Google's BigQuery is part of the Google Cloud Platform, a database-as-a-service (DBaaS) supporting the querying and rapid analysis of enterprise data.
$6.25
per TiB (after the 1st 1 TiB per month, which is free)
Amazon Redshift
Score 7.9 out of 10
N/A
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
$0.24
per GB per month
Snowflake
Score 9.0 out of 10
N/A
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
Google BigQuery needs minimal setup to get it up and running while Amazon Redshift and Oracle Analytics Cloud need moderate expertise and time to load a data set and run a query. Hadoop (open source) and its commercial version Cloudera do not provide a full out of the box …
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. …
Google BigQuery is less expensive to run and offers free storage of up to the first 10 GB of data. Google BigQuery is also easier (and faster) to get up and running. Unlike Snowflake, Google BigQuery does not require any manual scaling or performance tuning. Scaling is …
I personally find it by far simpler than Amazon Redshift due it's onboarding seamlessness. For a quick start and simplify tye access to read the data big query provide better user experience and a smoother user interface. More importantly, the fact that Big Query can be easily …
There are some areas in which this product is better while there are some in which others do better. It's not like Google BigQuery surpasses them in every metric. For a holistic view, I will say we use this because of - scalability, performance, ease of use, and seamless …
BigQuery can automatically scale to accommodate the data and query load, providing potentially unlimited scalability. At the same time, Redshift requires manual scaling efforts to increase or decrease capacity, which might affect performance during scaling operations.
Google BigQuery is the best among the ones we evaluated. It works really well with the Google Cloud workloads and comes with exceptional security controls. It can be combined easily with lots of products that Google Cloud has. It is a real game-changer.
Google BigQuery i would say is better to use than AWS Redshift but not SQL products but this could be due to being more experience in Microsoft and AWS products. It would be really nice if it could use standard SQL server coding rather than having to learn another dialect of …
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Google BigQuery is a fully managed, serverless data warehouse offered by Google Cloud Platform. It stands out for its scalability, performance, and ease of use compared to other data warehouse solutions. Here's how it stacks up against others. Google BigQuery is designed to …
Cost is the important factor for us compared with all of the other tools Google BigQuery stands top among all of them which charges very minimal charges for storage against all the apps that we have liked the most additionally, we can do query on our data, and can build …
I was already familiar with the Google Cloud Platform environment, and I was better equipped with the standard SQL language. Some of the syntax does not translate well to Redshift. It also seemed like many data source integrations relevant to our business were easier and more …
At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.
Both BigQuery and Redshift are two comparable fully managed petabyte-scale cloud data warehouses. They’re similar in many ways, but you should consider their unique features and how they can contribute to an organization’s data analytics infrastructure. When considering which …
Google BigQuery integrates seamlessly with Web Analytics data compared to the Azure cloud. Google BigQuery integrates natively with different digital media platforms compared to Azure and AWs.
We liked BQ because the cost of it is only dependent on the amount of data you store (and there are tiers of data access) and how much you search. For us, it is significantly less expensive to run BQ than an equivalent hosted RDBMS. Because most of our data pipelines are …
BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete …
Snowflake supports semi-structured data types and provided solutions to manage/process the semi-structured data. It supported sharing data between the different accounts and makes it easy in the scale and scale down process. Snowflake doesn't limit users on the database.
Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of …
We are currently on Redshift, because it was out before Snowflake. However, Snowflake looks promising. It's the new shiny toy that gives options that Redshift does not provide for. The big thing is that storage and compute can be scaled separately, whereas you cannot do that in …
We like Snowflake for its separation of computing and storage and also the separation of data warehouse different users. We replaced Redshift with Snowflake. However, Snowflake is great for its pay for performance kind of methodology.
Redshift leapfrogged Hive back when Hive was trying to figure out how to implement indexes, providing a more stable, standardized (postgres), easy to use (any postgres client), easier to administer, and scalable solution for querying server logs and raw usage data.
Azure SQL Database was discarded because of a less attractive licensing, costs, plus its integrates poorly with many of the Azure offerings as say Azure Data Factory - it is not a true ETL yet. Also, the rest of the tools used were of Open Source type and it did not look like a …
Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like Snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user …
Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective …
Amazon Redshifts has fewer features but at the same time, you also have some gains once it is running on AWS Cloud and it is really easy to set up. Besides that, in our case, it is a bit cheaper and we don't really need the extra features that you can find on Snowflake. Another …
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a …
Amazon Redshift is one of the fastest service offerings available in the market now. Plus you get an advantage of using a cutting edge compute service offering from AWS. Other technologies are fast but not as good as Amazon Redshift, I would say. Our business is interested in …
Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend …
As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. …
Snowflake is much faster and more intuitive than Amazon Redshift. We currently use AWS for other aspects of our data ingestion process but found that Snowflake is extremely compatible and the user interface is unmatched.
Redshift and Hive both have unique architecture. Both have their own cons. My guess is that Snowflake is made up by using the concepts of the two architecture concepts such as Amazon Redshift and Haddop, addressed the issues or gaps found in Redshift and Hadoop.
Since we switch from Amazon Redshift to Snowflake, we found Snowflake is much better than redshift in many ways, including the data integrate and data pull. However, comparing directly pull data from Amazon S3, Snowflake is quite slow in terms of data pull speed and the more …
Compared to Amazon Redshift, Snowflake is slightly easier and faster to achieve ROI but based on the user's perspective, the two tools have very little difference since both are leveraging SQL to pull data from AWS S3. Snowflake is also working with Microsoft Azure but it is …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Redshift compute and storage can be scaled up/down together (though they added some features recently, they don't quite add up). I haven't tried Avalanche or Firebolt but would love to in the near future, due to their pedigree or revolutionary billing methods.
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
For us our previous solution in this space was Redshift which we found to be much less reliable and was hardware capped. There may very well be cloud options that our company just wasn't utilizing. For us, queries constantly ran out of memory and failed. Even when they didn't …
Delivered as an easy-to-use data warehouse service, Snowflake enables you to process and analyze all your diverse data, build multiple databases, query with a common robust ANSI SQL environment, and execute ACID transnational capabilities.
The average percentage of time that a data warehouse is actually doing something is around 20%. Given this, the price by query estimate becomes an important pricing consideration.
For this, Snowflake crucially decouples of storage and compute. With Snowflake you pay for 1) …
More flexible and faster compared to Redshift, more functionality compared to BigQuery e.g. - per minute billing, instant spin up of warehouse. Overall, the cost and time savings swayed us in favor of Snowflake.
Instant provisioning of computing resources and data sharing is something we have not seen with any other vendor. Being HIPAA compliant at the time of evaluation was a must for us. Other vendors were late on this. Onboarding on support during implementation was also excellent.
I evaluated Redshift and Panoply when making the choice for Snowflake. Panoply is built on Redshift, so the two are equal in drawbacks: Redshift requires a cluster to be running 24/7 for your data to live there. We produce terabytes of data every day, so this was not an option …