Distributed system lamport's and vector algorithmpinki soni
Logical clocks are mechanisms for capturing chronological and causal relationships in distributed systems that lack a global clock. Some key logical clock algorithms are Lamport's timestamps and vector clocks. Lamport's timestamps assign monotonically increasing numbers to events, while vector clocks allow for partial ordering of events. The algorithms for Lamport's timestamps and vector clocks involve incrementing and propagating clock values to determine causal relationships between events in a distributed system.
A distributed system is a collection of computational and storage devices connected through a communications network. In this type of system, data, software, and users are distributed.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
presentation on design of a 2 pass assembler, and variant I and variant II in the subject of systems programming. especially helpful to GTU students, CSE and IT engineers
Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
Distributed system lamport's and vector algorithmpinki soni
Logical clocks are mechanisms for capturing chronological and causal relationships in distributed systems that lack a global clock. Some key logical clock algorithms are Lamport's timestamps and vector clocks. Lamport's timestamps assign monotonically increasing numbers to events, while vector clocks allow for partial ordering of events. The algorithms for Lamport's timestamps and vector clocks involve incrementing and propagating clock values to determine causal relationships between events in a distributed system.
A distributed system is a collection of computational and storage devices connected through a communications network. In this type of system, data, software, and users are distributed.
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
Distributed deadlock occurs when processes are blocked while waiting for resources held by other processes in a distributed system without a central coordinator. There are four conditions for deadlock: mutual exclusion, hold and wait, non-preemption, and circular wait. Deadlock can be addressed by ignoring it, detecting and resolving occurrences, preventing conditions through constraints, or avoiding it through careful resource allocation. Detection methods include centralized coordination of resource graphs or distributed probe messages to identify resource waiting cycles. Prevention strategies impose timestamp or age-based priority to resource requests to eliminate cycles.
presentation on design of a 2 pass assembler, and variant I and variant II in the subject of systems programming. especially helpful to GTU students, CSE and IT engineers
Distributed shared memory (DSM) provides processes with a shared address space across distributed memory systems. DSM exists only virtually through primitives like read and write operations. It gives the illusion of physically shared memory while allowing loosely coupled distributed systems to share memory. DSM refers to applying this shared memory paradigm using distributed memory systems connected by a communication network. Each node has CPUs, memory, and blocks of shared memory can be cached locally but migrated on demand between nodes to maintain consistency.
The document outlines the process for developing a MapReduce application including:
1) Writing map and reduce functions with unit tests, then a driver program to run on test data.
2) Running the program on a cluster with the full dataset and fixing issues.
3) Tuning the program for performance after it is working correctly.
The document discusses several key structures and components of operating systems, including:
1) System calls that provide interfaces to OS services like process control and file management.
2) The system call mechanism which generates interrupts to transfer control to the OS kernel.
3) System programs that perform tasks like file management and system status monitoring.
4) Operating system design approaches like layered structures, microkernels that separate kernel and services, and modular designs using loadable modules.
This document discusses hardware and software parallelism in computer systems. It defines hardware parallelism as parallelism enabled by the machine architecture through multiple processors or functional units. Software parallelism refers to parallelism exposed in a program's control and data dependencies. Modern computer architectures require support for both types of parallelism to perform multiple tasks simultaneously. However, there is often a mismatch between the hardware and software parallelism available. For example, a dual-processor system may be able to execute 12 instructions in 6 cycles, but the program's inherent parallelism may only allow completing the instructions in 7 cycles. Achieving optimal parallelism requires coordination between hardware design and software programming.
The document discusses common standards in cloud computing. It describes organizations like the Open Cloud Consortium and Distributed Management Task Force that develop standards. It then summarizes standards for application developers, messaging, and security including XML, JSON, LAMP, SMTP, OAuth, and SSL/TLS.
The network layer provides two main services: connectionless and connection-oriented. Connectionless service routes packets independently through routers using destination addresses and routing tables. Connection-oriented service establishes a virtual circuit between source and destination, routing all related traffic along the pre-determined path. The document also discusses store-and-forward packet switching, where packets are stored until fully received before being forwarded, and services provided to the transport layer like uniform addressing.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
There are 5 levels of virtualization implementation:
1. Instruction Set Architecture Level which uses emulation to run inherited code on different hardware.
2. Hardware Abstraction Level which uses a hypervisor to virtualize hardware components and allow multiple users to use the same hardware simultaneously.
