Top Big Data Technologies And Solutions to Watch

Things in the world of technology are constantly evolving. What was once in vogue and in high demand can quickly go out of date. Particularly in the context of big data, this is true. Big data solutions help businesses make 8% more money on average. By annual revenue, the big data market is valued at more than $56 billion globally. 

 

Information technology companies use big data to enhance operations, deliver better customer service, create specialized marketing campaigns, and take other steps to boost sales and profits. You need to be familiar with the leading big data technologies that will be in demand in 2023 if you want to stay ahead of the curve. These technologies will be covered in this blog post.  

The number of technology companies that provide big data solutions is seemingly endless. Currently, a lot of the big data solutions that are particularly well-liked fall into one of the 15 categories listed below:

It's nearly impossible to discuss big data without mentioning Apache Hadoop, an open-source framework for the distributed processing of enormous amounts of data, even though it may not be as popular as it once was.

 

Many commercial big data solutions are built on Hadoop, which has expanded over time to include an entire ecosystem of related software. In the previous year, Forrester predicted 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."

It is a Hadoop engine for big data processing and is up to a hundred times faster than the default MapReduce engine. Although Apache Spark is a component of the Hadoop ecosystem, its use is so pervasive that it merits its own category. There is obviously a sizable and expanding interest in the technology, and many vendors with Hadoop offerings also offer Spark-based products. 

R is a programming language and software environment created as another open-source project for working with statistics. The language is supported by many well-known integrated development environments (IDEs), such as Eclipse and Visual Studio. It is controlled by the R Foundation and made available under the GPL 2 license, making it the darling of data science. 

 

R has reportedly become one of the most well-liked programming languages worldwide, according to several organizations that rank the popularity of different programming languages. This is important because the top programming languages on these lists are frequently general-purpose languages that can be applied to a wide range of tasks.

Many businesses are creating data lakes to facilitate access to their enormous data stores. These are sizable data warehouses that gather data from numerous sources and preserve it in its original form. The lake and warehouse analogies apply here fairly well. This is distinct from a data warehouse, which also gathers information from various sources but structures and processes it for storage. If data were water, a data lake would be pure and unprocessed like a body of water, whereas a data warehouse would be more like a collection of water bottles kept on shelves.

 

Information is stored in structured, predefined columns and rows in conventional relational database management systems (RDBMSes). Although they don't offer the same level of consistency as RDBMSes, NoSQL databases are experts at storing unstructured data and offering quick performance. Numerous well-known NoSQL databases are in use today, including MongoDB, Redis, Cassandra, Couchbase, and many more.

 

Even top RDBMS providers like Oracle and IBM now provide NoSQL databases.  SQL is a specialized language that developers and database administrators use to query, manipulate, and manage the data in those RDBMSes. The popularity of NoSQL databases has grown along with the big data trend.

A subset of big data analytics called predictive analytics makes predictions about the course of the future based on historical data. Recent developments in artificial intelligence have made it possible for predictive analytics software to significantly improve its capabilities. To predict what will happen next, it uses data mining, modeling, and machine learning techniques. It is frequently used for business analysis, marketing, credit scoring, and fraud detection. Businesses are now spending more money on big data solutions with predictive capabilities as a result. 

A big data analytics solution can work much more quickly if it can process data that is kept in memory rather than on a hard drive. In-Memory also referred to as RAM, are orders of magnitude faster than long-term storage in any computer system. In-memory database technology does exactly that. In-memory database technology is now provided by a number of the top enterprise software providers, including SAP, Oracle, Microsoft, and IBM.

Big data security is a significant and expanding concern for businesses because big data repositories are a desirable target for hackers and advanced persistent threats. Big data security solutions are offered by dozens of vendors, and Apache Ranger, an open-source project from the Hadoop ecosystem, is also gaining popularity. Security was ranked as the second fastest-growing big data concern in the AtScale survey. 

Data availability, usability, and integrity processes are all included in the broad topic of data governance. The idea of governance is closely related to that of security. In addition to providing an audit trail so that business analysts or executives can see where data originated, it serves as the foundation for ensuring that the data used for big data analytics is accurate and appropriate.

Although the idea of artificial intelligence (AI) has existed for almost as long as there have been computers, the technology has only recently become truly practical. In many ways, the big data trend has fueled advancements in AI, especially in machine learning and deep learning, two of the field's subfields. Deep learning is a subset of machine learning that uses multiple layers of algorithms in addition to artificial neural networks to analyze data. It has a lot of potential for enabling analytics tools to distinguish between the content in images and videos and then process it appropriately.

 

In order to make sense of all the data available, we can anticipate seeing more artificial intelligence and machine learning applications, as well as a rise in the use of blockchain technology for big data management and security. The list of big data technologies we have discussed is by no means complete, but it should give you a good idea of the direction the industry is going in. Make sure you are knowledgeable about these big data technologies if you want to stay ahead of the curve in 2023 and beyond.

 

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