Big data vahidamiri-datastack.ir

Apr 22, 2017Download as PPTX, PDF1 like483 views

This document provides an overview of big data processing tools and NoSQL databases. It discusses how Hadoop uses MapReduce and HDFS to distribute processing across large clusters. Spark is presented as an alternative to Hadoop. The CAP theorem is explained as relating to consistency, availability, and network partitions. Different types of NoSQL databases are described including key-value, column, document and graph databases. Examples are provided for each type.

‌‫‌ای‌بر‌ابزارهای‬‫ه‬‫مقدم‬
‫پردازش‌داده‌در‌کالن‌داده‬
VAHID AMIRI
VAHIDAMIRY.IR
VAHID.AMIRY@GMAIL.COM

Big DataData Data Processing
Data Gathering
Data Storing

Big Data Definition
 No single standard definition…
“Big Data” is data whose scale, diversity, and complexity
require new architecture, techniques, algorithms, and
analytics to manage it and extract value and hidden
knowledge from it…

Solution
Big
Data
Big
Comput
ation
Big
Computer

 Hadoop is a software framework for distributed processing of large datasets
across large clusters of computers
 Hadoop implements Google’s MapReduce, using HDFS
 MapReduce divides applications into many small blocks of work.
 HDFS creates multiple replicas of data blocks for reliability, placing them on compute
nodes around the cluster
Hadoop

 More than just the Elephant in the room
 Over 120+ types of NoSQL databases
So many NoSQL options

 Extend the Scope of RDBMS
 Caching
 Master/Slave
 Table Partitioning
 Federated Tables
 Sharding
NoSql
 Relational database (RDBMS) technology
 Has not fundamentally changed in over 40 years
 Default choice for holding data behind many web apps
 Handling more users means adding a bigger server

RDBMS with Extended Functionality
Vs.
Systems Built from Scratch
with Scalability in Mind
NoSQL Movement

CAP Theorem
 “Of three properties of shared-data systems – data Consistency, system
Availability and tolerance to network Partition – only two can be achieved at
any given moment in time.”

“Of three properties of shared-data systems – data
Consistency, system Availability and tolerance to
network Partition – only two can be achieved at any
given moment in time.”
 CA
 Highly-available consistency
 CP
 Enforced consistency
 AP
 Eventual consistency
CAP Theorem

 Schema-less
 State (Persistent or Volatile)
 Example:
 Redis
 Amazon DynamoDB
Key / Value Database

 Wide, sparse column sets
 Schema-light
 Examples:
 Cassandra
 HBase
 BigTable
 GAE HR DS
Column Database

 Use for data that is
 document-oriented (collection of JSON documents) w/semi structured
data
 Encodings include XML, YAML, JSON & BSON
 binary forms
 PDF, Microsoft Office documents -- Word, Excel…)
 Examples: MongoDB, CouchDB
Document Database

Graph Database
Use for data with
 a lot of many-to-many relationships
 when your primary objective is quickly
finding connections, patterns and
relationships between the objects within
lots of data
 Examples: Neo4J, FreeBase (Google)

So which type of NoSQL? Back to CAP…
CP = noSQL/column
Hadoop
Big Table
HBase
MemCacheDB
AP = noSQL/document or key/value
DynamoDB
CouchDB
Cassandra
Voldemort
CA = SQL/RDBMS
SQL Sever / SQL
Azure
Oracle
MySQL

This document provides an overview of using OpenStack and Sahara to implement a big data architecture on cloud infrastructure. It discusses: - The characteristics and service models of cloud computing - An introduction to OpenStack, why it is used, and some of its key statistics - What Sahara is and its role in provisioning and managing Hadoop, Spark, and Storm clusters on OpenStack - Sahara's architecture, how it integrates with OpenStack, and examples of how it can be used to quickly provision data processing clusters and execute analytic jobs on cloud infrastructure.

عصر کلان داده، چرا و چگونه؟datastack

Big data vahidamiri-tabriz-13960226-datastack.irdatastack

Big data hadoop rdbmsArjen de Vries

The document discusses and compares MapReduce and relational database management systems (RDBMS) for large-scale data processing. It describes several hybrid approaches that attempt to combine the scalability of MapReduce with the query optimization and efficiency of parallel RDBMS. HadoopDB is highlighted as a system that uses Hadoop for communication and data distribution across nodes running PostgreSQL for query execution. Performance evaluations show hybrid systems can outperform pure MapReduce but may still lag specialized parallel databases.

