Module Overview
1. Module Overview
In this module, we cover storage and database services in Google Cloud. Every application needs to store data, whether it's business data, media to be streamed, or sensor data from devices. From an application-centered perspective, the technology stores and retrieves the data. Whether it's a database or an object store is less important than whether that service supports the application's requirements for efficiently storing and retrieving the data, given its characteristics. Google offers several data storage services to choose from. In this module, we will cover Cloud Storage, Filestore, Cloud SQL, Spanner, AlloyDB, Firestore, Bigtable, and Memorystore. Let me start by giving you a high-level overview of these different services. This table shows the storage and database services and highlights the storage service type, what each service is good for, and intended use. BigQuery is also listed on the right. I'm mentioning this service because it sits on the edge between data storage and data processing. You can store data in BigQuery, but the intended use for BigQuery is big data analysis and interactive querying. For this reason, BigQuery is covered later in the course. If tables aren't your preference, here's a decision tree to help you identify the solution that best fits your application. Let's walk through this together. First, ask yourself: Is your data structured? If it's not, then ask yourself if you need a shared file system. If you do, then choose Filestore. If you don't, then choose Cloud Storage. If your data is structured, does your workload focus on analytics? If it does, you will want to choose Bigtable or BigQuery, depending on your latency and update needs. BigQuery is recommended as a data warehouse, is the default storage for tabular data, and is optimized for large-scale, ad-hoc SQL-based analysis and reporting. While BigQuery data manipulation language enables you to update, insert, and delete data from your BigQuery tables, because it has a built-in cache, BigQuery works really well in cases where the data does not often change. Bigtable is a NoSQL wide-column database. It's optimized for low latency, large numbers of reads and writes, and maintaining performance at scale. In addition to analytics, Bigtable is also suited as a 'fast lookup' non-relational database for datasets too large to store in memory, with use cases in areas such as IoT, AdTech and FinTec. If your workload doesn't involve analytics, check whether your data is relational. If it's not relational, do you need application caching? If caching is a requirement, choose Memorystore, an in-memory database. Otherwise choose Firestore, a document database. If your data is relational and you need hybrid transactional and analytical processing, also known as HTAP, choose AlloyDB. If you don't need HTAP and don't need global scalability, choose Cloud SQL. If you don't need HTAP and need global scalability, choose Spanner. Depending on your application, you might use one or several of these services to get the job done. For more information on how to choose between these different services, please refer to the links provided in the course resources for this module. Before we dive into each of the data storage services, let's define the scope of this module. The purpose of this module is to explain which services are available and when to consider using them from an infrastructure perspective. I want you to be able to set up and connect to a service without detailed knowledge of how to use a database system. If you want a deeper dive into the design, organizations, structures, schemas and details on how data can be optimized, served and stored properly within those different services, I recommend Google Cloud's Data Engineering courses. Let's look at the agenda. This module covers all of the services we've mentioned so far. To become more comfortable with these services, you will apply them in two labs. We'll also provide a quick overview of Memorystore, which is Google Cloud's fully managed Redis service. Let's get started by diving into Cloud Storage and Filestore.2. Let's practice!
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