Amazon Redshift and Google BigQuery are leading data warehouse platforms. Redshift stands out for robust scalability and integration within the AWS ecosystem, while BigQuery's seamless integration with Google's ecosystem and serverless architecture offers adaptability.
Features: Redshift supports multiple data formats like CSV and JSON and features distributed query processing for speed. It integrates well with AWS services and offers customizable configurations for enhanced performance. BigQuery provides advanced analysis capabilities and handles unstructured data efficiently with its serverless architecture, contributing to cost-effectiveness and scalability.
Room for Improvement: Redshift is noted for its complexity in ETL handling, challenges in snapshot restorations, and limited real-time integrations. It lacks support for some complex SQL features. BigQuery requires optimization for handling special characters during migrations and lacks advanced caching functionalities. Users also report challenges with its cost management and adaptability with certain external systems.
Ease of Deployment and Customer Service: Amazon Redshift supports all main cloud deployment models, providing flexibility. While customer satisfaction is generally positive, improved support access is desired. BigQuery predominantly operates on public cloud deployment. Its documentation is widely regarded as helpful, even if direct technical support can feel limited.
Pricing and ROI: Redshift's pricing supports scalability but can be high for small datasets. Despite this, its capability to handle extensive queries is valued. BigQuery's pay-as-you-go model is flexible, making storage cost-efficient, although data processing can become costly with increased usage. Both platforms offer substantial ROI through enhanced data analytics, catering to diverse business needs.
Whenever we need support, if there is an issue accessing stored data due to regional data center problems, the Amazon team is very helpful and provides optimal solutions quickly.
It's costly when you enable support.
rating the customer support at ten points out of ten
I have been self-taught and I have been able to handle all my problems alone.
The scalability part needs improvement as the sizing requires trial and error.
It is a 10 out of 10 in terms of scalability.
The scalability is definitely good because we are migrating to the cloud since the computers on the premises or the big database we need are no longer enough.
Amazon Redshift is a stable product, and I would rate it nine or ten out of ten for stability.
They should bring the entire ETL data management process into Amazon Redshift.
Integration with AI could be a good improvement.
BigQuery is already integrating Gemini AI into the data extraction process directly in order to reduce costs.
Troubleshooting requires opening each pipeline individually, which is time-consuming.
In general, if I know SQL and start playing around, it will start making sense.
The cost of technical support is high.
It's a pretty good price and reasonable for the product quality.
The pricing of Amazon Redshift is expensive.
Being able to optimize the queries to data is critical. Otherwise, you could spend a fortune.
The price is perceived as expensive, rated at eight out of ten in terms of costliness.
The specific features of Amazon Redshift that are beneficial for handling large data sets include fast retrieval due to cloud services and scalability, which allows us to retrieve data quickly.
Scalability is also a strong point; I can scale it however I want without any limitations.
Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.
It is really fast because it can process millions of rows in just a matter of one or two seconds.
The features I find most valuable in this solution are the ability to run and handle large data sets in a very efficient way with multiple types of data, relational as SQL data.
BigQuery processes a substantial amount of data, whether in gigabytes or terabytes, swiftly producing desired data within one or two minutes.
Amazon Redshift is a fully administered, petabyte-scale cloud-based data warehouse service. Users are able to begin with a minimal amount of gigabytes of data and can easily scale up to a petabyte or more as needed. This will enable them to utilize their own data to develop new intuitions on how to improve business processes and client relations.
Initially, users start to develop a data warehouse by initiating what is called an Amazon Redshift cluster or a set of nodes. Once the cluster has been provisioned, users can seamlessly upload data sets, and then begin to perform data analysis queries. Amazon Redshift delivers super-fast query performance, regardless of size, utilizing the exact SQL-based tools and BI applications that most users are already working with today.
The Amazon Redshift service performs all of the work of setting up, operating, and scaling a data warehouse. These tasks include provisioning capacity, monitoring and backing up the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Amazon Redshift Functionalities
Amazon Redshift has many valuable key functionalities. Some of its most useful functionalities include:
Reviews from Real Users
“Redshift's versioning and data security are the two most critical features. When migrating into the cloud, it's vital to secure the data. The encryption and security are there.” - Kundan A., Senior Consultant at Dynamic Elements AS
“With the cloud version whenever you want to deploy, you can scale up, and down, and it has a data warehousing capability. Redshift has many features. They have enriched and elaborate documentation that is helpful.”- Aishwarya K., Solution Architect at Capgemini
BigQuery is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. ... You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
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