Millions of terabytes of data are getting generated by over 6 billion devices connected to the internet in the form of structured and unstructured data, referred to as Big Data. Big Data is going through trends that are governed by the transforming technologies in Machine Learning, Artificial Intelligence, and IoT and is directly impacting career and professional profiles of data scientists. Statisticians and software developers across the globe are increasingly subscribing to Data Science courses to gain the latest knowledge in data management.
The possibilities and effects of data management are far-reaching, giving rise to new employment opportunities for data scientists. Technologies like Predictive Analysis, In-Memory Technology, data security advancement, and Edge Computing are trending in Big Data. Open-source Big Data infrastructures like Hadoop, Spark and Apache, Data Warehouse and NoSQL are also gaining popularity among data scientists.
Some of the top-rated trends in Big Data technology include:
Hadoop
Hadoop is an open-source formatting technology that is favored by 41% of data scientists and data managers. It is formatted in Java computing language and is widely considered as the backbone of all Big Data applications. It provides a secure storage layer called Hadoop Distributed File System (HDFS), a data processing technology called MapReduce and a management layer called Yet Another Resource Negotiator (YARN).
R
R is favored by 38% of data science professionals and Big Data analysts worldwide. R is also an open-source computing language giving analytical solutions and is employed by educational institutions as a learning platform for analytics. R is primarily used in data handling and storage, and for making linear and non-linear statistical calculations. It categorizes large amounts of data into simple, understandable tabs making the data ready for interpretation and inference. R is still facing some performance issues but is nevertheless applied by management firms, publishing houses, and meteorological departments for data mining and analysis.
Visualization Models
Data visualization in a simplistic yet comprehensive way finds equal importance (if not more) as data analysis. Tableau, Infogram, ChartBlocks, Datawrapper, Plotly, RAW, Visual.ly, Google Charts are tools that you can use to assemble the mined data into a presentable and interactive form. With the help of these tools, analysts can visualize data in the real-time scenario and support it with graphs, pie-charts, matrices, etc. The better visualization also helps in presentations and client-servicing departments. Using the right data visualization tool to maximize the end result of data processing is a predicted trend for the year 2018.
Streaming Data
One of the most futuristic trends in Big Data analytics is the phenomena of Data Streaming. In this data is mined simultaneously from where it is sourced. For instance, data used to develop the RSS feed of a web-engine uses Data Streaming to decide which website is placed first on a specific search query. Data Streaming is done in agricultural industries to analyze the real-time growth of crops, and in financial sectors to measure real-time stock market changes. Various types of data streamers are also employed in recording and interpreting the fluctuations in oceanic temperatures and ocean surface height.
Variations in the height of the ocean will be considerably more than the fluctuation in temperature, and collectively using the analysis of these fluctuations, ocean-related policies can be devised. Thus, Data Streaming conducted across an array of industries is changing the dynamics of policymaking to cater to problems such as world debt, climate change etc. Streaming Data is the oil to the engine of artificial intelligence in making action-oriented decisions.
Predictive Analysis
Predictive Analysis uses statistical algorithms and machine learning tools over historical data to predict possible future outcomes.
Variables are added to the data analysis of the previously generated data to yield new analysis without using new data sets. This is also known as data forecasting and helps companies in making informed investments and decisions. This chain modeling of data has a ripple effect over the pre-existing sets to predict future data, aimed at improving the present-day conditions of an enterprise.
This is different from Streaming Data as there exists a percentage of prediction error in the generated data sets because it is subject to any change in consumer behavior. It is widely used to determine trends, better understand the psychographics of customers, generate niche and personalized services, and improve business performance.
In-Memory Technology
In-Memory Technology is used to analyze time-sensitive data. Instead of examining the data stored in a hard drive or a pen drive, the data that is stored in SSDs of the computer is analyzed.
This is a speedy and more reliable way of data analysis. However, In-Memory technology is more expensive than usual data analysis procedures as inbuilt storage costs more than external storage options.
One data, many treatments
A data scientist performs many functions with a single data set provided to them. The rate at which unstructured data is getting generated, data mining techniques will become more efficient and discrete. To keep pace with rapidly changing trends in Big Data, data scientists must be vigilant and sensitive to new developments in order to improve their performance and to gain better Business Intelligence.
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