3. Operating System Level which creates an isolated container on the physical server that functions like a virtual server.
4. Library Level which uses API hooks to control communication between applications and the system.
5. Application Level which virtualizes only a single application rather than an entire platform.
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
Virtualization is a technique, which allows to share single physical instance of an application or resource among multiple organizations or tenants (customers)..
Virtualization is a proved technology that makes it possible to run multiple operating system and applications on the same server at same time.
Virtualization is the process of creating a logical(virtual) version of a server operating system, a storage device, or network services.
The technology that work behind virtualization is known as a virtual machine monitor(VM), or virtual manager which separates compute environments from the actual physical infrastructure.
This document discusses different memory management techniques used in operating systems. It begins by describing the basic components and functions of memory. It then explains various memory management algorithms like overlays, swapping, paging and segmentation. Overlays divide a program into instruction sets that are loaded and unloaded as needed. Swapping loads entire processes into memory for execution then writes them back to disk. Paging and segmentation are used to map logical addresses to physical addresses through page tables and segment tables respectively. The document compares advantages and limitations of these approaches.
This document discusses OLAP (Online Analytical Processing) operations. It defines OLAP as a technology that allows managers and analysts to gain insight from data through fast and interactive access. The document outlines four types of OLAP servers and describes key multidimensional OLAP concepts. It then explains five common OLAP operations: roll-up, drill-down, slice, dice, and pivot.
Data-Intensive Technologies for CloudComputinghuda2018
This document provides an overview of data-intensive computing technologies for cloud computing. It discusses key concepts like data-parallelism and MapReduce architectures. It also summarizes several data-intensive computing systems including Google MapReduce, Hadoop, and LexisNexis HPCC. Hadoop is an open source implementation of MapReduce while HPCC provides distinct processing environments for batch and online query processing using its proprietary ECL programming language.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
Scheduling Definition, objectives and types Maitree Patel
Scheduling is the process of determining which process will use the CPU when multiple processes are ready to execute. The objectives of scheduling are to maximize CPU utilization, throughput, and fairness while minimizing response time, turnaround time, and waiting time. There are three main types of schedulers: long-term schedulers manage process admission to the system; short-term or CPU schedulers select the next process to run on the CPU; and medium-term schedulers handle process suspension during I/O waits.
Implementation levels of virtualizationGokulnath S
Virtualization allows multiple virtual machines to run on the same physical machine. It improves resource sharing and utilization. Traditional computers run a single operating system tailored to the hardware, while virtualization allows different guest operating systems to run independently on the same hardware. Virtualization software creates an abstraction layer at different levels - instruction set architecture, hardware, operating system, library, and application levels. Virtual machines at the operating system level have low startup costs and can easily synchronize with the environment, but all virtual machines must use the same or similar guest operating system.
RPC allows a program to call a subroutine that resides on a remote machine. When a call is made, the calling process is suspended and execution takes place on the remote machine. The results are then returned. This makes the remote call appear local to the programmer. RPC uses message passing to transmit information between machines and allows communication between processes on different machines or the same machine. It provides a simple interface like local procedure calls but involves more overhead due to network communication.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Object-oriented analysis and design (OOAD) is a popular approach for analyzing, designing, and developing applications using the object-oriented paradigm. It involves modeling a system as a group of interacting objects at various levels of abstraction. Key concepts in OOAD include objects, classes, attributes, methods, encapsulation, inheritance, polymorphism, and relationships like association, aggregation, and composition. Common OOAD techniques include use case diagrams, which show interactions between actors and the system, and class diagrams, which describe the structure and behavior of system objects and their relationships.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
The document provides an overview of developing a big data strategy. It discusses defining a big data strategy by identifying opportunities and economic value of data, defining a big data architecture, selecting technologies, understanding data science, developing analytics, and institutionalizing big data. A good strategy explores these subject domains and aligns them to organizational objectives to accomplish a data-driven vision and direct the organization.
The document outlines the process for developing a MapReduce application including:
1) Writing map and reduce functions with unit tests, then a driver program to run on test data.
2) Running the program on a cluster with the full dataset and fixing issues.
