{"id":206,"date":"2020-03-30T14:08:19","date_gmt":"2020-03-30T18:08:19","guid":{"rendered":"http:\/\/www.sierracreekconsulting.com\/?page_id=206"},"modified":"2024-09-29T10:53:26","modified_gmt":"2024-09-29T14:53:26","slug":"resources","status":"publish","type":"page","link":"https:\/\/www.sierracreekconsulting.com\/?page_id=206","title":{"rendered":"Resources"},"content":{"rendered":"\n<p class=\"has-large-font-size wp-block-paragraph\"><strong>Podcasts and Webinars<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Podcast discussions on data across the data lifecycle and AI.<\/strong>  Link to series:  <a href=\"https:\/\/podcasters.spotify.com\/pod\/show\/axsys\/episodes\/Data-Quality-Data--Quality-Decisions-e2op88r\">https:\/\/podcasters.spotify.com\/pod\/show\/axsys\/episodes\/Data-Quality-Data&#8211;Quality-Decisions-e2op88r<\/a><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-anchor-fm-inc wp-block-embed-anchor-fm-inc\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" title=\"Data: Quality Data, Quality Decisions  by AXSYS\" src=\"https:\/\/anchor.fm\/axsys\/embed\/episodes\/Data-Quality-Data--Quality-Decisions-e2op88r#?secret=2s2P1cXZIk\" data-secret=\"2s2P1cXZIk\" height=\"102px\" width=\"400px\" frameborder=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Data Capture and Create:  Recorded September 2024: Data Quality and strategies to improve AI solutions<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-anchor-fm-inc wp-block-embed-anchor-fm-inc\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" title=\"Data 2: Is data storage becoming too complex to manage without AI? by AXSYS\" src=\"https:\/\/anchor.fm\/axsys\/embed\/episodes\/Data-2-Is-data-storage-becoming-too-complex-to-manage-without-AI-e2oqscl#?secret=7bufxHdVYe\" data-secret=\"7bufxHdVYe\" height=\"102px\" width=\"400px\" frameborder=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Data Storage:  Is Data Storage becoming too complex to manage without AI?<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-anchor-fm-inc wp-block-embed-anchor-fm-inc\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" title=\"Data&#039;s Second Life: Maintenance and AI by AXSYS\" src=\"https:\/\/anchor.fm\/axsys\/embed\/episodes\/Datas-Second-Life-Maintenance-and-AI-e2os91n#?secret=Law42r2OLj\" data-secret=\"Law42r2OLj\" height=\"102px\" width=\"400px\" frameborder=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Data Maintenance:  How is AI revolutionizing Data Management?<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-anchor-fm-inc wp-block-embed-anchor-fm-inc\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" title=\"Data: From Data Chaos to Clarity: The AI Advantage by AXSYS\" src=\"https:\/\/anchor.fm\/axsys\/embed\/episodes\/Data-From-Data-Chaos-to-Clarity-The-AI-Advantage-e2otklk#?secret=FRmdK4O44q\" data-secret=\"FRmdK4O44q\" height=\"102px\" width=\"400px\" frameborder=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Data Usage and the AI Advantage<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-anchor-fm-inc wp-block-embed-anchor-fm-inc\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" title=\"AI and the End of Data: Managing Destruction, Compliance, and Cost by AXSYS\" src=\"https:\/\/anchor.fm\/axsys\/embed\/episodes\/AI-and-the-End-of-Data-Managing-Destruction--Compliance--and-Cost-e2oul59#?secret=x5oarZc1R6\" data-secret=\"x5oarZc1R6\" height=\"102px\" width=\"400px\" frameborder=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><figcaption class=\"wp-element-caption\">Data Disposal:  How AI can help with challenges related to finding and disposing of data to meet regulatory requirements.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"published-papers\"><strong>Published Papers<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Published July 22, 2021:  Improving Patient Insights With Textual ETL in the Lakehouse Paradigm <a rel=\"noreferrer noopener\" href=\"https:\/\/databricks.com\/blog\/2021\/07\/22\/improving-patient-insights-with-textual-etl-in-the-lakehouse-paradigm.html\" data-type=\"URL\" data-id=\"https:\/\/databricks.com\/blog\/2021\/07\/22\/improving-patient-insights-with-textual-etl-in-the-lakehouse-paradigm.html\" target=\"_blank\">https:\/\/databricks.com\/blog\/2021\/07\/22\/improving-patient-insights-with-textual-etl-in-the-lakehouse-paradigm.html<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Published May 19, 2021:  Evolution to the Data Lakehouse  <a rel=\"noreferrer noopener\" href=\"https:\/\/databricks.com\/blog\/2021\/05\/19\/evolution-to-the-data-lakehouse.html\" data-type=\"URL\" data-id=\"https:\/\/databricks.com\/blog\/2021\/05\/19\/evolution-to-the-data-lakehouse.html\" target=\"_blank\">https:\/\/databricks.com\/blog\/2021\/05\/19\/evolution-to-the-data-lakehouse.html<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"published-books\"><strong>Published