The document discusses big data and open source tools and technologies. It provides an overview of key challenges for data leaders, introduces the top 10 big data tools including Apache Spark, R, and Talend Open Studio. It outlines the benefits of open source including low costs, flexibility, and innovation. The document advocates adopting both corporate and open source software using a "bi-modal" approach to support innovative and engineered analytics. It provides a template for a 1-page big data strategy.
This document provides an overview of modern big data analytics tools. It begins with background on the author and a brief history of Hadoop. It then discusses the growth of the Hadoop ecosystem from early projects like HDFS and MapReduce to a large number of Apache projects and commercial tools. It provides examples of companies and organizations using Hadoop. It also outlines concepts like SQL on Hadoop, in-database analytics using MADLib, and the evolution of Hadoop beyond MapReduce with the introduction of YARN. Finally, it discusses new frameworks being built on top of YARN for interactive, streaming, graph and other types of processing.
This is my Deep Water talk for the TensorFlow Paris meetup.
Deep Water is H2O's integration with multiple open source deep learning libraries such as TensorFlow, MXNet and Caffe. On top of the performance gains from GPU backends, Deep Water naturally inherits all H2O properties in scalability. ease of use and deployment.
This document summarizes a presentation on machine learning and Hadoop. It discusses the current state and future directions of machine learning on Hadoop platforms. In industrial machine learning, well-defined objectives are rare, predictive accuracy has limits, and systems must precede algorithms. Currently, Hadoop is used for data preparation, feature engineering, and some model fitting. Tools include Pig, Hive, Mahout, and new interfaces like Spark. The future includes YARN for running diverse jobs and improved machine learning libraries. The document calls for academic work on feature engineering languages and broader model selection ontologies.
This document discusses various heuristics and principles for architecture design. It provides guidelines for creating simplified, evolvable systems using small modular components. Some key points discussed include using open architectures, building in options, and designing structures that are resilient to stress. The document also advocates for pattern-oriented, minimalist designs and evolutionary systems that can adapt over time without disrupting existing information. Overall, the document presents best practices for handling complexity, enabling flexibility, and ensuring architectures can withstand failures.
OpenVis Conference Report Part 1 (and Introduction to D3.js)Keiichiro Ono
This document summarizes a cytoscape team meeting on May 8, 2014. It discusses the OpenVis conference, which brings together practitioners in visualization including developers, designers, and analysts. The keynote speakers were introduced, including Mike Bostock who created the D3.js library. Bostock's talk focused on how D3 works and its use of data-driven documents to create interactive visualizations in web browsers. The document notes that while cytoscape uses Java for desktop apps, web technologies like cytoscape.js should be used for sharing data. It relates D3 and the team's projects, suggesting D3 could be used to visualize the cytoscape design process from Git commits.
This document discusses Pig Hive and Cascading, tools for processing large datasets using Hadoop. It provides background on each tool, including that Pig was developed by Yahoo Research in 2006, Hive was developed by Facebook in 2007, and Cascading was authored by Chris Wensel in 2008. It then covers typical use cases for each tool like web analytics processing, mining search logs for synonyms, and building a product recommender. Finally, it discusses how each tool works, mapping queries to MapReduce jobs, and compares features of the tools like philosophy, productivity and data models.
The document discusses big data and open source tools and technologies. It provides an overview of key challenges for data leaders, introduces the top 10 big data tools including Apache Spark, R, and Talend Open Studio. It outlines the benefits of open source including low costs, flexibility, and innovation. The document advocates adopting both corporate and open source software using a "bi-modal" approach to support innovative and engineered analytics. It provides a template for a 1-page big data strategy.
This document provides an overview of modern big data analytics tools. It begins with background on the author and a brief history of Hadoop. It then discusses the growth of the Hadoop ecosystem from early projects like HDFS and MapReduce to a large number of Apache projects and commercial tools. It provides examples of companies and organizations using Hadoop. It also outlines concepts like SQL on Hadoop, in-database analytics using MADLib, and the evolution of Hadoop beyond MapReduce with the introduction of YARN. Finally, it discusses new frameworks being built on top of YARN for interactive, streaming, graph and other types of processing.
This is my Deep Water talk for the TensorFlow Paris meetup.
