what is the difference between big data and data analytics?
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Big Data is a concept in Software Engineering which we use when we have a large sets of machine generated data, which in most of the cases is unstructured and not easy to use with traditional RDBMS concepts. Data Analytics on the other hand is more of analyzing data which could be structured or unstructured.
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Data Analytics focuses on algorithms to determine the relationship between data offering insights. The major difference between BI and Analytics is that Analytics has predictive capabilities whereas BI helps in informed decision-making based on analysis of past data.
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Whereas machine learning is a subfield of Computer Science and/or AI that gives computers the ability to learn without being explicitly programmed. Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it.
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Business Intelligence refers to the information required to enhance business decision-making activities. Data Analytics refers to modifying the raw data into a meaningful format. The prime purpose of business intelligence is to provide support in decision making and help the organizations to grow their business.
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Despite having similar aims and processes, there are two main differences between them: Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
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Data analytics is generally more focused than data science because instead of just looking for connections between data, data analysts have a specific goal in minding that they are sorting through data to look for ways to support. Data analytics is often automated to provide insights in certain areas.
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Fundamentally, a data warehouse is a collection of data that is separated from the operational systems. ... The size of a data warehouse is typically larger than 100 GB, whereas data marts are generally less than 100GB. Due to the difference in scope, it is comparatively easier to design and use data marts.
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Data Analysis is a process of collecting, transforming, cleaning, and modeling data with the goal of discovering the required information. ... Data visualization is at times used to portray the data for the ease of discovering the useful patterns in the data. The terms Data Modeling and Data Analysis mean the same.
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Big data analytics as the name suggest is the analysis of big data by discovering hidden patterns or extracting information from it. ... Big data has got more to do with High-Performance Computing, while Machine Learning is a part of Data Science. Machine learning performs tasks where human interaction doesn't matter.
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Improved insights for big data Not only is business intelligence which utilizes AI providing more insights, but it's providing them in virtually, real-time. As AI integrated software functions with an “adapt and learn” model, it's able to continually improve its decision making and even cut down on execution times.
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Data analytics is important because it helps businesses optimize their performances. ... A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.
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Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. ... Big data was originally associated with three key concepts: volume, variety, and velocity.
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Whereas in the Big Data environment, data is stored on a distributed file system (e.g. HDFS), rather than storing on a central server. ... BI solutions are more towards the structured data, whereas Big Data tools can process and analyze data in different formats, both structured and unstructured.
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Big data and artificial intelligence are two important branches of computer science today. ... Big data is inextricably linked with artificial intelligence. First, the development of big data technology depends on artificial intelligence, because it uses many artificial intelligence theories and methods.
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Data Engineers are focused on building infrastructure and architecture for data generation. In contrast, data scientists are focused on advanced mathematics and statistical analysis on that generated data.
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AI is a subset of advanced analytics, so advanced analytics is broader than AI when it comes to analytics capabilities. ... AI often involves the development of self-learning algorithms which enable analytics tools to learn from and make assumptions based on data.
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Analytics is a broad term without a narrow definition, which may refer to almost any type of data analysis, especially statistical analysis, data mining and machine learning. Statistics is the mathematics of estimating parameters of populations based on data from representative samples of those populations.