This document provides an introduction to common data structures including lists, arrays, stacks, queues, heaps, trees, and graphs. It explains that data structures organize data in memory and that the choice of data structure affects program performance for different tasks. The document then gives examples of queues and binary trees, explaining their structures and uses. It concludes that programmers choose appropriate data structures based on how the data will be used.
This document provides an introduction to data structures. It defines data structures as the logical organization of data storage and the relationships between data elements. Data structures allow for efficient data access and include primitive structures like integers and characters as well as nonprimitive structures like arrays, linked lists, queues, and trees. Different types of data structures are suited to different applications. The document outlines common linear and non-linear data structures and basic operations on data structures like insertion, deletion, traversal, search, and sorting.
Data structures are schemes for organizing data in computer memory. Common data structures include lists, arrays, stacks, queues, heaps, trees, and graphs. The organization of data affects program performance for different tasks. Programmers choose which data structures to use based on the nature of the data and the processes that need to be performed on that data.
SAP ABAP database tables are collection of fields, in which fields are made up of columns and rows. In SAP more than 8000 tables are defined. When table is created, its columns are named and data type is supplied for each columns. There can be only one data value in each column of each row in a table.
https://www.ducatindia.com/Best-sap-erp-training/
This document discusses data structures. It defines data as information stored in computers in various formats like numeric, non-numeric, and character. Data structures organize data in a way that allows for efficient operations. The simplest data structure is a variable, but arrays and structures allow storing multiple data. Linear data structures like stacks, queues, and linked lists as well as non-linear ones like trees and graphs support insertion, deletion and other operations better than variables and arrays. Data structures are used in nearly all programs and software to efficiently store and manipulate customer, contact, and other user data.
Data structures organize data in a computer so it can be used efficiently. They depict the logical relationship between data elements and allow efficient data manipulation. Common data structures include arrays, stacks, queues, and linked lists which are linear structures that arrange elements sequentially, and trees and graphs which are non-linear structures that arrange elements hierarchically. Data structures are important for handling large data sets, fetching data efficiently, and making algorithms simpler and faster.
This is a demo presentation prepared for the recruitment of Lecturer in CSE at Green University. In this presentation, an introduction to Data Structures is demonstrated in an intuitive way.
The document introduces data structures and provides examples of commonly used data structures like lists, arrays, stacks, queues, heaps, trees, and graphs. It discusses how data structures can be categorized based on their representation (linear vs non-linear) and memory allocation (static vs dynamic). The document explains that the choice of data structure depends on the nature of the data and the operations needed to be performed. It provides examples of queues and binary trees to illustrate how the structure of data impacts the efficiency of operations.
This document provides an introduction to common data structures including lists, arrays, stacks, queues, heaps, trees, and graphs. It explains that data structures organize data in memory and that the choice of data structure affects program performance for different tasks. The document then gives examples of queues and binary trees, explaining their structures and uses. It concludes that programmers choose appropriate data structures based on how the data will be used.
This document provides an introduction to data structures. It defines data structures as the logical organization of data storage and the relationships between data elements. Data structures allow for efficient data access and include primitive structures like integers and characters as well as nonprimitive structures like arrays, linked lists, queues, and trees. Different types of data structures are suited to different applications. The document outlines common linear and non-linear data structures and basic operations on data structures like insertion, deletion, traversal, search, and sorting.
Data structures are schemes for organizing data in computer memory. Common data structures include lists, arrays, stacks, queues, heaps, trees, and graphs. The organization of data affects program performance for different tasks. Programmers choose which data structures to use based on the nature of the data and the processes that need to be performed on that data.
SAP ABAP database tables are collection of fields, in which fields are made up of columns and rows. In SAP more than 8000 tables are defined. When table is created, its columns are named and data type is supplied for each columns. There can be only one data value in each column of each row in a table.
https://www.ducatindia.com/Best-sap-erp-training/
This document discusses data structures. It defines data as information stored in computers in various formats like numeric, non-numeric, and character. Data structures organize data in a way that allows for efficient operations. The simplest data structure is a variable, but arrays and structures allow storing multiple data. Linear data structures like stacks, queues, and linked lists as well as non-linear ones like trees and graphs support insertion, deletion and other operations better than variables and arrays. Data structures are used in nearly all programs and software to efficiently store and manipulate customer, contact, and other user data.
