Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures. The goal is to illustrate the types of data used and stored within the system, the relationships among these data types, the ways the data can be grouped and organized and its formats and attributes. Types of data models Like any design process, database and information system design begins at a high level of abstraction and becomes increasingly more concrete and specific. Data models can generally be divided into three categories, which vary according to their degree of abstraction. The process will start with a conceptual model, progress to a logical model and conclude with a physical model. Each type of data model is discussed in more detail below: Conceptual data models They are also referred to as domain models and offer a big-picture view of what the system will contain, how it will be organized, and which business rules are involved. Conceptual models are usually created as part of the process of gathering initial project requirements. Typically, they include entity classes (defining the types of things that are important for the business to represent in the data model), their characteristics and constraints, the relationships between them and relevant security and data integrity requirements. Logical data models They are less abstract and provide greater detail about the concepts and relationships in the domain under consideration. One of several formal data modeling notation systems is followed. These indicate data attributes, such as data types and their corresponding lengths, and show the relationships among entities. Logical data models don’t specify any technical system requirements. Logical data models can be useful in highly procedural implementation environments, or for projects that are data-oriented by nature, such as data warehouse design or reporting system development. Physical data models They provide a schema for how the data will be physically stored within a database. As such, they’re the least abstract of all. They offer a finalized design that can be implemented as a relational database, including associative tables that illustrate the relationships among entities as well as the primary keys and foreign keys that will be used to maintain those relationships. Physical data models can include database management system (DBMS)-specific properties, including performance tuning. What Is Bayesian Modeling & How Does It Work? Bayesian modeling is a statistical model where probability is influenced by the belief of the likelihood of a certain outcome. A Bayesian approach means that probabilities can be assigned to events that are neither repeatable nor random, such as the likelihood of a new novel becoming a New York Times bestseller. It wouldn’t make much sense to assign probabilities to this situation, but with a Bayesian approach, you can use prior probability to inform the outcome and then continually update that probability when new evidence is received. For example, was the author’s previous book on the bestseller list? Has there been a lot of buzz about the book on social media? One common example of Bayesian modeling is Naïve Bayes classifiers—this model is used in classification tasks only, and it falls under the umbrella of supervised ML algorithms, meaning that the model is trained with both categorical outputs and input features. Given two events “A” and “B”, where P(x) represents the probability of “x,” the Naïve Bayes theorem states that: P (A/B): How often A occurs given that B happens, also known as posterior probability. P (B/A): How often B occurs given that A happens, also known as likelihood probability. P (A): How likely it is for A to occur on its own. P (B): How likely it is for B to occur on its own. Both P(A/B) and P(B/A) are also known as conditional probabilities. The formula for Bayes Theorem looks like: P (A/B) = [P(B/A) P(A)] / P(B) Given two events “A” and “B”, where P(x) represents the probability of “x,” the Naïve Bayes theorem states that: P (A/B): How often A occurs given that B happens, also known as posterior probability. P (B/A): How often B occurs given that A happens, also known as likelihood probability. P (A): How likely it is for A to occur on its own. P (B): How likely it is for B to occur on its own. Both P(A/B) and P(B/A) are also known as conditional probabilities. The formula for Bayes Theorem looks like: P (A/B) = [P(B/A) P(A)] / P(B) How Can Businesses Use Bayesian Modeling to Their Advantage? Bayesian modeling is an efficient way to make real-time predictions—that’s why email servers like Google use this algorithm to determine whether emails belong in the spam folder or not. Let’s check out some other ways organizations can use Bayes’ theorem to their advantage. Testing & Comparing Models Bayes’ theorem offers an effective way to combine existing datasets with new observations within a theoretical framework. You can include past data about a certain variable and create a subsequent distribution for future analysis. Once you have new observations, the previous prediction can be used as old data. With these predictive distributions, businesses can perform in-depth testing of any aspect of their operations, including the effectiveness of a marketing campaign that’s currently running. As new data is collected (like click-through rate), marketers can update the probability of the campaign driving traffic, revenue, etc. and make updates as needed. Making the Most Accurate Predictions One of the best aspects of Bayesian modeling is its effectiveness at generating real-time predictions due to the fact that the probability of a hypothesis being true or not is constantly updated as new evidence is received. For instance, doctors can use a Naïve Bayes’ classifier to determine if a patient is at high risk for cancer, heart disease, diabetes, etc. based on previous medical history and conditions. They can then use new data, like how a patient is feeling on a certain day, or how they respond to treatment, to update their hypothesis and treatment plan with a high degree of accuracy. How Does a Naïve Bayes Model Work? Suppose we have a training dataset of weather conditions and a corresponding target variable, “Play,” which reflects the probability of playing a tennis match depending on the weather. We’ll follow these steps to determine if the conditions indicate good playing weather: First, put the information into a frequency table. Then, construct a probability table by calculating the probabilities of different conditions. Finally, use the Bayesian Model to compute the posterior probability (the probability that an outcome will occur given new data). The class with the highest posterior probability is the most likely outcome. If the weather is sunny and you want to calculate if the tennis match will go on, you can set the problem up like this and calculate the probability by inputting values appropriately: P(Yes/Sunny) = P(Sunny/Yes) * P(Yes)/P (Sunny) How Do Naive Bayes Algorithms Work? Let’s understand it using an example. Below I have a training data set of weather and corresponding target variable ‘Play’ (suggesting possibilities of playing). Now, we need to classify whether players will play or not based on weather condition. Let’s follow the below steps to perform it. Convert the data set into a frequency tableIn this first step data set is converted into a frequency table Create Likelihood table by finding the probabilitiesCreate Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64. Convert the data set into a frequency tableIn this first step data set is converted into a frequency table Create Likelihood table by finding the probabilitiesCreate Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64. Use Naive Bayesian equation to calculate the posterior probability Now, use Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of the prediction.
