In the context of this assignment on Redis, relational data are
inserted into a redis database while sql queries are properly
edited and transformed in order to retrieve information from the
redis database.
In the context of this assignment on Mongo, queries will be designed and executed on a mongo collection, simple operations on mongo will be executed with python while mapreduce jobs will also be designed and executed on a mongo collection.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
This document provides an introduction to the statistical programming language R. It outlines what R is, how to access and use its interface, and how to work with basic data types like vectors, matrices, and factors. It also demonstrates how to import and export data, perform basic plotting and graphics, and gives examples working with biological data from Affymetrix chips. The presenter encourages attendees to ask questions and notes they are not a perfect teacher.
How the query planner in PostgreSQL works? Index access methods, join execution types, aggregation & pipelining. Optimizing queries with WHERE conditions, ORDER BY and GROUP BY. Composite indexes, partial and expression indexes. Exploiting assumptions about data and denormalization.
This document provides an overview of data entry, management, and manipulation in R. It discusses how to create datasets using various functions like c(), matrix(), data.frame(), and list(). It also covers understanding dataset properties, importing data, creating new variables, and subsetting datasets. Useful functions for working with datasets include mode(), length(), dim(), names(), and attributes(). The document shows examples of entering data using these different methods.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
In the context of this assignment on Mongo, queries will be designed and executed on a mongo collection, simple operations on mongo will be executed with python while mapreduce jobs will also be designed and executed on a mongo collection.
The document discusses data structures and algorithms. It defines data structures as organized ways of storing data to allow efficient processing. Algorithms manipulate data in data structures to perform operations like searching and sorting. Big-O notation provides an asymptotic analysis of algorithms, estimating how their running time grows with input size. Common time complexities include constant O(1), linear O(n), quadratic O(n^2), and exponential O(2^n).
This document provides an outline of topics for learning the R programming language, including R basics, vectors and factors, arrays, matrices, lists, data frames, if/else statements, for loops, user defined functions, objects and classes, reading data files, string operations, and regular expressions. Key concepts covered are defining vectors and factors, performing operations on vectors, summarizing data, accessing and manipulating arrays and matrices, the structure and operations of data frames, using if/else statements and for/while loops, defining user functions, detecting object classes and converting between types, reading different file types into R, and using string and regular expression functions.
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
This document provides an introduction to the statistical programming language R. It outlines what R is, how to access and use its interface, and how to work with basic data types like vectors, matrices, and factors. It also demonstrates how to import and export data, perform basic plotting and graphics, and gives examples working with biological data from Affymetrix chips. The presenter encourages attendees to ask questions and notes they are not a perfect teacher.
How the query planner in PostgreSQL works? Index access methods, join execution types, aggregation & pipelining. Optimizing queries with WHERE conditions, ORDER BY and GROUP BY. Composite indexes, partial and expression indexes. Exploiting assumptions about data and denormalization.
This document provides an overview of data entry, management, and manipulation in R. It discusses how to create datasets using various functions like c(), matrix(), data.frame(), and list(). It also covers understanding dataset properties, importing data, creating new variables, and subsetting datasets. Useful functions for working with datasets include mode(), length(), dim(), names(), and attributes(). The document shows examples of entering data using these different methods.
This document provides a step-by-step guide to learning R. It begins with the basics of R, including downloading and installing R and R Studio, understanding the R environment and basic operations. It then covers R packages, vectors, data frames, scripts, and functions. The second section discusses data handling in R, including importing data from external files like CSV and SAS files, working with datasets, creating new variables, data manipulations, sorting, removing duplicates, and exporting data. The document is intended to guide users through the essential skills needed to work with data in R.
1. Fundamental Concept - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document provides an overview of key concepts related to data structures and algorithms using C++. It discusses fundamental topics like data types, data objects, abstract data types, and data structures. It also covers algorithms, including their characteristics, design tools like pseudocode and flowcharts, and complexity analysis using Big O notation. Finally, it introduces software engineering concepts like the software development life cycle and its main phases of analysis, design, implementation, testing and verification.