Data structures organize data in a computer so it can be used efficiently. They depict the logical relationship between data elements and allow efficient data manipulation. Common data structures include arrays, stacks, queues, and linked lists which are linear structures that arrange elements sequentially, and trees and graphs which are non-linear structures that arrange elements hierarchically. Data structures are important for handling large data sets, fetching data efficiently, and making algorithms simpler and faster.
This is a demo presentation prepared for the recruitment of Lecturer in CSE at Green University. In this presentation, an introduction to Data Structures is demonstrated in an intuitive way.
The document introduces data structures and provides examples of commonly used data structures like lists, arrays, stacks, queues, heaps, trees, and graphs. It discusses how data structures can be categorized based on their representation (linear vs non-linear) and memory allocation (static vs dynamic). The document explains that the choice of data structure depends on the nature of the data and the operations needed to be performed. It provides examples of queues and binary trees to illustrate how the structure of data impacts the efficiency of operations.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
Entities represent people, objects, or abstract concepts and have attributes that describe examples of the entity. Notation defines the entity name in capital letters and attributes in brackets. Entities and attributes are part of data modeling, where entities become database tables and attributes become fields during implementation. Examples provided include a PUPIL entity with attributes like name and DOB, a CAR entity with attributes like make and model, and a DOCTOR APPOINTMENT entity with date and time attributes.
This document discusses different types of data structures. It defines data structures as representations of logical relationships between data elements. It describes primitive data structures like integers and floats that are directly supported by machine instructions, and non-primitive structures like arrays, stacks, queues, lists, graphs and trees that are derived from primitive structures. Stacks are introduced as a non-primitive linear data structure that operates in a last-in, first-out manner, with elements only added or removed from the top of the stack.
This document provides an overview of data structures. It defines data structures as a way to organize and store data to allow for effective operations. The document outlines common data structure operations like traversing, searching, insertion, and deletion. It also categorizes data structures as primitive, non-primitive, linear, and non-linear. Linear structures discussed include stacks and queues. Non-linear structures covered are trees and graphs. Details are provided on representing graphs and trees.
A data dictionary contains metadata that describes the entities, attributes, data types, sizes, validation rules, and keys of data stored in a database. It is produced during database modeling and does not store actual data. The example shows a data dictionary for a school database that would store information about pupils and tutor classes, including each pupil's name and tutor class.
This document discusses different data structures, including their definitions, classifications, and common operations. It defines a data structure as a way to organize data by considering the elements stored and their relationships. Data structures covered include arrays, linked lists, stacks, queues, trees, graphs, and hash tables. They are classified as linear (processed sequentially) versus non-linear, and primitive (direct machine representation) versus non-primitive. Common operations on data structures are traversing, searching, inserting, and deleting.
Data structures are a way to organize data in a computer so it can be used efficiently. Common data structures include arrays, stacks, queues, linked lists, trees, and graphs. Arrays store elements in a linear order, stacks follow last-in first-out access with push and pop, queues are first-in first-out using enqueue and dequeue, and linked lists, trees and graphs have nodes connected by links or edges. These data structures enable efficient operations like inserting, deleting and searching for data.
Binary trees are hierarchical data structures that store nodes with zero, one or two child nodes. The root node is at the top and child nodes are directly connected below their parent node. Trees allow for quick access and search of hierarchical data, as well as faster insertion and deletion compared to arrays. Common applications of trees include manipulating hierarchical data like file systems, router algorithms, and tree traversal methods for easy searching of information.
Data structures deal with the organization and storage of data to facilitate efficient access and modification. They allow relationships between different data elements to be expressed and enable efficient processing and accessing of data. Data structures are classified as either primitive, like integers and characters, or non-primitive, like arrays and lists. Common operations on data structures include traversing, searching, inserting, deleting, and sorting data elements.
