Google BigQuery vs. Oracle Analytics

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)
Oracle Analytics
Score 7.5 out of 10
N/A
Oracle Analytics is a solution used to visually explore data to create and share compelling stories. Oracle Analytics Cloud is a cloud native service, and Oracle Analytics Server is the on-premise option.N/A
Pricing
Google BigQueryOracle Analytics
Editions & Modules
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
No answers on this topic
Offerings
Pricing Offerings
Google BigQueryOracle Analytics
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsMust contact sales team for pricing.
More Pricing Information
Community Pulse
Google BigQueryOracle Analytics
Features
Google BigQueryOracle Analytics
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
Oracle Analytics
-
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
Oracle Analytics
8.1
62 Ratings
1% below category average
Pixel Perfect reports00 Ratings8.055 Ratings
Customizable dashboards00 Ratings8.061 Ratings
Report Formatting Templates00 Ratings8.361 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
8.0
66 Ratings
0% below category average
Drill-down analysis00 Ratings8.564 Ratings
Formatting capabilities00 Ratings8.265 Ratings
Integration with R or other statistical packages00 Ratings7.345 Ratings
Report sharing and collaboration00 Ratings8.262 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
7.8
63 Ratings
5% below category average
Publish to Web00 Ratings7.755 Ratings
Publish to PDF00 Ratings7.762 Ratings
Report Versioning00 Ratings7.853 Ratings
Report Delivery Scheduling00 Ratings8.058 Ratings
Delivery to Remote Servers00 Ratings8.038 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
7.9
61 Ratings
1% below category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.659 Ratings
Location Analytics / Geographic Visualization00 Ratings7.652 Ratings
Predictive Analytics00 Ratings7.747 Ratings
Pattern Recognition and Data Mining00 Ratings8.63 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
8.4
61 Ratings
1% below category average
Multi-User Support (named login)00 Ratings8.160 Ratings
Role-Based Security Model00 Ratings8.060 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings8.560 Ratings
Report-Level Access Control00 Ratings8.54 Ratings
Single Sign-On (SSO)00 Ratings8.741 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
8.0
48 Ratings
4% above category average
Responsive Design for Web Access00 Ratings8.046 Ratings
Mobile Application00 Ratings8.033 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings8.044 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Google BigQuery
-
Ratings
Oracle Analytics
8.0
36 Ratings
3% above category average
REST API00 Ratings8.331 Ratings
Javascript API00 Ratings8.030 Ratings
iFrames00 Ratings8.323 Ratings
Java API00 Ratings8.031 Ratings
Themeable User Interface (UI)00 Ratings7.531 Ratings
Customizable Platform (Open Source)00 Ratings8.024 Ratings
Best Alternatives
Google BigQueryOracle Analytics
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
All AlternativesView all alternativesView all alternatives
User Ratings
Google BigQueryOracle Analytics
Likelihood to Recommend
8.8
(77 ratings)
8.2
(76 ratings)
Likelihood to Renew
8.1
(5 ratings)
9.1
(8 ratings)
Usability
7.0
(6 ratings)
8.2
(6 ratings)
Availability
7.3
(1 ratings)
9.1
(1 ratings)
Performance
6.4
(1 ratings)
9.1
(1 ratings)
Support Rating
5.3
(11 ratings)
9.1
(4 ratings)
In-Person Training
-
(0 ratings)
9.0
(1 ratings)
Online Training
-
(0 ratings)
9.1
(1 ratings)
Implementation Rating
-
(0 ratings)
9.1
(3 ratings)
Configurability
6.4
(1 ratings)
9.1
(1 ratings)
Contract Terms and Pricing Model
10.0
(1 ratings)
-
(0 ratings)
Ease of integration
7.3
(1 ratings)
8.2
(1 ratings)
Product Scalability
7.3
(1 ratings)
9.1
(1 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryOracle Analytics
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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Oracle
Oracle Data Visualization is very effective if used in an enterprise context with huge volumes of data coming from different systems. It supports dashboard and reporting capabilities and is easy to scale. It also allows you to leverage machine learning capabilities to extract hidden data trends. Visualization capabilities are powerful but not so various if compared to other solutions on the market. If you want to present a dashboard to an executive audience and you want to make your dashboards beautiful you must adapt them through PowerPoint.
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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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Oracle
  • Available without of the box connectors for Salesforce and oracle Saas Cloud. This is a huge plus for our business since we don't need another middleware solution just for this sake.
  • We are able to connect to our on-prem SQL Server database where we have our RMA database and other applications seamlessly without writing custom APIs.
  • OAC writes directly into ADW which is another advantage for loading Excel files into ADW after dataflow transformations.
  • OAC allows replication of the database from fusion ERP and lets us create subject areas using the data modeler.
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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.
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Oracle
  • More AI but with human back up.
  • Less mumbo jumbo so even IT guys/gals can more easily work.
  • More statistics (math) and orientation for those not familiar with it.
  • More live as well as virtual (live) show casing of real situations in business.
Read full review
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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Oracle
Scalability and rich integration capabilities. In the future, if we go with Hyperion for the Financial Consolidation and planning purposes -BI integration with Hyperion is going to be much simpler as it has native interface connectivity and even integration capabilities with well known CRM products (Siebel) and ERP Products (Oracle EBS, Peoplesoft, SAP) is going to be easy and straight forward.
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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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Oracle
Great, if you are limited to using it along with other Oracle products; sadly, not if you are integrating with other products, which can be a challenge. It is a great product with tons of functionality and great integration with other in-house platforms. Great visuals and customization for data and analytics to provide decision-making data and analysis.
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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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Oracle
No problems, it's that simple.
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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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Oracle
Other than anomalies or expected quirks, there were never any earth-shattering delays.
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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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Oracle
Oracle Analytics Support team is very proactive and I have never had a situation where I had to wait for more than a day or two to get my issues resolved. This is a very big help for us and we appreciate Oracle and its team for guaranteeing that experience.
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In-Person Training
Google
No answers on this topic
Oracle
Oracle University offers very in-depth training.
Read full review
Online Training
Google
No answers on this topic
Oracle
It was a private training site.
Read full review
Implementation Rating
Google
No answers on this topic
Oracle
A properly implemented Endeca solution performs extremely well on the largest of datasets and it positions your organization to immediately achieve your ROI.
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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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Oracle
Oracle Analytics Cloud, is one of the most agile and secure data analysis platforms that according to the budget and the amount of use, you can use the resources you need under the cloud. The Oracle brand is also very well known in this field and can meet all the needs of an organization or industry in any sector.
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Contract Terms and Pricing Model
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
Read full review
Oracle
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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Oracle
We have seen the results of this in our initial research and are not surprised that Oracle does this like it does soo many other things in this area, so well.
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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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Oracle
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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Oracle
  • We've used OBIEE (or it's previous named product) for over 13 years and it's still the most used tool for BI by the business.
  • We moved our largest business system off of Business Object into OBI so we could gain improved performance, reliability, and easier management of metadata.
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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.