Choosing the right data warehouse is a critical component of your general data and analytic business needs. We're not comparing apples and oranges here. This is apples to apples. But, there are real use cases that each of them excel at, and both solutions can be valuable depending upon your business's situational needs.

At Xplentywe support both solutions. So, this post will act as a guide for businesses looking to understand which data warehouse is best suited for their particular workflows and projects.

Data warehouses sometimes called columnar storage solutions are dumping grounds where you can throw all of your BI data for analytic processing. Both Redshift and BigQuery are data warehouses. You can throw all of your data from your blended tech stack and start to run analytics on it to help you make critical business decisions, forecast trends, budget, etc.

A typical data warehouse use case would be trend analysis. Businesses push all of their tech stack data e.

BigQuery vs Redshift: Pricing Strategy

Example: A business may want to know more about their sales leads. This will help them better understand their customers and personalize sales pitches and content delivery. To do this, that business can connect their Salesforce data with a data warehouse and run a query to discover which leads are the most valuable and which ones are most likely to churn.

This lets them distribute query requests across multiple servers to accelerate processing. So, multiple processors — each with their own memory and operating system — will handle specific segments of the query. OLTP or Online Transaction Processing is what most business use for processing transactions during day-to-day operations think ATMs, retail sales systems, text messaging, etc.

OLTP stores each row in a table as an object. OLTP's primary goal is data processing. Example: Let's say that two people withdraw money from the same online bank account at precisely the same moment. OLTP will take the first authorized user and process that transaction.

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And, it will ensure that neither user is able to withdraw more money than is present in the bank account — even if they both start the operation simultaneously.LookerStitchAmazon Redshiftdbt. It's already helping us create a solid process for reusable SQL-based data modeling, with consistent definitions across the entire organizations.

Looker allows us to collaboratively build these version-controlled models and push the limits of what we've traditionally been able to accomplish with analytics with a lean team. We're also evaluating the command line tool, dbt to manage data transformations. I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. However, running data viz tools directly connected to BigQuery will run pretty slow.

They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency. But the problem with the data is, it is in. PSV pipe separated values format and the size is also above GB. How would I optimize the performance and query result time?

Can anyone please help me out? BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.

Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level. Aggressive archiving of historical data to keep the production database as small as possible.

Data warehouse solution that fully separates compute and storage. Better management facility than directly using S3. Google's insanely fast, feature-rich, zero-maintenance column store.

Used for real-time customer data queries. Amazon Redshift Stacks. Google BigQuery Stacks. Snowflake Stacks.

redshift vs bigquery

Need advice about which tool to choose? Ask the StackShare community! Amazon Redshift. Google BigQuery. What is Amazon Redshift? What is Google BigQuery? Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure.

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Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. What is Snowflake? Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services AWS —no infrastructure to manage and no knobs to turn. Why do developers choose Amazon Redshift?Many of our customers ask us which data warehousing option is cheaper: BigQuery or Redshift?

BigQuery can be much more cost effective if you structure your data warehouse querying very well and split it into stages. Storage is bound to computing power for Redshift, unlike EC2 deployments. This means Redshift pricing will depend on your data size. This is on-demand pricing and as usual, AWS provides significant discounts if you pay upfront.

BigQuery pricing is much more complicated compared to Redshift.

redshift vs bigquery

Ingestion into a BigQuery warehouse is usually free of charge, but this is not the case for data streaming. You can check updated BigQuery pricing.

BigQuery uses columnar storage, and bills are based on scanned data within columns and not within rows. This query will cost you MB. This price is calculated as total timestamp column size. When a column is involved in a query, BigQuery calculates data as a whole column scan.

So how can query costs be reduced for such a filtering query? This is where BigQuery sharding and partitioning come into play. BigQuery sharding is implemented as wildcard table querying.

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You can query at once up to tables with a specified suffix. This approach is fast as well as cost effective. Unlike in the previous example, costs will be reduced to the size of columns of tables starting with gsod up to gsod Partitioning and sharding are not the only way to reduce your BigQuery costs.