A database organizes and stores data in files containing records made of fields. A flat-file database uses a single table which becomes inefficient as data grows. Relational databases avoid "anomalies" like insertion, update, and deletion errors from flat files by storing data across multiple linked tables using primary keys. Atomic data cannot be broken into smaller components while combined fields can be normalized.
Data structures allow for the effective organization and processing of data as a single unit. They involve determining how to logically represent data, choosing a data structure type, and developing operations to apply to the data. Common simple data structures include arrays and structures, while more complex structures include stacks, queues, linked lists, and trees. Key operations on data structures are insertion, deletion, searching, traversal, sorting, and merging.
This document provides an overview of classic data structures, including how they are classified and examples. It discusses both linear and non-linear data structures. Linear structures include arrays, stacks, queues, and linked lists. Non-linear structures include trees, graphs, hash tables, and sets. The document also describes the two phases of implementing data structures: how they are stored in computer memory, and expressing manipulating functions as algorithms using statements like if/then.
This document provides an overview of data structures and algorithms. It defines data structures as organized collections of data that allow for efficient use of data in a computer. Algorithms are step-by-step procedures to solve problems or achieve outputs. Common data structures discussed include arrays, stacks, queues, and linked lists. Linear data structures arrange elements sequentially while non-linear structures do not. Example non-linear structures mentioned are trees, binary search trees, and graphs. The document also gives examples of common algorithms like searching, sorting, insertion, updating, and deletion.
This document provides an overview of non-linear data structures, specifically trees and graphs. It defines non-linear data structures as those where data elements are not arranged sequentially. Trees are described as collections of nodes connected by edges, with one root node and potential child nodes. Common tree types are listed. Graphs are defined as collections of nodes connected by edges, where nodes represent data and edges represent relationships. Basic graph operations like adding nodes and edges are discussed. The document concludes by listing common operations on data structures like creation, selection, updating, searching, sorting, and destroying.
This document provides an introduction to data structures and algorithms. It defines data structures as organized groups of data elements that store and arrange data efficiently in a computer. Algorithms are defined as step-by-step procedures to solve problems or get desired outputs. Common data structure algorithms are searching, sorting, insertion, updating, and deletion. Data structures are classified as linear, where elements are arranged sequentially, and non-linear, where elements are connected hierarchically. Examples of each type are provided. The document aims to provide motivation and background knowledge for learning data structures and algorithms.
A table in a database consists of columns and rows, with the intersection of a column and row being a cell. Columns contain related data fields, such as name, gender, and hair color. Rows represent individual records of data, with each record containing the field values for a given person, company, or item.
This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
The document discusses different data structures including queues, binary trees, lists, and arrays. It provides examples of how queues and binary trees are structured and when each would be an appropriate data structure to use. Lists and arrays in Alice are also overviewed, noting that lists maintain order when items are removed while arrays leave empty spots. The key functions of each data structure and how programmers choose between them are summarized.
The document introduces different data structures commonly used in programming, including lists, arrays, stacks, queues, trees, and graphs. It provides examples of how queues and binary trees are structured and when each would be an appropriate data structure to use. Programmers must choose which data structure to implement based on the nature of the data and the necessary operations. Alice supports lists and arrays as basic built-in data structures that can be used to represent other data structures or organize program data.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
Entities represent people, objects, or abstract concepts and have attributes that describe examples of the entity. Notation defines the entity name in capital letters and attributes in brackets. Entities and attributes are part of data modeling, where entities become database tables and attributes become fields during implementation. Examples provided include a PUPIL entity with attributes like name and DOB, a CAR entity with attributes like make and model, and a DOCTOR APPOINTMENT entity with date and time attributes.
This document discusses different types of data structures. It defines data structures as representations of logical relationships between data elements. It describes primitive data structures like integers and floats that are directly supported by machine instructions, and non-primitive structures like arrays, stacks, queues, lists, graphs and trees that are derived from primitive structures. Stacks are introduced as a non-primitive linear data structure that operates in a last-in, first-out manner, with elements only added or removed from the top of the stack.
