Google Cloud’s Post

I'm happy to share that I've received my From Data to Insights certification from Google Cloud!   Great training with a dose of insightful knowledge.   I found especially inspiring the module forecasting ML models using SQL with BigQuery ML. #Google #GoogleCloud #BigQueryML #SQL #MachineLearning

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 👍

I am pleased to announce the successful completion of the "From Data to Insights with Google Cloud" specialization on Coursera. This comprehensive program, consisting of four courses, has provided a starting point for data wrangling and analysis using Google Cloud's suite of tools. The journey covered: 🔍 Exploring and Preparing Data with BigQuery 📊 Creating New BigQuery Datasets and Visualizing Insights 🚀 Achieving Advanced Insights with BigQuery 🤖 Applying Machine Learning to Data with Google Cloud Of particular note is the final course, focusing on applying Machine Learning to data with Google Cloud. It provided valuable insights into leveraging pre-trained ML APIs and developing custom ML models using SQL with BigQuery ML. This allows for streamlined, data-driven decision-making processes, enhancing the potential for actionable insights and improvements. The application of Machine Learning within BigQuery promises to revolutionize decision-making processes, enabling an efficient, easier way to uncover insights within large datasets and paving the way for innovative, data-driven solutions. I look forward to bringing these skills to future endeavors, continuing to dive deeper into Machine Learning, harnessing the power of data analysis and ML within the Google Cloud ecosystem. #BigQuery #GoogleCloud #MachineLearning #DataScience #dataanalytics

📚 I just finished the second of two Quests of hands-on labs derived from the exercises from the book "Data Science on Google Cloud Platform" by Valliappa Lakshmanan. 🌟 During these quests, I was able to practice various tasks such as ingestion, preparation, processing, querying, exploring, visualizing data sets, and running full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services. 🔍💻 Here are the labs from these quests: Part 1 📌 - Introduction to SQL for BigQuery and Cloud SQL 📊 - Ingesting Data Into The Cloud 🌩️ - Loading Data into Google Cloud SQL 💾 - Visualizing Data with Google Data Studio 🎨 - Processing Data with Google Cloud Dataflow 🔄 - Evaluating a Data Model 📈 Part 2 📌 - Bayes Classifier on Dataproc 🚻 - Machine Learning with Spark on Google Cloud Dataproc 🔥 - Machine Learning with BigQuery ML 🧠 - Machine Learning with TensorFlow in Vertex AI 🤖 - MLOps with Vertex AI ⚙️ - Real-Time Machine Learning with Cloud Dataflow and Vertex AI 🚀 #google #googlecloud #googlecloudplatform #googlecloudarcade #qwiklabs #qwiklabsarcade #googledevelopers #googlecloudskillsboost

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