3) Tuning the program for performance after it is working correctly.
The document discusses several key structures and components of operating systems, including:
1) System calls that provide interfaces to OS services like process control and file management.
2) The system call mechanism which generates interrupts to transfer control to the OS kernel.
3) System programs that perform tasks like file management and system status monitoring.
4) Operating system design approaches like layered structures, microkernels that separate kernel and services, and modular designs using loadable modules.
This document discusses hardware and software parallelism in computer systems. It defines hardware parallelism as parallelism enabled by the machine architecture through multiple processors or functional units. Software parallelism refers to parallelism exposed in a program's control and data dependencies. Modern computer architectures require support for both types of parallelism to perform multiple tasks simultaneously. However, there is often a mismatch between the hardware and software parallelism available. For example, a dual-processor system may be able to execute 12 instructions in 6 cycles, but the program's inherent parallelism may only allow completing the instructions in 7 cycles. Achieving optimal parallelism requires coordination between hardware design and software programming.
The document discusses common standards in cloud computing. It describes organizations like the Open Cloud Consortium and Distributed Management Task Force that develop standards. It then summarizes standards for application developers, messaging, and security including XML, JSON, LAMP, SMTP, OAuth, and SSL/TLS.
The network layer provides two main services: connectionless and connection-oriented. Connectionless service routes packets independently through routers using destination addresses and routing tables. Connection-oriented service establishes a virtual circuit between source and destination, routing all related traffic along the pre-determined path. The document also discusses store-and-forward packet switching, where packets are stored until fully received before being forwarded, and services provided to the transport layer like uniform addressing.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
There are 5 levels of virtualization implementation:
1. Instruction Set Architecture Level which uses emulation to run inherited code on different hardware.
2. Hardware Abstraction Level which uses a hypervisor to virtualize hardware components and allow multiple users to use the same hardware simultaneously.
3. Operating System Level which creates an isolated container on the physical server that functions like a virtual server.
4. Library Level which uses API hooks to control communication between applications and the system.
5. Application Level which virtualizes only a single application rather than an entire platform.
The document discusses temporal databases, which store information about how data changes over time. It covers several key points:
- Temporal databases allow storage of past and future states of data, unlike traditional databases which only store the current state.
- Time can be represented in terms of valid time (when facts were true in the real world) and transaction time (when facts were current in the database). Temporal databases may track one or both dimensions.
- SQL supports temporal data types like DATE, TIME, TIMESTAMP, INTERVAL and PERIOD for representing time values and durations.
- Temporal information can describe point events or durations. Relational databases incorporate time by adding timestamp attributes, while object databases
Virtualization is a technique, which allows to share single physical instance of an application or resource among multiple organizations or tenants (customers)..
Virtualization is a proved technology that makes it possible to run multiple operating system and applications on the same server at same time.
Virtualization is the process of creating a logical(virtual) version of a server operating system, a storage device, or network services.
The technology that work behind virtualization is known as a virtual machine monitor(VM), or virtual manager which separates compute environments from the actual physical infrastructure.
This document discusses different memory management techniques used in operating systems. It begins by describing the basic components and functions of memory. It then explains various memory management algorithms like overlays, swapping, paging and segmentation. Overlays divide a program into instruction sets that are loaded and unloaded as needed. Swapping loads entire processes into memory for execution then writes them back to disk. Paging and segmentation are used to map logical addresses to physical addresses through page tables and segment tables respectively. The document compares advantages and limitations of these approaches.
This document discusses OLAP (Online Analytical Processing) operations. It defines OLAP as a technology that allows managers and analysts to gain insight from data through fast and interactive access. The document outlines four types of OLAP servers and describes key multidimensional OLAP concepts. It then explains five common OLAP operations: roll-up, drill-down, slice, dice, and pivot.
Data-Intensive Technologies for CloudComputinghuda2018
This document provides an overview of data-intensive computing technologies for cloud computing. It discusses key concepts like data-parallelism and MapReduce architectures. It also summarizes several data-intensive computing systems including Google MapReduce, Hadoop, and LexisNexis HPCC. Hadoop is an open source implementation of MapReduce while HPCC provides distinct processing environments for batch and online query processing using its proprietary ECL programming language.