This document provides an overview of data structures. It defines data structures as a way to organize and store data to allow for effective operations. The document outlines common data structure operations like traversing, searching, insertion, and deletion. It also categorizes data structures as primitive, non-primitive, linear, and non-linear. Linear structures discussed include stacks and queues. Non-linear structures covered are trees and graphs. Details are provided on representing graphs and trees.
A data dictionary contains metadata that describes the entities, attributes, data types, sizes, validation rules, and keys of data stored in a database. It is produced during database modeling and does not store actual data. The example shows a data dictionary for a school database that would store information about pupils and tutor classes, including each pupil's name and tutor class.
This document discusses different data structures, including their definitions, classifications, and common operations. It defines a data structure as a way to organize data by considering the elements stored and their relationships. Data structures covered include arrays, linked lists, stacks, queues, trees, graphs, and hash tables. They are classified as linear (processed sequentially) versus non-linear, and primitive (direct machine representation) versus non-primitive. Common operations on data structures are traversing, searching, inserting, and deleting.
Data structures are a way to organize data in a computer so it can be used efficiently. Common data structures include arrays, stacks, queues, linked lists, trees, and graphs. Arrays store elements in a linear order, stacks follow last-in first-out access with push and pop, queues are first-in first-out using enqueue and dequeue, and linked lists, trees and graphs have nodes connected by links or edges. These data structures enable efficient operations like inserting, deleting and searching for data.
Binary trees are hierarchical data structures that store nodes with zero, one or two child nodes. The root node is at the top and child nodes are directly connected below their parent node. Trees allow for quick access and search of hierarchical data, as well as faster insertion and deletion compared to arrays. Common applications of trees include manipulating hierarchical data like file systems, router algorithms, and tree traversal methods for easy searching of information.
Data structures deal with the organization and storage of data to facilitate efficient access and modification. They allow relationships between different data elements to be expressed and enable efficient processing and accessing of data. Data structures are classified as either primitive, like integers and characters, or non-primitive, like arrays and lists. Common operations on data structures include traversing, searching, inserting, deleting, and sorting data elements.
A database organizes and stores data in files containing records made of fields. A flat-file database uses a single table which becomes inefficient as data grows. Relational databases avoid "anomalies" like insertion, update, and deletion errors from flat files by storing data across multiple linked tables using primary keys. Atomic data cannot be broken into smaller components while combined fields can be normalized.
Data structures allow for the effective organization and processing of data as a single unit. They involve determining how to logically represent data, choosing a data structure type, and developing operations to apply to the data. Common simple data structures include arrays and structures, while more complex structures include stacks, queues, linked lists, and trees. Key operations on data structures are insertion, deletion, searching, traversal, sorting, and merging.
This document provides an overview of classic data structures, including how they are classified and examples. It discusses both linear and non-linear data structures. Linear structures include arrays, stacks, queues, and linked lists. Non-linear structures include trees, graphs, hash tables, and sets. The document also describes the two phases of implementing data structures: how they are stored in computer memory, and expressing manipulating functions as algorithms using statements like if/then.
This document provides an overview of data structures and algorithms. It defines data structures as organized collections of data that allow for efficient use of data in a computer. Algorithms are step-by-step procedures to solve problems or achieve outputs. Common data structures discussed include arrays, stacks, queues, and linked lists. Linear data structures arrange elements sequentially while non-linear structures do not. Example non-linear structures mentioned are trees, binary search trees, and graphs. The document also gives examples of common algorithms like searching, sorting, insertion, updating, and deletion.
This document provides an overview of non-linear data structures, specifically trees and graphs. It defines non-linear data structures as those where data elements are not arranged sequentially. Trees are described as collections of nodes connected by edges, with one root node and potential child nodes. Common tree types are listed. Graphs are defined as collections of nodes connected by edges, where nodes represent data and edges represent relationships. Basic graph operations like adding nodes and edges are discussed. The document concludes by listing common operations on data structures like creation, selection, updating, searching, sorting, and destroying.
