{"id":23011,"date":"2019-07-29T08:30:11","date_gmt":"2019-07-29T15:30:11","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23011"},"modified":"2019-07-30T08:36:08","modified_gmt":"2019-07-30T15:36:08","slug":"supercharge-data-science-applications-with-the-intel-distribution-for-python","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/","title":{"rendered":"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python"},"content":{"rendered":"\n<p style=\"text-align:center\"><em>Sponsored Post<\/em><\/p>\n\n\n\n<p>The Python language plays a prominent role in almost every data scientist\u2019s workflow. There are countless easy-to-use Python data science packages, ranging from exploratory data analysis (EDA) and visualization, to machine learning, to AutoML platforms that enable rapid iteration over data and models. Python is integral to many high-profile use cases such as <a href=\"https:\/\/nbviewer.jupyter.org\/github\/ogrisel\/notebooks\/blob\/master\/Labeled%20Faces%20in%20the%20Wild%20recognition.ipynb\">facial recognition<\/a>,&nbsp;<a href=\"https:\/\/nbviewer.jupyter.org\/github\/marrrcin\/ml-twitter-sentiment-analysis\/blob\/develop\/twitter_sentiment_analysis.ipynb\">sentiment analysis<\/a>,&nbsp;<a href=\"https:\/\/nbviewer.jupyter.org\/github\/llSourcell\/anomaly_detection_for_CERN\/blob\/master\/Credit%20Card%20Fraud%20Detection.ipynb\">fraud detection<\/a>,&nbsp;<a href=\"https:\/\/nbviewer.jupyter.org\/github\/TebogoNakampe\/Treatise-of-Medical-Image-Processing\/blob\/master\/TMIP_BrainTumour.ipynb\">brain tumor classification<\/a>, and much more. <\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"506\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist-1024x506.png\" alt=\"\" class=\"wp-image-23012\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist-1024x506.png 1024w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist-150x74.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist-300x148.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist-768x380.png 768w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist.png 1282w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<p><strong>Intel\u00ae Distribution for Python Accelerates\nData Science Workflows<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/software.intel.com\/en-us\/distribution-for-python\/choose-download\">Intel\u00ae Distribution for Python<\/a> is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel\u00ae Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who can gain advantage with this product include: machine learning developers, data scientists, numerical and scientific computing developers, and HPC developers.<\/p>\n\n\n\n<p>Intel\u00ae\u2019s accelerated Python packages enable data scientists to\ntake advantage of the ease-of-use and productivity of Python, while taking\nadvantage of the ever-increasing performance of modern hardware. Intel\u00ae\u2019s\noptimized implementation of scikit-learn (leveraging Intel\u00ae Data Analytics\nAcceleration Library), as well as Intel\u00ae\u2019s optimized implementations of&nbsp;<a href=\"https:\/\/software.intel.com\/en-us\/ai-academy\/frameworks\/tensorflow\">Tensorflow<\/a>\nand&nbsp;<a href=\"https:\/\/software.intel.com\/en-us\/ai-academy\/frameworks\/caffe\">Caffe<\/a>&nbsp;(leveraging\nIntel\u00ae MKL-DNN), achieve highly efficient data layout, cache blocking,\nmulti-threading, and vectorization. Intel\u00ae\u2019s optimized implementations of numpy\nand scipy provide drop-in performance enhancement to the expansive complement of\nstatistics, mathematical optimizations, and many other data-centric\ncomputations already built on top of numpy and scipy. In addition, Intel\u00ae now\nprovides&nbsp;<a href=\"https:\/\/intelpython.github.io\/daal4py\/index.html\">daal4py<\/a>,\nwhich combines the API simplicity familiar to users of scikit-learn, with\nautomatic scaling over multiple compute nodes. This rich feature-set helps data\nscientists deliver better predictions faster, and enable analysis of higher\nvolume data sets with the same compute and memory resources.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Using Intel\u00ae Distribution for Python, data scientists and data engineers\nare able to:<\/h4>\n\n\n\n<ul><li>Achieve faster Python application performance with minimal or no changes to your code.<\/li><li>Accelerate NumPy, SciPy, and scikit-learn with integrated Intel\u00ae Performance Libraries such as Intel\u00ae Math Kernel Library and Intel\u00ae Data Analytics Acceleration Library.<\/li><li>Access the latest vectorization and multithreading instructions, Numba and Cython, composable parallelism with Threading Building Blocks, etc.<\/li><\/ul>\n\n\n\n<p>Performance improvements\ninclude: faster machine learning with\nscikit-learn key algorithms accelerated with Intel\u00ae Data Analytics Acceleration\nLibrary, the latest TensorFlow and Caffe libraries optimized for Intel\u00ae\narchitecture, the XGBoost package included in the Intel\u00ae Distribution for\nPython (Linux* only). Intel\u00ae Distribution\nfor Python is included in the company\u2019s flagship product, Intel\u00ae Parallel\nStudio XE.