Deep Water is H2O's integration with multiple open source deep learning libraries such as TensorFlow, MXNet and Caffe. On top of the performance gains from GPU backends, Deep Water naturally inherits all H2O properties in scalability. ease of use and deployment.
This document summarizes a presentation on machine learning and Hadoop. It discusses the current state and future directions of machine learning on Hadoop platforms. In industrial machine learning, well-defined objectives are rare, predictive accuracy has limits, and systems must precede algorithms. Currently, Hadoop is used for data preparation, feature engineering, and some model fitting. Tools include Pig, Hive, Mahout, and new interfaces like Spark. The future includes YARN for running diverse jobs and improved machine learning libraries. The document calls for academic work on feature engineering languages and broader model selection ontologies.
This document discusses various heuristics and principles for architecture design. It provides guidelines for creating simplified, evolvable systems using small modular components. Some key points discussed include using open architectures, building in options, and designing structures that are resilient to stress. The document also advocates for pattern-oriented, minimalist designs and evolutionary systems that can adapt over time without disrupting existing information. Overall, the document presents best practices for handling complexity, enabling flexibility, and ensuring architectures can withstand failures.
OpenVis Conference Report Part 1 (and Introduction to D3.js)Keiichiro Ono
This document summarizes a cytoscape team meeting on May 8, 2014. It discusses the OpenVis conference, which brings together practitioners in visualization including developers, designers, and analysts. The keynote speakers were introduced, including Mike Bostock who created the D3.js library. Bostock's talk focused on how D3 works and its use of data-driven documents to create interactive visualizations in web browsers. The document notes that while cytoscape uses Java for desktop apps, web technologies like cytoscape.js should be used for sharing data. It relates D3 and the team's projects, suggesting D3 could be used to visualize the cytoscape design process from Git commits.
This document discusses Pig Hive and Cascading, tools for processing large datasets using Hadoop. It provides background on each tool, including that Pig was developed by Yahoo Research in 2006, Hive was developed by Facebook in 2007, and Cascading was authored by Chris Wensel in 2008. It then covers typical use cases for each tool like web analytics processing, mining search logs for synonyms, and building a product recommender. Finally, it discusses how each tool works, mapping queries to MapReduce jobs, and compares features of the tools like philosophy, productivity and data models.
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Intro to Machine Learning with H2O and Python - DenverSri Ambati
This document provides an overview of H2O.ai, an open-source in-memory predictive analytics platform. It was founded in 2011 and has 50+ core developers. H2O supports many machine learning algorithms like generalized linear models, random forest, gradient boosting, and deep learning. It can handle large datasets across various environments and programming interfaces like R, Python, and REST APIs. H2O provides scalable supervised and unsupervised learning algorithms for tasks like classification, regression, clustering, and dimensionality reduction.
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
Big Data Everywhere Chicago: Leading a Healthcare Company to the Big Data Pro...BigDataEverywhere
Mohammad Quraishi, Senior IT Principal, Cigna
Like Moses seeing the Promised Land from afar, we knew the big data journey would be worth it, but we didn't know how hard it would be. In this talk, I'll delve into the details of our big data and analytics initiative at Cigna,
H2O.ai - Road Ahead - keynote presentation by Sri AmbatiSri Ambati
Artificial Intelligence for Business Transformation.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Introduction to Deep Learning and AI at Scale for ManagersDataWorks Summit
Deep Learning and the new wave of AI are inevitably coming to your business area. If you are a manager and if you are trying to make sense of all the buzzwords, this session is four you. We will show you what is Deep Learning in a way that you will understand how it works and how can you apply it. We then expand the scope and apply the deep learning and AI techniques in the Big Data context. You will learn about things that don't work out so well, the risks and challenges in both applying and developing with deep learning and AI technologies. We conclude with practical guidance on how to add the exciting deep learning and AI capabilities to your next project.
Outline:
- The path to Deep Learning
- From machine learning to Deep Learning
- But how does it work?
- Deep Learning architectures
- Deep Learning applications
- Deep Learning at scale
- Running AI at scale
- Deep learning at Scale using Spark
- The trouble with AI
- Application challenges
- Development challenges
- How to start your first Deep Learning project
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...Maurice Nsabimana
Volunteers around the world increasingly act as human sensors to collect millions of data points. A team from the World Bank trained deep learning models, using Apache Spark and BigDL, to confirm that photos gathered through a crowdsourced data collection pilot matched the goods for which observations were submitted.
