Overview of Amazon Redshift and Google BigQuery

The age-old debate over cloud data warehouses continues. In the war of database engines, Amazon Redshift and Google BigQuery are the two databases most used by companies. In this article, we compare the two data warehouses in terms of usability, pricing, scalability, and performance.

Redshift was released by Amazon in 2012 as a beta version, and the technology is based on PostgreSQL 8.0.2 and created by ParAccel, a database management system designed for advanced BI analytics. Even if applied in OLAP and BI applications, Redshift is inspired by the relational nature of Postgre SQL.

BigQuery was promoted by Google for internal use and developed from Dremel.

Technology is a web service that presents Dremel on top of the REST interface. BigQuery looks like a hybrid system due to its column-based operations and serves as an excellent supporter of integrated data.

Environment: The two companies have built a solid and complete technological environment, which supports systems with data integration, BI optimized with analytical tools, and developer and consulting communities

Pricing: Compared to BigQuery Redshift is more expensive, costing $ 0.08 per GB, compared to BigQuery which costs $ 0.02 per GB. However, BigQuery only offers storage and not queries. The platform invoices data-based queries separately at $ 5 / TB. Because of BigQuery lacks an index and various analytical queries, data analysis is a huge and expensive process. In most cases, users opt for Amazon Redshift because it is predictable, simple, and encourages the use and analysis of data.

Data Flexibility: In the case of Redshift, if something happens during a transaction, Amazon Redshift allows users to restore to ensure that the data returns to a consistent state. BigQuery works on the principle of adding data only and its storage engine strictly follows this technique. This becomes a major inconvenience for the user when something goes wrong during the transaction process, forcing him to restart from the beginning or from a specific point.

Another key point is that duplicating data in BigQuery is difficult and expensive. Both technologies have reservations about inserting streaming data, with Redshift taking over by ensuring data storage with extra care from the user. On the other hand, BigQuery supports de-duplication of streaming data in the most efficient way using the time window.

Uniformity: BigQuery takes advantage over Redshift in the consistency scenario because BigQuery separates the details of the underlying hardware components, databases, and other forms. BigQuery works outside the framework, in which the Redshift case requires having in-depth knowledge and specific skills in order to analyze and optimize effectively.

Allowance and support allocation: BigQuery measures the number of slots needed for each query that a user wants to execute. Technology increases available depending on the situation.

Redshift, meanwhile, follows a classic procedure by capping the devices needed to form a cluster.

The other the drawback of RedShift is it's resizing because the user is forced to move all the data to the new cluster.

Security: The two giants offer conventional authentication and security features for their technologies.

Google query supports its Cloud Identity and Access Management. Users are allowed to use OAuth as a conventional procedure to obtain the cluster, especially when third-party authorization exists.

Amazon Redshift relies on IAM for Amazon user access and management identity. The system is a robust complex feature that extends the exceptional versatility of a company to monitor complex situations in the event of access and identity management.

Outlook: Redshift and BigQuery are engaging in cloud-hosted technologies providing similar analytical databases. However, depending on the requirements and the financial situation of the company, they have to choose a database technology. For small businesses and startups, it would be advisable to choose Google BigQuery due to simple and affordable features. It's also good for people who are very new to cloud database technology as it doesn't cause too many complications. Amazon Redshift may not be flexible as it involves creating clusters and the technology cannot be offered by financially weaker companies. Redshift can provide a detailed analysis of specific financial topics with its predictable technology and the use of clusters. Users should, therefore, consider the above points before choosing the preferred data warehouse service.

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