"Explain all this in simple and understandable words"
Sure! I'd be happy to break down the concept of data modeling and explain Bayesian modeling and Naïve Bayes algorithms in simple terms.
Data Modeling: Think of data modeling as creating a detailed plan or map for organizing information in a system. There are three main types of data models:
Bayesian Modeling: Bayesian modeling is a statistical approach where probability is influenced by beliefs about the likelihood of an outcome. It allows for assigning probabilities to events even when they are not repeatable or random. For example, it can be used to predict the likelihood of a new novel becoming a bestseller by considering prior beliefs and updating them with new evidence over time.
Naïve Bayes Algorithms: Naïve Bayes algorithms are a specific type of Bayesian modeling used for classification tasks. They work by calculating the probability of a certain outcome given specific input data. For example, they can predict whether a tennis match will happen based on weather conditions. The algorithm uses probability calculations to make predictions, considering the likelihood of different outcomes and selecting the most likely one.
Overall, these techniques help businesses make predictions, compare different models, and analyze data to make informed decisions in various fields such as marketing, healthcare, and more.
The fundamental concept of Fuzzy Logic is the membership function, which defines the degree of membership of an input value to a certain set or category. The membership function is a mapping from an input value to a membership degree between 0 and 1, where 0 represents non-membership and 1 represents full membership.
Fuzzy Logic is implemented using Fuzzy Rules, which are if-then statements that express the relationship between input variables and output variables in a fuzzy way. The output of a Fuzzy Logic system is a fuzzy set, which is a set of membership degrees for each possible output value.
In summary, Fuzzy Logic is a mathematical method for representing vagueness and uncertainty in decision-making, it allows for partial truths, and it is used in a wide range of applications. It is based on the concept of membership function and the implementation is done using Fuzzy rules.
In the boolean system truth value, 1.0 represents the absolute truth value and 0.0 represents the absolute false value. But in the fuzzy system, there is no logic for the absolute truth and absolute false value. But in fuzzy logic, there is an intermediate value too present which is partially true and partially false.
Explain in simple words
In simple terms, fuzzy logic is a way of dealing with information that's not always clear-cut or black-and-white. Instead of just "true" or "false," it allows for in-between answers.
Imagine if you were describing how hot or cold the weather is. In normal logic, you might say it's either "hot" or "cold," but in fuzzy logic, you could say it's "kind of hot" or "sort cold." This helps to capture the uncertainty or vagueness in many real-life situations.
Fuzzy logic uses membership functions, which assign a value from 0 to 1 to show how well something fits into a. If it's 0, it doesn't fit at all, and if it's 1, it fits perfectly. most of the time, things are somewhere in between.
Fuzzy logic is used in many areas because it good for representing things that aren't always clear, like human language, decision-making, and controlling things that change gradually, like temperature or speed.
ARCHITECTURE
Its Architecture contains four parts :
RULE BASE: It contains the set of rules and the IF-THEN conditions provided by the experts to govern the decision-making system, on the basis of linguistic information. Recent developments in fuzzy theory offer several effective methods for the design and tuning of fuzzy controllers. Most of these developments reduce the number of fuzzy rules. FUZZIFICATION: It is used to convert inputs i.e. crisp numbers into fuzzy sets. Crisp inputs are basically the exact inputs measured by sensors and passed into the control system for processing, such as temperature, pressure, rpm’s, etc. INFERENCE ENGINE: It determines the matching degree of the current fuzzy input with respect to each rule and decides which rules are to be fired according to the input field. Next, the fired rules are combined to form the control actions. DEFUZZIFICATION: It is used to convert the fuzzy sets obtained by the inference engine into a crisp value. There are several defuzzification methods available and the best-suited one is used with a specific expert system to reduce the error.
The architecture of a system based on fuzzy logic typically comprises four components:
Rule Base: This part consists of a set of rules that are provided by experts to govern the decision-making process. This is typically based on linguistic information and specifies the IF-THEN conditions. Recent advancements in fuzzy theory aim to streamline the design and tuning of fuzzy controllers by the number of fuzzy rules.
Fuzzification: Fuzzification is the process of converting crisp, exact inputs - such as data from sensors like temperature, pressure, or rpm - into fuzzy sets. These crisp inputs are transformed into fuzzy sets to accommodate the uncertainty and vagess in the data.
Inference Engine: The inference engine evaluates the degree of match between the current fuzzy input and each rule, determining which rules should be activated based on the input. Subsequently, the rules are combined to produce the control actions for the system.
Defuzzification: Defification is the process of converting the fuzzy sets generated by the inference engine into a precise, crisp value. There are various defuzzification methods available, and the most suitable one is chosen minimize errors in the specific expert system.