Books<\/strong><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"building-the-data-lakehouse\"><strong>Building the Data Lakehouse<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Published October 2021 https:\/\/technicspub.com\/data-lakehouse\/<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2021\/10\/image.png\"><img loading=\"lazy\" decoding=\"async\" width=\"503\" height=\"745\" src=\"https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2021\/10\/image.png\" alt=\"\" class=\"wp-image-287\" srcset=\"https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2021\/10\/image.png 503w, https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2021\/10\/image-203x300.png 203w\" sizes=\"auto, (max-width: 503px) 100vw, 503px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Description<\/strong> <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today&#8217;s complex and ever-changing analytics, machine learning, and data science requirements.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"data-architecture-a-primer-for-the-data-scientist-2nd-edition\">Data Architecture: A Primer for the Data Scientist, 2nd Edition<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Published 2019  <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"591\" height=\"727\" src=\"http:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2020\/03\/Book-1.jpg\" alt=\"\" class=\"wp-image-214\" srcset=\"https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2020\/03\/Book-1.jpg 591w, https:\/\/www.sierracreekconsulting.com\/wp-content\/uploads\/2020\/03\/Book-1-244x300.jpg 244w\" sizes=\"auto, (max-width: 591px) 100vw, 591px\" \/><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"description\">Description<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the &#8220;bigger picture&#8221; and to understand where their data fit into the grand scheme of things.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Data Architecture: A Primer for the Data Scientist, Second Edition<\/em>&nbsp;addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"key-features\">Key Features<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>New case studies include expanded coverage of textual management and analytics<\/li>\n\n\n\n<li>New chapters on visualization and big data<\/li>\n\n\n\n<li>Discussion of new visualizations of the end-state architecture<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"book-contributions\">Book Contributions<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information&nbsp;by Danette McGilvray<\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-rich is-provider-amazon wp-block-embed-amazon\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM)\" type=\"text\/html\" width=\"1140\" height=\"550\" frameborder=\"0\" allowfullscreen style=\"max-width:100%\" src=\"https:\/\/read.amazon.com\/kp\/card?preview=inline&#038;linkCode=kpd&#038;ref_=k4w_oembed_SOeywrvIrokcM5&#038;asin=0128180153&#038;tag=kpembed-20\"><\/iframe>\n<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Podcasts and Webinars Podcast discussions on data across the data lifecycle and AI. Link to series: https:\/\/podcasters.spotify.com\/pod\/show\/axsys\/episodes\/Data-Quality-Data&#8211;Quality-Decisions-e2op88r Published Papers Published July 22, 2021: Improving Patient Insights With Textual ETL in the Lakehouse Paradigm https:\/\/databricks.com\/blog\/2021\/07\/22\/improving-patient-insights-with-textual-etl-in-the-lakehouse-paradigm.html Published May 19, 2021: Evolution to the Data Lakehouse https:\/\/databricks.com\/blog\/2021\/05\/19\/evolution-to-the-data-lakehouse.html Published Books Building the Data Lakehouse Published October 2021 https:\/\/technicspub.com\/data-lakehouse\/ Description [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-206","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/pages\/206","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=206"}],"version-history":[{"count":9,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/pages\/206\/revisions"}],"predecessor-version":[{"id":329,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=\/wp\/v2\/pages\/206\/revisions\/329"}],"wp:attachment":[{"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=206"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=206"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sierracreekconsulting.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=206"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}