Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 23, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Sep 22, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Sep 20, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Sep 18, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned Sep 16, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Sep 12, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Sep 22, 2021 EDT
[DEPRECATED] Data Engineering
Earned Sep 22, 2021 EDT
Building Codeless Pipelines on Cloud Data Fusion
Earned Sep 21, 2021 EDT
Scientific Data Processing
Earned Sep 21, 2021 EDT
BigQuery for Marketing Analysts
Earned Sep 20, 2021 EDT
Applied Data: Blockchain
Earned Sep 20, 2021 EDT
Data Catalog Fundamentals
Earned Sep 20, 2021 EDT
Prepare Data for ML APIs on Google Cloud
Earned Sep 20, 2021 EDT
Build a Data Warehouse with BigQuery
Earned Sep 19, 2021 EDT
Create ML Models with BigQuery ML
Earned Sep 19, 2021 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Sep 17, 2021 EDT
Derive Insights from BigQuery Data
Earned Sep 16, 2021 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Sep 16, 2021 EDT
Data Science on Google Cloud
Earned Sep 16, 2021 EDT
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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Mit dem Skill-Logo zum Kurs Engineer Data for Predictive Modeling with BigQuery ML weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Erstellen von Pipelines für die Datentransformation nach BigQuery mithilfe von Dataprep von Trifacta; Extrahieren, Transformieren und Laden (ETL) von Workflows mit Cloud Storage, Dataflow und BigQuery; Erstellen von Machine-Learning-Modellen mithilfe von BigQuery ML; standortübergreifendes Kopieren von Daten mit Cloud Composer. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über Produkte und Dienste von Google Cloud belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Umgebung anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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This advanced-level quest is unique amongst the other catalog offerings. The labs have been curated to give IT professionals hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud data engineering knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, you will need other preparation, too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
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This quest offers hands-on practice with Cloud Data Fusion, a cloud-native, code-free, data integration platform. ETL Developers, Data Engineers and Analysts can greatly benefit from the pre-built transformations and connectors to build and deploy their pipelines without worrying about writing code. This Quest starts with a quickstart lab that familiarises learners with the Cloud Data Fusion UI. Learners then get to try running batch and realtime pipelines as well as using the built-in Wrangler plugin to perform some interesting transformations on data.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this advanced-level quest, you will get hands-on practice with GCP services like Big Query, Dataproc, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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Want to turn your marketing data into insights and build dashboards? Bring all of your data into one place for large-scale analysis and model building. Get repeatable, scalable, and valuable insights into your data by learning how to query it and using BigQuery. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this Quest you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. This Quest brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this Quest utilizes advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start. The final lab is an advanced challenge-style lab in which there are elements where you are not provided the answer but must solve it for yourself.
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Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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Mit dem Skill-Logo zum Kurs Prepare Data for ML APIs on Google Cloud weisen Sie Grundkenntnisse in folgenden Bereichen nach: Bereinigen von Daten mit Dataprep von Trifacta, Ausführen von Datenpipelines in Dataflow, Erstellen von Clustern und Ausführen von Apache Spark-Jobs in Dataproc sowie Aufrufen von ML-APIs, einschließlich der Cloud Natural Language API, Cloud Speech-to-Text API und Video Intelligence API. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Geschäftssituation anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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Mit dem Skill-Logo zum Kurs Build a Data Warehouse with BigQuery weisen Sie fortgeschrittene Kenntnisse in folgenden Bereichen nach: Daten zusammenführen, um neue Tabellen zu erstellen, Probleme mit Joins lösen, Daten mit Unions anhängen, nach Daten partitionierte Tabellen erstellen und JSON, Arrays sowie Strukturen in BigQuery nutzen. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud vergeben wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, wie Sie Ihr Wissen in einer praxisnahen Geschäftssituation anwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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Mit dem Skill-Logo zum Kurs Create ML Models with BigQuery ML weisen Sie fortgeschrittene Kenntnisse in folgendem Bereich nach: Erstellen und Bewerten von Machine-Learning-Modellen mit BigQuery ML, um Datenvorhersagen zu treffen. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Umgebung anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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Mit dem Skill-Logo zum Kurs Derive Insights from BigQuery Data weisen Sie Grundkenntnisse in folgenden Bereichen nach: Schreiben von SQL-Abfragen, Abfragen öffentlicher Tabellen, Laden von Beispieldaten in BigQuery, Beheben häufig auftretender Syntaxfehler mithilfe der Abfragevalidierung in BigQuery und Erstellen von Berichten in Looker Studio durch Herstellen einer Verbindung zu BigQuery-Daten. Ein Skill-Logo ist ein exklusives digitales Abzeichen, das von Google Cloud ausgestellt wird und Ihre Kenntnisse über unsere Produkte und Dienste belegt. In diesem Zusammenhang wird auch die Fähigkeit bewertet, Ihr Wissen in einer interaktiven praxisnahen Geschäftssituation anzuwenden. Absolvieren Sie eine kursspezifische Aufgabenreihe und die Challenge-Lab-Prüfung, um ein Skill-Logo zu erhalten, das Sie in Ihrem Netzwerk posten können.
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In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
Google Cloud Big Data and Machine Learning Fundamentals
Earned Sep 23, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Sep 22, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Sep 20, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Sep 18, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned Sep 16, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Sep 12, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Sep 22, 2021 EDT
![Badge für [DEPRECATED] Data Engineering](https://cdn.qwiklabs.com/%2BCsWnL49dO1EiQScypoKfAJdI8oWEgDWqu5PBt%2FWepc%3D)
[DEPRECATED] Data Engineering
Earned Sep 22, 2021 EDT
Building Codeless Pipelines on Cloud Data Fusion
Earned Sep 21, 2021 EDT
Scientific Data Processing
Earned Sep 21, 2021 EDT
BigQuery for Marketing Analysts
Earned Sep 20, 2021 EDT
Applied Data: Blockchain
Earned Sep 20, 2021 EDT
Data Catalog Fundamentals
Earned Sep 20, 2021 EDT
Prepare Data for ML APIs on Google Cloud
Earned Sep 20, 2021 EDT
Build a Data Warehouse with BigQuery
Earned Sep 19, 2021 EDT
Create ML Models with BigQuery ML
Earned Sep 19, 2021 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Sep 17, 2021 EDT
Derive Insights from BigQuery Data
Earned Sep 16, 2021 EDT
NCAA® March Madness®: Bracketology with Google Cloud
Earned Sep 16, 2021 EDT
Data Science on Google Cloud
Earned Sep 16, 2021 EDT