Best Cloud Databases for Data Science/Scientist

Cloud Databases are very crucial in cloud based approach of today’s business landscape.With highly in demand data science projects and applications, the cloud databases provide an easy way to connect in applications developed using programming languages such as python.

Various DB APIs are also available to connect with python application. Cloud databases are available in the cloud where users can host the data and use it as a service. Cloud databases are more reliable when it comes to data recovery and backup.

What is DBaaS?

DBaaS or Database as a service is a cloud service that manages various operations in database in cloud. These operations include the basic CRUD (Create, read, update and delete) operations among other advanced DB services . The use of DBaaS is ideal for high volumes of data as some of these platforms can handle 100s of TBs of data speedily and reliably.

List of best DBaaS /Cloud Databases

IBM Db2

IBM Db2 on Cloud is a great way to use cloud databases in your python notebooks. It is an ideal option for Data Scientists. IBM Db2 is very modern and combines the features of enterprise data management systems and Artificial intelligence integration for better and faster query processing and data management capabilities. This makes it one of the top choices for data scientists. It gained immense popularity due to its efficient cross platform integration. IBM Cloud Pack for Data also enhances data management by helping you to grow your data and use it efficiently in the desired application while taking into consideration its costs and resources.

PostgreSQL

Build by the University of California, Berkely, PostgreSQL is a great DB tool to include database capabilities across various platforms. PostgreSQL is a reliable option for database administrators as well as data steered application developers. PostgreSQL is an open source library and a relational cloud database management system with high levels of extensibility and SQL compliance with other platforms and languages. It is very useful in embedding SQL code in a python programming based application. It is also provides a JDCB driver for Java programming language.

DB API for Python: psycopg2

DB API for Java: PgJDBC

Microsoft Azure SQL Database

Microsoft Azure SQL DB is based on the azure cloud environment. It can host several databases from different customers. It is also available as VM/Managed services. Database restoration in Microsoft Azure is quick and offers efficient and reliable backup facilities. Transaction time is faster regardless of the data size. Some other great features of Microsoft Azure SQL Database include data patching, failure detection, replication, bug detection, database maintenance task management, network failure etc. The Azure platform fully manages the Azure SQL Database and the chances of data loss are very minimal. It provides API, SDK and libraries for Python: azure-storage-blob, azure-mgmt-storage

Amazon Relational DB services

Amazon provides a great cloud database alternative for high end applications. Amazon Relational Database Service or simply RDS is easy to set up, operate, and scale a relational database in the cloud. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching and backups. It frees you to focus on your applications so you can give them the fast performance, high availability, security and compatibility they need. It is easy to administer, highly scalable, secure, fast and durable.

Google BigQuery

Google Big Query is one of the best enterprise cloud warehouse. It is used to add SQL like queries to manage and control your data in an effective and efficient manner. Query federation supports both MySQL (second generation) and PostgreSQL instances in Cloud SQL. The BigQuery data warehouse is based on the Dremel Technology and provides successful approach to manage a server-less database design. It provides more dynamic data storage and warehousing abilities.

Python client for Google BigQuery: Google-cloud-bigquery

Last words….

Cloud Databases have a lot of advantages such as remote accessibility to many working on the same database, good management of resources, faster retrieval and management among others. Cloud database solve various problems of traditional databases by enabling super-fast, SQL queries against tables in use, by using the infrastructure of the platform.

Be First to Comment

Leave a Reply

Your email address will not be published. Required fields are marked *