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Unleash the ML power right at the heart of your Data warehouse in BigQuery

Read the docs. If you don’t hear this early in your career, well, you wish you had. When it’s time to pick up a new tool or new language, reading and referencing the docs is a great way to start learning. As an example, I am picking up Terraform as we build out our cloud infrastructure in GCP. As a configuration language, it’s a little different than coding something new. But it has great docs. When I need to learn about cloud functions or creating BigQuery tables or whatever, it’s easy to find the right doc and get the syntax I need. Need to make an API call? Check the API reference. Learn how to find the docs for tools and parse them for what you need. It will accelerate your learning. —— I write about how to start a new career in analytics. Follow me for more insights.

🚀 Surprised and impressed by the power of Google Cloud's BigQuery 🤯🔍 Who knew you could create Machine Learning models right within BigQuery itself? 🤖💡Definitely not me 😅 Just earned my skill badge for "Create ML Models with BigQuery ML" and I am blown away by the possibilities this opens up for data analysis 🌟💻 #BigQueryML #MachineLearning #GoogleCloud #DataAnalytics #AlwaysLearning

Positive change for BigQuery partitioned tables this week: the previous limit of 4000 partitions per table is now raised to 10000 🎉 . From a practical point of view, 4000 partitions is almost 11 years of daily partitions, and 10000 is 27 years of daily partitions. I had in my head that partitions were introduced in BigQuery around 2014 so when I initially read the news, I thought to myself "Right on time to save those who started using daily partitions from the start" I wanted to double-check when partitions were introduced to BigQuery so like it's custom nowadays I asked AI: - Gemini was off, replying that a public date is not available but from Stackoverflow it estimated that it was before July 2017 - Chat GPT 4 pointed to 19th September 2016 and the official blog post as a source. However, when I asked to print the URL, it wasn't able to do so, i.e. it printed a blank URL, when I tried to get the real URL it changed the announcement date to 2nd June 2016 but still without a concrete URL So I got all the articles from the Google Cloud blog website to get to the post that introduced partitions and indeed it was 2nd June 2016. https://lnkd.in/ervusxVx (side note of scraping: there are in total 8230 articles on the official blog website, the first one published on 16th June 2010) However, looking at the BigQuery release page (I should have looked here in the first place), it looks like the initial max partition number was 2500 and on 4th May it was raised to 4000. In other words, they raised the buffer, so now they have some 20 years to figure out how to increase partitions to accommodate the period afterward. Of course, I'm ignoring the usage of partitioned historical data 😀 #rainysaturday #nonsence #googlecloud p.s. I started with my newsletter GCP Weekly on 3rd October 2016 so the post wasn't in the archives, of course that was the first place I went 👍

Leveling up my cloud game! I’m excited to share my yet another skill badge from GCP. I’ve earned my "Create ML Models with BigQuery ML Skill Badge" #GoogleCloudLearning #GoogleCloudSkillBadge

Completed the Google Cloud Big Data and Machine Learning Fundamentals course! I am thrilled to have gained expertise in using Google Cloud Platform tools such as BigQuery, Dataflow, Dataproc, and TensorFlow for managing large data sets and implementing machine learning solutions. With this certification, I can confidently say that I have a deep understanding of GCP and can effectively tackle real-world data projects. #GoogleCloud #MachineLearning #BigData #DataScience

I'm excited to share that I've completed the “Exploring and Preparing your Data with BigQuery" course offered by Google Cloud. This course was a game-changer for me as it delved into data analysts' common challenges, providing hands-on solutions using powerful big data tools on Google Cloud. Throughout the journey, I learned valuable SQL skills and became proficient in utilizing tools like BigQuery and Dataprep for efficient analysis and transformation of datasets along with Data Fusion for creating data pipelines. This course has equipped me to navigate the difficulties of data analysis, offering practical insights that I'm eager to apply in real-world scenarios. #DataAnalysis #BigQuery #BigData #DataPrep #DataFusion #GoogleCloud #CourseraCertificate #SQLSkills #OnlineLearning

Just earned the 'Create ML Models with BigQuery ML' skill badge and I'm over the moon! This badge is no small feat; it's a testament to mastering the art of creating and evaluating machine learning models using BigQuery ML. It's like having a crystal ball, but the magic is in the algorithms and the data sets. If you're looking to showcase your proficiency with Google Cloud products and services, this is the badge to aim for. It's not just a badge, it's a digital high-five from Google Cloud recognizing your ability to apply knowledge in a hands-on environment. So, if you're ready to boost your cloud career and make data predictions that can transform businesses, dive into the BigQuery ML pool. The water's fine, and the skills you'll develop are even finer! #GoogleCloudLearning #GoogleCloudSkillBadge #ContinuousLearning

Leveling up my cloud game! I’m excited to share I’ve earned my Create ML Models with BigQuery ML Skill Badge #GoogleCloudLearning #GoogleCloudSkillBadge #JuaraGCPS10!

Hi #linkedconnections, I have successfully learned and implemented Bigquery ML Modelling in GCP. After completion of all the labs (~5hrs), I have successfully implemented the challenge lab, Thanks to Google and Google Cloud Training Online for starting the #certified2024 program and providing us the great platform to learn and showcase our skill set. Looking forward for more learnings and stay tune for more upskilling and more badges. #bigquery #gcp #modelling #dataengineer #weekendlearning #learning #cloud #bigqueryML