Google BigQuery vs. QlikView

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Google BigQuery
Score 8.8 out of 10
N/A
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)
QlikView
Score 8.2 out of 10
N/A
QlikView® is Qlik®’s original BI offering designed primarily for shared business intelligence reports and data visualizations. It offers guided exploration and discovery, collaborative analytics for sharing insight, and agile development and deployment.N/A
Pricing
Google BigQueryQlikView
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
QlikView
Custom
per user
Offerings
Pricing Offerings
Google BigQueryQlikView
Free Trial
YesYes
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeOptional
Additional DetailsOn an perpetual license basis, based on server plus number of users. Contact vendor for pricing.
More Pricing Information
Community Pulse
Google BigQueryQlikView
Features
Google BigQueryQlikView
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
QlikView
-
Ratings
Automatic software patching8.017 Ratings00 Ratings
Database scalability9.179 Ratings00 Ratings
Automated backups8.524 Ratings00 Ratings
Database security provisions8.773 Ratings00 Ratings
Monitoring and metrics8.475 Ratings00 Ratings
Automatic host deployment8.013 Ratings00 Ratings
BI Standard Reporting
Comparison of BI Standard Reporting features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
8.4
68 Ratings
3% above category average
Pixel Perfect reports00 Ratings8.050 Ratings
Customizable dashboards00 Ratings9.366 Ratings
Report Formatting Templates00 Ratings8.060 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
8.1
67 Ratings
1% above category average
Drill-down analysis00 Ratings8.366 Ratings
Formatting capabilities00 Ratings7.767 Ratings
Integration with R or other statistical packages00 Ratings8.336 Ratings
Report sharing and collaboration00 Ratings8.362 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
8.6
62 Ratings
4% above category average
Publish to Web00 Ratings8.049 Ratings
Publish to PDF00 Ratings9.056 Ratings
Report Versioning00 Ratings7.542 Ratings
Report Delivery Scheduling00 Ratings10.048 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
7.4
58 Ratings
8% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.955 Ratings
Location Analytics / Geographic Visualization00 Ratings7.546 Ratings
Predictive Analytics00 Ratings6.85 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
7.0
60 Ratings
19% below category average
Multi-User Support (named login)00 Ratings8.159 Ratings
Role-Based Security Model00 Ratings5.855 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings6.055 Ratings
Report-Level Access Control00 Ratings8.13 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
QlikView
8.0
47 Ratings
3% above category average
Responsive Design for Web Access00 Ratings8.044 Ratings
Mobile Application00 Ratings9.028 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings8.038 Ratings
Best Alternatives
Google BigQueryQlikView
Small Businesses
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Yellowfin
Yellowfin
Score 8.7 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Reveal
Reveal
Score 10.0 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 7.4 out of 10
Kyvos Semantic Layer
Kyvos Semantic Layer
Score 9.5 out of 10
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User Ratings
Google BigQueryQlikView
Likelihood to Recommend
8.8
(77 ratings)
7.7
(88 ratings)
Likelihood to Renew
8.1
(5 ratings)
8.8
(29 ratings)
Usability
7.0
(6 ratings)
8.0
(15 ratings)
Availability
7.3
(1 ratings)
9.8
(4 ratings)
Performance
6.4
(1 ratings)
8.6
(4 ratings)
Support Rating
5.3
(11 ratings)
3.3
(15 ratings)
Online Training
-
(0 ratings)
8.0
(3 ratings)
Implementation Rating
-
(0 ratings)
7.4
(13 ratings)
Configurability
6.4
(1 ratings)
-
(0 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Ease of integration
7.3
(1 ratings)
-
(0 ratings)
Product Scalability
7.3
(1 ratings)
8.9
(2 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryQlikView
Likelihood to Recommend
Google
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).
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Qlik
Sales data validations have helped manage our justifications in the past, especially with regard to new product development and new business introduction. It has also been helpful in identifying trends with business impact and direction specific to quarter and monthly sales from ERP data as well as decisions to purchase equipment of staffing based on run rates and product demand.
One thing that can get out of hand is data output - if you aren't careful in your query, you may be overloaded with data dumps and drown in the amount of info you have to filter through. This is a user caution, not a comment on the software itself.
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Pros
Google
  • Realtime integration with Google Sheets.
  • 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.
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Qlik
  • QlikView has a simple, relational data model that's REALLY fast. Filtering and changing data is dead simple results are almost immediately available.
  • The free version of Qlikview is almost completely featured, so you roll a pro-level product out to an entire department for really cheap.
  • QlikView is really flexible--if you can imagine it, you can build it.
Read full review
Cons
Google
  • 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.
Read full review
Qlik
  • We found that QlikView can be a bit slow in supporting some forms of encryption. It is web-based and we needed to upgrade all of our server to not support the older SSL and TLS 1 protocols, only support TLS 1.2 and TLS 1.3. However, QlikView could not run with TLS 1.2 and TLS 1.3. We had to wait over six months to get a version that would handle the newer TLS versions.
  • There are so many options with QlikView that you can get lost when developing a visualization. There are still items I have not yet figured out, such as labeling a graph with the name of a selected detail item.
  • QlikView works by pulling the data it is going to use for visualization into its database. I am a security reviewer and I need to make certain that PII and PHI is not pulled by QlikView for a visualization, otherwise this could become a reportable indecent.
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Likelihood to Renew
Google
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.
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Qlik
Ease of use, ability to load from pretty much any data source. today I created an application that loaded time sheets from excel that are not in a table format. With Qlik's "enable transformation steps" I was able to automate loads of multiple spreadsheets and multiple tabs easily. Could not do that with any other tool.
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Usability
Google
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.
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Qlik
QlikView is very easy to implement. The installation is very straight forward. QlikView has several different data connectors that can connect to different data sources very smoothly. The user interface to build the reports is very easy to understand. This helps to have a smaller learning curve. Something very helpful is that QlikView is a browser application for the end users. So, you don't need to install any applications on the user's computer.
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Reliability and Availability
Google
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.
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Qlik
We have not had any downtime issues with the product nor uncovered any significant bugs
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Performance
Google
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.
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Qlik
It is not a SAAS product.
Read full review
Support Rating
Google
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.
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Qlik
My experience with the Qlik support team has been somewhat limited, but every interaction I have had with them has been very professional and I received a response quickly. Typically if there is a technical issue, our IT team will follow up. My inquiries are specific to product functionality, and Qlik has been very helpful in clarifying any questions I might have.
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In-Person Training
Google
No answers on this topic
Qlik
My team attended, but I cannot myself rate, but I think it was good as they've successfully launched a training program at our company themselves for users. It was 3-4 day training.
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Online Training
Google
No answers on this topic
Qlik
Training was as expected. The demo environments tend to be more fully featured that our own environment, but the training was clear and well delivered.
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Implementation Rating
Google
No answers on this topic
Qlik
"Implementation" can mean a few things... so I'm not sure that this is the answer you want.... but here it goes: To me, implementation means: "Is the user interface intuitive and can I produce meaningful reports with ease?" On that score, I'd say YES. The amount of training required was minimal and the results were powerful. The desktop implementation is a simple, "blank" interface just waiting for your creativity. The pre-populated templates give you a reasonable start to any project -- and a good set of objects to "play around with" if you're just getting started. Finally, note that the "implementation" I used was baked into QuickBooks 2016 Enterprise -- called "Advanced Reporting"..... That integration makes it ultra useful and simple.
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Alternatives Considered
Google
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.
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Qlik
The only other vendor product that I have worked with that provides a similar experience to Qlikview is Tableau. I would recommend Tableau if your use case is to build a fixed dashboard. You can share reports for free without needing to buy additional licenses. I would recommend Qlikview if your users are looking for a more interactive experience. They can create new objects to represent the data which can't be accomplished as easily in Tableau
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Qlik
No answers on this topic
Scalability
Google
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.
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Qlik
No answers on this topic
Professional Services
Google
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.
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Qlik
No answers on this topic
Return on Investment
Google
  • 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.
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Qlik
  • You can use the free desktop version to do a lot of reporting and analysis work more quickly so the ROI is huge
  • QlikView is great at finding outliers such as data entry errors
  • QlikView is great at helping you quickly discover new insights about your business that can prompt you to take action that can immediately affect your cash flow.
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ScreenShots

