Your application needs to store data with strong transactional consistency, and you want seamless scaling up. which storage option is the best choice for your application?

In this article Modern business systems manage increasingly large volumes of heterogeneous data. This heterogeneity means that a single data store is usually not the best approach. Instead, it's often better to store different types of data in different data stores, each focused toward a specific workload or usage pattern. The term polyglot persistence is used to describe solutions that use a mix of data store technologies. Therefore, it's important to understand the main storage models and their tradeoffs. Selecting the right data store for your requirements is a key design decision. There are literally hundreds of implementations to choose from among SQL and NoSQL databases. Data stores are often categorized by how they structure data and the types of operations they support. This article describes several of the most common storage models. Note that a particular data store technology may support multiple storage models. For example, a relational database management systems (RDBMS) may also support key/value or graph storage. In fact, there is a general trend for so-called multi-model support, where a single database system supports several models. But it's still useful to understand the different models at a high level. Not all data stores in a given category provide the same feature-set. Most data stores provide server-side functionality to query and process data. Sometimes this functionality is built into the data storage engine. In other cases, the data storage and p...

Manage Datastore from the console • Overview • Edit an entity in the Console • View statistics • Use the admin console • Exporting and importing entities • Scheduling an export • Deleting entities in bulk • Manage data retention with time-to-live policies • Configure data locality with regional endpoints • Concepts • All concepts • Choosing between Native mode and Datastore mode • Automatic upgrade to Cloud Firestore • Entities, properties, and keys • Indexes • Queries • Aggregation queries • Limits • Metadata • Statistics • Transactions • Errors and error handling • Identity and Access Management (IAM) • Storage size calculations •

• • AI & Machine Learning • API Management • Application Development • Application Modernization • Chrome Enterprise • Compute • Containers & Kubernetes • Data Analytics • Databases • DevOps & SRE • Maps & Geospatial • Security & Identity • Infrastructure • Infrastructure Modernization • Networking • Productivity & Collaboration • SAP on Google Cloud • Storage & Data Transfer • Sustainability • • IT Leaders • • Financial Services • Healthcare & Life Sciences • Manufacturing • Media & Entertainment • Public Sector • Retail • Supply Chain • Telecommunications • Partners • Startups & SMB • Training & Certifications • Inside Google Cloud • Google Cloud Next & Events • Google Maps Platform • Google Workspace • Developers & Practitioners • Transform with Google Cloud Do you like complex application logic? We don’t either. One of the things we’ve learned here at Google is that application code is simpler and development schedules are shorter when developers can rely on underlying data stores to handle complex transaction processing and keeping data ordered. To quote the original Spanner paper, “we believe it is better to have application programmers deal with performance problems due to overuse of transactions as bottlenecks arise, rather than always coding around the lack of transactions.” 1 Put another way, data stores that provide transactions and consistency across the entire dataset by default lead to fewer bugs, fewer headaches and easier-to-maintain application code. Defin...

GSP184 Overview The Quiz application skeleton has already been written. You clone the repository that contains the skeleton using Google Cloud Shell, review the code using the Cloud Shell editor, and view it using the Cloud Shell web preview feature. You then modify the code that stores data to use Cloud Datastore. Objectives In this lab, you learn how to perform the following tasks: • Harness Cloud Shell as your development environment • Preview the application • Update the application code to integrate Cloud Datastore Setup and requirements Before you click the Start Lab button Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you. This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab. To complete this lab, you need: • Access to a standard internet browser (Chrome browser recommended). Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account. • Time to complete the lab---remember, once you start, you cannot pause a lab. Note: If you already have your own personal Google Cloud account or proj...

Initially, thinking D is the best answer but when question is re-re-read, A seems to be correct answer for following reasons 1. Is payment TRANSACTION -- DB should able to perform full blown transaction (updating inventory, sales info etc, though not specified) , not just ATOMIC which DataStore provides 2. Its point-of-sale application, not ONLINE STORE where HIGH number of concurrent users ordering stuff. 3. User Base could grow exponentially - again more users does mot mean concurrent users and more processing power. Its only about storage. 4. Do not want to Manage infrastructure scaling. - Cloud SQL can scale in terms of storage. 5. CloudStore is poor selection for OLTP application - Each property is index - so higher latency Not sure, during exam 2 min is enough to think on various point.. I may be wrong or wrong path ... lets brainstrom.. That link explains how to set MySQL autoscaling with Google Compute Engine instances (you install and manage MySQL on the VM). This can not be applied to Cloud SQL (managed service). In Cloud SQL, only the storage can be automatically increased, and changing the Cloud SQL instance size requires a manual edit of the instance type. yes. and that is ok since this is a point of sale. an exponential increase in number of clients still means reduced parallel processing (how many customers can buy in the very same time) so an increase in memory and CPU is very unlikely to be necessary. yes, an exponential increase in the number of customers...

In this article This article describes the comparison criteria you should use when evaluating a data store. The goal is to help you determine which data storage types can meet your solution's requirements. General considerations Keep the following considerations in mind when making your selection. Functional requirements • Data format. What type of data are you intending to store? Common types include transactional data, JSON objects, telemetry, search indexes, or flat files. • Data size. How large are the entities you need to store? Will these entities need to be maintained as a single document, or can they be split across multiple documents, tables, collections, and so forth? • Scale and structure. What is the overall amount of storage capacity you need? Do you anticipate partitioning your data? • Data relationships. Will your data need to support one-to-many or many-to-many relationships? Are relationships themselves an important part of the data? Will you need to join or otherwise combine data from within the same dataset, or from external datasets? • Consistency model. How important is it for updates made in one node to appear in other nodes, before further changes can be made? Can you accept eventual consistency? Do you need ACID guarantees for transactions? • Schema flexibility. What kind of schemas will you apply to your data? Will you use a fixed schema, a schema-on-write approach, or a schema-on-read approach? • Concurrency. What kind of concurrency mechanism do ...