know more about data science course! Get Free Demo Classes!
Top Big Data Technologies
1: Which of the following algorithms is most suitable for classification tasks?
Artificial Intelligence
Along with supplementary technologies like Machine Learning (ML) and Deep Learning, Artificial Intelligence (AI) is driving change not only in the IT landscape but also in other sectors of the economy. Building human capabilities into machines is a topic covered by an interdisciplinary area of computer science and engineering. Applications include accurate weather forecasts, self-driving cars, robotic surgery, and voice-activated assistants. Additionally, business analytics are being powered by AI and ML in a way that allows the organization to innovate at a higher level. The biggest benefit comes from staying one step ahead of the competition by spotting potential issues that people might miss.
SQL-based Technologies
The computer language known as SQL, or Structured Query Language, is used to organize, manipulate, and manage data that is stored in databases. For roles in software development, familiarity with SQL-based technologies like MySQL is a requirement. Practical expertise in NoSQL databases emerges as organizations move beyond querying structured data from relational databases to enable faster performance. A wider variety of technologies that can be used for creating and designing contemporary applications can be found within NoSQL. You can provide specific data collection and retrieval techniques, which can then be used in real-time web applications and Big Data analytics software. Some of the most well-known NoSQL databases on the market include MongoDB, Redis, and Cassandra.
R Programming
R is free software that facilitates statistical computation, visualization, and communication in environments built on the Eclipse platform. R provides a wide range of coding and pacing tools as a programming language. R is primarily used by statisticians and data miners for data analytics. It makes it possible to plot, graph, and report with high quality. Additionally, you can integrate it with Hadoop and other database management systems, or pair it with languages like C, C++, Python, and Java.
Looking for a Data Science Career? Explore Here!
Data Lakes
Consolidated repositories for both structured and unstructured data are known as data lakes. Unstructured data can be saved in its current form during the accumulation process, or you can run various data analytics on it to convert it to structured data. You would need to use dashboards, data visualization, real-time data analytics, etc. in the latter scenario. The likelihood of obtaining more accurate business conclusions would rise even further as a result. Nowadays, many of the capabilities needed for data lake projects are already built into AI-enabled platforms and microservices. Machine learning is being increasingly used by data analytics companies on new data sources like log files, social media, clickstreams, and Internet of Things (IoT) devices. Organizations that take advantage of these big data technologies can better respond to opportunities and advance their growth through active involvement and informed decisions.
Predictive Analytics
Based on historical data, predictive analytics, a subset of big data analytics, forecasts future behavior and events. It is fueled by various technologies, including Data modeling, statistical and mathematical modeling, and machine learning. Regression techniques and classification algorithms are frequently needed for the creation of predictive models. Any business using big data to predict trends needs to be extremely precise. Because of this, software and IT professionals need to be able to apply such models to investigate and uncover relationships between different parameters. Their abilities and contributions can significantly reduce business risks when used properly.
Hadoop
An open-source software framework called Hadoop uses a distributed cluster to store data. It employs the MapReduce programming model to accomplish this. Here are some key elements of Hadoop that you should be aware of:
- Performs resource management duties YARN (for example, allocating to applications and scheduling jobs.)
- Data processing can be done using MapReduce on top of a distributed storage system.
- HIVE: Allows professionals with SQL expertise to conduct data analytics.
PIG: As a high-level scripting language for Hadoop, PIG facilitates data transformation. - Unstructured data is imported into the file system using Flume.
- Relational databases can be used to import and export structured data using Sqoop.
- ZooKeeper: In the Hadoop environment, it synchronizes distributed services to aid in configuration management.
- Oozie: Connects various logical tasks in order to fully carry out a specific task.
Apache Spark
For aspiring software developers, Spark, a real-time data processing framework, is another essential tool. SQL, machine learning, graph processing, and streaming analytics are all built-in features. Systems for detecting credit card fraud and eCommerce recommendation engines are a couple of use cases. Additionally, it is simple to integrate with Hadoop to carry out quick actions in accordance with business requirements. Spark is favored by data scientists because it is thought to process data more quickly than MapReduce.
When it comes to utilizing Big Data, businesses place a high priority on speed. They are looking for solutions that can compile data from various sources, process it, and produce insightful findings and practical trends. The need is so urgent and immediate that technologies like Streaming Analytics are in demand. Such applications are anticipated to expand even more with the growth of IoT.
Prescriptive Analytics
In a given situation, prescriptive analytics is concerned with directing actions toward desired outcomes. For instance, by offering potential courses of action, it can assist businesses in responding to market changes like the appearance of questionable products. It combines descriptive and predictive analysis in this manner. One of the most sought-after Big Data technologies in 2024 is prescriptive analytics because it goes beyond data monitoring. It places a strong emphasis on operational effectiveness and customer satisfaction, the two pillars of any 21st-century business.
In-memory Database
Data engineers must have a solid understanding of database design and architecture. Nevertheless, it’s crucial to stay current and experiment with new technologies. In-memory Computing (IMC) is one instance where numerous computers dispersed across various locations share data processing tasks. Any scale and instant access to data are possible. IMC applications are already thriving in the IoT, retail, and healthcare industries. It is being used by businesses like e-Therapeutics for network-driven drug discovery. While in-memory databases have enabled online clothing retailers like Zalando to manage growing data volumes with greater flexibility.
Blockchain
The main technology behind cryptocurrencies like bitcoin is blockchain. It captures structured data in a special way that, once recorded, cannot be changed or deleted. As a result, a very secure ecosystem is created, which is ideal for the banking, finance, securities, and insurance industries (BFSI). Blockchain applications are becoming more popular outside of the BFSI industry in fields related to social welfare, like education and healthcare. As a result, software professionals with advanced database technology knowledge have many options. We have now given you a brief overview of some of the top Big Data applications to watch in 2023. The scope of the future appears broad and promising at the rate of technological development that is currently occurring. Let’s examine how receiving specialized training in higher education can assist you in making a mark in this.
Tableau Software
One of the top Big data technologies for displaying business analytics is Tableau. To gather and process information, this tool can also be connected to files, relational sources, and large sources. Companies can quickly and affordably analyze vast amounts of data thanks to Tableau software. The ability of this tool to retrieve materials from numerous locations is its key differentiator. Engineers can therefore load anything to get a complete picture, from an excel file to a large database. Additionally, Tableau supports all file types, including Excel and Oracle.
Kubernetes
One of the Big data cloud technologies created by Google is Kubernetes. Its primary applications are vendor-neutral clustering and container management. Kubernetes wasn’t designed to handle Big data workloads in the beginning. These days, new developments make it possible for this tool to support massive infrastructures. Big data solutions are simple to deploy thanks to Kubernetes operators like Apache Kafka and Cassandra. By implementing Big data software in a variety of environments, they also realize the portability potential.
ElasticSearch
ElasticSearch comes in last place on our list of cloud Big data technologies. It is a quick, scalable horizontal search and analytics engine. It is a hybrid no-SQL database that works with all kinds of data. ElasticSearch has the ability to instantly analyze large amounts of data. ElasticSearch is used by multinational corporations, particularly start-up businesses, to perform full-text search, log analysis, business intelligence, and process monitoring. It can scale up to thousands of servers and conduct extremely quick searches.
Get the latest updates on data science in the Entri app