In this talk, Maurice Nsabimana, a statistician at the World Bank, and Jiao Wang, a software engineer on the Big Data Technology team at Intel, demonstrate a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world. BigDL is a distributed deep learning library designed from the ground up to run natively on Apache Spark. It enables data engineers and scientists to write deep learning applications in Scala or Python as standard Spark programs-without having to explicitly manage distributed computations. Attendees of this session will learn how to get started with BigDL, which runs in any Apache Spark environment, whether on-premise or in the Cloud.
LinkedIn is a large professional social network with 50 million users from around the world. It faces big data challenges at scale, such as caching a user's third degree network of up to 20 million connections and performing searches across 50 million user profiles. LinkedIn uses Hadoop and other scalable architectures like distributed search engines and custom graph engines to solve these problems. Hadoop provides a scalable framework to process massive amounts of user data across thousands of nodes through its MapReduce programming model and HDFS distributed file system.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Deep Water - Bringing Tensorflow, Caffe, Mxnet to H2OSri Ambati
Arno Candel introduces Deep Water, which brings Tensorflow, Caffe, Mxnet to H2O. It also brings support for GPUs, image classification, NLP and much more to H2O.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses big data trends and challenges. It begins by defining big data as data that requires a cluster of computers to process due to infrastructure limitations. It then discusses improvements in cluster computing techniques and exponential growth in compute capability, storage density, and data volume. The document notes that while data and compute capabilities are growing exponentially, only a small percentage of available data is actually analyzed. It provides examples of data sources and tools for structured, unstructured, and semi-structured data. Finally, it discusses the evolution of processing structured data on Hadoop from MapReduce to SQL and Spark and IBM's leadership in these areas.
Jupyter Notebook is a popular open-source tool that allows users to create documents containing code, equations, visualizations, and text. It supports Python, R, Scala, and Julia and is commonly used for tasks like data cleaning, transformation, modeling, and visualization. R Studio is also open-source and used for operations on data using the R language, including packages for manipulation and visualization. SAS was one of the first analytics tools and was designed for descriptive and predictive analytics. It has been used for over 40 years for statistical analysis and decision making.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document discusses various tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++; databases like MySQL, NoSQL, SQL Server and Oracle; data analytics tools like SAS, Tableau, SPSS and Excel; APIs like TensorFlow; servers and frameworks like Hadoop and Spark; and compares SQL and NoSQL databases. It provides details on languages and tools like R, Python, Excel, SAS, SPSS and discusses their uses and popularity in data science.
- Data science domains like statistics, natural language processing, predictive analytics, and visualization have entered the market, while image processing, internet of things, and artificial intelligence are still in exploration.
- The "3 V's of BIG DATA" are volume, variety, and velocity.
- Popular programming languages for data science include R, Python, and SQL.
- Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core Hadoop modules are Hadoop Common, HDFS, YARN, and MapReduce.
- A sample data science methodology includes defining a problem statement, choosing an appropriate machine learning algorithm, running models/analysis in R/Python
Big data technologies can be categorized as operational or analytical. Operational technologies deal with raw daily data like online transactions, while analytical technologies analyze operational data for business decisions. The document describes several examples of big data technologies categorized by data storage, mining, analytics, and visualization. Common storage technologies include Hadoop, MongoDB, and Cassandra. Data mining tools include Presto, RapidMiner, and Elasticsearch. Analytics are performed using Apache Kafka, Splunk, KNIME, Spark, and R. Popular visualization technologies are Tableau and Plotly.
This document provides an overview of architecting a first big data implementation. It defines key concepts like Hadoop, NoSQL databases, and real-time processing. It recommends asking questions about data, technology stack, and skills before starting a project. Distributed file systems, batch tools, and streaming systems like Kafka are important technologies for big data architectures. The document emphasizes moving from batch to real-time processing as a major opportunity.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
An overview of Tensorflow, and then we'll walk through how to utilize this library within the H2O platform. Tensorflow is an open source, deep learning framework utilized by Google and Deepmind. #h2ony
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Intro to Machine Learning with H2O and Python - DenverSri Ambati
This document provides an overview of H2O.ai, an open-source in-memory predictive analytics platform. It was founded in 2011 and has 50+ core developers. H2O supports many machine learning algorithms like generalized linear models, random forest, gradient boosting, and deep learning. It can handle large datasets across various environments and programming interfaces like R, Python, and REST APIs. H2O provides scalable supervised and unsupervised learning algorithms for tasks like classification, regression, clustering, and dimensionality reduction.
