Google BigQuery - Advanced Analytics and Data Management

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nfoPublished 7/2024
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 14 Lectures ( 9h 50m ) | Size: 6.6 GB/info]

Google BigQuery: Scalable Solutions for Modern Data Challenges. Efficient Queries, Data Warehousing, Real-Time Insights.
What you'll learn:
Introduction to GCP: Understand what Google Cloud Platform is, including its key services and features, and learn how to set up a GCP account.
Navigating the GCP Console: Gain proficiency in navigating the GCP Console, using Cloud Shell, and Google Cloud SDK for managing resources.
BigQuery Fundamentals: Learn what BigQuery is, its key features and benefits, how it works, and its various use cases.
Setting Up and Using BigQuery: Set up BigQuery by creating a GCP project, enabling the BigQuery API, and understanding datasets and tables in BigQuery.
Data Loading & Exporting: Master loading data into BigQuery from various sources such as CSV, JSON, Google Cloud Storage, and understand supported data formats.
SQL Querying in BigQuery: Develop skills in writing basic and advanced SQL queries in BigQuery, using joins, subqueries, aggregations, window functions.
BigQuery Data Management: Manage datasets and tables, perform data transformation and cleaning using SQL, and move public datasets under your project.
Performance Optimization and Cost Management: Optimize query performance with best practices, query execution plans, caching, and materialized views.
Learn strategies for cost management and monitoring in BigQuery.
Requirements:
Enthusiasm and determination to make your mark on the world!
Description:
A warm welcome to the Google Cloud BigQuery course by Uplatz.Google BigQuery is a fully managed, serverless, and highly scalable data warehouse designed for large-scale data analysis. It's part of the Google Cloud Platform (GCP) and allows users to perform super-fast SQL queries using the processing power of Google's infrastructure.How BigQuery works:Serverless ArchitectureBigQuery eliminates the need to set up and manage infrastructure. You don't need to provision resources or configure servers; it automatically scales to accommodate the size of your data and query complexity.StorageData is stored in columnar format, which optimizes for read performance and data compression. This is particularly effective for analytical queries that often need to scan large amounts of data.Query ExecutionUses SQL for querying data. BigQuery's execution engine optimizes the query plan and distributes the workload across multiple nodes in Google's infrastructure.It leverages a highly parallel execution model to perform large-scale data processing efficiently.IntegrationIntegrates with other Google Cloud services such as Google Cloud Storage, Google Cloud Dataflow, Google Cloud Dataproc, and Google Sheets.Supports standard SQL dialect, making it accessible for users familiar with SQL.Data Loading and ExportingSupports various data formats (CSV, JSON, Avro, Parquet) for loading data.Data can be exported to formats like CSV and JSON.Security and ComplianceProvides robust security features including encryption at rest and in transit, identity and access management, and support for compliance standards such as GDPR.Benefits of Learning BigQuery:Learning BigQuery can provide a significant edge in data analysis and engineering roles, given the increasing importance of big data in various industries. It equips you with the skills to manage and analyze large datasets efficiently, leading to better insights and decision-making.Scalability and PerformanceHandle petabytes of data with ease. BigQuery's architecture is designed to scale seamlessly, which is critical for big data applications.Cost-EffectivenessPay only for the data you query (on-demand pricing) or opt for flat-rate pricing if your usage is predictable. This can lead to significant cost savings compared to traditional data warehousing solutions.Ease of UseUser-friendly with SQL support, making it accessible to a wide range of users from data analysts to data scientists.Integration with Data EcosystemEasily integrates with various data sources and tools, including Google Cloud services and third-party applications, enhancing its utility in different data workflows.Real-Time AnalyticsSupport for real-time data ingestion and analysis enables timely insights, crucial for dynamic and fast-paced environments.Managed ServiceAs a fully managed service, it reduces the overhead associated with managing and maintaining infrastructure, allowing you to focus more on data analysis and insights.Advanced FeaturesIncludes advanced analytical capabilities such as machine learning (BigQuery ML), geospatial analysis (BigQuery GIS), and integration with BI tools like Looker and Data Studio.Practical Use Cases of BigQuery:Business IntelligenceUse BigQuery to analyze sales data, customer behavior, and market trends to make data-driven business decisions.Log AnalysisAnalyze large volumes of log data for monitoring, troubleshooting, and improving application performance.Real-Time Data ProcessingPerform real-time analytics on streaming data for applications like fraud detection, recommendation systems, and IoT analytics.Data WarehousingServe as the central repository for integrating data from various sources and performing complex queries for reporting and analytics.Google Cloud BigQuery - Course CurriculumThis course is designed to introduce learners to Google BigQuery, a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. The curriculum covers fundamental concepts, hands-on exercises, and practical use cases to provide a comprehensive understanding of BigQuery.Module 1: Introduction to Google Cloud Platform (GCP)Overview of GCPWhat is Google Cloud Platform?Key services and featuresSetting up a GCP accountNavigating the GCP ConsoleUnderstanding the GCP Console interfaceIntroduction to Cloud ShellIntroduction to Google Cloud SDKModule 2: Introduction to BigQueryWhat is BigQuery?Overview of BigQueryKey features and benefitsWorking of BigQueryUse cases for BigQueryBigQuery SandboxSetting Up BigQueryCreating a GCP projectEnabling the BigQuery APIUnderstanding BigQuery datasets and tablesModule 3: Working with BigQueryBigQuery InterfaceNavigating the BigQuery ConsoleUsing the BigQuery command-line toolGoogle Cloud SDK· Introduction to BigQuery client librariesLoading and Exporting DataData formats supported by BigQueryLoading data into BigQuery from various sources (CSV, JSON, Cloud Storage)Google Cloud Storage (GCS) bucketModule 4: Querying Data in BigQueryBigQuery SQL BasicsIntroduction to SQLUnderstanding SQL syntax in BigQueryWriting and running queries in BigQueryAdvanced SQL QueriesUsing joins and subqueriesAggregations and window functionsPartitioning and clustering for performanceModule 5: BigQuery Data ManagementManaging Datasets and TablesCreating and managing datasetsManaging Table SchemasMove a BigQuery Public Dataset Under Your ProjectData Transformation and CleaningUsing SQL for data transformationData cleaning techniquesModule 6: BigQuery Performance OptimizationOptimizing QueriesQuery performance best practicesUsing query execution plansCaching and materialized viewsCost ManagementUnderstanding BigQuery pricingCost optimization strategiesMonitoring and managing BigQuery costs
Who this course is for:
Data Analysts
Data Scientists
Data Engineers
Machine Learning Engineers
Anyone aspiring to become a Cloud Architect/Engineer
Newbies and beginners who wish to learn Google Cloud Platform and BigQuery database design & management
Business Intelligence Professionals
Database Administrators
Cloud Architects
Cloud Engineers
Software Engineers
Software Developers
IT Professionals
Project Managers
Students and Researchers in Data Science and Analytics
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Код:

https://www.udemy.com/course/google-cloud-bigquery/

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