سکوهای ابری و مدل های برنامه نویسی در ابرdatastack

Big Data , Big Problem?Mohammadhasan Farazmand

Hadoop vs. RDBMS for Advanced Analyticsjoshwills

The document discusses when to use Hadoop instead of a relational database management system (RDBMS) for advanced analytics. It provides examples of when queries like count distinct, cursors, and alter table statements become problematic in an RDBMS. It contrasts analyzing simple, transactional data like invoices versus complex, evolving data like customers or website visitors. Hadoop is better suited for problems involving complex objects, self-joins on large datasets, and matching large datasets. The document encourages structuring data in HDFS in a flexible way that fits the problem and use cases like simple counts on complex objects, self-self-self joins, and matching problems.

Hadoop and Hive in Enterprisesmarkgrover

Comparison - RDBMS vs Hadoop vs ApacheSandeepTaksande

Introduction to Apache Hadoop Eco-SystemMd. Hasan Basri (Angel)

The document provides information about Hadoop, its core components, and MapReduce programming model. It defines Hadoop as an open source software framework used for distributed storage and processing of large datasets. It describes the main Hadoop components like HDFS, NameNode, DataNode, JobTracker and Secondary NameNode. It also explains MapReduce as a programming model used for distributed processing of big data across clusters.

Big Data and HadoopFlavio Vit

Introduction To Hadoop EcosystemInSemble

PPT on HadoopShubham Parmar

The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.

Big data conceptsSerkan Özal

This document provides an overview of big data concepts, including NoSQL databases, batch and real-time data processing frameworks, and analytical querying tools. It discusses scalability challenges with traditional SQL databases and introduces horizontal scaling with NoSQL systems like key-value, document, column, and graph stores. MapReduce and Hadoop are described for batch processing, while Storm is presented for real-time processing. Hive and Pig are summarized as tools for running analytical queries over large datasets.

What is hadoopAsis Mohanty

Otimizações de Projetos de Big Data, Dw e AI no Microsoft AzureLuan Moreno Medeiros Maciel

The Hadoop Path by Subash DSouza of Archangel Technology Consultants, LLC.Data Con LA

Hadoop is moving towards improved security and real-time analytics. For security, Hadoop vendors have made acquisitions and implemented features like Kerberos authentication and Apache Sentry authorization. For real-time analytics, tools are focusing on real-time streaming (like Storm, Spark Streaming, and Samza) and real-time querying of data (like Hive on Tez, Impala, Drill, and Spark). The right tool depends on use cases, and enterprises should choose what is easiest and most scalable.

Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan

The document discusses Hadoop and big data. It defines Hadoop as an open source, scalable, and fault tolerant platform for storing and processing large amounts of unstructured data distributed across machines. It describes Hadoop's core components like HDFS for data storage and MapReduce/YARN for data processing. It also discusses how Hadoop fits into big data scenarios and landscapes, applying Hadoop to save money, the concept of data lakes, Hadoop in the cloud, and big data analytics with Hadoop.

HDFSVardhman Kale

Hadoop distributions - ecosystemJakub Stransky

Hadoop Tutorial For BeginnersDataflair Web Services Pvt Ltd

Hd insight essentials quick viewRajesh Nadipalli

Hw09 Welcome To Hadoop WorldCloudera, Inc.

Big data technology unit 3RojaT4

Hadoopronit gaikwad

Hadoop: An Industry PerspectiveCloudera, Inc.

This document provides an overview of Hadoop and how it can be used for data consolidation, schema flexibility, and query flexibility compared to a relational database. It describes the key components of Hadoop including HDFS for storage and MapReduce for distributed processing. Examples of industry use cases are also presented, showing how Hadoop enables affordable long-term storage and scalable processing of large amounts of structured and unstructured data.