In the past decade a number of technologies have revolutionized the way we do analytics in banking. In this talk we would like to summarize this journey from classical statistical offline modeling to the latest real-time streaming predictive analytical techniques.
In particular, we will look at hadoop and how this distributing computing paradigm has evolved with the advent of in-memory computing. We will introduce Spark, an engine for large-scale data processing optimized for in-memory computing.
Finally, we will describe how to make data science actionable and how to overcome some of the limitations of current batch processing with streaming analytics.
Big Data Everywhere Chicago: Leading a Healthcare Company to the Big Data Pro...BigDataEverywhere
Mohammad Quraishi, Senior IT Principal, Cigna
Like Moses seeing the Promised Land from afar, we knew the big data journey would be worth it, but we didn't know how hard it would be. In this talk, I'll delve into the details of our big data and analytics initiative at Cigna,
H2O.ai - Road Ahead - keynote presentation by Sri AmbatiSri Ambati
Artificial Intelligence for Business Transformation.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Introduction to Deep Learning and AI at Scale for ManagersDataWorks Summit
Deep Learning and the new wave of AI are inevitably coming to your business area. If you are a manager and if you are trying to make sense of all the buzzwords, this session is four you. We will show you what is Deep Learning in a way that you will understand how it works and how can you apply it. We then expand the scope and apply the deep learning and AI techniques in the Big Data context. You will learn about things that don't work out so well, the risks and challenges in both applying and developing with deep learning and AI technologies. We conclude with practical guidance on how to add the exciting deep learning and AI capabilities to your next project.
Outline:
- The path to Deep Learning
- From machine learning to Deep Learning
- But how does it work?
- Deep Learning architectures
- Deep Learning applications
- Deep Learning at scale
- Running AI at scale
- Deep learning at Scale using Spark
- The trouble with AI
- Application challenges
- Development challenges
- How to start your first Deep Learning project
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...Maurice Nsabimana
Volunteers around the world increasingly act as human sensors to collect millions of data points. A team from the World Bank trained deep learning models, using Apache Spark and BigDL, to confirm that photos gathered through a crowdsourced data collection pilot matched the goods for which observations were submitted.
In this talk, Maurice Nsabimana, a statistician at the World Bank, and Jiao Wang, a software engineer on the Big Data Technology team at Intel, demonstrate a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world. BigDL is a distributed deep learning library designed from the ground up to run natively on Apache Spark. It enables data engineers and scientists to write deep learning applications in Scala or Python as standard Spark programs-without having to explicitly manage distributed computations. Attendees of this session will learn how to get started with BigDL, which runs in any Apache Spark environment, whether on-premise or in the Cloud.
LinkedIn is a large professional social network with 50 million users from around the world. It faces big data challenges at scale, such as caching a user's third degree network of up to 20 million connections and performing searches across 50 million user profiles. LinkedIn uses Hadoop and other scalable architectures like distributed search engines and custom graph engines to solve these problems. Hadoop provides a scalable framework to process massive amounts of user data across thousands of nodes through its MapReduce programming model and HDFS distributed file system.
Software Analytics with Jupyter, Pandas, jQAssistant, and Neo4j [Neo4j Online...Markus Harrer
Let’s tackle problems in software development in an automated, data-driven and reproducible way!
As developers, we often feel that there might be something wrong with the way we develop software. Unfortunately, a gut feeling alone isn’t sufficient for the complex, interconnected problems in software systems.
We need solid, understandable arguments to gain budgets for improvement projects or to defend us against political decisions. Though, we can help ourselves: Every step in the development or use of software leaves valuable, digital traces. With clever analysis, these data can show us root causes of problems in our software and deliver new insights – understandable for everybody.
If concrete problems and their impact are known, developers and managers can create solutions and take sustainable actions aligned to existing business goals.
In this meetup, I talk about the analysis of software data by using a digital notebook approach. This allows you to express your gut feelings explicitly with the help of hypotheses, explorations and visualizations step by step.
