Non-relational Database by Google

Non-relational Database by Google
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  1. BigTable: -
    This Bigtable database is created by Google, it is compressed, high performance and proprietary data storage system which is created by Google and is built on top of google file system, LevelDB and other few google technologies. This database development is starting in 2004 and now it is widely used by google like web indexing,google maps, google book search and google earth, blogger, google code, youtube. The google is designed its own database because they need high scalability and better control over performance. Bigtable is like wide column store. It maps 2 arbitrary string values which are row key and column key and timestamp for 3-dimensional mappings into an arbitrary byte array. Remember it is not a relational database and it is more kind of sparse, distributed multi-dimensional sorted map.
    BigTable is designed to scale in petabyte range across hundreds and thousands of machines and also to add additional commodity hardware machines is very easy in this and there is no need for reconfiguration.
    Ex.

Suppose google copy of web can be stored in BigTable where row key is what is domain URL and Bigtable columns describe various properties of a web page and every column holds different versions of webpage. He columns can have different timestamped versions of describing different copies of web page and it stores timestamp page when google retrieves that page or fetch that page. Every cell in Bigtable has zero or timestamped versions of data.
In short, Bigtable is like a map-reduce worker pool.

A google cloud Bigtable is a petabyte-scale, fully managed and it is NoSQL database service provided by Google and it is mainly for large analytical and operational workloads.

Features Of Google Cloud BigTable:

  1. It is having low latency and massively scalable NoSQL. It is mainly ideal for ad tech, fin-tech, and IoT. By using replication it provides high availability, higher durability, and resilience in the face of zonal failures. It is designed for storage for machine learning applications.
  2. Fast and Performant
  3. Seamless scaling and replication
  4. Simple and integrated- it means it integrates easily with popular big data tools like Hadoop, cloud dataflow and Cloud Dataproc also support HBase API.
  5. Fully Managed- means google manage the database and configuration related task and developer needs only focus on development.

Do you have some kind of question? I am not seeing one here.