This document discusses various data structures in R programming including vectors, matrices, arrays, data frames, lists, and factors. It provides examples of how to create each structure and access elements within them. Various methods for importing and exporting data in different file formats like Excel, CSV, and text files are also covered.
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
R is a free and open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It was originally developed in the 1990s at Bell Laboratories by statisticians John Chambers and colleagues. Key points about R include that it is an interpreted language, supports functional programming, and is object-oriented. R can be used for tasks like statistical analysis, data visualization, and machine learning. It has a large community of users and developers contributing packages for specialized analysis techniques.
The document discusses stacks and their applications. It describes stacks as last-in, first-out data structures and covers stack operations like push and pop. Common uses of stacks include expression evaluation, recursion, reversing data structures, and printing job queues. The document also discusses time and space complexity analysis of algorithms, conversion between infix, postfix and prefix notation, and software engineering principles like the software development life cycle.
9. Searching & Sorting - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses various searching and sorting algorithms. It covers sequential search, binary search, Fibonacci search, hashed search, indexed sequential search and their time complexities. Sorting algorithms like bubble sort, insertion sort, selection sort are explained along with their analysis. Internal sorting techniques like quicksort, heapsort, radix sort and bucket sort are also mentioned. The document provides details on sorting methods, order, stability and efficiency.
Redis project : Relational Databases to Key-Value systemsLamprini Koutsokera
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
This document provides an agenda for an R programming presentation. It includes an introduction to R, commonly used packages and datasets in R, basics of R like data structures and manipulation, looping concepts, data analysis techniques using dplyr and other packages, data visualization using ggplot2, and machine learning algorithms in R. Shortcuts for the R console and IDE are also listed.
The document discusses Oracle system catalogs which contain metadata about database objects like tables and indexes. System catalogs allow accessing information through views with prefixes like USER, ALL, and DBA. Examples show how to query system catalog views to get information on tables, columns, indexes and views. Query optimization and evaluation are also covered, explaining how queries are parsed, an execution plan is generated, and the least cost plan is chosen.
This document outlines algorithms for query processing and optimization in database systems. It discusses translating SQL queries to relational algebra, algorithms for sorting and joining large datasets that exceed available memory, including nested loop joins, sort-merge joins, and hash joins. It also describes query optimization techniques and factors that influence query performance.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
This document discusses queues and their implementation using data structures in C++. It covers:
1) Defining queues and their operations of insertion at the rear and deletion at the front.
2) Implementing queues using arrays and avoiding their drawbacks using circular queues.
3) Other applications that use queues like simulation, job scheduling, and priority queues.
4) Different queue implementations like multi-queue, deque, and priority queue data structures.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
The document summarizes external sorting techniques used in database management systems. It describes a two-phase sorting approach using limited buffer space in memory. The first phase creates runs by sorting each page individually. The second phase repeatedly merges runs by pairs until a single sorted run is produced, using three buffer pages - two for input runs and one for the output merged run. The process of merging two sorted runs by comparing elements and writing the smallest to the output page is also explained.
R is a free programming language and software environment for statistical analysis and graphics. It contains functions for data manipulation, calculation, and graphical displays. Some key features of R include being free, running on multiple platforms, and having extensive statistical and graphical capabilities. Common object types in R include vectors, matrices, data frames, and lists. R also has packages that add additional functions.
The document summarizes several papers presented at SIGMOD 2011 related to Hadoop and distributed data processing. It then provides more detail on Apache Hadoop's real-time capabilities at Facebook, the Nova system for continuous Pig/Hadoop workflows, and an approach for loading data from Hadoop into parallel data warehouses more efficiently.