This document provides an introduction to data structures and algorithms. It defines data structures as organized groups of data elements that store and arrange data efficiently in a computer. Algorithms are defined as step-by-step procedures to solve problems or get desired outputs. Common data structure algorithms are searching, sorting, insertion, updating, and deletion. Data structures are classified as linear, where elements are arranged sequentially, and non-linear, where elements are connected hierarchically. Examples of each type are provided. The document aims to provide motivation and background knowledge for learning data structures and algorithms.
A table in a database consists of columns and rows, with the intersection of a column and row being a cell. Columns contain related data fields, such as name, gender, and hair color. Rows represent individual records of data, with each record containing the field values for a given person, company, or item.
This document discusses elementary data organization, including primitive and non-primitive data types, data structures, and common data structure operations. It defines data as values assigned to entities, and information as meaningful, processed data. Primitive data types directly supported by machines are listed. Non-primitive data types require additional processing. Data structures arrange data in memory and include common examples like arrays and linked lists. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Data structures are classified as linear or non-linear based on how elements are arranged.
The document discusses different data structures including queues, binary trees, lists, and arrays. It provides examples of how queues and binary trees are structured and when each would be an appropriate data structure to use. Lists and arrays in Alice are also overviewed, noting that lists maintain order when items are removed while arrays leave empty spots. The key functions of each data structure and how programmers choose between them are summarized.
The document introduces different data structures commonly used in programming, including lists, arrays, stacks, queues, trees, and graphs. It provides examples of how queues and binary trees are structured and when each would be an appropriate data structure to use. Programmers must choose which data structure to implement based on the nature of the data and the necessary operations. Alice supports lists and arrays as basic built-in data structures that can be used to represent other data structures or organize program data.
This document introduces common data structures including lists, arrays, stacks, queues, trees, and graphs. It explains that data structures organize data in memory and that the choice of data structure affects a program's performance for different tasks. The document provides examples of using a queue and binary tree data structure. It describes how queues process data in first-in, first-out order and how binary trees can efficiently search sorted data. Programmers choose appropriate data structures based on how the data will be used. In Alice, lists and arrays are the main built-in data structures that can be used to organize data or create other structures.
This document introduces common data structures including lists, arrays, stacks, queues, trees, and graphs. It explains that data structures organize data in memory and that the choice of data structure affects a program's performance for different tasks. The document provides examples of using a queue and binary tree data structure. It describes how queues process data in first-in, first-out order and how binary trees can efficiently search sorted data. Programmers choose appropriate data structures based on how the data will be used. In Alice, lists and arrays are the main built-in data structures that can be manipulated.
This document introduces common data structures including lists, arrays, stacks, queues, trees, and graphs. It explains that data structures organize data in memory and that the choice of data structure affects a program's performance for different tasks. The document provides examples of using a queue and binary tree data structure. It describes how queues process data in first-in, first-out order and how binary trees can efficiently search sorted data. Programmers choose appropriate data structures based on how the data will be used. Alice supports lists and arrays for organizing data.
The document introduces various data structures including lists, arrays, stacks, queues, heaps, trees, and graphs. It provides examples of how a queue and binary tree are structured and used. The key point is that the way data is organized affects how efficiently a program can perform different tasks on that data. Programmers choose appropriate data structures based on the nature of the data and the processes needed. Alice supports lists and arrays as basic built-in data structures that can be used to organize data or create other structures.
Unit.1 Introduction to Data Structuresresamplopsurat
The document provides an introduction to data structures. It defines a data structure as a way of storing and organizing data efficiently to allow operations to be performed quickly. Data structures can be static or dynamic. An abstract data type (ADT) is a mathematical description of an object and its operations. Algorithms implement ADTs using data structures. There are many data structures because there are tradeoffs between speed, memory usage, elegance, and other factors. Common data structures include lists, trees, hash tables. Operations on data structures include traversing, searching, insertion, deletion and others. Static structures have fixed sizes while dynamic structures have variable sizes.
