Dynamic Community Detection for Large-scale e-Commerce data with Spark Streaming and GraphX-(Ming Huang, Taobao)

This document provides guidelines for developing databases and writing SQL code. It includes recommendations for naming conventions, variables, select statements, cursors, wildcard characters, joins, batches, stored procedures, views, data types, indexes and more. The guidelines suggest using more efficient techniques like derived tables, ANSI joins, avoiding cursors and wildcards at the beginning of strings. It also recommends measuring performance and optimizing for queries over updates.

A comparison of different solutions for full-text search in web applications using PostgreSQL and other technology. Presented at the PostgreSQL Conference West, in Seattle, October 2009.

This document covers entire C language thoroughly. Its for all the students or professionals who would like to learn C or would like to brush up their knowledge with a quick recap.

C language supports a character set of 256 characters including lowercase and uppercase English alphabets (a-z and A-Z), digits (0-9), and special symbols like mathematical, logical, and punctuation symbols. Every character has a corresponding ASCII value. A C program is provided that prints all the characters in the C character set along with their ASCII values to demonstrate the set of characters supported.

Programs transform input data into output data using programming languages that support different data types and operations on those types. A data type specifies a set of values and operations on those values and is used to declare variables, return values, and function parameters. Identifiers refer to data types, variables, and functions and have specific naming rules. Common built-in data types include integers, characters, floating points, pointers, arrays, strings, and structures.

The document discusses the MySQL query optimizer. It begins by explaining how the optimizer works, including analyzing statistics, determining optimal join orders and access methods. It then describes how the optimizer trace can provide insight into why a particular execution plan was selected. The remainder of the document provides details on the various phases the optimizer goes through, including logical transformations, cost-based optimizations like range analysis and join order selection.

The document discusses various primitive data types including integer, floating point, decimal, boolean, character, and string types. It covers the implementation and design considerations of these types in different programming languages such as C, C++, Java, and C#. Enumeration types are also introduced as user-defined ordinal types.

This presentation features the fundamentals of SQL tunning like SQL Processing, Optimizer and Execution Plan, Accessing Tables, Performance Improvement Consideration Partition Technique. Presented by Alphalogic Inc : https://www.alphalogicinc.com/

python provides data structures such as List, Tuples, dictionaries and regular expressions. This slide having programming examples in python.

A dictionary in Python is an unordered collection of key-value pairs where keys must be unique and immutable. It allows storing and accessing data values using keys. Keys can be of any immutable data type while values can be of any data type. Dictionaries can be created using curly braces {} or the dict() constructor and elements can be added, accessed, updated, or removed using keys. Common dictionary methods include copy(), clear(), pop(), get(), keys(), items(), and update().

Postgres expert, Bruce Momjian, as he discusses common table expressions (CTEs) and the ability to allow queries to be more imperative, allowing looping and processing hierarchical structures that are normally associated only with imperative languages.

더 많은 샘플코드는 아래 주소에서 보실 수 있습니다. https://github.com/KennethanCeyer/pycon-kr-2018

Data Structures in Python, second in the series of Introduction to Python. Upcoming series will cover Functions and OOPs concept in Python.

Data Definition Language (DDL), Data Definition Language (DDL), Data Manipulation Language (DML) , Transaction Control Language (TCL) , Data Control Language (DCL) - , SQL Constraints

Nested structures in C allow one structure to be defined within another. This document demonstrates a nested structure with an address structure defined within an emp structure. It declares variables of each structure type, assigns values to their members, and prints the member values. The address structure contains phone, city, and pin members, while the emp structure contains name, emp_no, and salary members.

The document defines and describes various data types in the C programming language. It discusses integer data types like char, short int, int, long int; floating point data types like float, double, long double; void data type; and derived data types like arrays, pointers, structures, unions, enumerated data types, and user-defined data types using typedef. Each data type is explained along with its size, range of values it can hold, and examples.

This document discusses Netflix's use of Spark and Spark Streaming. Key points include: - Netflix uses Spark on its Berkeley Data Analytics Stack (BDAS) to enable rapid experimentation for algorithm engineers and provide business value through more A/B tests. - Use cases for Spark at Netflix include feature selection, feature generation, model training, and metric evaluation using large datasets with many users. - Netflix BDAS provides notebooks, access to the Netflix ecosystem and services, and faster computation and scaling. It allows for ad-hoc experimentation and "time machine" functionality. - Netflix processes over 450 billion events per day through its streaming data pipeline, which collects, moves, and processes events at cloud scale

This document discusses two key problems with modularity-based community detection in networks: incomparability and resolution limit. The modularity measure cannot reliably distinguish between networks with genuine communities and networks with no communities. Additionally, the size of communities detected depends on the overall network size, so smaller true communities may not be detected in large networks. The document provides examples of networks where modularity fails to identify the true partition into communities.

This document summarizes an analysis of complex networks using open source software tools. It provides an overview of network graph analysis and statistical and visual measures used to assess network patterns. It then demonstrates these concepts through case studies of the Miles Davis album collaboration network, the Boston Red Sox player network, and a GDELT news event network. The document concludes that network graph analysis is a powerful technique for understanding relationships in connected data.

Este documento describe Hadoop y Hive en AWS. Explica que Hadoop es un framework para procesar grandes cantidades de datos de manera distribuida a través de múltiples nodos, utilizando HDFS para almacenamiento y MapReduce para procesamiento. También describe cómo AWS ofrece servicios como EC2, S3 y EMR que permiten implementar fácilmente clusters Hadoop en la nube de manera elástica y a escala. Finalmente, introduce Hive como una capa SQL sobre Hadoop que facilita el análisis de datos a gran escala.