This document compares RDBMS and NoSQL databases. RDBMS uses SQL and follows ACID properties, storing data in tables and columns. NoSQL databases are non-relational, distributed, and horizontally scalable. Common NoSQL databases include MongoDB, Cassandra, and HBase. NoSQL databases sacrifice consistency for availability and partition tolerance as described by CAP theorem.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Query Analyzing
Introduction into indexes
Indexes In Mongo
Managing indexes in MongoDB
Using index to sort query results.
When should I use indexes.
When should we avoid using indexes.
Learn the current state of the NoSQL landscape and discover the different data models within it. From document stores and key value databases to graph and Wide Column. Then you’ll learn why wide column databases are the most appropriate for scalable high performance use cases, including capabilities for massive scale-out architecture, peer-to-peer clustering to avoid bottlenecking and built-in multi-datacenter replication.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Redis is an open source, advanced key-value data store,Often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
In this Masterclass presentation we will:
• Explore the architecture and fundamental characteristics of Amazon Redshift
• Show you how to launch Redshift clusters and to load data into them
• Explain out how to use the AWS Console to monitor and manage Redshift clusters
• Help you to discover best practices and other resources to help you get the most from Redshift
Watch the recording here: http://youtu.be/-FmCWcxRvXY
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Query Analyzing
Introduction into indexes
Indexes In Mongo
Managing indexes in MongoDB
Using index to sort query results.
When should I use indexes.
When should we avoid using indexes.
Learn the current state of the NoSQL landscape and discover the different data models within it. From document stores and key value databases to graph and Wide Column. Then you’ll learn why wide column databases are the most appropriate for scalable high performance use cases, including capabilities for massive scale-out architecture, peer-to-peer clustering to avoid bottlenecking and built-in multi-datacenter replication.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Slides for Data Syndrome one hour course on PySpark. Introduces basic operations, Spark SQL, Spark MLlib and exploratory data analysis with PySpark. Shows how to use pylab with Spark to create histograms.
Redis is an open source, advanced key-value data store,Often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets
HDFS is a Java-based file system that provides scalable and reliable data storage, and it was designed to span large clusters of commodity servers. HDFS has demonstrated production scalability of up to 200 PB of storage and a single cluster of 4500 servers, supporting close to a billion files and blocks.
Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. You can start small for just $0.25 per hour with no commitment or upfront costs and scale to a petabyte or more for $1,000 per terabyte per year, less than a tenth of most other data warehousing solutions.
In this Masterclass presentation we will:
• Explore the architecture and fundamental characteristics of Amazon Redshift
• Show you how to launch Redshift clusters and to load data into them
• Explain out how to use the AWS Console to monitor and manage Redshift clusters
• Help you to discover best practices and other resources to help you get the most from Redshift
Watch the recording here: http://youtu.be/-FmCWcxRvXY
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
The rising interest in NoSQL technology over the last few years resulted in an increasing number of evaluations and comparisons among competing NoSQL technologies From survey we create a concise and up-to-date comparison of NoSQL engines, identifying their most beneficial use from the software engineer point of view.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
Session Presented @IndicThreads Cloud Computing Conference, Pune, India ( http://u10.indicthreads.com )
------------
More and more Enterprises are moving their IT infrastructure to Cloud platforms. Out of the entire components, Data Storage still remains a tricky part of the puzzle. I would like to present an overview of the choices, their advantages and limitations, we as Software Developers have currently. Based upon the choices, we may need to think about the design and architecture of the data-manipulation components of the application, we plan to put on Cloud. Following is an overview of the proposed agenda:
* Existing “Cloud Capable” and “Cloud Native” Relational DBMS
* Existing “Cloud Capable” and “Cloud Native” Non-Relational DBMS
* Main differences between Relational and Non-Relational DBMS’s
* Advantages and Limitations of Relational DBMS on Cloud Platforms
* Advantages and Limitations of Non-Relational DBMS on Cloud Platforms
* Design Patterns while using Non-Relational DBMS in the application
* Code Walk-through showing Integration of “Cloud Capable” and “Cloud Native” Non-Relational DBMS with a Web-Application
Takeaways from the session
* Overview of current Market Situation w.rt. Data Storage on Cloud
* Helpful Pointers towards making the right choice of Data Storage platform
* How Non-Relational DBMS’s can be integrated into our applications
More and more Enterprises are moving their IT infrastructure to Cloud platforms. Out of the entire components, Data Storage still remains a tricky part of the puzzle. I would like to present an overview of the choices, their advantages and limitations, we as Software Developers have currently. Based upon the choices, we may need to think about the design and architecture of the data-manipulation components of the application, we plan to put on Cloud. Following is an overview of the proposed agenda:
Existing “Cloud Capable” and “Cloud Native” Relational DBMS
Existing “Cloud Capable” and “Cloud Native” Non-Relational DBMS
Main differences between Relational and Non-Relational DBMS’s
Advantages and Limitations of Relational DBMS on Cloud Platforms
Advantages and Limitations of Non-Relational DBMS on Cloud Platforms
Design Patterns while using Non-Relational DBMS in the application
Code Walk-through showing Integration of “Cloud Capable” and “Cloud Native” Non-Relational DBMS with a Web-Application
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
1. RDBMS Vs. NoSQL
SQL Database
SQL Standard
SQL Characterstics
SQL Database Example
NoSQL Database
NoSQL Database Definition
General Characterstics
NoSQL Database Example
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2. What is RDBMS
RDBMS stands for Relational Database Management System.