I show the collaboration of open source analysis tools (Jupyter, Pandas, jQAssistant and, of course, Neo4j) to inspect problems in Java applications and their environment. We have a look at performance hotspots, knowledge loss and worthless code parts – completely automated from raw data up to visualizations for management.
Participants learn how they can translate their unsafe gut feelings into solid evidence for obtaining budgets for dedicated improvement projects with the help of data analysis.
Deep Water - Bringing Tensorflow, Caffe, Mxnet to H2OSri Ambati
Arno Candel introduces Deep Water, which brings Tensorflow, Caffe, Mxnet to H2O. It also brings support for GPUs, image classification, NLP and much more to H2O.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
This document discusses big data trends and challenges. It begins by defining big data as data that requires a cluster of computers to process due to infrastructure limitations. It then discusses improvements in cluster computing techniques and exponential growth in compute capability, storage density, and data volume. The document notes that while data and compute capabilities are growing exponentially, only a small percentage of available data is actually analyzed. It provides examples of data sources and tools for structured, unstructured, and semi-structured data. Finally, it discusses the evolution of processing structured data on Hadoop from MapReduce to SQL and Spark and IBM's leadership in these areas.
Jupyter Notebook is a popular open-source tool that allows users to create documents containing code, equations, visualizations, and text. It supports Python, R, Scala, and Julia and is commonly used for tasks like data cleaning, transformation, modeling, and visualization. R Studio is also open-source and used for operations on data using the R language, including packages for manipulation and visualization. SAS was one of the first analytics tools and was designed for descriptive and predictive analytics. It has been used for over 40 years for statistical analysis and decision making.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document discusses various tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++; databases like MySQL, NoSQL, SQL Server and Oracle; data analytics tools like SAS, Tableau, SPSS and Excel; APIs like TensorFlow; servers and frameworks like Hadoop and Spark; and compares SQL and NoSQL databases. It provides details on languages and tools like R, Python, Excel, SAS, SPSS and discusses their uses and popularity in data science.
- Data science domains like statistics, natural language processing, predictive analytics, and visualization have entered the market, while image processing, internet of things, and artificial intelligence are still in exploration.
- The "3 V's of BIG DATA" are volume, variety, and velocity.
- Popular programming languages for data science include R, Python, and SQL.
- Apache Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. The core Hadoop modules are Hadoop Common, HDFS, YARN, and MapReduce.
- A sample data science methodology includes defining a problem statement, choosing an appropriate machine learning algorithm, running models/analysis in R/Python
Big data technologies can be categorized as operational or analytical. Operational technologies deal with raw daily data like online transactions, while analytical technologies analyze operational data for business decisions. The document describes several examples of big data technologies categorized by data storage, mining, analytics, and visualization. Common storage technologies include Hadoop, MongoDB, and Cassandra. Data mining tools include Presto, RapidMiner, and Elasticsearch. Analytics are performed using Apache Kafka, Splunk, KNIME, Spark, and R. Popular visualization technologies are Tableau and Plotly.
This document provides an overview of architecting a first big data implementation. It defines key concepts like Hadoop, NoSQL databases, and real-time processing. It recommends asking questions about data, technology stack, and skills before starting a project. Distributed file systems, batch tools, and streaming systems like Kafka are important technologies for big data architectures. The document emphasizes moving from batch to real-time processing as a major opportunity.
Introduction to Data Science - Week 4 - Tools and Technologies in Data ScienceFerdin Joe John Joseph PhD
This document discusses tools and technologies used in data science. It covers popular programming languages like Python, R, Java and C++. It also discusses databases, data analytics tools, APIs, servers, and frameworks. Specific tools mentioned include Hadoop, Spark, Tableau, IBM SPSS, SAS, and Excel. The document provides brief descriptions and examples of how these various tools are used in data science.
Sudipta Mukherjee has over 18 years of experience as a software developer and leader with expertise in machine learning, compilers, and functional programming. They have authored 6 books on programming topics and regularly presents at international conferences. Their skills include C#, F#, Python, machine learning, domain-specific languages, and data analytics.
Top 10 Data analytics tools to look for in 2021Mobcoder
This write-up has surrounded the top 10 tools used by data analysts, architects, scientists, and other professionals. Each tool has some specific feature that makes it an ideal fit for a specific task. So choose wisely depending on your business need, type of data, the volume of information, experience in analytical thinking.
