What Are The Most Famous Data Handling Technologies Of The Decade?
The term data handling involves recording and representation of data in a way that can be helpful to you or others. Companies such as computer firms generate data in the upwards of thousands of terabytes daily, and they need to store that generated data somewhere. With the turn of the new decade, companies have begun to rely heavily on data handling and management tools. The goal of these tools and technologies is to provide individuals and companies with data optimization so they can make accurate decisions to increase their revenue generation. For example, imagine the amounts of data Facebook compiles every minute. There is a need for solutions that can record, manage, and interpret data at a high rate of speed.
For individuals and companies looking for such data-handling tools and technologies, this article will be of great help to them. Not only will we share the most reliable data technologies with you, but we will also give you an idea of how they work. Some of these technologies are listed as follows;
Table of Contents
An in-memory database refers to a data handling system that stores data directly into the random access memory(RAM) of a computer. With the help of the random access memory, an in-memory database has quicker data access speeds. Due to it, data stored on an in-memory database is accessible whenever needed. In-memory databases have been around for the last twenty years, and it is the go-to for companies such as computer firms, who want to gain access to large amounts of data quickly regularly.
You can usually find an in-memory database inside a data warehouse. These warehouses store data on their databases, compressing it, and preparing it for analysis. However, there is another advantage other than quick access speeds. An in-memory database makes it possible to store both structured and unstructured data. Nowadays, you can choose between various in memory database solutions out there and satisfy all your data storage requirements for your company. Give them a try if your company wants high-speed access to large amounts of data.
Predictive analytics, as the word predictive, suggests, forecasts behavior, and future events with the help of stored data. It uses data modeling, machine learning techniques, and data mining to draw future predictions. For example, companies use predictive analytics to detect fraud, for predictive marketing, credit scoring, and for finances evaluation.
Due to the advancements in artificial intelligence during the last few years, predictive analytics has become more accurate than ever before. As a result, companies have started to invest huge money in a data handling solution that comes with a predictive analytics feature. Many companies such as Microsoft, SAS, IBM, and RapidMiner offer database solutions that come with predictive analytics.
Artificial intelligence has been around for more than two decades now. However, this technology has recently seen a spike in usage. In many ways, machine and deep learning have seen the most advancements due to their ability to help in analyzing and recording data. The term machine learning refers to a computer’s ability to learn without the need of any programming from the user. With the help of machine learning, companies can recognize patterns, look at historical data, and predict outcomes.
With deep learning, companies can use multiple layers of algorithms to analyze and record data. Deep learning relies mainly on artificial neural networks. It allows data analysis tools to record content from videos and images and store them as a text file. Vendors such as Google, Amazon Web, and Microsoft offer AI deep learning and machine learning solutions to companies that can make use of such features.
Many companies have set up data lakes to allow them to have access to their data quickly and easily. Data lakes contain a giant repository to data collected from various sources and stored in its original state. Compared to a traditional database warehouse, a data lake is different. A data warehouse also collects data from a variety of sources, but stores it in a form structured for storage only.
In such a case, consider a data lake as a natural source of water, such as a lake, which contains unfiltered water, while a data warehouse is like filtered bottled water. A data lake is beneficial for companies that want to store data but do not know how to use it. Due to the internet of things(IoT), there has been a spike in the usage of data lakes.
Due to companies familiarising themselves with the capabilities of a data analytics solution, the demand for faster access to insights has seen an increase along with it. These companies consider the ability to analyze data along with streaming analytics as to the holy grail of all data analytics tools. They look for solutions that allow them to return quick insight after accepting data from multiple sources.
Vendors such as IBM, SAP, and TIBCO, offer streaming analytics solutions nowadays. According to a study conducted by MarketsandMarkets, streaming analytics generated around three billion dollars in revenue in the year 2016, which they predict will increase to thirteen billion in the year 2021.
SQL is a computer language used in the management and manipulation of data inside an RDBMSes(relational database management systems). An RDBMSes stores data in its structured form, categorizing it in columns and rows. With the help of a NoSQL database, developers and administrators can store data in its unstructured form. It also provides fast performance and quick access to this form of data. However, it is much less reliable than RDBMSes.
To counter this, companies should use both SQL and NoSQL based databases, if they want to store both structured and unstructured data. MongoDB, Cassandra, and Redis are examples of NoSQL databases. Even companies such as IBM offer NoSQL databases, along with their RDBMSes solutions.
You might know about cloud computing. Well, edge computing is the opposite. Edge computing analyzes data close to its source of creation. While with cloud computing, data analysis takes place on a centralized server. The best feature of edge computing is that it reduces network traffic and its related costs, due to the reduction of data transmission over a network. Data analysts and venture capitalists consider edge computing as the next big thing when it comes to data analysis. However, edge computing, mainly edge analytics, is still in its development phases.
Due to the increase in demand for IT solutions, the big data ecosystem continues to evolve and grow to meet these demands. As days pass by, new data handling technologies emerge, providing companies with enhanced data analysis tools. However, no one solution fits all. Companies need to use a combination of various data handling techniques to achieve their data analysis, recording, and management needs. To get on top of the competition, we have shared with you some data handling technologies of this decade. So give them a try.
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