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
The document discusses heaps and their implementation and applications. It defines heaps as binary trees that satisfy the heap property, where each node is greater than or equal to its children (for min-heaps) or less than or equal (for max-heaps). Heaps can be implemented efficiently using arrays, allowing for fast insertion, deletion, and finding the maximum/minimum element. The document outlines heap operations and provides examples of heap construction. Common applications of heaps discussed are priority queues, selection algorithms, and heapsort.
This document provides an overview of basic relational database management system (RDBMS) concepts. It defines key terms like tables, records, fields and relationships. It also describes the relational model, ER diagrams and SQL. Common RDBMS like MySQL, SQL Server and Oracle are introduced. Basic SQL operators for queries are shown along with examples. The document serves as an introduction to fundamental RDBMS concepts.
1. Fundamental Concept - Data Structures using C++ by Varsha Patilwidespreadpromotion
This document provides an overview of key concepts related to data structures and algorithms using C++. It discusses fundamental topics like data types, data objects, abstract data types, and data structures. It also covers algorithms, including their characteristics, design tools like pseudocode and flowcharts, and complexity analysis using Big O notation. Finally, it introduces software engineering concepts like the software development life cycle and its main phases of analysis, design, implementation, testing and verification.
This document discusses various data structures in R programming including vectors, matrices, arrays, data frames, lists, and factors. It provides examples of how to create each structure and access elements within them. Various methods for importing and exporting data in different file formats like Excel, CSV, and text files are also covered.
- R is a free software environment for statistical computing and graphics. It has an active user community and supports graphical capabilities.
- R can import and export data, perform data manipulation and summaries. It provides various plotting functions and control structures to control program flow.
- Debugging tools in R include traceback, debug, browser and trace which help identify and fix issues in functions.
R is a free and open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It was originally developed in the 1990s at Bell Laboratories by statisticians John Chambers and colleagues. Key points about R include that it is an interpreted language, supports functional programming, and is object-oriented. R can be used for tasks like statistical analysis, data visualization, and machine learning. It has a large community of users and developers contributing packages for specialized analysis techniques.
The document discusses stacks and their applications. It describes stacks as last-in, first-out data structures and covers stack operations like push and pop. Common uses of stacks include expression evaluation, recursion, reversing data structures, and printing job queues. The document also discusses time and space complexity analysis of algorithms, conversion between infix, postfix and prefix notation, and software engineering principles like the software development life cycle.
9. Searching & Sorting - Data Structures using C++ by Varsha Patilwidespreadpromotion
The document discusses various searching and sorting algorithms. It covers sequential search, binary search, Fibonacci search, hashed search, indexed sequential search and their time complexities. Sorting algorithms like bubble sort, insertion sort, selection sort are explained along with their analysis. Internal sorting techniques like quicksort, heapsort, radix sort and bucket sort are also mentioned. The document provides details on sorting methods, order, stability and efficiency.
Redis project : Relational Databases to Key-Value systemsLamprini Koutsokera
Avaliable at: https://github.com/dbsmasters/bdsmasters
The current project is implemented in the context of the course "Big Data Management Systems" taught by Prof. Chatziantoniou in the Department of Management Science and Technology (AUEB). The aim of the project is to familiarize the students with big data management systems such as Hadoop, Redis, MongoDB and Azure Stream Analytics.
This document provides an agenda for an R programming presentation. It includes an introduction to R, commonly used packages and datasets in R, basics of R like data structures and manipulation, looping concepts, data analysis techniques using dplyr and other packages, data visualization using ggplot2, and machine learning algorithms in R. Shortcuts for the R console and IDE are also listed.
The document discusses Oracle system catalogs which contain metadata about database objects like tables and indexes. System catalogs allow accessing information through views with prefixes like USER, ALL, and DBA. Examples show how to query system catalog views to get information on tables, columns, indexes and views. Query optimization and evaluation are also covered, explaining how queries are parsed, an execution plan is generated, and the least cost plan is chosen.
