BigQuery, Memorystore, and product comparisons
1. BigQuery, Memorystore, and product comparisons
Next, we look at BigQuery and Memorystore, and then compare the storage services. BigQuery is a fully managed, serverless enterprise data warehouse for analytics. BigQuery has built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery can scan terabytes in seconds, and petabytes in minutes. It's a great solution for Online Analytical Processing, or OLAP, workloads, for big data exploration and processing, and for reporting with Business Intelligence tools. Applications that you run on Google Cloud can achieve high levels of performance by using either Redis or Memcached without the burden of managing complex deployments. Memorystore supports both of these highly scalable, available, and secure open source caching engines, and is fully protocol compatible with each engine. Memorystore is ideal for scalable web applications, gaming, and stream processing, where a distributed in-memory data store allows for fast, real-time access or processing of data. As a fully managed service, provisioning, replication, failover, and patching are all automated. You can also monitor instances and set up alerts with Cloud Monitoring. Memorystore can be protected from the internet by using VPC networks and internal IP addresses. Memorystore also integrates with Identity and Access Management (IAM). Here are the Google Cloud storage options at a glance. To choose the right storage option for your application, it’s important to understand what a product is and isn’t ideal for by design. This slide includes a simple description of the products and use cases that are ideal for each product. Use cases that are not ideal for each product are also listed. Other considerations for choosing a storage option for your application include read/write latency, typical size of your data, and storage type. When you're designing the database needs of your applications, remember that you are not limited to a single database. Choose the database that is most suitable for each use case. Size limits are per database, so you can exceed the size limits shown here by splitting your data into multiple databases. Refer to the table or Google Cloud documentation to identify the best storage option for your application.2. Let's practice!
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