Big Data on the Microsoft PlatformAndrew Brust

The future of Big Data toolingData Science Society

Alexander Aldev - Co-founder and CTO of MammothDB, currently focused on the architecture of the distributed database engine. Notable achievements in the past include managing the launch of the first triple-play cable service in Bulgaria and designing the architecture and interfaces from legacy systems of DHL Global Forwarding's data warehouse. Has lectured on Hadoop at AUBG and MTel. "The future of Big Data tooling" will briefly review the architectural concepts of current Big Data tools like Hadoop and Spark. It will make the argument, from the perspective of both technology and economics, that the future of Big Data tools is in optimizing local storage and compute efficiency.

Big Data SSD Architecture: Digging Deep to Discover Where SSD Performance Pay...Samsung Business USA

Which storage technology, HDDs or SSDs, excels in big data architecture? SSDs clearly win on speed, offering higher sequential read/write speeds and higher IOPS. However, deploying SSDs in hundreds or thousands of nodes could add up to a very expensive proposition. A better approach identifies critical locations where SSDs enable immediate cost-per-performance wins. This whitepaper will look at the basics of big data tools, review two performance wins with SSDs in a well-known framework, as well as present some examples of emerging opportunities on the leading edge of big data technology.

More Related Content

What's hot (20)

Hadoop and Hive in Enterprisesmarkgrover

Comparison - RDBMS vs Hadoop vs ApacheSandeepTaksande

Introduction to Apache Hadoop Eco-SystemMd. Hasan Basri (Angel)

Big Data and HadoopFlavio Vit

Introduction To Hadoop EcosystemInSemble

PPT on HadoopShubham Parmar

Big data conceptsSerkan Özal

What is hadoopAsis Mohanty

Otimizações de Projetos de Big Data, Dw e AI no Microsoft AzureLuan Moreno Medeiros Maciel

The Hadoop Path by Subash DSouza of Archangel Technology Consultants, LLC.Data Con LA

Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan

HDFSVardhman Kale

Hadoop distributions - ecosystemJakub Stransky

Hadoop Tutorial For BeginnersDataflair Web Services Pvt Ltd

Hd insight essentials quick viewRajesh Nadipalli

Hw09 Welcome To Hadoop WorldCloudera, Inc.

Big data technology unit 3RojaT4

Hadoopronit gaikwad

Hadoop: An Industry PerspectiveCloudera, Inc.

Big Data on the Microsoft PlatformAndrew Brust

Hadoop and Hive in Enterprisesmarkgrover

Comparison - RDBMS vs Hadoop vs ApacheSandeepTaksande

Introduction to Apache Hadoop Eco-SystemMd. Hasan Basri (Angel)

Big Data and HadoopFlavio Vit

Introduction To Hadoop EcosystemInSemble

PPT on HadoopShubham Parmar

Big data conceptsSerkan Özal

What is hadoopAsis Mohanty

Otimizações de Projetos de Big Data, Dw e AI no Microsoft AzureLuan Moreno Medeiros Maciel

The Hadoop Path by Subash DSouza of Archangel Technology Consultants, LLC.Data Con LA

Introduction to Big Data Analytics on Apache HadoopAvkash Chauhan

HDFSVardhman Kale

Hadoop distributions - ecosystemJakub Stransky

Hadoop Tutorial For BeginnersDataflair Web Services Pvt Ltd

Hd insight essentials quick viewRajesh Nadipalli

Hw09 Welcome To Hadoop WorldCloudera, Inc.

Big data technology unit 3RojaT4

Hadoopronit gaikwad

Hadoop: An Industry PerspectiveCloudera, Inc.

Big Data on the Microsoft PlatformAndrew Brust

Similar to Big data vahidamiri-datastack.ir (20)

The future of Big Data toolingData Science Society

Big Data SSD Architecture: Digging Deep to Discover Where SSD Performance Pay...Samsung Business USA

Managing Big data with HadoopNalini Mehta

The data management industry has matured over the last three decades, primarily based on relational database management system(RDBMS) technology. Since the amount of data collected, and analyzed in enterprises has increased several folds in volume, variety and velocityof generation and consumption, organisations have started struggling with architectural limitations of traditional RDBMS architecture. As a result a new class of systems had to be designed and implemented, giving rise to the new phenomenon of “Big Data”. In this paper we will trace the origin of new class of system called Hadoop to handle Big data.