This document outlines algorithms for query processing and optimization in database systems. It discusses translating SQL queries to relational algebra, algorithms for sorting and joining large datasets that exceed available memory, including nested loop joins, sort-merge joins, and hash joins. It also describes query optimization techniques and factors that influence query performance.
This document provides an introduction to the basics of R programming. It begins with quizzes to assess the reader's familiarity with R and related topics. It then covers key R concepts like data types, data structures, importing and exporting data, control flow, functions, and parallel computing. The document aims to equip readers with fundamental R skills and directs them to online resources for further learning.
This document discusses queues and their implementation using data structures in C++. It covers:
1) Defining queues and their operations of insertion at the rear and deletion at the front.
2) Implementing queues using arrays and avoiding their drawbacks using circular queues.
3) Other applications that use queues like simulation, job scheduling, and priority queues.
4) Different queue implementations like multi-queue, deque, and priority queue data structures.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
The document discusses query processing and optimization. It describes several key activities in query processing including translating queries to a format executable by the database, applying optimization techniques, and evaluating the queries. It then provides details on three specific operations: selection using linear searches and indices, sorting, and join operations. It explains different algorithms for implementing each operation and factors to consider when choosing algorithms such as indexing and data sizes.
The document summarizes external sorting techniques used in database management systems. It describes a two-phase sorting approach using limited buffer space in memory. The first phase creates runs by sorting each page individually. The second phase repeatedly merges runs by pairs until a single sorted run is produced, using three buffer pages - two for input runs and one for the output merged run. The process of merging two sorted runs by comparing elements and writing the smallest to the output page is also explained.
R is a free programming language and software environment for statistical analysis and graphics. It contains functions for data manipulation, calculation, and graphical displays. Some key features of R include being free, running on multiple platforms, and having extensive statistical and graphical capabilities. Common object types in R include vectors, matrices, data frames, and lists. R also has packages that add additional functions.
The document summarizes several papers presented at SIGMOD 2011 related to Hadoop and distributed data processing. It then provides more detail on Apache Hadoop's real-time capabilities at Facebook, the Nova system for continuous Pig/Hadoop workflows, and an approach for loading data from Hadoop into parallel data warehouses more efficiently.
This document discusses extendible hashing, which is a hashing technique for dynamic files that allows efficient insertion and deletion of records. It works by using a directory to map hash values to buckets, and dynamically expanding the directory size and number of buckets as needed to accommodate new records. When a bucket overflows, it is split into two buckets, and the directory is expanded to distinguish them. The directory size can also be contracted when buckets can be combined due to deletions. Alternative approaches like dynamic hashing and linear hashing that address the same problem of dynamic files are also overviewed.
The document discusses heaps and their implementation and applications. It defines heaps as binary trees that satisfy the heap property, where each node is greater than or equal to its children (for min-heaps) or less than or equal (for max-heaps). Heaps can be implemented efficiently using arrays, allowing for fast insertion, deletion, and finding the maximum/minimum element. The document outlines heap operations and provides examples of heap construction. Common applications of heaps discussed are priority queues, selection algorithms, and heapsort.
This document provides an overview of basic relational database management system (RDBMS) concepts. It defines key terms like tables, records, fields and relationships. It also describes the relational model, ER diagrams and SQL. Common RDBMS like MySQL, SQL Server and Oracle are introduced. Basic SQL operators for queries are shown along with examples. The document serves as an introduction to fundamental RDBMS concepts.
A relational database management system (RDBMS) is a database management system (DBMS) based on the relational model invented by Edgar F. Codd at IBM's San Jose Research Laboratory. Most databases in widespread use today are based on his relational database model.[1]
The document discusses MySQL and SQL concepts including relational databases, database management systems, and the SQL language. It introduces common SQL statements like SELECT, INSERT, UPDATE, and DELETE and how they are used to query and manipulate data. It also covers topics like database design with tables, keys, and relationships between tables.
