Structured data storage
1. Structured data storage
In the previous lesson, you saw how Cloud Storage is used to store unstructured data. Now let’s explore some Google Cloud data storage products that are suited for storing structured data. Structured data consists of numbers and values that are organized in a predefined format that’s easily searchable in a relational database. Earlier in the course, we mentioned that a relational database stores information in tables, rows, and columns that have a clearly defined schema that represents the structure or logical configuration of the database. Cloud SQL offers fully managed relational databases, including MySQL, PostgreSQL, and SQL Server as a service. It’s designed to transfer mundane—but necessary and often time-consuming—tasks to Google, like applying patches and updates, managing backups, and configuring replications, so you can focus on building great applications. Trusted by thousands of the largest enterprises around the world, organizations that use Cloud SQL obtain various benefits. It doesn't require any software installation or maintenance. It supports managed backups, so backed-up data is securely stored and accessible if a restore is required. It encrypts customer data when on Google’s internal networks and when stored in database tables, temporary files, and backups. And it includes a network firewall, which controls network access to each database instance. Spanner is a fully managed, mission-critical, relational database service that scales horizontally to handle unexpected business spikes. Battle tested by Google’s own mission-critical applications and services, Spanner is the service that powers Google’s multi-billion dollar business. Spanner is especially suited for applications that require a SQL relational database management system with joins and secondary indexes, built-in high availability, which provides data redundancy to reduce downtime when a zone or instance becomes unavailable (the goal is to prevent a single point of failure), strong global consistency, which ensures that all locations where data is stored are updated to the most recent data version quickly, and high numbers of input and output operations per second (tens of thousands of reads and writes per second or more). Both Cloud SQL and Spanner are fully managed database services, but how do they differ? Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server with greater than 99.95% availability. Database Migration Service (DMS) makes it easy to migrate your production databases to Cloud SQL with minimal downtime. And then there is Spanner, which is a fully managed relational database with unlimited scale, strong consistency, and up to 99.999% availability with zero downtime for planned maintenance and schema changes. This globally distributed, ACID-compliant cloud database automatically handles replicas, sharding, and transaction processing, so you can quickly scale to meet any usage pattern and ensure success of products. When considering which option is best for your business, consider this: if you have outgrown any relational database, are sharding your databases for throughput high performance, need transactional consistency, global data, and strong consistency, or just want to consolidate your database, consider using Spanner. If you don’t need horizontal scaling or a globally available system, Cloud SQL is a cost-effective solution. The final structured data storage solution that we’ll explore is BigQuery. BigQuery is a fully-managed data warehouse. As we’ve already learned, a data warehouse is a large store that contains petabytes of data gathered from a wide range of sources within an organization and is used to guide management decisions. Because it’s fully managed, BigQuery takes care of the underlying infrastructure, so users can focus on using SQL queries to answer business questions, without having to worry about deployment, scalability, and security. BigQuery provides two services in one: storage and 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. Data in BigQuery is encrypted at rest by default without any action required from a user. Encryption at rest is encryption used to protect data that’s stored on a disk, including solid-state drives, or backup media. BigQuery provides seamless integration with the existing partner ecosystem. Businesses can tap into our ecosystem of system integrators and data integration partners to help enhance analytics and reporting. These integrations mean that BigQuery lets organizations make the most of existing investments in business intelligence, data ingestion, and data integration tools. Industry research shows that 90% of organizations have a multicloud strategy, which adds complexity to data integration, orchestration, and governance. BigQuery works in a multicloud environment, which lets data teams eradicate data silos by using BigQuery to securely and cost effectively analyze data across multiple cloud providers. BigQuery also has built-in machine learning features so that ML models can be written directly in BigQuery by using SQL. And if other professional tools—such as Vertex AI from Google Cloud—are used to train ML models, datasets can be exported from BigQuery directly into Vertex AI for a seamless integration across the data-to-AI lifecycle.2. Let's practice!
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