The document discusses different models for distributed systems including physical, architectural and fundamental models. It describes the physical model which captures the hardware composition and different generations of distributed systems. The architectural model specifies the components and relationships in a system. Key architectural elements discussed include communicating entities like processes and objects, communication paradigms like remote invocation and indirect communication, roles and responsibilities of entities, and their physical placement. Common architectures like client-server, layered and tiered are also summarized.
Scheduling Definition, objectives and types Maitree Patel
Scheduling is the process of determining which process will use the CPU when multiple processes are ready to execute. The objectives of scheduling are to maximize CPU utilization, throughput, and fairness while minimizing response time, turnaround time, and waiting time. There are three main types of schedulers: long-term schedulers manage process admission to the system; short-term or CPU schedulers select the next process to run on the CPU; and medium-term schedulers handle process suspension during I/O waits.
Implementation levels of virtualizationGokulnath S
Virtualization allows multiple virtual machines to run on the same physical machine. It improves resource sharing and utilization. Traditional computers run a single operating system tailored to the hardware, while virtualization allows different guest operating systems to run independently on the same hardware. Virtualization software creates an abstraction layer at different levels - instruction set architecture, hardware, operating system, library, and application levels. Virtual machines at the operating system level have low startup costs and can easily synchronize with the environment, but all virtual machines must use the same or similar guest operating system.
RPC allows a program to call a subroutine that resides on a remote machine. When a call is made, the calling process is suspended and execution takes place on the remote machine. The results are then returned. This makes the remote call appear local to the programmer. RPC uses message passing to transmit information between machines and allows communication between processes on different machines or the same machine. It provides a simple interface like local procedure calls but involves more overhead due to network communication.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
Object-oriented analysis and design (OOAD) is a popular approach for analyzing, designing, and developing applications using the object-oriented paradigm. It involves modeling a system as a group of interacting objects at various levels of abstraction. Key concepts in OOAD include objects, classes, attributes, methods, encapsulation, inheritance, polymorphism, and relationships like association, aggregation, and composition. Common OOAD techniques include use case diagrams, which show interactions between actors and the system, and class diagrams, which describe the structure and behavior of system objects and their relationships.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
The document provides an overview of developing a big data strategy. It discusses defining a big data strategy by identifying opportunities and economic value of data, defining a big data architecture, selecting technologies, understanding data science, developing analytics, and institutionalizing big data. A good strategy explores these subject domains and aligns them to organizational objectives to accomplish a data-driven vision and direct the organization.
This document provides an overview of MapReduce and HBase in big data processing. It discusses how MapReduce distributes tasks across nodes in a cluster and uses map and reduce functions to process large datasets in parallel. It also explains how HBase can be used for storage with MapReduce, providing fast access and retrieval of large amounts of flexible, column-oriented data.
The document discusses MapReduce, a framework for processing large datasets in a distributed manner. It begins by explaining how MapReduce addresses issues around scaling computation across large networks. It then provides details on the key features and working of MapReduce, including how it divides jobs into map and reduce phases that operate in parallel on data blocks. Examples are given to illustrate how MapReduce can be used to count word frequencies in text and tally population statistics from a census.
MapReduce is a programming model for processing large datasets in a distributed system. It allows parallel processing of data across clusters of computers. A MapReduce program defines a map function that processes key-value pairs to generate intermediate key-value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. The MapReduce framework handles parallelization of tasks, scheduling, input/output handling, and fault tolerance.
Hadoop eco system with mapreduce hive and pigKhanKhaja1
This document provides an overview of MapReduce architecture and components. It discusses how MapReduce processes data using map and reduce tasks on key-value pairs. The JobTracker manages jobs by scheduling tasks on TaskTrackers. Data is partitioned and sorted during the shuffle and sort phase before being processed by reducers. Components like Hive, Pig, partitions, combiners, and HBase are described in the context of how they integrate with and optimize MapReduce processing.
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
The document discusses cloud computing systems and MapReduce. It provides background on MapReduce, describing how it works and how it was inspired by functional programming concepts like map and reduce. It also discusses some limitations of MapReduce, noting that it is not designed for general-purpose parallel processing and can be inefficient for certain types of workloads. Alternative approaches like MRlite and DCell are proposed to provide more flexible and efficient distributed processing frameworks.
MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers.