This presentation provides an overview of big data open source technologies. It defines big data as large amounts of data from various sources in different formats that traditional databases cannot handle. It discusses that big data technologies are needed to analyze and extract information from extremely large and complex data sets. The top technologies are divided into data storage, analytics, mining and visualization. Several prominent open source technologies are described for each category, including Apache Hadoop, Cassandra, MongoDB, Apache Spark, Presto and ElasticSearch. The presentation provides details on what each technology is used for and its history.
Hadoop is an open source framework that allows processing and storage of large datasets across clusters of commodity hardware. It was created in 2006 by Doug Cutting and Mike Cafarella to support distributed processing for the Nutch search engine. Hadoop uses a distributed file system and MapReduce programming model to store and process data in a fault-tolerant way across large clusters of servers. It became an Apache project in 2006 and is now widely used by companies like Yahoo, Facebook, and Amazon to manage their big data.
Big Data Warsaw v 4 I "The Role of Hadoop Ecosystem in Advance Analytics" - R...Dataconomy Media
What is Big Data? What is Hadoop? What is MapReduce? How do the other components such as: Oozie, Hue, Hive, Impala works? Which are the main Hadoop distributions? What is Spark? What are the differences between Batch and Streaming processing? What are some Business Intelligence Solutions by focusing on some business cases?
Orange, R, RapidMiner, and WEKA are open source data mining and machine learning tools. Orange has an elegant scripting interface and can be run in GUI or ETL mode. R has elegant scripting integrated with extensive statistical libraries. RapidMiner has many features and good connectivity. WEKA has the easiest GUI but more limited connectivity than the other tools. The document compares the tools on factors such as supported data formats, user interfaces, connectivity, and provides examples of companies using the different tools.
Orange is an open-source data visualization and analysis tool for novice and expert users. It was developed in Python and is available for Windows, Mac OS X, and Linux. Orange provides tools for data mining, machine learning, and statistical analysis through a graphical user interface and Python scripting. Some key features include visual programming, data visualization, interaction and analytics capabilities, a large toolbox of algorithms, and extensibility. Orange has been used by organizations like AstraZeneca for drug development.
This document discusses various business analytics tools including programming languages, self-serve tools, auto ML platforms, visualization tools, and deep learning frameworks. It outlines popular languages like R, Python, Scala, and JavaScript. It also lists self-serve tools like SAS, SPSS, and Alteryx. Popular auto ML platforms mentioned are H2O.ai, AWS, KNIME, and GCP. Visualization tools covered are Tableau, Qlik, Power BI, Domo, and Board. Finally, deep learning frameworks discussed are TensorFlow, Keras, PyTorch, MXNet, and Gluon.
The document discusses big data, including what it is, sources of big data like social media and stock exchange data, and the three Vs of big data - volume, velocity, and variety. It then discusses Hadoop, the open-source framework for distributed storage and processing of large datasets across clusters of computers. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed computation, and YARN which manages computing resources. The document also provides overviews of Pig and Jaql, programming languages used for analyzing data in Hadoop.
BigData refers to large and complex datasets that are difficult to process using traditional database management systems. It includes both structured and unstructured data from sources like social media, sensors, business transactions, and more. Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It solves BigData problems through massively parallel processing using its core components - HDFS for storage and MapReduce for distributed computing.
The document provides information about various open source software tools that can be used for education. It discusses tools for image editing like GIMP, office suites like LibreOffice, web development tools like Brackets, programming environments like Scratch and Greenfoot, animation tools like Stykz and TuxPaint, 3D modeling software like Blender and FreeCAD, and collaborative tools like OwnCloud. It also provides links to websites about open source education resources and discusses some common questions around open source software licensing.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
2. Excel (software
application)
• What is it?
• A spreadsheet application that helps you
analyse data efficiently. It is an elite member
of the Microsoft Office suite of software
applications. If you weren’t living under a rock
all these years, you would have surely worked
on Excel. From schools to industries, everybody
uses Excel. It is an indispensable tool in the
data analyst’s arsenal.
• Who made it?
• It was developed by Microsoft for Windows,
macOS, Android and iOS.
3. R (programming
language)
• What is it?
• An open source (freely available) language for
statistical investigation and visualization. It is
the descendant of the S language. You can call
R, the “Batman” of the data science world.
Current version (as of May 2018) : 3.5.0
• R has a commercial sibling called S-PLUS.
