Google Cloud Bigtable

Cloud Bigtable is managed NoSQL ● Fully managed NoSQL, wide-column database service for terabyte applications ● Integrated ○ Accessed using HBase API ○ Native compatibility with big data, Hadoop ecosystems

Cloud Bigtable is Google's NoSQL big data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

You can use Cloud Bigtable to store and query all of the following types of data: ● Marketing data, such as purchase histories and customer preferences ● Financial data, such as transaction histories, stock prices, and currency exchange rates ● Internet of Things (IoT) data, such as usage reports from energy meters and home appliances ● Time-series data, such as CPU and memory usage over time for multiple servers ● Personalisation ● Recommendation ●  Geo spatial datasets ● Graphs

Why choose Cloud Bigtable? ● Replicated storage ● Data encryption in-flight and at rest ● Role-based ACLs ● Drives major applications such as Google Analytics and Gmail

Customers frequently choose Bigtable if the data is: Big ● Large quantities (>1 TB) of semi-structured or structured data Fast ● Data is high throughput or rapidly changing NoSQL ● Transactions, strong relational semantics not required And especially if it is: Time series ● Data is time-series or has natural semantic ordering Big data ● You run asynchronous batch or real-time processing on the data Machine learning ● You run machine learning algorithms on the data ● Low latency read-write access ● High throughput analytics ● Native time series support ● Source is from application API, streaming, batch-processing

Bigtable Access Patterns 

● Application API : Data can be read from and written to Cloud Bigtable through a data service layer like Managed VMs, the HBase REST Server, or a Java Server using the HBase client. Typically this will be to serve data to applications, dashboards, and data services. ● Streaming : Data can be streamed in (written event by event) through a variety of popular stream processing frameworks like Cloud Dataflow Streaming, Spark Streaming, and Storm. ● Batch Processing : Data can be read from and written to Cloud Bigtable through batch processes like Hadoop MapReduce, Dataflow, or Spark. Often, summarised or newly calculated data is written back to Cloud Bigtable or to a downstream database.


Alternatives for Google cloud Bigtable

  • Microsoft Access. 
  • MongoDB. 
  • Amazon DynamoDB. 

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