In the previous section of this course, you explored dataflow and Pub/Sub, Google Cloud solutions to processing streaming data. Now let's focus your attention on BigQuery. You'll begin by exploring BigQueries, two main services, storage and analytics, and then get a demonstration of BigQuery in use. After that, you'll see how BigQuery ML provides a data to AI lifecycle all within one place. You'll also learn about BigQuery ML project phases, as well as key commands. Finally, you'll get hands-on practice using BigQuery ML to build a custom ML model. Let's get started. BigQuery is a fully managed data warehouse. A data warehouse is a large store containing terabytes and petabytes of data gathered from a wide range of sources within an organization that's used to guide management decisions. Being fully managed means that BigQuery takes care of the underlying infrastructure, so you can focus on using SQL queries to answer business questions without worrying about deployment, scalability, and security. Let's look at some of the key features of BigQuery. BigQuery provides two services in one, storage plus analytics. It's a place to store petabytes of data. For reference, one petabyte is equivalent to 11,000 movies at 4k quality. BigQuery is also a place to analyze data with built-in features like machine-learning, geospatial analysis, and business intelligence, which we'll explore a bit later on. BigQuery is a fully managed serverless solution, meaning that you can use SQL queries to answer your organization's biggest questions in the front end without worrying about infrastructure in the back end. If you've never written SQL before, don't worry. This course provides resources and labs to help. BigQuery has a flexible pay-as-you-go pricing model, where you pay for the number of bytes of data your query processes and for any permanent table storage. If you prefer to have a fixed bill every month, you can also subscribe to flat-rate pricing, where you have a reserved amount of resources for use. Data in BigQuery is encrypted at rest by default without any action required from a customer. By encryption at rest, we mean encryption used to protect data that is stored on a disk, including solid-state drives or backup media. BigQuery has built-in machine learning features, so you can write ML models directly in BigQuery using SQL. Also, if you decide to use other professional tools such as Vertex AI from Google Cloud to train your ML models, you can export data sets from BigQuery directly into Vertex AI for seamless integration across the data to AI lifecycle. What does a typical data warehouse solution architecture look like? The input data can be either real-time or batch data. If you think back to the last section of the course, you'll recall that there are four challenges of big data in modern organizations. They are that data can be any format, variety, any size, volume, any speed, velocity, and possibly inaccurate veracity. If it's streaming data, which can be either structured or unstructured, high-speed and large volume, Pub/Sub is needed to digest the data. If it's batch data, it can be directly uploaded to Cloud Storage. After that, both pipelines lead to Dataflow to process the data. Dataflow is where we ETL, extract, transform, and load the data if needed. BigQuery sits in the middle to link data processing using dataflow and data access through analytics, AI, and ML tools. The job of the analytics engine of BigQuery at the end of a data pipeline is to ingest all the process data after ETL, store and analyze it and possibly output it for further use, such as data visualization and machine learning. BigQuery outputs usually feed into two buckets, business intelligence tools and AI and ML tools. If you're a business analyst or data analyst, you can connect to visualization tools like Looker, Data Studio, Tableau, and other BI tools. If you prefer to work in spreadsheets, you can query both small or large BigQuery datasets directly from Google Sheets, and even perform common operations like pivot tables. Alternatively, if you're a data scientist or machine-learning engineer, you can directly call the data from BigQuery through AutoML or Workbench. These AI and ML tools are part of Vertex AI, Google's unified ML platform. BigQuery is like a common staging area for data analytics workloads. When your data is their business analysts, BI developers, data scientists, and machine learning engineers can be granted access to your data for their own insights.