Big Data Analytics with HadoopPhilippe Julio

What is Hadoop? Key Concepts, Architecture, and ApplicationsMikeKelvin1

Hadoop is an open-source framework for distributed storage and processing of large data sets. Key components include HDFS (storage), MapReduce (processing), YARN (resource management), and Hadoop Common (utilities). Its architecture follows a master-slave model with Master Nodes (NameNode, JobTracker) managing data and tasks, and Slave Nodes (DataNodes, TaskTrackers) storing data and performing computations. Hadoop is used in data warehousing, business intelligence, machine learning, and large-scale data processing, making it essential for big data applications. Feel free to download the PPT for more detailed information or read about the topic by visiting: https://www.techgabbing.com/post/what-is-hadoop-key-concepts-architecture-and-its-applications

Rameez Rangrez_Hadoop_AdminRameez Rangrez

This document contains a resume for Ramez Rangrez, who is a Hadoop Administrator with over 5 years of experience in configuring, deploying, maintaining, and monitoring Hadoop clusters. He has worked at Persistent Systems Limited since 2016 and CloudAge from 2013 to 2016. His skills include SQL, Hadoop ecosystem tools, Linux, AWS, and he is working towards Cloudera certification. His responsibilities have involved Hadoop administration, troubleshooting issues, capacity planning, and performance tuning. He also has experience with a customer churn analysis project using Hadoop and cloud systems.

Nosql Presentation.pdf for DBMS understandingHUSNAINAHMAD39

How Hadoop Revolutionized Data Warehousing at Yahoo and FacebookAmr Awadallah

Hadoop was developed to solve problems with data warehousing systems at Yahoo and Facebook that were limited in processing large amounts of raw data in real-time. Hadoop uses HDFS for scalable storage and MapReduce for distributed processing. It allows for agile access to raw data at scale for ad-hoc queries, data mining and analytics without being constrained by traditional database schemas. Hadoop has been widely adopted for large-scale data processing and analytics across many companies.

1.demystifying big data & hadoopdatabloginfo

This document provides an overview of big data and Hadoop. It defines big data as large, complex datasets that are difficult to store and process using traditional systems. Examples of big data sources are listed. Hadoop is introduced as an open source framework for distributed processing of large datasets across commodity computers. Key components of Hadoop like HDFS for data storage and MapReduce for parallel processing are described. Reasons for moving to Hadoop include its ability to handle large, unstructured datasets across clusters of servers in a scalable way. The future of data growth and the Hadoop ecosystem are also discussed briefly.

HadoopMayuri Gupta

Big data refers to large volumes of data that are diverse in type and are produced rapidly. It is characterized by the V's: volume, velocity, variety, veracity, and value. Hadoop is an open-source software framework for distributed storage and processing of big data across clusters of commodity servers. It has two main components: HDFS for storage and MapReduce for processing. Hadoop allows for the distributed processing of large data sets across clusters in a reliable, fault-tolerant manner. The Hadoop ecosystem includes additional tools like HBase, Hive, Pig and Zookeeper that help access and manage data. Understanding Hadoop is a valuable skill as many companies now rely on big data and Hadoop technologies.

Big Data Analytics Presentation on the resourcefulness of Big datanextstep013

Nosql seminarShreyashkumar Nangnurwar

The document provides an introduction to NOSQL databases. It begins with basic concepts of databases and DBMS. It then discusses SQL and relational databases. The main part of the document defines NOSQL and explains why NOSQL databases were developed as an alternative to relational databases for handling large datasets. It provides examples of popular NOSQL databases like MongoDB, Cassandra, HBase, and CouchDB and describes their key features and use cases.

NOSQL- Presentation on NoSQLRamakant Soni

NOSQL in big data is the not only structure langua.pdfajajkhan16

This presentation discusses the limitations of relational database management systems (RDBMS) in handling large datasets and introduces NoSQL databases as an alternative. It begins by defining RDBMS and describing issues with scaling RDBMS to big data through techniques like master-slave architecture and sharding. It then defines NoSQL databases, explaining why they emerged and classifying them into key-value, columnar, document, and graph models. The presentation concludes that both RDBMS and NoSQL databases have advantages, suggesting a polyglot approach is optimal to handle different data storage needs.

Which no sql databaseNitin KR

The document compares different NoSQL database types: key-value databases for fast access to large numbers of objects but limited querying; document databases for flexible schemas and querying within documents; columnar databases optimized for analytics on entire columns but no joins; and graph databases suited for interconnected data modeled as nodes and edges but difficult to scale for large generic graphs. The document provides examples, use cases, limitations, and databases for each type.