Google BigQuery Screenshots

Screenshot of Migrating data warehouses to BigQuery - Features a streamlined migration path from Netezza, Oracle, Redshift, Teradata, or Snowflake to BigQuery using the fully managed BigQuery Migration Service.Screenshot of bringing any data into BigQuery - Data files can be uploaded from local sources, Google Drive, or Cloud Storage buckets, using BigQuery Data Transfer Service (DTS), Cloud Data Fusion plugins, by replicating data from relational databases with Datastream for BigQuery, or by leveraging Google's data integration partnerships.Screenshot of generative AI use cases with BigQuery and Gemini models - Data pipelines that blend structured data, unstructured data and generative AI models together can be built to create a new class of analytical applications. BigQuery integrates with Gemini 1.0 Pro using Vertex AI. The Gemini 1.0 Pro model is designed for higher input/output scale and better result quality across a wide range of tasks like text summarization and sentiment analysis. It can be accessed using simple SQL statements or BigQuery’s embedded DataFrame API from right inside the BigQuery console.Screenshot of insights derived from images, documents, and audio files, combined with structured data - Unstructured data represents a large portion of untapped enterprise data. However, it can be challenging to interpret, making it difficult to extract meaningful insights from it. Leveraging the power of BigLake, users can derive insights from images, documents, and audio files using a broad range of AI models including Vertex AI’s vision, document processing, and speech-to-text APIs, open-source TensorFlow Hub models, or custom models.Screenshot of event-driven analysis - Built-in streaming capabilities automatically ingest streaming data and make it immediately available to query. This allows users to make business decisions based on the freshest data. Or Dataflow can be used to enable simplified streaming data pipelines.Screenshot of predicting business outcomes AI/ML - Predictive analytics can be used to streamline operations, boost revenue, and mitigate risk. BigQuery ML democratizes the use of ML by empowering data analysts to build and run models using existing business intelligence tools and spreadsheets.

QlikView Screenshots

Screenshot of QlikView Sales DashboardScreenshot of QlikView on all devicesScreenshot of QlikView using mobile touch screen