Data structures are used to organize data efficiently to perform operations on large amounts of data. They include primitive structures like integers and floats, as well as linear structures like arrays, stacks, and queues and non-linear structures like trees and graphs. Common operations on data structures include traversing, inserting, deleting, searching, and sorting data elements. Understanding which data structure to use for a given problem is important to write efficient programs as data volumes continue growing rapidly.
The document discusses data structures and provides details about various types of data structures:
1) It describes linear and non-linear data structures, and lists arrays, stacks, queues, trees and graphs as examples.
2) It explains that primitive data structures like integers and characters are basic types directly used by machines, while non-primitive structures like arrays and lists are more sophisticated structures derived from primitive ones.
3) It provides details about common operations on data structures like creation, destruction, selection, updating, searching, sorting, splitting and merging.
The document discusses data structures and provides information on various types of data structures including linear and non-linear data structures. It defines data structures as specialized formats for organizing, processing, retrieving and storing data. Some key points discussed include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They provide efficient methods for storing and accessing data.
- Linear data structures like stacks and queues arrange data in a sequential order while non-linear structures like trees and graphs connect data in a non-sequential manner.
- Common operations on data structures include creation, destruction, selection, updating, searching, sorting, splitting and merging of data.
- Arrays are a basic data structure that
data structure is a storage that is used to store and organize data .pptxVicky Singh
A data structure is a way of organizing and storing data in a computer so that it can be accessed efficiently. There are different types of data structures that are used for processing, retrieving, and storing data in programs. Data structures arrange data in memory and are essential for organizing, accessing, and storing data efficiently. Common types of data structures include linear structures like arrays, stacks, and queues, as well as non-linear structures like trees and graphs.
The document provides an overview of data structures and algorithms. It defines data structures as collections of data organized in a way that allows efficient access and modification. Algorithms are sets of instructions to solve problems or accomplish tasks. Common categories of algorithms include sort, search, delete, insert, and update. Data structures can be classified as primitive, linear, or non-linear. Linear structures include arrays, linked lists, stacks, and queues while non-linear structures include trees and graphs. Common operations on data structures are searching, insertion, deletion, traversing, sorting, and merging.
This document discusses data structures and provides an introduction and overview. It defines data structures as specialized formats for organizing and storing data to allow efficient access and manipulation. Key points include:
- Data structures include arrays, linked lists, stacks, queues, trees and graphs. They allow efficient handling of data through operations like traversal, insertion, deletion, searching and sorting.
- Linear data structures arrange elements in a sequential order while non-linear structures do not. Common examples are discussed.
- Characteristics of data structures include being static or dynamic, homogeneous or non-homogeneous. Efficiency and complexity are also addressed.
- Basic array operations like traversal, insertion, deletion and searching are demonstrated with pseudocode examples
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose.
What are Data Structures? - Definition from WhatIs.com
TechTarget
The document discusses different types of databases including relational databases, analytical databases, operational databases, and object-oriented databases. It describes key characteristics of each type of database such as how they model and store data. Relational databases use tables to store data and link tables using relationships while analytical databases store archived data for analysis and operational databases manage dynamic data. Object-oriented databases integrate object-oriented programming with databases.
Arrays store fixed-size sequences of elements and provide random access by index. Lists are similar but dynamically resize. Stacks follow LIFO, adding and removing from the top. Queues follow FIFO, adding to the rear and removing from the front. Trees are hierarchical with nodes and edges. Graphs collect nodes connected by edges to represent relationships. When choosing a data structure, consider the type, usage, and organization of data as well as space/time constraints and your own experience. The choice significantly impacts performance and maintainability.
introduction about data structure_i.pptxpoonamsngr
This document provides an introduction to data structures. It defines data and data structures, and describes different types of data structures including primitive and non-primitive, linear and non-linear structures. It also discusses operations on data structures like creation, destruction, selection and updating. Finally, it covers analyzing the time and space complexity of algorithms.