This architecture allows for the of uncertain or imprecise information and is commonly employed in various applications to make decisions and control systems based on this type of information. These components work together to process input data, determining appropriate control actions based on vague or uncertain input information.
Considerations while designing Schema in MongoDB Design your schema according to user requirements. Combine objects into one document if you will use them together. Otherwise separate them (but make sure there should not be need of joins). Duplicate the data (but limited) because disk space is cheap as compare to compute time. Do joins while write, not on read. Optimize your schema for most frequent use cases. Do complex aggregation in the schema.
When designing a schema in MongoDB, it's important to consider several key factors to ensure the efficient performance and flexibility of the database. Here are some important considerations:
User Requirements: The schema should be aligned with the specific requirements and use cases of the application. Understanding how the data will be accessed and manipulated is crucial for designing an effective schema.
Document Structure: Combine objects into one document if they are frequently used together. This can help in minimizing the need for joins and can improve data retrieval performance.
Data Duplication: It's acceptable to duplicate data, but do so in a limited and thoughtful manner. Disk space is often more affordable than the additional compute time required for complex data retrieval operations.
Joins vs. Embedded Data: As a part of the schema design, consider performing joins at write-time rather than at read-time, as MongoDB's document-oriented nature is optimized for retrieval of whole documents rather than scattered data.
Optimization for Use Cases: Tailor the schema to optimize the database for the most frequent and critical use cases. This ensures that the database performs efficiently for its primary functions.
Complex Aggregation: Consider incorporating complex aggregation operations into the schema, such as pre-aggregating some data as per the application's requirements to optimize query performance and response times.
By considering these factors when designing the schema, developers can create a well-structured and efficient MongoDB database schema that meets the specific needs of the application.
arrays in mongodb
In MongoDB, arrays are a flexible way to store and manipulate data. Here are several important aspects to consider when working with arrays in MongoDB:
Array Data Type: MongoDB allows fields in a document to hold arrays as a data type. These arrays can store a variety of elements, including other documents, arrays, and scalar values.
Array Operators: MongoDB provides a rich set of operators for querying and updating array fields. These include operators like $push, $pull, $addToSet, $pop, $elemMatch, and $slice, among others. These operators enable efficient manipulation and modification of array data.
Array Indexing: MongoDB supports indexing on array fields. This allows for efficient querying and retrieval of data within arrays, providing improved performance for array-related operations.
Array Limitations: While arrays in MongoDB offer flexibility, it's important to consider the potential impact on performance and complexity when dealing with large arrays. Additionally, deeply nested arrays can impact query performance and readability.
Schema Design: Careful consideration should be given to the schema design when using arrays. It is important to structure arrays meaningfully based on how the data will be queried and updated. This includes determining whether an array of embedded documents or a separate collection is more appropriate.
By understanding and leveraging arrays effectively in MongoDB, developers can take advantage of the flexibility offered by arrays while ensuring efficient querying, indexing, and schema design. This allows for effective management and manipulation of array data within MongoDB documents.
with example
Certainly! Let's explore the use of arrays in MongoDB with an example related to a simple schema for a blog application. Consider a collection called "posts" that includes an array of comments for each post.
Here's an example document representing a post with an array of comments:
{ "_id": ObjectId("60a5c3c2c21e100af6550714"), "title": " to MongoDB Arrays", "content": "In this post we will explore the use of arrays in MongoDB.", "comments": [ { "username": "1", "comment": "Great post! Very informative." }, { "username": "user2", "comment": "I learned a lot from this article." } }
In this example:
You can use MongoDB's array operators to perform various operations on the "comments" array field. For instance, you can use the $push to add a new comment to the array:
db.posts.update( { _id: ObjectId("605c3c2c21e100af6550714") }, { $push: { comments: {: "user3", comment: "Thanks for sharing!" } } );
This operation adds a new comment to the "comments" array for the post with the specified _id.
, if there is a need to retrieve posts based on the comments, you can use array operators such as $elemMatch to query documents based on specific criteria within the array.
, arrays in MongoDB are powerful for storing and working with related data within a single document, offering a great deal of flexibility in schema design and data manipulation.
Principal Component Analysis (PCA)
Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set. Reducing the number of variables of a data set naturally comes at the expense of accuracy, but the trick in dimensionality reduction is to trade a little accuracy for simplicity. Because smaller data sets are easier to explore and visualize and make analyzing data much easier and faster for machine learning algorithms without extraneous variables to process. So to sum up, the idea of PCA is simple — reduce the number of variables of a data set, while preserving as much information as possible. HOW DO YOU DO A PCA?
Step by Step Explanation of PCA STEP 1: STANDARDIZATION The aim of this step is to standardize the range of the continuous initial variables so that each one of them contributes equally to the analysis. More specifically, the reason why it is critical to perform standardization prior to PCA, is that the latter is quite sensitive regarding the variances of the initial variables. That is, if there are large differences between the ranges of initial variables, those variables with larger ranges will dominate over those with small ranges (For example, a variable that ranges between 0 and 100 will dominate over a variable that ranges between 0 and 1), which will lead to biased results. So, transforming the data to comparable scales can prevent this problem. Mathematically, this can be done by subtracting the mean and dividing by the standard deviation for each value of each variable.
Once the standardization is done, all the variables will be transformed to the same scale.