No SQL Databases as modern database conceptsdebasisdas225831

How can Hadoop & SAP be integratedDouglas Bernardini

Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer

NoSQL Data Stores in Research and Practice - ICDE 2016 Tutorial - Extended Ve...Felix Gessert

This document provides an overview of NoSQL data stores and techniques for scalable data management. It begins with an introduction to NoSQL and the motivations for using specialized data systems instead of traditional relational databases. It then covers the four main classes of NoSQL databases - key-value stores, wide-column stores, document stores, and graph databases. The document also discusses the CAP theorem and its implications, as well as common techniques like sharding, replication, and query processing that NoSQL databases employ to achieve scalability and high availability. The goal is to help readers understand how to approach decisions around which database system may be best for their needs and requirements.

Hadoop introduction , Why and What is Hadoop ?sudhakara st

The future of Big Data toolingData Science Society

Big Data SSD Architecture: Digging Deep to Discover Where SSD Performance Pay...Samsung Business USA

Managing Big data with HadoopNalini Mehta

Big Data Analytics with HadoopPhilippe Julio

What is Hadoop? Key Concepts, Architecture, and ApplicationsMikeKelvin1

Rameez Rangrez_Hadoop_AdminRameez Rangrez

Nosql Presentation.pdf for DBMS understandingHUSNAINAHMAD39

How Hadoop Revolutionized Data Warehousing at Yahoo and FacebookAmr Awadallah

1.demystifying big data & hadoopdatabloginfo

HadoopMayuri Gupta

Big Data Analytics Presentation on the resourcefulness of Big datanextstep013

Nosql seminarShreyashkumar Nangnurwar

NOSQL- Presentation on NoSQLRamakant Soni

NOSQL in big data is the not only structure langua.pdfajajkhan16

Which no sql databaseNitin KR

No SQL Databases as modern database conceptsdebasisdas225831

How can Hadoop & SAP be integratedDouglas Bernardini

Big Data Analytics with Hadoop, MongoDB and SQL ServerMark Kromer

NoSQL Data Stores in Research and Practice - ICDE 2016 Tutorial - Extended Ve...Felix Gessert

Hadoop introduction , Why and What is Hadoop ?sudhakara st

Recently uploaded (20)

Data Analytics Overview and its applicationsJanmejayaMishra7

Conic Sectionfaggavahabaayhahahahahs.pptxtaiwanesechetan

1. Briefing Session_SEED with Hon. Governor Assam - 27.10.pdfSimran112433

Cleaned_Lecture 6666666_Simulation_I.pdfalcinialbob1234

GenAI for Quant Analytics: survey-analytics.aiInspirient

Principles of information security Chapter 5.pptEstherBaguma

Day 1 - Lab 1 Reconnaissance Scanning with NMAP, Vulnerability Assessment wit...Abodahab

Molecular methods diagnostic and monitoring of infection - Repaired.pptx7tzn7x5kky

Developing Security Orchestration, Automation, and Response ApplicationsVICTOR MAESTRE RAMIREZ

Digilocker under workingProcess Flow.pptxsatnamsadguru491

VKS-Python Basics for Beginners and advance.pptxVinod Srivastava

VKS-Python-FIe Handling text CSV Binary.pptxVinod Srivastava

183409-christina-rossetti.pdfdsfsdasggsagfardin123rahman07

How to join illuminati Agent in uganda call+256776963507/0741506136illuminati Agent uganda call+256776963507/0741506136

Calories_Prediction_using_Linear_Regression.pptxTijiLMAHESHWARI

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

Classification_in_Machinee_Learning.pptxwencyjorda88

04302025_CCC TUG_DataVista: The Design Storyccctableauusergroup

Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation. Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.