The document discusses linear data structures and lists. It introduces the list abstract data type (ADT) and describes common list operations like finding an element or inserting and deleting elements. It also describes different types of lists, including singly linked lists, circularly linked lists, and doubly linked lists. The document then discusses stack and queue ADTs and their applications.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdf
Struktur data ppt
1. Introduction to Data
Structures
TRI SUCI HANDAYANI
SISTEM INFORMASI
SAINS DAN TEKNOLOGI
UNIVERSITAS ISLAM NEGERI SULTAN SYARIF KASIM
RIAU
http://sif.uin-suska.ac.id/
http://fst.uin-suska.ac.id/
http://www.uin-suska.ac.id/
2. Data Structures
A data structure is a scheme
for organizing data in the
memory of a computer.
Some of the more commonly
used data structures include
lists, arrays, stacks, queues,
heaps, trees, and graphs.
Binary Tree
3. Data Structures
The way in which the data
is organized affects the
performance of a program
for different tasks.
Computer programmers
decide which data
structures to use based on
the nature of the data and
the processes that need to
be performed on that data.
Binary Tree
4. Example: A Queue
A queue is an example of commonly used simple data
structure. A queue has beginning and end, called the
front and back of the queue.
Data enters the queue at one end and leaves at the
other. Because of this, data exits the queue in the same
order in which it enters the queue, like people in a
checkout line at a supermarket.
5. Example: A Binary Tree
A binary tree is another
commonly used data
structure. It is organized
like an upside down tree.
Each spot on the tree,
called a node, holds an item
of data along with a left
pointer and a right pointer.
Binary Tree
6. Example: A Binary Tree
The pointers are lined up
so that the structure
forms the upside down
tree, with a single node at
the top, called the root
node, and branches
increasing on the left and
right as you go down the
tree. Binary Tree
7. Choosing Data Structures
By comparing the queue with
the binary tree, you can see
how the structure of the data
affects what can be done
efficiently with the data.
8. Choosing Data Structures
A queue is a good data structure
to use for storing things that
need to be kept in order, such as
a set of documents waiting to be
printed on a network printer.
.
9. Choosing Data Structures
The jobs will be printed in the
order in which they are
received.
Most network print servers
maintain such a print queue.
.
10. Choosing Data Structures
A binary tree is a good data
structure to use for searching
sorted data.
The middle item from the list is
stored in the root node, with
lesser items to the left and
greater items to the right.
11. Choosing Data Structures
A search begins at the root. The
computer either find the data, or
moves left or right, depending
on the value for which you are
searching.
Each move down the tree cuts
the remaining data in half.
12. Choosing Data Structures
Items can be located very
quickly in a tree.
Telephone directory assistance
information is stored in a tree,
so that a name and phone
number can be found quickly.
13. Choosing Data Structures
For some applications, a queue
is the best data structure to use.
For others, a binary tree is
better.
Programmers choose from
among many data structures
based on how the data will be
used by the program.
14. Data Structures in Alice
Alice has two built-in data structures
that can be used to organize data, or
to create other data structures:
• Lists
• Arrays
15. Lists
A list is an ordered set of data. It is often used to store
objects that are to be processed sequentially.
A list can be used
to create a queue.
16. Arrays
An array is an indexed set of variables, such as
dancer[1], dancer[2], dancer[3],… It is like a set of
boxes that hold things.
A list is a set of items.
An array is a set of
variables that each
store an item.
17. Arrays and Lists
You can see the difference between arrays and
lists when you delete items.
18. Arrays and Lists
In a list, the missing spot is filled in when
something is deleted.
19. Arrays and Lists
In an array, an empty variable is left behind
when something is deleted.
20. Lists
A list is created in Alice by checking the make a
list box when creating a new variable.
Make a list box
21. Lists
The For all in order and For all together tiles can
be used to work with lists. They are at the
bottom of the editor area.
22. Arrays
Arrays can be created in a similar manner, but
more often they are created using the array
visualization object from the Alice local gallery.
The Array Visualization object
has special properties and
methods for manipulating
the elements in an array.
23. Arrays
Alice has a set of built-in functions that can be
performed on arrays.
Editor's Notes
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.
The Redo button will reverse the last use of the Undo button, restoring the previous action.
The Ctrl-Y keyboard shortcut also can be used in place of the Redo button.