STEP 2: COVARIANCE MATRIX COMPUTATION The aim of this step is to understand how the variables of the input data set are varying from the mean with respect to each other, or in other words, to see if there is any relationship between them. Because sometimes, variables are highly correlated in such a way that they contain redundant information. So, in order to identify these correlations, we compute the covariance matrix. The covariance matrix is a p × p symmetric matrix (where p is the number of dimensions) that has as entries the covariances associated with all possible pairs of the initial variables. For example, for a 3-dimensional data set with 3 variables x, y, and z, the covariance matrix is a 3×3 matrix of this from: Covariance Matrix for 3-Dimensional Data Since the covariance of a variable with itself is its variance (Cov(a,a)=Var(a)), in the main diagonal (Top left to bottom right) we actually have the variances of each initial variable. And since the covariance is commutative (Cov(a,b)=Cov(b,a)), the entries of the covariance matrix are symmetric with respect to the main diagonal, which means that the upper and the lower triangular portions are equal. STEP 3: COMPUTE THE EIGENVECTORS AND EIGENVALUES OF THE COVARIANCE MATRIX TO IDENTIFY THE PRINCIPAL COMPONENTS Eigenvectors and eigenvalues are the linear algebra concepts that we need to compute from the covariance matrix in order to determine the principal components of the data. Before getting to the explanation of these concepts, let’s first understand what do we mean by principal components. Principal components are new variables that are constructed as linear combinations or mixtures of the initial variables. These combinations are done in such a way that the new variables (i.e., principal components) are uncorrelated and most of the information within the initial variables is squeezed or compressed into the first components. So, the idea is 10-dimensional data gives you 10 principal components, but PCA tries to put maximum possible information in the first component, then maximum remaining information in the second and so on, until having something like shown in the scree plot below. Organizing information in principal components this way, will allow you to reduce dimensionality without losing much information, and this by discarding the components with low information and considering the remaining components as your new variables. An important thing to realize here is that, the principal components are less interpretable and don’t have any real meaning since they are constructed as linear combinations of the initial variables. Geometrically speaking, principal components represent the directions of the data that explain a maximal amount of variance, that is to say, the lines that capture most information of the data. The relationship between variance and information here, is that, the larger the variance carried by a line, the larger the dispersion of the data points along it, and the larger the dispersion along a line, the more the information it has. To put all this simply, just think of principal components as new axes that provide the best angle to see and evaluate the data, so that the differences between the observations are better visible.
STEP 4: FEATURE VECTOR As we saw in the previous step, computing the eigenvectors and ordering them by their eigenvalues in descending order, allow us to find the principal components in order of significance. In this step, what we do is, to choose whether to keep all these components or discard those of lesser significance (of low eigenvalues), and form with the remaining ones a matrix of vectors that we call Feature vector. So, the feature vector is simply a matrix that has as columns the eigenvectors of the components that we decide to keep. This makes it the first step towards dimensionality reduction, because if we choose to keep only p eigenvectors (components) out of n, the final data set will have only p dimensions. Example: Continuing with the example from the previous step, we can either form a feature vector with both of the eigenvectors v1 and v2:
Or discard the eigenvector v2, which is the one of lesser significance, and form a feature vector with v1 only:
Discarding the eigenvector v2 will reduce dimensionality by 1, and will consequently cause a loss of information in the final data set. But given that v2 was carrying only 4% of the information, the loss will be therefore not important and we will still have 96% of the information that is carried by v1.
So, as we saw in the example, it’s up to you to choose whether to keep all the components or discard the ones of lesser significance, depending on what you are looking for. Because if you just want to describe your data in terms of new variables (principal components) that are uncorrelated without seeking to reduce dimensionality, leaving out lesser significant components is not needed. LAST STEP: RECAST THE DATA ALONG THE PRINCIPAL COMPONENTS AXES In the previous steps, apart from standardization, you do not make any changes on the data, you just select the principal components and form the feature vector, but the input data set remains always in terms of the original axes (i.e, in terms of the initial variables). In this step, which is the last one, the aim is to use the feature vector formed using the eigenvectors of the covariance matrix, to reorient the data from the original axes to the ones represented by the principal components (hence the name Principal Components Analysis). This can be done by multiplying the transpose of the original data set by the transpose of the feature vector.
Thank you for sharing an in-depth explanation of Principal Component Analysis (PCA). It provides a comprehensive understanding of the PCA process and its underlying mathematical concepts. To summarize, PCA can be broken down into several key steps:
Standardization: The initial variables are standardized to bring them to a comparable scale, ensuring that each variable contributes equally to the analysis.
Covariance Matrix Computation: The covariance matrix is computed to understand the relationships and correlations between the input. This allows the identification of redundant information and relationships between variables.
Compute the Eigenvectors and Eigenvalues: Eigenvectors and eigenvalues of the covariance matrix are computed. These represent the principal components of the data. The principal are new variables that are constructed as linear combinations of the initial variables and are uncorrelated. They capture the most information from the initial variables and are ordered by significance.
Feature Vector: A feature vector is formed by choosing whether to keep all the principal components or discard those of lesser significance. The feature vector is a matrix that contains the selected eigenvectors and is the first step towards dimensionality reduction.
Recasting the Data Along the Principal Components Axes: In the final step, the data is reoriented from the axes to the axes represented by the principal components. This is achieved by multiplying the transpose of the original data set by the transpose of the feature vector, effectively transforming the data into the new coordinate system by the principal components.
