Many information technology companies use big data to boost sales and revenue, improve processes, provide excellent client service, and design targeted marketing campaigns. You must be knowledgeable about the top big data technologies if you want to keep one step ahead of the competition. The below article represents these technologies:
Several IT businesses provide big data solutions. Many of the popular big data solutions at the moment fit into one of the categories listed below:
The Hadoop Ecosystem
Even if Apache Hadoop may not be as well-known as it once was, it is virtually only possible to talk about big data by discussing this open-source framework for the distributed processing of massive volumes of data. Forrester forecasted in the prior year that “within the next two years, 100% of all large enterprises will adopt it (Hadoop and related technologies such as Spark) for big data analytics.”
R
The programming language and software environment R was developed as another open-source project for working with statistics. Numerous well-known integrated development environments (IDEs), including Eclipse and Visual Studio, support the language. It is the darling of data science since it is run by the R Foundation and distributed under the GPL 2 license.
According to several organizations that rank the popularity of various programming languages, R has emerged as one of the most well-liked programming languages globally. This is significant since many of the top programming languages on these rankings are general-purpose languages, which may be used for various purposes.
Spark
It is a Hadoop engine for handling large amounts of data and is up to a hundred times quicker than the standard MapReduce engine. Despite being a part of the Hadoop ecosystem, Apache Spark deserves its category because of its widely used. The technology has a substantial and growing following, and several manufacturers with Hadoop solutions also provide Spark-based products.
NoSQL Databases
Conventional relational database management systems (RDBMSes) store data in organized, predetermined columns and rows. NoSQL databases excel in storing unstructured data and providing rapid speed, even if they provide a different level of consistency than RDBMSes. MongoDB, Redis, Cassandra, Couchbase, and many more well-known NoSQL databases are now in use.
Renowned RDBMS vendors like Oracle and IBM currently offer NoSQL databases. Developers and database administrators query, alter and manage the data in such RDBMSes using SQL, a specialized language. Along with the big data movement, NoSQL databases have become increasingly popular.
Data Lakes
Several firms are constructing data lakes to make access to their massive data warehouses easier. These are enormous data warehouses that gather information and maintain its integrity from several sources. Here, the warehouse and lake analogies work rather nicely. Unlike a data warehouse, which similarly collects information from numerous sources but organizes and processes it for storage, this is different. If data were water, a data lake would be clean and undisturbed like a body of water, whereas a data warehouse would resemble a collection of water bottles stored on shelves.
Predictive Analytics
The predictive kind of big data analytics uses previous data to create future predictions. Predictive analytics software may now considerably enhance its capabilities thanks to recent advancements in artificial intelligence. It employs data mining, modeling, and machine learning strategies to make future predictions. It is commonly used for marketing, credit scoring, fraud detection, and business analysis. Companies are spending more money on big data solutions with predictive capabilities as a result.
Big Data Security Solutions
Because big data repositories are a desired target for hackers and sophisticated, persistent threats, so big data security is a growing problem for enterprises. Numerous companies provide big data security solutions, and Apache Ranger, an open-source project from the Hadoop ecosystem, is also gaining prominence. According to the AtScale poll, security is the second most big data issue.
In-Memory Databases
If a big data analytics system can handle data saved in memory rather than on a hard disk, it will function considerably more swiftly. In any computer system, the speed of in-memory, often known as RAM, is orders of magnitude greater than that of long-term storage. This is exactly what in-memory database technology accomplishes. Many leading corporate software vendors, including SAP, Oracle, Microsoft, and IBM, now provide in-memory database technology.
Solutions for Big Data Governance
The wide issue of data governance includes data availability, usefulness, and integrity protocols. Security and the concept of governance go hand in hand. It provides the basis for guaranteeing that the data utilized for big data analytics is valid and suitable. It also offers an audit trail so business analysts or executives can understand where data originated.
Artificial Intelligence
Artificial intelligence (AI) is a concept that has been around for almost as long as computers themselves, but the technology has only lately been really useful. The big data trend has helped AI progress, particularly in machine learning and deep learning subfields. A form of machine learning known as “deep learning” analyzes data using numerous layers of algorithms in addition to artificial neural networks. It offers a lot of potential for allowing analytics systems to recognize the information in photographs and videos and analyze it accordingly.
Bottom Line
We may expect to see more applications of artificial intelligence and machine learning, as well as an increase in the usage of blockchain technology for Big Data Management and security to make sense of all the data that is already accessible. Although by no means exhaustive, the list of big data technologies we have covered should give you a decent notion of the direction of the sector. Be familiar with these big data technologies to stay ahead of the curve in 2023 and beyond.