MapReduce is a programming model and implementation for processing large datasets in a distributed environment. It allows users to write map and reduce functions to process key-value pairs. The MapReduce library handles parallelization across clusters, automatic parallelization, fault-tolerance through task replication, and load balancing. It was designed at Google to simplify distributed computations on massive amounts of data and aggregates the results across clusters.
Map reduce advantages over parallel databases Ahmad El Tawil
MapReduce has several advantages over parallel databases for processing large datasets:
1) MapReduce can handle heterogeneous systems with different storage systems more easily than parallel databases which require data copying and analysis.
2) Complex functions are more straightforward to express in MapReduce's simple map and reduce model compared to SQL in parallel databases which can require complicated user defined functions.
3) MapReduce provides better fault tolerance than parallel databases by using techniques like batching, sorting, grouping and smart task scheduling during data transfers between mapping and reducing tasks.
Spark is an open-source cluster computing framework that uses in-memory processing to allow data sharing across jobs for faster iterative queries and interactive analytics, it uses Resilient Distributed Datasets (RDDs) that can survive failures through lineage tracking and supports programming in Scala, Java, and Python for batch, streaming, and machine learning workloads.
Mapreduce - Simplified Data Processing on Large ClustersAbhishek Singh
The document describes MapReduce, a programming model and implementation for processing large datasets across clusters of computers. It allows users to write map and reduce functions to parallelize tasks. The MapReduce library automatically parallelizes jobs, distributes data and tasks, handles failures and coordinates communication between machines. It is scalable, processing terabytes of data on thousands of machines, and easy for programmers without parallel experience to use.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Hadoop and Mapreduce for .NET User GroupCsaba Toth
This document provides an introduction to Hadoop and MapReduce. It discusses big data characteristics and challenges. It provides a brief history of Hadoop and compares it to RDBMS. Key aspects of Hadoop covered include the Hadoop Distributed File System (HDFS) for scalable storage and MapReduce for scalable processing. MapReduce uses a map function to process key-value pairs and generate intermediate pairs, and a reduce function to merge values by key and produce final results. The document demonstrates MapReduce through an example word count program and includes demos of implementing it on Hortonworks and Azure HDInsight.
The document describes MapReduce, a programming model and implementation for processing large datasets across clusters of computers. The model uses map and reduce functions to parallelize computations. Map processes key-value pairs to generate intermediate pairs, and reduce merges values with the same intermediate key. The implementation handles parallelization, distribution, and fault tolerance transparently. Hundreds of programs have been implemented using MapReduce at Google, processing terabytes of data on thousands of machines daily.
This slide is about detecting concealed object from images using Search Identification Network (SINet) just like predator searches its prey and identifies it.
Depth of neural network is crucial for its success. However, network training becomes more difficult with increasing depth. New architecture designed to ease gradient-based training of very deep network is highway network.
Concept Sorting in Knowledge ElicitationAdarshaDhakal
This document discusses knowledge acquisition and concept sorting. It defines knowledge acquisition as the process of acquiring knowledge for an unknown domain, which is an important first step in knowledge engineering. Concept sorting is presented as a knowledge elicitation technique where domain experts organize concepts into groups based on common attributes. The document provides details on how concept sorting is performed and analyzed, noting its benefits in articulating an expert's domain knowledge, as well as its limitations for large numbers of concepts.
Shape Preserving Interpolation Using C2 Rational Cubic SplineAdarshaDhakal
Linear interpolation, cubic interpolation, rational cubic spline with positivity, monotony and convexity preserving technique and shape preserving interpolation.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Open Source Contributions to Postgres: The Basics POSETTE 2024ElizabethGarrettChri
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
3. Introduction
• MapReduce is a programming model introduced by Google for processing
and generating large data sets on clusters of computers.
• Google first formulated the framework for the purpose of serving Google’s
Web page indexing, and the new framework replaced earlier indexing
algorithms.
• Beginner developers find the MapReduce framework beneficial because
library routines can be used to create parallel programs without any worries
about infra-cluster communication, task monitoring or failure handling
processes.
• MapReduce runs on a large cluster of commodity machines and is highly
scalable.
• It has several forms of implementation provided by multiple programming
languages, like Java, C# and C++.