Overall, PCA allows for dimensionality reduction while preserving much information as possible by transforming the data into a new, uncorrelated set of variables. This facilitates exploration, visualization, and analysis, and is particularly useful in data for machine learning algorithms.
It is impossible for a web service to provide following three guarantees at the same time:
Consistency
Availability
Partition-tolerance
Consistency – Consistency means that the nodes will have the same copies of a replicated data item visible for various transactions. A guarantee that every node in a distributed cluster returns the same, most recent and a successful write. Consistency refers to every client having the same view of the data. There are various types of consistency models. Consistency in CAP refers to sequential consistency, a very strong form of consistency.
Availability – Availability means that each read or write request for a data item will either be processed successfully or will receive a message that the operation cannot be completed. Every non-failing node returns a response for all the read and write requests in a reasonable amount of time. The key word here is “every”. In simple terms, every node (on either side of a network partition) must be able to respond in a reasonable amount of time.
PartitionTolerance – Partition tolerance means that the system can continue operating even if the network connecting the nodes has a fault that results in two or more partitions, where the nodes in each partition can only communicate among each other. That means, the system continues to function and upholds its consistency guarantees in spite of network partitions. Network partitions are a fact of life. Distributed systems guaranteeing partition tolerance can gracefully recover from partitions once the partition heals.
The CAP theorem states that distributed databases can have at most two of the three properties: consistency, availability, and partition tolerance. As a result, database systems prioritize only two properties at a time.
CA(Consistency and Availability)-
The system prioritizes availability over consistency and can respond with possibly stale data.
Example databases: Cassandra, CouchDB, Riak, Voldemort.
AP(Availability and Partition Tolerance)-
The system prioritizes availability over consistency and can respond with possibly stale data. The system can be distributed across multiple nodes and is designed to operate reliably even in the face of network partitions. Example databases: Amazon DynamoDB,
CP(Consistency and Partition Tolerance)- The system prioritizes consistency over availability and responds with the latest updated data. The system can be distributed across multiple nodes and is designed to operate reliably even in the face of network partitions. Example databases: Apache HBase, MongoDB, Redis. Consistency: All nodes should see the same data at the same time Availability: Node failures do not prevent survivors from continuing to operate Partition-tolerance: The system continues to operate despite network partitions A distributed system can satisfy any two of these guarantees at the same time but not all three
The future of databases is distributed (Big Data Trend, etc.) CAP theorem describes the trade-offs involved in distributed systems A proper understanding of CAP theorem is essential to making decisions about the future of distributed database design Misunderstanding can lead to erroneous or inappropriate design choices
Explain this all in understandable words and simple terms
The CAP theorem outlines the challenges of distributed systems, stating that's impossible for a web service to simultaneously provide three guarantees: consistency, availability, and partition tolerance. Here's a simplified breakdown:
Consistency: This means that all nodes in a distributed system have the same copies of data visible for different transactions. It ensures that every client has the same view of the data, providing a strong of consistency known as sequential consistency.
Availability: This guarantee ensures that every or write request for data will be processed successfully or receive a message that the operation cannot be completed. Every non-failing node processes requests in a reasonable amount of time.
Partition Tolerance: This means the system can continue operating even if network faults result in two or more partitions where nodes can only within their own partition. Essentially, the system can continue to function and maintain its guarantees despite network partitions.
According to the CAP theorem, distributed databases can prioritize two of these guarantees at a time:
CA (Consistency and Availability): Prioritizes availability over consistency and may respond with possibly stale data. Examples include Cassandra, CouchDB, Riak, Voldemort.
AP (Availability and Partition Tolerance): Priorizes availability over consistency and can also respond with possibly stale data. It's built to operate reliably even in the face of network partitions. Example databases include Amazon DynamoDB.
CP (Consistency and Partition Tolerance): Prioritizes consistency over availability and with the latest updated data. It's designed to operate reliably even in the face of network partitions. Examples of databases that follow this model include Apache HBase, MongoDB, and Redis.
The future of databases involves distributed systems, and understanding the CAP theorem is crucial when making decisions about database design. Misunderstanding the CAP theorem can lead to erroneous or inappropriate choices.
What is HBase? HBase is a distributed column-oriented database built on top of the Hadoop file system. It is an open-source project and is horizontally scalable. HBase is a data model that is similar to Google’s big table designed to provide quick random access to huge amounts of structured data. It leverages the fault tolerance provided by the Hadoop File System (HDFS). It is a part of the Hadoop ecosystem that provides random real-time read/write access to data in the Hadoop File System. One can store the data in HDFS either directly or through HBase. Data consumer reads/accesses the data in HDFS randomly using HBase. HBase sits on top of the Hadoop File System and provides read and write access. In HBase, tables are split into regions and are served by the region servers. Regions are vertically divided by column families into “Stores”. Stores are saved as files in HDFS. Shown below is the architecture of HBase. Note: The term ‘store’ is used for regions to explain the storage structure. HBase has three major components: the client library, a master server, and region servers. Region servers can be added or removed as per requirement. MasterServer The master server - Assigns regions to the region servers and takes the help of Apache ZooKeeper for this task. Handles load balancing of the regions across region servers. It unloads the busy servers and shifts the regions to less occupied servers. Maintains the state of the cluster by negotiating the load balancing. Is responsible for schema changes and other metadata operations such as creation of tables and column families. Regions Regions are nothing but tables that are split up and spread across the region servers. Region server The region servers have regions that - Communicate with the client and handle data-related operations. Handle read and write requests for all the regions under it. Decide the size of the region by following the region size thresholds. When we take a deeper look into the region server, it contain regions and stores as shown below: The store contains memory store and HFiles. Memstore is just like a cache memory. Anything that is entered into the HBase is stored here initially. Later, the data is transferred and saved in Hfiles as blocks and the memstore is flushed. Zookeeper Zookeeper is an open-source project that provides services like maintaining configuration information, naming, providing distributed synchronization, etc. Zookeeper has ephemeral nodes representing different region servers. Master servers use these nodes to discover available servers. In addition to availability, the nodes are also used to track server failures or network partitions. Clients communicate with region servers via zookeeper. In pseudo and standalone modes, HBase itself will take care of zookeeper.