• Who made it?
• This incredible tool was created by Ross Ihaka
and Robert Gentleman. You can easily guess
how the language got its name. R is currently
developed by the R Development Core Team.
4. R Studio (integrated
development
environment for R)
• What is it?
• An open source tool for implementing the R
language. Whenever you hear about R, you will
also hear about R Studio. R Studio is like the
“Batcave” where you can perform all your
statistical analysis. It is just as intuitive as
Google in completing your sentences -
commands. It is important to download R
along with R Studio.
• Who made it?
• RStudio was founded by JJ Allaire, creator of
the programming language ColdFusion.
5. Python
(programming
language)
• What is it?
• An open source language used for general
purpose programming. It can used for
statistical computing, implementing AI,
creating games, and web applications. You can
call it the “Superman” of the data science
world. Current version (as of May 2018) : 3.7
• Who made it?
• Created by Guido van Rossum and first
released in 1991.
6. Jupyter (a non-profit,
open-source project)
• What is it?
• Project Jupyter is a revolutionary non-profit open-
source project which builds software applications
for interactive computing andsuch applications
support dozens of programming languages. A
popular web-based application used by data
scientists and data enthusiasts is the Jupyter
notebook.
• The Jupyter Notebook is an incredibly powerful
tool for interactively developing and presenting
data science projects.
• Who made it?
• Jupyter is developed in the open on GitHub,
through the consensus of the Jupyter community.
7. Anaconda (an open
source distribution
for Python and R)
• What is it?
• An open source distribution of the Python and
R programming languages for data science
and machine learning related applications. It
comes with all the necessary tools and
packages for data analysis, eliminating the
burden from the user who will be on a pursuit
for such tools.
• The distribution includes Jupyter Notebook.
• Who made it?
• Developed by Anaconda Inc.
8. SPSS (software
application)
• What is it?
• SPSS is a commercially available software
package for performing statistical analysis. It
offers a rich set of capabilities for every stage
of the analytical process.
• SPSS stands for “Statistical Package for the
Social Sciences”, and is officially known as
IBM SPSS Statistics, but most users refer to it
as “SPSS”.
• Who made it?
• The software was developed by the SPSS Inc.
• It was later acquired by IBM in 2009.
9. Java (programming
language)
• What is it?
• Java is a general purpose programming
language that can be used for data analysis,
statistical modelling and to build virtually
anything. Java is instrumental in the creation
of popular data science applications that are
used today. A prime example would be
Hadoop.
• As Java is one of the oldest languages, it
comes with a great many libraries and tools
for machine learning and data science.
• Who made it?
• Developed by Sun Microsystems (now owned
by Oracle Corporation) and designed by James
Gosling.
10. Julia (programming
language)
• What is it?
• Julia is a open source programming language
for technical computing, data exploration, and
analysis. It is relatively new.
• It has attracted some high-profile clients, from
investment manager BlackRock, which uses it
for time-series analytics, to the British insurer
Aviva, which uses it for risk calculations.
• Who made it?
• Designed by Jeff Bezanson, Alan Edelman,
Stefan Karpinski, and Viral B. Shah.
11. MATLAB
(programming
language)
• What is it?
• MATLAB stands for Matrix Laboratory. It is a
commercially available programming
language for mathematical computing, data
processing and visualization. It is the easiest
and most productive software environment for
engineers and scientists.
• Who made it?
• Designed by Cleve Moler and developed by
MathWorks.
12. GNU Octave
(programming
language)
• What is it?
• GNU Octave is an open source programming
language used for numerical computations and
data analysis. Octave is one of the major free
alternatives to MATLAB. It can be used for
creating data visualizations in 2D and 3D.
• Octave has support for various statistical
methods. This includes basic descriptive
statistics, probability distributions, statistical
tests, random number generation, and much
more. It was named after a chemical engineer
professor Octave Levenspiel.
• Who made it?
• Developed by John W. Eaton and many others[
13. Database (any data
management
system)
• What is it?
• A Database is a general term for an organized collection of
data.
• Databases support storage and manipulation of data.
• The data is organized into rows and columns which is in the
form of a table. This is referred as a Relational Database.
• SQL is a popular language used by 90% of data scientists
for inserting, searching, updating, and deleting database
records. It stands for Structured Query Language.
