BigQuery and Datastore: Google Cloud and its Intricacies V6 (Cloud Storage Finale):
BigQuery
BigQuery sits on the edge between data storage and data processing.
Your data needs to run more in the way of exploring a vast sea of data, If you want to do ad-hoc SQL queries on a massive data set, then BigQuery got you covered completely.
Performance in BigQuery
BigQuerry is Google’s fully-managed, petabyte-scale, low-cost analytics data warehouse. This is simply because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights, use familiar SQL and take advantage of our pay-as-you-go model. It’s easy to get data into BigQuery.
You can load it from cloud storage or cloud data store, or stream it into BigQuery at up to 100,000 rows per second. Once it’s in there, you can run super-fast SQL queries against multiple terabytes of data in seconds using the processing power of Google’s infrastructure.
Scaling and Availability in Big Querry:
In addition to SQL queries, you can easily read and write data in BigQuery via Cloud Dataflow, Hadoop, and Spark. BigQuery is used by all types of organizations from startups to Fortune 500 companies, including smaller organizations like Big Query’s free monthly quotas, while bigger organizations love its seamless scaling, and its available 99.9 per cent service level agreement.
Location, Storage and Cost Advantage in BigQuery
Google’s infrastructure is global and so is BigQuery. BigQuery lets you specify the region where your data will be kept.
For instance, if you want to keep data in Europe, you don’t have to go set up a cluster in Europe. Just specify the EU location where you create your data set. US and Asia's locations are also available. Because BigQuery separates storage and computation, you pay for your data storage separately from queries.
This means you pay for queries only when they are actually running and you have full control over who has access to the data stored in BigQuery, including sharing data sets with people in different projects. If you share data sets that won’t impact your cost or performance. People you share with pay for their own queries, not you. Long-term storage pricing is an automatic discount for data residing in BigQuery for extended periods of time. When the age of your data reaches 90 days in BigQuery, Google will automatically drop the price of storage.
Cloud Datastore.
One of the main use cases of Cloud Datastore is to store structured data from App Engine apps.
You can also build solutions that span App Engine and Compute Engine with Cloud Datastore as the integration point. As you would expect from a fully managed service.
Performance
Cloud Datastore automatically handles sharding and replication, providing you with a highly available and durable database that scales automatically to handle the load. Unlike Cloud Bigtable, it also offers transactions that affect multiple database rows, and it lets you do SQL-like queries. To get you started, Cloud Datastore has a free daily quota that provides storage, reads, writes, deletes and small operations at no charge.
Cloud Datastore databases can span App Engine and Compute Engine applications. It is highly scalable and is a NoSQL database.
Cloud Datastore is the best for semi-structured application data that is used in app engines’ applications.
TEXT EDITORS IN CLOUD
Nano Text
Nano is a text editor for Unix-like computing systems or operating environments using a command-line interface. It emulates the Pico text editor, part of the Pine email client, and also provides additional functionality. It is used in the cloud environment.
Cloud Storage and its best practice
Conclusion:
Cloud Datastore is the best for semi-structured application data that is used in app engines’ applications. Bigtable is best for analytical data with heavy read/write events like AdTech, Financial or IoT data.
Consider using Cloud Datastore if you need to store unstructured objects or if you require support for transactions and SQL-like queries. This storage service provides terabytes of capacity with a maximum unit size of one megabyte per entity.
Cloud Storage is best for structured and unstructured, binary or object data like images, large media files and backups. SQL is best for web frameworks and in existing applications like storing user credentials and customer orders.
Consider using Cloud Storage if you need to store immutable blobs larger than 10 megabytes such as large images or movies.
This storage service provides petabytes of capacity with a maximum unit size of five terabytes per object.
Cloud Spanner is best for large scale database applications that are larger than two terabytes; for example, for financial trading and e-commerce use cases.
Consider using Cloud SQL or Cloud Spanner if you need full SQL support for an online transaction processing system. Cloud SQL provides terabytes of capacity, while Cloud Spanner provides petabytes.
Cloud SQL provides terabytes of capacity, while Cloud Spanner provides petabytes. If Cloud SQL does not fit your requirements because you need horizontal scalability not just through reading replicas, consider using Cloud Spanner.
Bigtable is best for analytical data with heavy read/write events like AdTech, Financial or IoT data.
Consider using Cloud Bigtable if you need to store a large number of structured objects. Cloud Bigtable does not support SQL’s queries nor does it support multi-row transactions. This storage service provides petabytes of capacity with a maximum unit size of 10 megabytes per cell and 100 megabytes per row.
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Credits: Google Cloud Platform, Essentials and Infrastructure.
Image Credit: Google Cloud