Explain this in simple words
Sure, I'd be happy to explain HBase in simple terms.
HBase a type of database that's a part of the Hadoop ecosystem, designed to large volumes of structured data while providing quick random access. It's built on top of the Hadoop file system is open-source and horizontally scalable, meaning it can grow to handle more data as needed.
Here's an overview of how it works:
Data: HBase can store data directly in the Hadoop file (HDFS) or access it from there. It's designed quick and random access to the data, making it suitable for that need to retrieve specific pieces of information efficiently.
Architecture: In HBase, tables are split into and served by region servers. These regions are further divided into "stores" and are saved as files in HDFS. client library, master server, and region servers are its three components.
Components:
Zookeeper:keeper is another open-source project that helps in coordinating and managing the HBase cluster. It maintains information about the, naming, distributed synchronization, and availability of region servers. It's also used to track server failures or network partitions.
In a nutshell, HBase is a powerful database that's well-suited for handling massive amounts of data and providing quick access to specific pieces of information, making it a valuable part of the Hadoop ecosystem.
3.Yarn: Yet another Resource Negotiator is used for job scheduling and manage the cluster. 4. Hadoop Common: These Java libraries are used to start Hadoop and are used by other Hadoop modules.
It seems like you've provided additional information about components of the Hadoop ecosystem. YARN (Yet Another Resource Negotiator) is indeed responsible for job scheduling and cluster resource management. Hadoop Common consists of the Java libraries used to start Hadoop and are utilized by other Hadoop modules for various functionalities.
Is there anything specific you would like to know about these components?
Hadoop is an open source framework from Apache and is used to store process and analyze data which are very huge in volume. Hadoop is written in Java. It is used for batch/offline processing. It is being used by Facebook, Yahoo, Google, Twitter, LinkedIn and many more. Moreover it can be scaled up just by adding nodes in the cluster. Modules of Hadoop HDFS: Hadoop Distributed File System. Google published its paper GFS and on the basis of that HDFS was developed. It states that the files will be broken into blocks and stored in nodes over the distributed architecture. Map Reduce: This is a framework which helps Java programs to do the parallel computation on data using key value pair. The Map task takes input data and converts it into a data set which can be computed in Key value pair. The output of Map task is consumed by reduce task and then the out of reducer gives the desired result. 3.Yarn: Yet another Resource Negotiator is used for job scheduling and manage the cluster. 4. Hadoop Common: These Java libraries are used to start Hadoop and are used by other Hadoop modules.
This is a comprehensive summary of Hadoop and its major components. It seems like you have a good understanding of Hadoop's capabilities, including its distributed file system (HDFS), the MapReduce framework, YARN for job scheduling and resource management, and the Hadoop Common Java libraries. Hadoop's ability to handle large volumes of data, its scalability, and its usage by major tech companies are indeed key aspects of its appeal.
Is there anything specific you would like to explore further about Hadoop or its components?
Fundamental Statistical Concepts for Data Science
Statistics is “a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data”. Throw programming and machine learning into the mix and you have a pretty good description of the core skills for data science. Statistics is used in almost all aspects of data science. It is used to analyse, transform and clean data. Evaluate and optimise machine learning algorithms. It is also used in the presentation of insights and findings. The field of statistics is extremely broad and determining what exactly you need to learn and in what order can be difficult. Additionally, a lot of material for learning this subject is very complex and in some cases can be quite difficult to digest. Particularly if you don’t have an advanced maths degree and are transitioning into data science from a field such as software engineering. In the following article, I am going to introduce eight fundamental statistical concepts you need to be able to grasp when learning data science. These are not particularly advanced techniques but they are a selection of the basic requirements you need to know before moving onto learning more complex methods.
Statistical sampling In statistics, the entire set of raw data that you may have available for a test or experiment is known as the population. For a number of reasons, you cannot necessarily measure the patterns and trends across the entire population. For that reason statistics allows us to take a sample, perform some computations on that set of data, and using probability and some assumptions we can with a certain degree of certainty understand trends for the entire population or predict future events. Statistics allows us to take a sample, or portion of the population, perform some computations on that set of data and using probability and some assumptions we can with a certain degree of certainty understand trends for the entire population Let’s say, for example, that we want to understand the prevalence of a disease such as breast cancer in the entire population of the United Kingdom. For practical reasons, it is not possible to screen the entire population. Instead, we may take a random sample and measure the prevalence among them. Assuming our sample is sufficiently randomised and representative of the entire population we can achieve a measure of prevalence.