• Relational databases like MySQL Database, Oracle, Ms SQL
server, Sybase, etc uses SQL. SQL can be pronounced as
“sequel” or “es-que-el”.
• Who made it?
• SQL was developed by Donald D. Chamberlin and Raymond
F. Boyce
14. Tableau (software
company)
• What is it?
• Tableau is the provider of various interactive data
visualization tools focused on business intelligence.
Their commercially available product is called Tableau
Desktop and it comes with 14-days trail period.
• Tableau can connect to almost any database, and
allows the user to drag and drop data to create
interesting visualizations.
• Tableau is also freely available as Tableau Public.
• Tableau is based on VizQL (visual query language) which
allows simple drag and drop approach to create
incredible data visualizations.
• Who made it?
• Tableau was founded by Pat Hanrahan, Christian
Chabot, and Chris Stolte
15. Qlik (software
company)
• What is it?
• Qlik is the provider of QlikView and Qlik Sense,
business intelligence & visualization software.
• QlikView allows users to rapidly build and
deploy analytic apps without the need for
professional development skills
• Who made it?
• Qlik was founded by Björn Berg and Staffan
Gestrelius
16. Hadoop (a big
data framework)
• What is it?
• Hadoop is an open source, Java-based programming
framework where you can work on large volumes and
varieties of data that cannot be stored and processed in
relational databases.
• The name Hadoop is a made-up name. It owes its name
to a stuffed toy elephant owned by the creator Doug
Cutting’s son.
• Hadoop consists of three key parts – HDFS(distributed
file storage layer), Map-Reduce (distributed processing
layer) and YARN (data management layer).
• Who made it?
• Hadoop was created by Doug Cutting and Mike
Cafarella and presently developed by Apache Software
Foundation.
• Hadoop's MapReduce and HDFS components drew
inspiration from Google papers on MapReduce and
Google File System.
17. Hive (a data
warehouse
software)• What is it?
• Hive is a data warehouse software built on top
of Hadoop for providing data summarization,
query and analysis.
• Hive provides a mechanism to work on data
using a SQL like language called HiveQL.
• HiveQL automatically translates SQL-like
queries into MapReduce jobs executed on
Hadoop.
• Who made it?
• While initially developed by Facebook, Hive is
used and developed by other companies such
as Netflix and the Financial Industry
Regulatory Authority (FINRA).
18. Pig (an open-
source
technology)• What is it?
• Pig is a high-level platform for creating
programs that run on Hadoop. The scripting
language used for this platform is called Pig
Latin.
• Pig Latin enables users to write complex data
transformations without knowing Java. Map-
reduce programs were primarily written in
Java.
• Pig scripts are translated into a series of
MapReduce jobs that are executed on Hadoop.
• Who made it?
• Pig was a result of development effort at
19. Spark (a big data
processing
framework)
• What is it?
• Apache Spark is a fast and efficient big data
processing framework with built-in modules for
streaming, SQL, machine learning and graph
processing.
• While Hadoop suits for batch processing of
data, Spark is specially useful for real-time
streaming data.
• Who made it?
• Spark was authored by Matei Zaharia.
• It is developed by Apache Software
Foundation, UC Berkeley AMPLab, and
Databricks.
20. Github (software
development
platform)
• What is it?
• Github is a web-based hosting platform for
computer science projects. Its main
implementation is version control. This helps in
keeping tabs on changes to a project. GitHub
allows developers to discover, share, and build
better software.
• A budding data scientist can present her/his data
science projects on GitHub. If a Facebook account
is your personal profile and a Linkedin account is
your professional profile, think of Github as your
technical profile.
• Who made it?
• Github was founded by Tom Preston-Werner
21. Kaggle (a data
science platform)
• What is it?
• Kaggle is a platform for learning data science
and hosting analytics competitions in which
users compete to build the best models for
analysing and predicting the datasets
uploaded by companies and users.
• Datasets are available on everything from
government, health, and science to popular
games and dating trends.
• Who made it?
• Kaggle was founded by Anthony Goldbloom
and its parent organization is Google.
22. DataCamp (a web-
based learning
platform)
• What is it?
• DataCamp is a popular online interactive
training and education platform in the field of
data analytics.
• DataCamp offers free and premium interactive
online training by experts from various fields.
• Who made it?
• DataCamp was founded by Martijn Theuwissen
and Jonathan Cornelissen.