From our perspective, they are not the main one as well. If you use a table suffix filter instead of a date filter, you can reduce costs to MB per query. In such types of queries, the date filter is usually variable, which leads to a lot of queries. Although the table suffix filter allows you to reduce costs dramatically, they will still be high if you query too often.

The typical solution here is to introduce a roll up table first and then query it. The first query costs us 3. On the other hand, the second query is KB, which is at least times less than suffix filtering.

redshift vs bigquery

As you see, there is no absolute winner in the Bigquery vs Redshift comparison. Meanwhile, BigQuery will allow you to query only about queries per 1TB of data stored for that price per day. At the same time, we saw a lot of BigQuery deployments, which are at least 4 times cheaper than Redshift due to multi-stage querying.

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Replacing Hadoop with Snowflake

Redshift pricing Redshift pricing is pretty simple to understand. They charge as usual for AWS: per machine, per hour. AWS constantly updates prices so please check their site for up-to-date information.

BigQuery pricing BigQuery pricing is much more complicated compared to Redshift. Bytes billed explained BigQuery uses columnar storage, and bills are based on scanned data within columns and not within rows. Data Stack.

Subscribe for the Cube.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. 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.

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. 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. 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 an 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. 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.

They are being actively promoted by their respective companies and both the products work as marketed. Still, we recommend one over the other in the following scenarios:. Redshift Vs BigQuery: Performance On many head-to-head tests, Redshift has proved to show better query times when configured and tweaked correctly.Corporate history is full of business rivalries that we love reading about. In this post, we will compare two products, from two great companies.

Redshift from Amazon and BigQuery from Google. Not as exciting as Batman vs. Spiderman but both products have managed to create quite some noise with their launch, and there are plenty of posts out there, trying to convince that one is better than the other. Here at Blendo, we admire and respect both products as each one is an amazing engineering feat.

For this reason, we will make an as unbiased as possible comparison of the two. Also, we will not talk about performance; we will not use artificial datasets to measure speeds and latencies. First of all, any artificial dataset no matter how good it is, it will always be something that will differ from the data that each user will use.

Also, there are plenty of comparisons out there that are using a dataset to measure performance. Finally, these performance tests are best to be performed by each customer, using a subset of the actual data that will be loaded into the data warehouse at the end.

In this way, a performance benchmark will make more sense. Instead, we will focus on other aspects of data warehouse solutions, and we will compare the two solutions on these fronts like, data modeling, data consistency guarantees, and maintenance effort that is required. Amazon Redshift was released in as a beta version. The story behind ParAccel and Redshift is quite interesting.

InParAccel was acquired by Actian. Initially, Amazon claimed that it could scale to petabytes while with the introduction of Spectrum, it should scale to exabytes of data. On the other hand, BigQuery was developed internally by Google, and it is the evolution of Dremel. It can be perceived more like a hybrid system because it is columnar, but it also has excellent support for nested data.

Especially for the last difference. Think of all the debates about SQL vs. NoSQL systems. Developers are finding it easier to work with a system where nested data structures are natively supported while an analyst finds it much easier to interact with a database system that speaks SQL well. Amazon Redshift and Google BigQuery support both, bulk and streaming inserts.

That is the most common way of loading data into both systems, and probably the most natural one as both are intended for OLAP and BI use cases where real-time is not usually the case.

Nevertheless, both systems also support inserting data in a streaming fashion. Amazon Redshift does this through Kinesis while Google BigQuery supports it more natively as part of the solution. Finally, Google BigQuery also supports the direct import of data from Google Analytics, but you need to have a premium account there which is quite pricey.

When it comes to the data serializations supported by the two systems, there are no surprises there. Both of them support the following:. Of course, both Amazon and Google has made sure that loading data from the rest of the infrastructure that each company supports, is easy. When it comes to data modeling, there are similarities but also some significant differences between the two systems. In both of them, data is organized on two different levels. Although the names are different, the functionality of Schemas and Datasets is similar.

