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)
Semrush
Score 8.6 out of 10
N/A
Semrush is a relatively popular search engine optimization tool set from the company of the same name based in Pennsylvania and founded in 2008. Largely the platforms relies on competitive intelligence, and features SEO staples like backlink checking, keyword analysis to refine SEO and PPC campaigning and locate low-cost / high-yield keywords, analysis of competitors who co-occupy desired ad and listing spaces, domain vs. domain analysis, as well as site audit and domain tracking.
Semrush can…
$139.95
per month
Pricing
Google BigQuery
Semrush
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Pro
$139.95
per month
Guru
$249.95
per month
Business
$499.95
per month
Offerings
Pricing Offerings
Google BigQuery
Semrush
Free Trial
Yes
Yes
Free/Freemium Version
Yes
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
—
17% discount for annual pricing.
More Pricing Information
Community Pulse
Google BigQuery
Semrush
Features
Google BigQuery
Semrush
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
0% above category average
Semrush
-
Ratings
Automatic software patching
8.017 Ratings
00 Ratings
Database scalability
9.179 Ratings
00 Ratings
Automated backups
8.524 Ratings
00 Ratings
Database security provisions
8.773 Ratings
00 Ratings
Monitoring and metrics
8.475 Ratings
00 Ratings
Automatic host deployment
8.013 Ratings
00 Ratings
SEO
Comparison of SEO features of Product A and Product B
Google BigQuery
-
Ratings
Semrush
7.9
154 Ratings
3% above category average
Keyword analysis
00 Ratings
8.6154 Ratings
Backlink management
00 Ratings
8.3147 Ratings
SERP ranking tracking
00 Ratings
8.3150 Ratings
Page grader
00 Ratings
7.6129 Ratings
Competitive analysis
00 Ratings
8.5149 Ratings
Site audit / diagnostics
00 Ratings
8.0150 Ratings
Site recommendations
00 Ratings
7.6139 Ratings
Task management
00 Ratings
6.691 Ratings
SEO Channels
Comparison of SEO Channels features of Product A and Product B
Google BigQuery
-
Ratings
Semrush
8.2
140 Ratings
8% above category average
Local SEO
00 Ratings
8.1128 Ratings
Social SEO
00 Ratings
8.0106 Ratings
Mobile SEO
00 Ratings
8.3113 Ratings
Global SEO
00 Ratings
8.2114 Ratings
SEO Platform & Account Management
Comparison of SEO Platform & Account Management features of Product A and Product B
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
This is suited to plan for keywords in a single market, but it struggles when you need to consolidate several markets. It is well-suited to discover new keywords directly related to the ones being used currently to expand the content the company is creating to get better results.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
We have to use this product as its a 3rd party supplier choice to utilise this product for their data side backend so will not be likely we will move away from this product in the future unless the 3rd party supplier decides to change data vendors.
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
The interface is a little less intuitive than it could be. The data is often available but filtering and manipulating the data can be a little difficult at times. Expanded comparisons would be helpful since most of the time seeing more than a few competitors at a time allows for a better sense of how to forecast.
I have never had any significant issues with Google Big Query. It always seems to be up and running properly when I need it. I cannot recall any times where I received any kind of application errors or unplanned outages. If there were any they were resolved quickly by my IT team so I didn't notice them.
I think Google Big Query's performance is in the acceptable range. Sometimes larger datasets are somewhat sluggish to load but for most of our applications it performs at a reasonable speed. We do have some reports that include a lot of complex calculations and others that run on granular store level data that so sometimes take a bit longer to load which can be frustrating.
BigQuery can be difficult to support because it is so solid as a product. Many of the issues you will see are related to your own data sets, however you may see issues importing data and managing jobs. If this occurs, it can be a challenge to get to speak to the correct person who can help you.
I'm in a FB group for Semrush paid users and it amazing! They are fast to respond, take suggestions and help with questions. I have not felt alone in using this product at all. Highly recommend their support team. When I had an issue I can jump into the group and they will help get me the right person to help or even tag their programmers to look at something that is going on. Love the group!
PowerBI can connect to GA4 for example but the data processing is more complicated and it takes longer to create dashboards. Azure is great once the data import has been configured but it's not an easy task for small businesses as it is with BigQuery.
We have used both Ahrefs and Semrush extensively at our agency and Semrush remains the tool of choice. We find that Semrush has the largest selection of tools & features to use, with the highest accuracy, and provides top-tier analysis recommendations. It's really is the ultimate all-in-one tool when comparing other performance measurement tools in the digital marketing space.
We have continued to expand out use of Google Big Query over the years. I'd say its flexibility and scalability is actually quite good. It also integrates well with other tools like Tableau and Power BI. It has served the needs of multiple data sources across multiple departments within my company.
Google Support has kindly provide individual support and consultants to assist with the integration work. In the circumstance where the consultants are not present to support with the work, Google Support Helpline will always be available to answer to the queries without having to wait for more than 3 days.
Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
Using Semrush on behalf of our clients, we have seen some impressive growth in organic visibility, traffic, conversion and revenue, across multiple industries.
The site checks available with Semrush have helped us to pitch and sell our services, securing new clients.