Relational Database Management System stores data in the form of rows and columns
RDBMS is the basis for SQL, and for all modern database systems like MS SQL Server,
IBM DB2, Oracle, MySQL, and Microsoft Access.
A relational database has following major components:
Table, Record / Tuple, Field & Column /Attribute.
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3. SQl Charachteristics
Data stored in columns and tables
Relationships represented by data
Data Manipulation Language
Data Definition Language
Transactions
Abstraction from physical layer
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4. Transactions – ACID Properties
Atomic – All of the work in a transaction completes (commit) or none of it completes
Consistent – A transaction transforms the database from one consistent state to another
consistent State. Consistency is defined in terms of constraints.
Isolated – The results of any changes made during a transaction are not visible until the
transaction has committed.
Durable – The results of a committed transaction survive failures
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5. Example of SQL /RDBMS
Oracle: An object-relational database management system (DBMS) that is written in the C++ language.
IBM DB2: A family of database server products from IBM.
Sybase: A relational database server product for businesses that is primarily used on the Unix operating
system.
MS SQL Server: An RDBMS for enterprise-level databases that supports both SQL and NoSQL
architectures. MS SQL Server was developed by Microsoft.
Maria DB: An enhanced, drop-in version of MySQL.
PostgreSQL: An enterprise-level, object-relational DBMS that uses procedural languages such as Perl and
Python in addition to SQL-level code.
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6. Limitation of SQL
Scalability: Users have to scale relational database on powerful servers that are expensive
and difficult to handle. To scale relational database it has to be distributed on to multiple
servers. Handling tables across different servers is a chaos.
Complexity: In SQL server’s data has to fit into tables anyhow. If your data doesn’t fit
into tables, then you need to design your database structure that will be complex and again
difficult to handle.
RDBMS is a great tool for solving ACID problems when data validity is crucial, when you
need to support dynamic queries.
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7. NoSQL Introduction
No SQL stands for Not only Sql.
Next Generation Databases mostly addressing some of the points: being non-relational, distributed,
opensource and horizontal scalable.
The original intention has been modern web-scale databases.
The movement began early 2009 and is growing rapidly. Often more characteristics apply as: schema-
free,easy replication support, simple API, eventually consistent / BASE (not ACID), a huge data
amount, and more
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8. NoSQL Database
MongoDB: The most popular open-source NoSQL system. MongoDB is a document-oriented
database that stores JSON-like documents in dynamic schemas. Craigslist, eBay, and Foursquare
use MongoDB.
CouchDB: An open source, web-oriented database developed by Apache. CouchDB uses the
JSON data exchange format to store its documents; JavaScript for indexing, combining, and
transforming documents; and HTTP for its API.
HBase: An open source Apache project that was developed as a part of Hadoop. HBase is a
column store database written in Java. It has capabilities similar to those that BigTable provides.
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9. Oracle NoSQL Database: Oracle’s NoSQL database.
Cassandra DB: A distributed database that excels at handling extremely large amounts of
structured data. Cassandra DB is also highly scalable. Cassandra DB was created at
Facebook. It is used by Instagram, Comcast, Apple, and Spotify.
Riak: An open source, key-value store database written in Erlang. Riak has built-in fault-
tolerance replication and automatic data distribution that enable it to offer excellent
performance.
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11. Architecture NoSql
It Consist Two Layer User Interface and
Data Modeling and Storage System
The key concepts of the NoSQL movement is to have DBs focus on the task of high-
performance scalable data storage, and provide low-level access to a data management
layer in a way that allows data management tasks to be conveniently written in the
programming language of choice rather than having data management logic spread across
Turing-complete application languages, SQL, and sometimes even DB-specific stored
procedure languages.
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12. NoSQL Distinguishing Characteristics
(Scalability) Large data volumes.
Flexibility (query handling ,eg.FB)
It is an unstructured way of storing data.
NoSQL databases are the collection of key-value pair, documents, graph databases or wide-
column stores which do not have any standard schema definitions that has to be adhered to.
It is highly and easily Scalable replication and distribution.
Queries need to return answers quickly
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13. • ACID transaction properties are not needed – BASE
• CAP Theorem
• Open source development
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15. Consistency
all nodes see the same data at the same time .
client perceives that a set of operations has occurred all at once .
More like Atomic in ACID transaction properties
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16. Availability
Node failures do not prevent survivors from continuing to operate .
Every operation must terminate in an intended Response.
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17. Partition Tolerance
The system continues to operate despite arbitrary message loss .
Operations will complete, even if individual components are unavailable.
But Not all of the C , A , P can be Satisfied Simultaneously
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19. NoSQL Database Types
There are a variety of types:
Column Store – Each storage block contains
Data from only one column
Document Store – stores documents made up of tagged elements
Key-Value Store – Hash table of keys
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20. Map Reduce
Technique for indexing and searching large data volumes
Two Phases, Map and Reduce
Map
Extract sets of Key-Value pairs from underlying data
Potentially in Parallel on multiple machines
Reduce
Merge and sort sets of Key-Value pairs
Results may be useful for other searches
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