Descriptive statistics Descriptive statistics, as the name suggests, helps us to describe the data. In other words, it enables us to understand the underlying characteristics. It doesn’t predict anything, make assumptions or infer anything it simply provides a description of what the data sample we have looks like. Descriptive statistics are derived from calculations, often called parameters. These include things like the: • Mean — the central value, commonly called the average. • Median — the middle value if we ordered the data from low to high and divide it exactly in half. • Mode- the value which occurs most often.
Distributions Descriptive statistics are useful but they can often hide important information about the data set. For example, if a data set contains several numbers that are much larger than the others then the mean may be skewed and will not give us a true representation of the data. A distribution is a chart, often a histogram, that displays the frequency with which each value appears in a data set. This type of chart gives us information about the spread and skewness of the data. A distribution will usually form a curve-like graph. This may be skewed more to the left or right.
In some cases, the curve may not be as smooth.
One of the most important distributions is the normal distribution, commonly referred to as the bell curve due to its shape. It is symmetrical in shape with most of the values clustering around the central peak and the further away values distributed equally on each side of the curve. Many variables in nature will form a normal distribution such as peoples heights and IQ scores.
Probability Probability, in simple terms, is the likelihood of an event occurring. In statistics, an event is the outcome of an experiment which could be something like the rolling of a dice or the results of an AB test. Probability for a single event is calculated by dividing the number of events by the number of total possible outcomes. In the case of, say, rolling a six on a dice there are 6 possible outcomes. So the chance of rolling a six is 1/6 = 0.167, sometimes this is also expressed as a percentage so 16.7%. Events can be either independent or dependent. With dependent events, a prior event influences the subsequent event. Let’s say we have a bag of M&M’s and we wanted to determine the probability of randomly picking a red M&M. If every time we removed the selected M&M from the bag the probability of picking red would change due to the effect of prior events. Independent events are not affected by prior events. In the case of the bag of M&M’s if each time we selected one we put it back in the bag. The probability of selecting red would remain the same each time. Whether an event is independent or not is important, as the way in which we calculate the probability of multiple events changes depending on the type. The probability of multiple independent events is calculated by simply multiplying the probability of each event. In the example of the dice roll, say we wanted to calculate the chance of rolling a 6 three times. This would look like the following: 1/6 = 0.167 1/6 = 0.167 1/6 = 0.167 0.167 * 0.167 * 0.167 = 0.005 The calculation is different for dependent events, also known as conditional probability. If we take the M&M’s example, imagine we have a bag with only two colours red and yellow, and we know that the bag contains 3 red and 2 yellow and we want to calculate the probability of picking two reds in a row. On the first pick, the probability of picking a red is 3/5 = 0.6. On the second pick we have removed one M&M, which happened to be red, so our second probability calculation is 2/4 = 0.5. The probability of picking two reds in a row is therefore 0.6 * 0.5 = 0.3.
Bias As we have previously discussed in statistics we frequently use samples of data to make estimates about the whole data set. Similarly, for predictive modelling, we will use some training data and attempt to build a model that can make predictions about new data. Bias is the tendency of a statistical or predictive model to over or underestimates a parameter. This is often due to the method used to obtain a sample or the way that errors are measured. There are several types of bias commonly found in statistics. Here is a brief description of two of them.
Selection bias — this occurs when the sample is selected in a non-random way. In data science, an example may be stopping an AB test early when the test is running or selecting data for training a machine learning model from one time period which could mask seasonal effects.
Confirmation bias — this occurs when the person performing some analysis has a predetermined assumption about the data. In this situation, there can be a tendency to spend more time examining variables that are likely to support this assumption.
Variance As we discussed earlier in this article the mean in a sample of data is the central value. Variance measures how far each value in the data set is from the mean. Essentially it is a measurement of the spread of numbers in a data set. Standard deviation is a common measure of variation for data that has a normal distribution. It is a calculation that gives a value to represent how widely distributed the values are. A low standard deviation indicates that the values tend to lie quite close to the mean, whilst a high standard deviation indicates that the values are more spread out. If the data does not follow a normal distribution then other measures of variance are used. Commonly the interquartile range is used. This measurement is derived by first ordering the values by rank and then dividing the data points into four equal parts, called quartiles. Each quartile describes where 25% of the data points lie according to the median. The interquartile range is calculated by subtracting the median for the two central quarters, also known as Q1 and Q3.
Bias/Variance tradeoff The concepts of bias and variance are very important for machine learning. When we build a machine learning model we use a sample of data known as the training data set. The model learns patterns in this data and generates a mathematical function that is able to map the correct target label or value (y) to a set of inputs (X). When generating this mapping function the model will use a set of assumptions to better approximate the target. For example, the linear regression algorithm assumes a linear (straight line) relationship between the input and the target. These assumptions generate bias in the model. As a computation, bias is the difference between the mean prediction generated by the model and the true value. If we were to train a model using different samples of training data we would get a variation in the predictions that are returned. Variance in machine learning is a measure of how large this difference is. In machine learning bias and variance make up the overall expected error for our predictions. In an ideal world, we would have both low bias and low variance. However, in practice minimizing bias will usually result in an increase in variance and vice versa. The bias/variance trade-off describes the process of balancing these two errors to minimise the overall error for a model.