<\/p>\n\n\n\n<p><strong>Close-to-Native Code\nPerformance<\/strong><\/p>\n\n\n\n<p>Intel\u00ae Distribution for Python\nincorporates multiple libraries and techniques to bridge the performance gap\nbetween Python and equivalent functions written in C and C++ languages,\nincluding:<\/p>\n\n\n\n<ul><li>Intel\u00ae Math Kernel Library (Intel\u00ae MKL) for BLAS and LAPACK<\/li><li>Intel MKL vector math library for universal functions (uMath)<\/li><li>Intel\u00ae Data Analytics Acceleration Library (Intel\u00ae DAAL) for machine learning and data analytics<\/li><li>Integration with Intel\u00ae Advanced Vector Extensions (Intel\u00ae AVX), a feature of the latest Intel\u00ae Xeon\u00ae processors<\/li><\/ul>\n\n\n\n<p>A series of <a href=\"https:\/\/software.intel.com\/en-us\/distribution-for-python\/benchmarks\">benchmarks<\/a> were performed to show the efficiency of optimized functions for areas\u2014linear algebra, Fast Fourier Transforms (FFT), uMath, machine learning, composable parallelism, Amazon Elastic Compute Cloud, and Black Scholes formula\u2014and compare Intel\u00ae Distribution for Python to its respective open source Python packages. The benchmarks measure Python against native C code equivalent, which is considered to be representative of optimal performance. The higher the efficiency, the faster the function and the closer to native C speed. <\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"1024\" height=\"570\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic-1024x570.png\" alt=\"\" class=\"wp-image-23013\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic-1024x570.png 1024w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic-150x83.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic-300x167.png 300w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic-768x427.png 768w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_perf_pic.png 1166w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n\n\n<p><strong>Summary<\/strong><\/p>\n\n\n\n<p>The\nfollowing groups can benefit from the Intel\u00ae Distribution for Python:<\/p>\n\n\n\n<ul><li>Machine learning developers, data scientists, and analysts &#8211; easily implement performance-packed, production-ready\nscikit-learn algorithms.<\/li><li>Numerical and scientific computing developers &#8211; accelerate and scale the compute-intensive Python packages\nNumPy, SciPy, and mpi4py.<\/li><li>High-performance computing (HPC) developers &#8211; unlock the power of modern hardware to accelerate your Python\napplications.<\/li><\/ul>\n\n\n\n<p>Intel\u00ae Distribution for Python is a free software package available for\nWindows, Linux, and macOS. Each OS option comes with specialized packages for accelerated workflows\nand advanced functionality. Download immediately <a href=\"https:\/\/software.intel.com\/en-us\/distribution-for-python\/choose-download\">HERE<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Intel\u00ae Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel\u00ae Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who can gain advantage with this product include: machine learning developers, data scientists, numerical and scientific computing developers, and HPC developers.<\/p>\n","protected":false},"author":37,"featured_media":23015,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[182,87,180,210,773,56,1],"tags":[781,133,337,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Supercharge Data Science Applications with the Intel\u00ae Distribution for Python - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"Intel\u00ae Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel\u00ae Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who can gain advantage with this product include: machine learning developers, data scientists, numerical and scientific computing developers, and HPC developers.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2019-07-29T15:30:11+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-07-30T15:36:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_logo.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"300\" \/>\n\t<meta property=\"og:image:height\" content=\"157\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/\",\"url\":\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/\",\"name\":\"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2019-07-29T15:30:11+00:00\",\"dateModified\":\"2019-07-30T15:36:08+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning &amp; Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2540da209c83a68f4f5922848f7376ed\",\"name\":\"Daniel Gutierrez\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/5780282e7e567e2a502233e948464542?s=96&d=mm&r=g\",\"caption\":\"Daniel Gutierrez\"},\"description\":\"Daniel D. Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.\",\"sameAs\":[\"http:\/\/www.insidebigdata.com\",\"https:\/\/twitter.com\/@AMULETAnalytics\"],\"url\":\"https:\/\/insidebigdata.com\/author\/dangutierrez\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/","og_locale":"en_US","og_type":"article","og_title":"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python - insideBIGDATA","og_description":"Intel\u00ae Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_logo.