As analytics in your company graduates from a MySQL/PostgreSQL/SQL Server, a pertinent question that you need to answer is which data warehouse is best suited for you. This blog tries to compare Redshift vs BigQuery – two very famous cloud data warehouses today.
At Hevo, we make it easier for our customers to bring all their data to the data warehouse of their choice. Naturally, our customers come to us seeking our recommendations on choosing a data warehouse. Our customers want to know which data warehouse will give them faster query times, how much data will it be able to handle and what will it cost. The answer depends on various inputs like the size of data, the nature of use and the technical capability of users managing the warehouse. After learning about the BigQuery vs Redshift comparison in this blog, you can let Hevo take the work of data migration and transformation for you.
In this post, we are going to talk about the two most popular data warehouses: Amazon Redshift and Google BigQuery. Honestly, in the Redshift vs BigQuery comparison, similarities are greater than the differences. Still, there are nuanced differences that you need to be aware of while making a choice.
Here’s how this blog is structured for you:
- Introduction to Redshift
- Introduction to BigQuery
- Redshift Vs BigQuery: Performance
- Redshift Vs BigQuery: Manageability and Usability
- Redshift Vs BigQuery: Pricing
- Redshift Vs BigQuery: Security
Introduction to Redshift
Amazon Redshift is a fully managed cloud-based data warehouse which is designed for handling large scale data set storage. It is also used to perform large scale of database migration. The architecture of Redshift involves nodes and clusters. The initial process involves launching a set of computing resources called nodes. These nodes are organised into large groups called clusters. Queries can be processed after it. Know more about Amazon Redshift from their official documentation.
Introduction to BigQuery
Google BigQuery is a fully-managed and serverless data warehouse. It allows analysis over petabytes of data. BigQuery also supports querying using ANSI SQL. It has machine learning capabilities. It runs on Google Cloud Storage and can be accessed using REST API. You can gain insights with real-time and predictive analysis using Google BigQuery. Know more about Google BigQuery from their official document.
This blog will provide you a brief BigQuery vs Redshift comparison.
Redshift Vs BigQuery: Performance
On many head-to-head tests, Redshift has proved to show better query times when configured and tweaked correctly. There are several benchmarks available over the internet. You can also refer to the official AWS blog from here. Know more about the performance of Redshift and BigQuery from here.
Hevo Data: Load Data in your Data Warehouse Seamlessly
Hevo is a No-code Data Pipeline. You can easily load your data in your desired data warehouse in real-time. Hevo makes sure that you fulfil your needs at a reasonable price. It offers pre-built data integrations from 100+ data sources. Using Hevo, you can get data into Redshift or BigQuery for simplifying the process of data analysis.
Let’s look at some unbeatable features of Hevo:
- Easy Implementation: Hevo offers a simple and intuitive user interface for their customers.
- Fully-Automated: You can automate your entire ETL process without writing any custom code.
- Zero Maintenance: Once you’re done with the setup, Hevo manages all future tasks automatically.
- Fault-Tolerant: Hevo can automatically detect anomalies from the incoming data and informs you immediately. All the affected rows are kept aside for correction so that it doesn’t hamper your workflow.
- Security: Hevo makes sure that your data is secured by offering two-factor authentication and end-to-end encryption.
- Zero Data Loss: Hevo makes sure that data is transferred from source to destination in real-time without any loss or change in data.
Are you ready to use Hevo? If yes, then give it a try by signing up for a 14-day free trial today.
Redshift Vs BigQuery: Manageability and Usability
Redshift gives you a lot more flexibility on how you want to manage your resources. This means that you get more control at the cost of some management overhead. To operate a decently sized Redshift cluster efficiently, you need a deep understanding and skill-set around warehousing concepts. For example, Redshift will expect you know about how to distribute your data across nodes and will require you to do vacuuming operations on a periodic basis.
BigQuery, on the other hand, does not expect you to manage your resources. It abstracts away the details of the underlying hardware, database, and all configurations. It mostly works out of the box.
Redshift Vs BigQuery: Pricing
In the case of Redshift, you need to predetermine the size of your cluster. That means you are billed irrespective of whether you query your data on not. Shutting down clusters when not needed is left to the user. Billing is done on hourly usage of the cluster. This makes Redshift more costly when your query volumes are low. But, if your query volumes are higher, predictable and uniformly distributed over time Redshift may turn out to be a lot cheaper. Also, the costs are more predictable because you always know the size of your cluster. Know more about Redshift pricing here.
BigQuery, on the other hand, has segregated compute resources from storage. Thus, you are only charged when you are running queries. Billing is done on the amount of data processed during queries. On the surface this pricing might seem to be cheaper but, this approach makes costs for BigQuery unpredictable and it will turn out to be more expensive than Redshift when query volumes are high. Know more about BigQuery pricing here.
Redshift Vs BigQuery: Security
In the case of Redshift, it banks on AWS IAM (Identity and Access Management), an Amazon management access and identifies its users. The system extends exceptional versatility for the company to monitor and manage the complex situation in the case of IAM.
BigQuery has the support from its Cloud IAM. Users can use OAuth as a conventional procedure to obtain the cluster, especially when a third party authorization exists.
Ecosystems around both Amazon Redshift and Google BigQuery are buzzing. They are being actively promoted by their respective companies and both the products work as marketed. You wouldn’t be too wrong for choosing either of them. Still, we recommend one over the other in the following scenarios:
- Redshift: When you are okay spending some time optimizing your data for fast queries- when your resource utilization is going to be fairly distributed across time and a large proportion of data being actually queried rather than just sitting in the database.
- BigQuery: When you want something that just works and don’t want to spend time tuning the database when you are okay having query response times of a few minutes and you have a lot of data that is being queried rarely.
We hope that this BigQuery vs Redshift comparison was useful for you. As both the platform provides top-notch features, so it depends on you which data warehouse suits you the most. After you have decided on the data warehouse, you can initiate your data migration using Hevo Data.
Hevo is a No-code Data Pipeline. It supports pre-built integrations from 100+ data sources. Hevo offers a reliable, consistent and secure solution to you. It will simplify the process of data migration to your desired data warehouse.
Give Hevo a try by signing up for a 14-day free trial today.
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