This document provides an overview of a lecture on fundamentals of computer systems that covers topics such as logic, Boolean algebra, memory, CPU, file management, databases, cyber security, data modeling, HTML, CSS, and color properties in CSS. The lecture discusses logic gates, truth tables, logical variables, memory concepts, fetch-execute CPU cycles, relational databases, creating HTML documents, CSS syntax, style rules, selector types like type, id, class selectors, inheritance, and CSS color, font, and comment properties. The document also includes examples and questions to help explain the concepts.
Get up to Speed (Quick Guide to data.table in R and Pentaho PDI)Serban Tanasa
1) The document provides a quick guide to using data.table in R and Pentaho Data Integration (PDI) for fast data loading and manipulation. It discusses benchmarks showing data.table is 2-20x faster than traditional methods for reading, ordering, and transforming large data.
2) The outline discusses how to use basic data.table functions for speed gains and to overcome R's scaling limitations. It also provides a very brief overview of PDI's capabilities for Extract/Transform/Load (ETL) workflows without writing code.
3) The benchmarks section shows data.table is up to 500% faster than traditional R methods for reading large CSV files and orders of magnitude faster for sorting and aggregating
This document provides an introduction and overview of key concepts related to SQL Server databases including:
- The database engine and its role in storing, processing, and securing data
- System and user databases
- Database objects like tables, views, indexes, stored procedures
- Structured Query Language (SQL) and its sublanguages for data definition, manipulation, and transaction control
- Guidelines for writing SQL statements
- Creating and using databases along with creating tables and defining data types and constraints
Introduction to the Structured Query Language SQLHarmony Kwawu
Our world depends on data in order to thrive. There are many different methods for storing data but the idea of relational database technology has proved the most advantageous. At the heart of all major relational database approach is the SQL, standing for Structured Query Language. SQL is based on set theory or relational principles.
The document provides an overview and discussion of Cassandra including its architecture, data model, and real world applications. It discusses Cassandra's distributed architecture based on BigTable and Dynamo, as well as key concepts like nodes, clusters, consistency levels, and tunable consistency. The document also covers data modeling techniques in Cassandra like compound primary keys, materialized views, secondary indexes, counters, and using time to live for expiring data. Real world examples are provided for many of these techniques.
This document discusses why SQL has endured as the dominant language for data analysis for over 40 years. SQL provides a powerful yet simple framework for querying data through its use of relational algebra concepts like projection, filtering, joining, and aggregation. It also allows for transparent optimization by the database as SQL is declarative rather than procedural. Additionally, SQL has continuously evolved through standards while providing access to a wide variety of data sources.
This document provides an overview of Cassandra data modeling concepts and techniques. It discusses Cassandra's data model, architecture, data types, consistency levels, and more. Key concepts covered include defining primary keys, including compound primary keys, working with wide rows for time series data, using materialized views, secondary indexes, counters, and time to live for expiring data. The document uses examples to illustrate these Cassandra features and how to apply different data modeling patterns.
This document discusses databases and SQL. It defines a database as an integrated collection of data managed by a database management system (DBMS) using SQL. The most popular type is the relational database which organizes data into tables related through primary keys. SQL is used for queries with statements like SELECT, INSERT, DELETE, and UPDATE. Database interfaces like Perl DBI, PHP dbx, and Python DB-API allow access from programming languages. ADO.NET is an API for database access in .NET.
Mining Code Examples with Descriptive Text from Software ArtifactsPreetha Chatterjee
The document describes an exploratory study conducted to understand the types of information provided about code snippets embedded in different software-related documents. The study analyzed 60 documents across 12 categories and identified 17 labels and sub-labels for annotating the information about code snippets. Research papers were found to contain the most code snippets on average (8.6 per paper) with the longest descriptions (439 lines of text on average). The study aims to help develop techniques for mining relevant information from various document types to assist with software engineering tasks.