4. • MapReduce is a general-purpose programming model for data-
intensive computing.
• It was introduced by Google in 2004 to construct its web index.
• It is also used at Yahoo, Facebook etc. It uses a parallel computing
model that distributes computational tasks to large number of
nodes(approximately 1000-10000 nodes.)
• It is fault-tolerable. It can work even when 1600 nodes among 1800
nodes fails.
• Hadoop framework from Apache Software Foundation is an
implementation of MapReduce Programming Model
8. Steps for MapReduce
• Step 1: Transform raw data into key/value pairs in parallel.
• The mapper will get the data file and make the Rating the key and
the values will be the reviews. We will add number 1 for reviews.
• Step 2: Shuffle and sort by the MapReduce model.
• The process of transferring mappers’ intermediate output to the
reducer is known as shuffling. It will collect all the reviews(number
1s) together with the individual key and it will sort them. it will get
sorted by key.
• Step3: Process the data using Reduce.
• Reduce will count each value(number 1) for each key.
9. • Although, the map and reduce functions in MapReduce model is not
exactly same as in functional programming.
• Map and Reduce functions in MapReduce model:
• Map: It process a (key, value) pair and returns a list of
(intermediate key, value) pairs
map(k1, v1)→list(k2, v2)
• Reduce: It merges all intermediate values having the same
intermediate key
reduce(k2, list(v2))→list(v3)
10.
11. Basic Concept
• In MapReduce model, user has to write only two functions map and
reduce.
• Few examples that can be easily expressed as MapReduce
computations:
• Distributed Grep ( is an efficient way to utilize a Hadoop cluster to
find log messages hidden within terabytes of log data)
• Count of URL Access Frequency
• Inverted Index
• Mining
12.
13. Advantages
• MapReduce facilitates automatic parallelization and distribution,
reducing the time required to run the programs
• MapReduce provides fault tolerance by re-executing, writing map
output to a distributed file system, and restarting failed map or reducer
task
• MapReduce is a cost-effective solution for processing of data
• MapReduce processes large volume of unprocessed data very quickly
• MapReduce utilizes simple programming model to handle tasks more
efficiently and quickly and is easy to learn
• MapReduce is flexible and works with several Hadoop languages to
handle and store data
14. Limitations
• MapReduce is a low-level programming model which involves a lot of
writing code
• The batch-based processing nature of MapReduce makes it unsuitable for
real-time processing
• It does not support data pipelining or overlapping of Map and Reduce
functions
• Task initialization, coordination, monitoring, and scheduling take up a large
chunk of MapReduce's execution time and reduce its performance
• MapReduce cannot cache the intermediate data in memory, thereby
diminishing Hadoop’s performance
15. The data we have has 20491 rows and 2 columns, and
our task is to provide individual count of ratings.
16. MAPPING each rating with a shuffle and giving counter of 1.
Later sorting the ratings with the count.
17. REDUCING leads to giving lesser number of data.
Each rating has their total count from the data from Review of Hotel
18. Implementing MapReduce Programming
Model
• Hadoop, developed by Apache
• Spark, developed by AMPLab at UC Berkley
• Phoenix++, developed at Stanford University
• MARISSA (MApReduce Implementation for Streaming Science Application,
developed at SUNY Binghamton
• DRYAD and DRYADLINQ, developed by Microsoft
• MapReduce-MPI, Developed by Steve Plimpton (Sandia)
• Disco, developed by NOKIA
• Themis, developed by Rasmussen et al
• MR4C, developed by Skybox Imaging
19. Bibliography
• MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean and Sanjay Ghemawat Google, Inc.
• MapReduce Tutorial, https://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html
• Hadoop – MapReduce, https://www.tutorialspoint.com/hadoop/hadoop_mapreduce.htm
• MapReduce-Implementation-in-Python, https://github.com/rshah204/MapReduce-Implementation-in-
Python/blob/master/MapReduce.ipynb
• Hotel Reviews, https://www.kaggle.com/datasets/yash10kundu/hotel-reviews?resource=download
• MAP-REDUCE IMPLEMENTATIONS: SURVEY AND PERFORMANCE COMPARISON, Zeba Khanam
and Shafali Agarwal, Department of Computer Application, JSSATE, Noida, IJCSIT Vol 7, No 4, August
2015