The important differences between the two systems are of the supported data types. More specifically, Redshift is closer to the standard SQL data types, e. Google BigQuery, supports a smaller set of datatypes which also deviates more from the standard SQL set of datatypes but there are mappings to them.

Because of that, nested data structures are first class citizens, and you can query them directly from BigQuery. It is advised not to flatten out nested data when inserted in BigQuery and instead use the native support the system has and query the data directly.As we speak the future of cloud computing is being decided.

Amazon and Googleas well as MicrosoftSnowflakeand a few othersoffer multiple cloud solutions for practically everything. Both are releasing colossal features and services on almost a weekly basis, and we, the developers, are richer for it. This post compares Redshift vs. BigQuery in detail. As our platform delivers full-stack data automation, a critical chunk of the stack hinges not only on the massively parallel data warehouse used internally to store hundreds of terabytes of data, but the capability to analyze it in minutes.

This choice would define us so we were determined to do a thorough comparison of the two and pick the one best for us. Spoiler Alert! On almost all fronts we found Amazon Redshift cluster to deliver superior results. Significantly so for usability, performance, and cost for almost all analytical use-cases, especially at scale. And yes, at a glance there are apparent complexities to Redshift, but what it surrenders in terms of simplicity it gains in terms of functionality.

Basics: Redshift vs. This article assumes some familiarity with Redshift and BigQuery, as well as basic knowledge in columnar MPP data warehouses. If you already got this covered feel free to skip ahead.

It all comes down to how transactional operations and analytical queries differ from each other. Normal transactional operations are usually indexed and very fast. This is because they must support a huge number of concurrent queries, each targeted to a specific row, which are resolved in the sub-second range.

Analytical queries on the other hand are normally performed by just a few analysts where each query is a batch process over a huge dataset that can take minutes or even hours to compute. Normal relational database system, like Postgres and MySQL, store data internally in row form: all data rows are stored together and are usually indexed by a primary key to facilitate efficient accessibility.

For example, computing the average age of all of our users, grouped by location. Performing these queries on classic row-oriented databases requires them to read through the entire database, along with all unused columns, to produce the results. This massive inefficiency is addressed with the advent of columnar databases.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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Comparing Google BigQuery vs. Amazon Redshift shows that both can answer same set of requirements, differ mostly by cost plans. It seems that Redshift is more complex to configure defining keys and optimization work vs.

Google BigQuery that perhaps has an issue with joining tables. I posted this comparison on reddit. Quickly enough a long term RedShift practitioner came to comment on my statements. To try BigQuery you don't need a credit card or any setup time. Just try it quick instructions to try BigQuery.

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See this in depth guide to data warehouse pricing on the cloud: Understanding Cloud Pricing Part 3. These features also require you to conform your data model somewhat to get the best performance. It supports a large amount of the SQL standard and most tools that can speak to Postgres can use it unchanged. It's a unique service with it's own API and interfaces. It provides limited support for SQL queries but most users interact with via custom code Java, Python, etc.

Some 3rd party tools have added support for BigQuery but existing tools will not work without modification. BigQuery is better for custom coded interactions and teams who dislike SQL. TL;DR - Redshift is usually faster and will be cheaper if you query the data somewhat regularly.

Learn more. Amazon Redshift [closed] Ask Question. Asked 5 years, 6 months ago. Active 1 year, 3 months ago.

System Properties Comparison Amazon Redshift vs. Google BigQuery vs. Microsoft Azure Cosmos DB

Viewed 12k times. Amazon Redshift? Try quora, your question is more suitable there Oct 13 '14 at Thanks anyway. Active Oldest Votes. BigQuery doesn't care. Use it whenever you want, no provisioning needed. Hourly costs when doing nothing: Redshift will ask you to pay per hour of each of these servers running, even when you are doing nothing. Speed of queries: Redshift performance is limited by the amount of CPUs you are paying for BigQuery transparently brings in as many resources as needed to run your query in seconds.

Indexing: Redshift will ask you to index correction: distribute your data under certain criteria, and you'll only be able to run fast queries based on this index.