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Last updated: May 25, 2022

Data Warehousing and Data Mining

Hey guys, doing something with data and you are confused about where to store and how to manage it. When dealing with data, we come across these techniques of managing data. So before going into these topics let's get to know what is data warehousing? and what is data mining? Data warehousing is a storehouse of data that we use for reporting and analyzing data. When dealing with business intelligence, we usually work with data warehouses as while dealing with businesses we have various types of data in bulk. Thus for storing such data we manage using data warehousing. Data mining is the method of processing the data for some patterns and characters that data depicts. In this article, we will be looking in-depth about what is data warehousing and mining. Don't worry we won't stop there until we teach you how to use data warehousing tools and data mining tools. We will go there slowly because slow and steady wins the race. Thus taking baby steps, we will first teach the architecture of data warehousing and mining. Then we will step further into what are its techniques and then we will illustrate various tools and also give you hands-on practice regarding these tools by giving from scratch information on one of the tools. I know it's a lot to take in at present but don't worry, we will be by your side with every step and ready for your questions, so ask them a lot.

Data Mining vs KDD

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Data Warehousing

Just as the word 'warehouse' means a place to store bulk produce or goods in large quantities. Adding the word, "data" means storing a large amount of data. Data Warehousing is the method used for analytical purposes and business reporting. It mainly involves collecting and managing the data from various sources and then using it for business purposes. This data is also used to extract business insights. There are various types of Data warehousing architecture such as single-tier, two-tier, and three-tier architecture.

Data Warehousing

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How does Data Warehousing Work?

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In this article, we will discuss the components of a data warehouse.

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Types of Data Warehouse EASY

In this article, we will explain the different types of data warehouses, their characteristics, limitations, & use cases.

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In this article, we will be covering the difference between a Data lake and vs Data warehouse.

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Databases vs Data Warehouses EASY

In this article, we shall discuss the Databases and Data Warehouses

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This article discusses How are Data Warehouses Built?

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Types of Data Warehouse Architecture There are three main types of Data warehousing architecture single-tier architecture - which aims to minimize the data volume. There should be no duplicate records in the data warehouse only single layering. It mainly focuses on generating a dense collection of data and reducing the amount of data storage, two-tier architecture- differentiates the physically accessible sources and data storage. Three-tier architecture has a top layer, reconciled layer, and the layer of the data warehouse, it contains both data warehouses and the data marts.

Approaches For Data Storage Layer Data Storage Layer is the layer where our Big data resides after being generated. The Data Warehouse holds the processed data. This data has been cleaned, converted, and provided with a defined format, allowing employers to utilize the data as needed by the business. Data will be stored in both Data Warehouses and Data Marts, depending on the Architecture approach. Data Marts will be covered further down the road. An Operational Data Store is also included in some. It includes the following types of Data storage:- Direct Attached Storage, Network Attached Storage. There are various devices that are used for data storage such as Flash drives Arrays, hybrid cloud storage, backup software, and appliances.

Data Warehousing Tools Introducing to you, the various tools that are helpful in implementing Data Warehousing. You might find the topic tough initially but slow and steady with the proper understanding you will find it quite easy. And we are here to provide all the material on it and give you a proper outline and in-depth explanation about various tools. Starting with a bit of introduction and common features of data warehousing tools, we would introduce some of them such as Amazon Redshift - a brief introduction and setup details will be covered here, and Microsoft Azure - how it can be helpful as a data warehousing tool, Google Big Query - introduction to what it is and Snowflake- details including what it is and how it can be used as a data warehousing tool. We will also be taking up one data warehousing tool that is oracle Warehouse builder and shows you how you can install and set it on your local machine and what are its feature. We will be comparing The three bigs - AWS, Azure, and google big query, laying out various features and comparing them.

Data Mining

Data mining is the practice of analyzing large databases to generate new information from them. It is also uncovering the patterns or hidden data from the given databases and analyzing them. It combines statistical and artificial intelligence to analyze these large data sets which we obtain using various techniques and then discover useful information from these. Using data warehousing and the growth of big data, and other such techniques of data mining helped in transforming the raw data into useful knowledge. It involves the discussion of various data mining techniques and architectures.

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In this article, we will cover the differences between data mining and machine learning.

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Data Mining vs Big Data

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In this blog, we will learn about Discretization in Data Mining. We will understand its core concepts, its usage, advantages, and much more for better understanding.

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Data Mining vs Web Mining EASY

In this blog, we will be discussing the features and applications of Data Mining and Web Mining followed by the differences between them.

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Data Mining vs Data Snooping

In this article, we will first discuss data mining and data snooping with their types, pros, and cons. Then the difference between data mining and data snooping.

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Data Mining vs OLAP EASY

In this blog, we will be discussing the features and applications of Data Mining and Olap followed by the differences between them.

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Data Mining vs Deep Learning

In this article, we will first discuss data mining and deep learning with their types, pros, and cons. Then the difference between data mining and deep learning.

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Data Mining vs Data Profiling

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Types Of Data Mining Architecture EASY

In this article, we will learn about Data Mining Architecture, its types, advantages, and disadvantages.

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Bayesian Classification in Data Mining EASY

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KDD in Data Mining EASY

KDD is a method of extracting relevant, unknown, and useful data from massive databases(also known as big data).

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Outlier Detection in Data Mining EASY

In this blog, we will learn about the different methods of Outlier Detection in Data Mining, along with their application and challenges.

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Detecting Phishing in Data Mining MEDIUM

In this article, we will discuss what Phishing is, the method of detecting phishing in Data Mining, along with the Random Forest Algorithm.

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What is Regression in Data Mining? EASY

Regression in data mining is a statistical technique used to predict a numerical value based on one or more independent variables. Regression analysis predicts the value of the dependent variable based on the values of the independent variables.

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Applications of Data Mining

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Clustering in Data Mining MEDIUM

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Data Mining Techniques There are various methods and technologies, because of which it becomes a priority to know how to process data and make predictions and conclusions on the basis of it. But again what methods can be used for all this processing? What do you think? Let's see what are the data mining techniques? Starting with the introduction to it, where we will introduce various techniques. Then we will take up each of them individually and explain each of them with proper explanation to you. It includes classification, clustering, regression, association, and outlier detection.

Data Mining Tools After knowing about the techniques of data mining, the next stop is the machine or tool where we can execute the data mining techniques. Data mining tools are like R-studio or tableau where you can frame and execute the data mining techniques, thus creating the data models and also text these models. there are many open source and sophisticated tools. In this, we will be looking at some of them. But before that, we need an introduction to these data mining tools and many more than that. After that introduction, we will take up some of them and discuss them. Here we will be discussing - Rapid Miner, Python, orange, and kaggle.

Data Mining Algorithms

A very important station has arrived. You better sit tight and take your pens out. You might be well versed with the word "Algorithms". Now we will introduce to you what is the data mining algorithm. There are many but here we will discuss a few important ones. Let's first take a look at the part about choosing the right algorithm. What and how can one choose the right algorithm? Later we will be doing one-to-one discussions on these algorithms. We will briefly explain what it is and then dive into the mathematical details.

Top 10 Common Data Mining Algorithms

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K-mean Algorithm EASY

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Support Vector Machines Algorithm EASY

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Apriori Algorithm EASY

Explore the Apriori algorithm: its properties, components, advantages, disadvantages, and tips to enhance efficiency. Learn how to optimize your data mining process.

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PageRank Algorithm

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AdaBoost Algorithm

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K-Nearest Neighbour Algorithm

In this article, we will discuss the K-nearest neighbour(KNN) algorithm and its implementation in Python.

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Naive Bayes Algorithm EASY

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