{"id":1889,"date":"2024-12-05T20:00:25","date_gmt":"2024-12-05T20:00:25","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=1889"},"modified":"2024-12-06T14:57:14","modified_gmt":"2024-12-06T14:57:14","slug":"to-succeed-in-2025-data-engineers-need-to-become-more-lazy","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/to-succeed-in-2025-data-engineers-need-to-become-more-lazy\/","title":{"rendered":"To Succeed In 2025, Data Engineers Need to Become More Lazy!"},"content":{"rendered":"

When asked how he addresses difficult and complex challenges, Microsoft founder Bill Gates replied,\u00a0\u201cI will always choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it.\u201d<\/p>\n

Gates’ advice seems counterintuitive regarding the difficult and complex challenges data engineers will face in 2025. That is until you consider recent advancements in AI-enabled data engineering tools.<\/p>\n

Data Engineering Success = Business Success\u00a0<\/strong><\/h4>\n

Data volumes, diversity, distribution, user demand, and more are among the overwhelming challenges facing data engineers in 2025.<\/p>\n

Expectations are high to streamline data flows, optimize performance, reduce costs, and implement next-gen technologies.<\/p>\n

Given data\u2019s strategic value, especially in today’s increasingly AI-driven business environment, failure is not an option.<\/p>\n

AI-enabled Data Engineering Tools to the Rescue<\/strong><\/h4>\n

AI-enabled data engineering tool capabilities have improved dramatically over the past several years. These include advanced tooling for\u00a0these #dataengineering domains:<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Observability<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Pipeline Development<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Data Quality<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Resource Allocation<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Workload Management\u00a0<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Query Performance\u00a0<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Cost Management<\/strong><\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0And More<\/strong><\/p>\n

Following Gates\u2019 maxim, \u201clazy\u201d data engineers make their jobs \u201ceasy\u201d by smartly offloading increasing portions of their workloads onto these automated tools.<\/p>\n

Not only does this help them complete Gates\u2019\u00a0<\/strong>\u201chard job\u201d sooner and more efficiently, but it also frees up time to take on additional complex data engineering jobs essential to business success.<\/p>\n

The Art of the Possible in AI-enabled Data Engineering 2025<\/strong><\/h4>\n

As a data engineering leader, building your 2025 plans requires understanding the wealth of AI-enabled data engineering capabilities available today and how best to take advantage of them.<\/p>\n

The lazy way to learn this essential information is to attend our\u00a0Insight Jam Session: To Succeed In 2025, Data Engineers Need to Become More Lazy!<\/a><\/em>\u00a0on Friday, December 6 at Noon EST.<\/p>\n

There, you will learn about tangible 2025 opportunities from three leading authorities on AI-enabled data engineering:<\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Barzan Mozafari<\/a>,\u00a0CEO and Co-Founder at\u00a0Keebo<\/a><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Kyle Kirwan<\/a>, CEO and Founder of\u00a0BigEye<\/a><\/p>\n

\u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Vinayak Mitty<\/a>, Director of Data Science and Engineering at\u00a0PPLSI<\/a><\/p>\n

In that session, our panelists will address these critical questions:<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0What is driving the rise of AI-enabled data engineering tools today?<\/p>\n

\u25cb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Business needs?<\/p>\n

\u25cb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0Technology breakthroughs?<\/p>\n

\u25cb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0New thinking about old problems?<\/p>\n

\u25cb\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0All of the above?<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0What were some of the most significant AI-enabled data engineering tooling advancements in 2024?<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0What\u2019s coming in 2025?<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0How are data engineering teams and their internal customers benefitting?<\/p>\n

\u25cf\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0What starting points will yield the quickest return?<\/p>\n

Sign up now<\/a>.\u00a0\u00a0See you then and there.<\/p>\n","protected":false},"excerpt":{"rendered":"

When asked how he addresses difficult and complex challenges, Microsoft founder Bill Gates replied,\u00a0\u201cI will always choose a lazy person to do a hard job. Because a lazy person will find an easy way to do it.\u201d Gates’ advice seems counterintuitive regarding the difficult and complex challenges data engineers will face in 2025. That is […]<\/p>\n","protected":false},"author":414,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[11],"tags":[],"acf":[],"yoast_head":"\nTo Succeed In 2025, Data Engineers Need to Become More Lazy!<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"To Succeed In 2025, Data Engineers Need to Become More Lazy!\" \/>\n<meta property=\"og:description\" content=\"When asked how he addresses difficult and complex challenges, Microsoft founder Bill Gates replied,\u00a0\u201cI will always choose a lazy person to do a hard job. 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