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-5Z9","jetpack-related-posts":[{"id":15122,"url":"https:\/\/insidebigdata.com\/2016\/06\/01\/technical-collaboration-expanding-anaconda-ecosystem\/","url_meta":{"origin":23011,"position":0},"title":"Intel and Continuum Analytics Work Together to Extend the Power of Python-based Analytics Across the Enterprise","date":"June 1, 2016","format":false,"excerpt":"Continuum Analytics, the creator and driving force behind Anaconda, a leading open data science platform powered by Python, welcomes Intel into the Anaconda ecosystem. Intel has adopted the Anaconda packaging and distribution and is working with Continuum to provide interoperability.","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":23195,"url":"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/","url_meta":{"origin":23011,"position":1},"title":"Interview: Terry Deem and David Liu at Intel","date":"September 3, 2019","format":false,"excerpt":"I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel\u00ae Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel\u00ae Distribution for Python*, both from Intel, to discuss the Intel\u00ae Distribution for Python (IDP): targeted classes of developers, use with\u2026","rel":"","context":"In &quot;Data Storage&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_IPD_logo.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":33215,"url":"https:\/\/insidebigdata.com\/2023\/08\/24\/anaconda-distribution-for-python-brings-data-science-to-hundreds-of-millions-of-microsoft-excel-users\/","url_meta":{"origin":23011,"position":2},"title":"Anaconda Distribution for Python Brings Data Science to Hundreds of Millions of Microsoft Excel Users","date":"August 24, 2023","format":false,"excerpt":"Anaconda Inc., the provider of one of the world\u2019s most widely used and trusted data science and AI platforms, announced the beta availability of Anaconda Distribution for Python in Excel, a new integration with\u00a0Microsoft\u00a0Excel.\u00a0 Anaconda\u2019s Python distribution is fully embedded and integrated into the Excel grid toolboxes for manipulating, analyzing,\u2026","rel":"","context":"In &quot;Cloud&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Data_Science_shutterstock_1247255884_special.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":18798,"url":"https:\/\/insidebigdata.com\/2017\/09\/06\/julia-high-level-language-supercomputing-big-data\/","url_meta":{"origin":23011,"position":3},"title":"Julia: A High-Level Language for Supercomputing and Big Data","date":"September 6, 2017","format":false,"excerpt":"Julia is a new language for technical computing that is meant to address the problem of language environments not designed to run efficiently on large compute clusters. It reads like Python or Octave, but performs as well as C. It has built-in primitives for multi-threading and distributed computing, allowing applications\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/09\/Intel_Julia_fig.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":18392,"url":"https:\/\/insidebigdata.com\/2017\/07\/20\/using-python-drive-new-insights-innovation-big-data\/","url_meta":{"origin":23011,"position":4},"title":"Using Python to Drive New Insights and Innovation from Big Data","date":"July 20, 2017","format":false,"excerpt":"In a recent white paper \"Management's Guide - Unlocking the Power of Data Science & Machine Learning with Python,\" ActiveState - the Open Source Language Company - provides a summary of Python\u2019s attributes in a number of important areas, as well as considerations for implementing Python to drive new insights\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2017\/07\/ActiveState_cover.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":29033,"url":"https:\/\/insidebigdata.com\/2022\/04\/12\/anaconda-announces-collaboration-with-esri-setting-the-enterprise-standard-for-python-across-the-geospatial-community\/","url_meta":{"origin":23011,"position":5},"title":"Anaconda Announces Collaboration with Esri, Setting the Enterprise Standard for Python Across the Geospatial Community","date":"April 12, 2022","format":false,"excerpt":"Anaconda Inc., provider of the popular data science platform, announced a collaboration with\u00a0Esri, the global market leader in geographic information system (GIS) software, location intelligence, and mapping. This collaboration supports Esri and the geospatial community by providing users of Esri\u2019s software with preloaded geospatial packages for use with Python.","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23011"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=23011"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/23011\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/23015"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=23011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=23011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=23011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}