Correlation Correlation is a statistical technique that measures relationships between two variables. Correlation is assumed to be linear (forming a line when displayed on a graph) and is expressed as a number between +1 and -1, this is known as the correlation coefficient. A correlation coefficient of +1 denotes a perfectly positive correlation (when the value for one variable increases the value of the second variable also increases), a coefficient of 0 denotes no correlation and a coefficient of -1 denotes a perfect negative correlation.
Statistical Resampling Once we have a data sample, it can be used to estimate the population parameter. The problem is that we only have a single estimate of the population parameter, with little idea of the variability or uncertainty in the estimate. One way to address this is by estimating the population parameter multiple times from our data sample. This is called resampling. Statistical resampling methods are procedures that describe how to economically use available data to estimate a population parameter. The result can be both a more accurate estimate of the parameter (such as taking the mean of the estimates) and a quantification of the uncertainty of the estimate (such as adding a confidence interval). Resampling methods are very easy to use, requiring little mathematical knowledge. They are methods that are easy to understand and implement compared to specialized statistical methods that may require deep technical skill in order to select and interpret. The resampling methods […] are easy to learn and easy to apply. They require no mathematics beyond introductory high-school algebra, et are applicable in an exceptionally broad range of subject areas. — Page xiii, Resampling Methods: A Practical Guide to Data Analysis, 2005. A downside of the methods is that they can be computationally very expensive, requiring tens, hundreds, or even thousands of resamples in order to develop a robust estimate of the population parameter. The key idea is to resample form the original data — either directly or via a fitted model — to create replicate datasets, from which the variability of the quantiles of interest can be assessed without long-winded and error-prone analytical calculation. Because this approach involves repeating the original data analysis procedure with many replicate sets of data, these are sometimes called computer-intensive methods. — Page 3, Bootstrap Methods and their Application, 1997. Each new subsample from the original data sample is used to estimate the population parameter. The sample of estimated population parameters can then be considered with statistical tools in order to quantify the expected value and variance, providing measures of the uncertainty of the estimate. Statistical sampling methods can be used in the selection of a subsample from the original sample. A key difference is that process must be repeated multiple times. The problem with this is that there will be some relationship between the samples as observations that will be shared across multiple subsamples. This means that the subsamples and the estimated population parameters are not strictly identical and independently distributed. This has implications for statistical tests performed on the sample of estimated population parameters downstream, i.e. paired statistical tests may be required. Two commonly used resampling methods that you may encounter are k-fold cross-validation and the bootstrap. • Bootstrap. Samples are drawn from the dataset with replacement (allowing the same sample to appear more than once in the sample), where those instances not drawn into the data sample may be used for the test set. • k-fold Cross-Validation. A dataset is partitioned into k groups, where each group is given the opportunity of being used as a held out test set leaving the remaining groups as the training set. The k-fold cross-validation method specifically lends itself to use in the evaluation of predictive models that are repeatedly trained on one subset of the data and evaluated on a second held-out subset of the data. Generally, resampling techniques for estimating model performance operate similarly: a subset of samples are used to fit a model and the remaining samples are used to estimate the efficacy of the model. This process is repeated multiple times and the results are aggregated and summarized. The differences in techniques usually center around the method in which subsamples are chosen. — Page 69, Applied Predictive Modeling, 2013. The bootstrap method can be used for the same purpose, but is a more general and simpler method intended for estimating a population parameter.
Explain in simple terms and understandable words?
Absolutely, I'd be happy to explain these fundamental statistical concepts in simple and understandable terms.
Statistical Sampling: When we can't measure or observe an entire population, we take a representative sample and use it to make predictions about the entire. For example, instead of screening the entire UK population for breast cancer prevalence, we can take a random sample to estimate the prevalence based on that sample.
Descriptive Statistics: This helps us understand the characteristics of the sample without making predictions. It includes measures like the average (mean), middle value (median), and most common value (mode).
Distributions: shows how frequently each value appears in a data set. It gives us insights into how the data is spread out and whether it follows a typical pattern like the "bell curve."
Probability: This measures the likelihood of an event occurring, such as rolling a 6 on a dice. can be independent (not influenced by prior events) or dependentinfluenced by prior events), and probability calculations differ for type.
Bias: Bias refers to the tendency of or predictive models to consistently over or underestimates a parameter. Types of bias include selection bias (non-random sample selection) and confirmation bias (preconceived assumptions affecting analysis).
Variance: This measures how far each in a data set is from the mean, essentially assessing the spread of the data.
Bias/Variance Tradeoff: In machine learning, we balance bias (difference between the model's prediction and the true value) and variance (variation in the predictions) to minimize the overall error in our predictions.
Correlation: This measures the relationship between two variables and is expressed as a number between +1 and -1, where +1 denotes a positive correlation, 0 denotes no correlation, and -1 denotes a perfect negative.
Statistical Resampling: This technique involves repeatedly estimating population parameter from a data sample to understand its variability and uncertainty, which provides a more accurate estimate and quantifies the uncertainty. like k-fold cross-validation and the bootstrap are used for this purpose.
I hope this breakdown helps clarify these fundamental statistical concepts for you Let me know if you have any further questions.