CTS EXCEPTIONSPrediction of Aluminium wire rod physical properties through AI...ThanushsaranS

Data Analytics Overview and its applicationsJanmejayaMishra7

Conic Sectionfaggavahabaayhahahahahs.pptxtaiwanesechetan

1. Briefing Session_SEED with Hon. Governor Assam - 27.10.pdfSimran112433

Cleaned_Lecture 6666666_Simulation_I.pdfalcinialbob1234

GenAI for Quant Analytics: survey-analytics.aiInspirient

Principles of information security Chapter 5.pptEstherBaguma

Day 1 - Lab 1 Reconnaissance Scanning with NMAP, Vulnerability Assessment wit...Abodahab

Molecular methods diagnostic and monitoring of infection - Repaired.pptx7tzn7x5kky

Developing Security Orchestration, Automation, and Response ApplicationsVICTOR MAESTRE RAMIREZ

Digilocker under workingProcess Flow.pptxsatnamsadguru491

VKS-Python Basics for Beginners and advance.pptxVinod Srivastava

VKS-Python-FIe Handling text CSV Binary.pptxVinod Srivastava

183409-christina-rossetti.pdfdsfsdasggsagfardin123rahman07

How to join illuminati Agent in uganda call+256776963507/0741506136illuminati Agent uganda call+256776963507/0741506136

Calories_Prediction_using_Linear_Regression.pptxTijiLMAHESHWARI

Adobe Analytics NOAM Central User Group April 2025 Agent AI: Uncovering the S...gmuir1066

Classification_in_Machinee_Learning.pptxwencyjorda88

04302025_CCC TUG_DataVista: The Design Storyccctableauusergroup

Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

CTS EXCEPTIONSPrediction of Aluminium wire rod physical properties through AI...ThanushsaranS

Big data vahidamiri-datastack.ir

1. ‌‫‌ای‌بر‌ابزارهای‬‫ه‬‫مقدم‬ ‫پردازش‌داده‌در‌کالن‌داده‬ VAHID AMIRI VAHIDAMIRY.IR [email protected]

2. Big DataData Data Processing Data Gathering Data Storing

4. Big Data Definition  No single standard definition… “Big Data” is data whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it…

6. Solution Big Data Big Comput ation Big Computer

7. Big Data Solutions

8.  Hadoop is a software framework for distributed processing of large datasets across large clusters of computers  Hadoop implements Google’s MapReduce, using HDFS  MapReduce divides applications into many small blocks of work.  HDFS creates multiple replicas of data blocks for reliability, placing them on compute nodes around the cluster Hadoop

10. Spark Stack

11.  More than just the Elephant in the room  Over 120+ types of NoSQL databases So many NoSQL options

12.  Extend the Scope of RDBMS  Caching  Master/Slave  Table Partitioning  Federated Tables  Sharding NoSql  Relational database (RDBMS) technology  Has not fundamentally changed in over 40 years  Default choice for holding data behind many web apps  Handling more users means adding a bigger server

13. RDBMS with Extended Functionality Vs. Systems Built from Scratch with Scalability in Mind NoSQL Movement

14. CAP Theorem  “Of three properties of shared-data systems – data Consistency, system Availability and tolerance to network Partition – only two can be achieved at any given moment in time.”

15. “Of three properties of shared-data systems – data Consistency, system Availability and tolerance to network Partition – only two can be achieved at any given moment in time.”  CA  Highly-available consistency  CP  Enforced consistency  AP  Eventual consistency CAP Theorem

16. Flavors of NoSQL

17.  Schema-less  State (Persistent or Volatile)  Example:  Redis  Amazon DynamoDB Key / Value Database

18.  Wide, sparse column sets  Schema-light  Examples:  Cassandra  HBase  BigTable  GAE HR DS Column Database

19.  Use for data that is  document-oriented (collection of JSON documents) w/semi structured data  Encodings include XML, YAML, JSON & BSON  binary forms  PDF, Microsoft Office documents -- Word, Excel…)  Examples: MongoDB, CouchDB Document Database

20. Graph Database Use for data with  a lot of many-to-many relationships  when your primary objective is quickly finding connections, patterns and relationships between the objects within lots of data  Examples: Neo4J, FreeBase (Google)

21. So which type of NoSQL? Back to CAP… CP = noSQL/column Hadoop Big Table HBase MemCacheDB AP = noSQL/document or key/value DynamoDB CouchDB Cassandra Voldemort CA = SQL/RDBMS SQL Sever / SQL Azure Oracle MySQL

Big data vahidamiri-datastack.ir

Recommended

More Related Content

What's hot (20)

Similar to Big data vahidamiri-datastack.ir (20)

Recently uploaded (20)

Big data vahidamiri-datastack.ir