The document contains questions related to database management systems (DBMS). It covers topics like data modeling, relational algebra, SQL, transaction processing, concurrency control, and database design. Some key questions ask about the differences between primary and candidate keys, entity relationship modeling, normalization, and query optimization techniques.
This document contains a set of short and long questions related to database management systems. Some key topics covered include the entity-relationship model, relational data model, normalization, transaction processing, concurrency control, and database recovery. The questions range from definitions and short explanations to examples and multi-step problems involving conceptual and practical database concepts.
- Oracle Database 10g is an object-relational database management system that allows for grid computing. It is based on the relational model and supports multimedia, large objects, and user-defined data types.
- The course aims to teach students to interact with Oracle using SQL, retrieve and manipulate data, run queries, create reports, and obtain metadata from dictionary views.
- Key tables used in the course include EMPLOYEES, DEPARTMENTS, and JOB_GRADES.
- Oracle Database 10g is an object-relational database management system that allows for grid computing. It is based on the relational model and supports multimedia, large objects, and user-defined data types.
- The course aims to teach students how to perform tasks with Oracle like retrieving and updating data using SQL, obtaining metadata from dictionary views, and creating reports.
- Key tables used in the course include EMPLOYEES, DEPARTMENTS, and JOB_GRADES.
ADO.NET provides a bridge between front-end controls and back-end databases. It uses a two-tier architecture with objects that encapsulate data access operations. These objects interact with controls to display data without exposing details of moving data. ADO.NET supports connecting to diverse data sources using the same methodology including SQL Server via different classes.
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End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
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A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
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Redis Project: Relational databases & Key-Value systems
1. Redis Project
Relational databases & Key-Value systems
Athens University of Economics and Business
Dpt. Of Management Science and Technology
Prof. Damianos Chatziantoniou
| lkoutsokera@gmail.com
| stratos.gounidellis@gmail.com
Lamprini Koutsokera (8130074)
Stratos Gounidellis (8130029)
BDSMasters
2. SQL Server vs. Redis
2
Description Microsoft’s relational DBMS In-memory data structure store,
used as database
Database model Relational DBMS Key-value store
Implementation language C++ C
Data scheme yes schema-free
Triggers yes no
Replication methods yes, depending the SQL-Server Edition Master-slave replication
Partitioning methods tables can be distributed across Sharding
several files, sharding through
federation
Project
from a relational database to a key-value systemReferences: [1]
13. 13
Python coding [7] – code metrics
Query Execution
Unit Testing
Relational Data Insertion
14. 14
Assumptions - Restrictions
The text file follows the structure described below:
o first line (SELECT): a list of table_name.attribute_name, delimited by the character ",".
o second line (FROM): a list of table names, delimited by the character ",".
o third line (WHERE): a simple condition, consisting only of AND, OR, NOT, =, <>, >, <, <=, >= and
parentheses.
o fourth line (ORDER BY): a simple clause, containing either an attribute name and the way of
ordering (ASC or DESC) or RAND().
o fifth line (LIMIT): a number, specifying the number of rows to be displayed.
The ORDER BY clause contains only one attribute.
The sql query is correct according to the sql syntax.
The names of the tables and the attributes are correct.
In case a clause is skipped then the corresponding line remains blank.
19. References
[1] Db-engines.com. (n.d.). Memcached vs. Microsoft SQL Server vs. Redis Comparison. [online] Available at:
https://db-engines.com/en/system/Memcached%3bMicrosoft+SQL+Server%3bRedis [Accessed 13 Apr. 2017].
[2] Redis.io. Redis Quick Start. https://redis.io/topics/quickstart [Accessed 12 Apr. 2017].
[3] Peter Cooper. Redis 101 - A whirlwind tour of the next big thing in NoSQL data storage.
https://www.scribd.com/document/33531219/Redis-Presentation [Accessed 12 Apr. 2017].
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Lamprini Koutsokera (8130074)
Stratos Gounidellis (8130029)
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