While working in the field of machine learning and data analytics, data profiling and data mining are used quite extensively with various definitions scattered across. The two terms are often confused, and people even use it interchangeably in some cases. While both may seem to be the same thing, they are not. Firstly, data mining has been in use for quite some time, whereas data profiling is a relatively rare and new topic.
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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Data mining is a world itself, which is why it can easily get very confusing. There is an incredible number of data mining tools available in the market. However, while some might be more suitable for handling data mining in Big Data, others stand out for their data visualization features.
As is explained in this article, data mining is about discovering patterns in data and predicting trends and behaviors. Simply put, it is the process of converting vasts sets of data into relevant information. There is not much use in having massive amounts of data if we do not actually know what it means.
This process encompasses other fields such as machine learning, database systems, and statistics. Additionally, data mining functions can vary greatly from data cleansing to artificial intelligence, data analytics, regression, clustering, etc. Consequently, many tools are being developed and updated to fulfill these functions and ensure the quality of large data sets (since poor data quality results in poor and irrelevant insights). This article seeks to explain the best options for each function and context. Keep reading to find out our 21 top mining tools!
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In the digital era that we live in, data has become the biggest and most valuable asset for most organisations. Data is rapidly transforming the way we live and communicate, and it is by collecting, sorting and studying this data, that organisations across the world are looking for ways to impact their bottom lines.
When working with all terminology related to data, it is essential to have a clear understanding of the different scope of work related to it. In this article, we’ll discuss the differences between Big Data and Data Science. Though these terms are interlinked and often used interchangeably, there’s a vast underlying difference between them in all aspects.
Let us begin by defining the two terms.
Big Data is a standard way to define it is as an assortment of data which is too large to be stored or processed using the traditional database systems within a given period. A common misconception while referring to it is when the term is used to refer to data whose size of the volume is of the order of terabytes or more. However, it is a purely contextual term. For example, even a file of 250MB is Big Data in the context of an email attachment.
Data exhibits key attributes that must be taken into consideration when processing a dataset. They are most commonly known as the 5 Vs. Each of the Vs has specific implications in terms of handling them, but, when all of them are seen in combination, they present even bigger challenges.
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With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.
Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.
Now, addressing the main topic of interest – how are data analysis and data science different from each other.
As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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