Google BigQuery vs. Pentaho

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)
Pentaho
Score 5.1 out of 10
N/A
Pentaho is a suite of open source business intelligence and analytics products, now offered and supported by Hitachi Data Systems since the June 2015 acquisition.N/A
Pricing
Google BigQueryPentaho
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 BigQueryPentaho
Free Trial
YesNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Google BigQueryPentaho
Features
Google BigQueryPentaho
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Google BigQuery
8.5
80 Ratings
0% below category average
Pentaho
-
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
Pentaho
9.0
20 Ratings
9% above category average
Pixel Perfect reports00 Ratings8.618 Ratings
Customizable dashboards00 Ratings9.918 Ratings
Report Formatting Templates00 Ratings8.718 Ratings
Ad-hoc Reporting
Comparison of Ad-hoc Reporting features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
8.7
19 Ratings
8% above category average
Drill-down analysis00 Ratings7.618 Ratings
Formatting capabilities00 Ratings8.319 Ratings
Integration with R or other statistical packages00 Ratings9.312 Ratings
Report sharing and collaboration00 Ratings9.717 Ratings
Report Output and Scheduling
Comparison of Report Output and Scheduling features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
9.7
20 Ratings
16% above category average
Publish to Web00 Ratings9.618 Ratings
Publish to PDF00 Ratings9.819 Ratings
Report Versioning00 Ratings9.713 Ratings
Report Delivery Scheduling00 Ratings9.917 Ratings
Delivery to Remote Servers00 Ratings9.310 Ratings
Data Discovery and Visualization
Comparison of Data Discovery and Visualization features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
8.1
17 Ratings
1% above category average
Pre-built visualization formats (heatmaps, scatter plots etc.)00 Ratings7.916 Ratings
Location Analytics / Geographic Visualization00 Ratings8.216 Ratings
Predictive Analytics00 Ratings8.314 Ratings
Access Control and Security
Comparison of Access Control and Security features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
9.1
20 Ratings
7% above category average
Multi-User Support (named login)00 Ratings9.320 Ratings
Role-Based Security Model00 Ratings9.619 Ratings
Multiple Access Permission Levels (Create, Read, Delete)00 Ratings9.918 Ratings
Single Sign-On (SSO)00 Ratings7.610 Ratings
Mobile Capabilities
Comparison of Mobile Capabilities features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
8.3
11 Ratings
7% above category average
Responsive Design for Web Access00 Ratings9.710 Ratings
Mobile Application00 Ratings6.97 Ratings
Dashboard / Report / Visualization Interactivity on Mobile00 Ratings8.711 Ratings
Application Program Interfaces (APIs) / Embedding
Comparison of Application Program Interfaces (APIs) / Embedding features of Product A and Product B
Google BigQuery
-
Ratings
Pentaho
8.6
10 Ratings
10% above category average
REST API00 Ratings8.310 Ratings
Javascript API00 Ratings9.09 Ratings
iFrames00 Ratings7.39 Ratings
Java API00 Ratings8.79 Ratings
Themeable User Interface (UI)00 Ratings8.910 Ratings
Customizable Platform (Open Source)00 Ratings9.610 Ratings
Best Alternatives
Google BigQueryPentaho
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 BigQueryPentaho
Likelihood to Recommend
8.8
(77 ratings)
9.1
(31 ratings)
Likelihood to Renew
8.1
(5 ratings)
8.8
(11 ratings)
Usability
7.1
(6 ratings)
9.3
(6 ratings)
Availability
7.3
(1 ratings)
-
(0 ratings)
Performance
6.4
(1 ratings)
-
(0 ratings)
Support Rating
5.5
(11 ratings)
9.3
(7 ratings)
Online Training
-
(0 ratings)
9.5
(2 ratings)
Implementation Rating
-
(0 ratings)
5.0
(1 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)
-
(0 ratings)
Professional Services
8.2
(2 ratings)
-
(0 ratings)
User Testimonials
Google BigQueryPentaho
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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Hitachi Vantara
Pentaho is very well suited to perform data extraction & data mining from various cloud storage & transform that data using various available data models. However, the software struggles when it comes to visualizing the extracted data in an appealing manner & can be difficult for end-users to get an understanding of data tables created using those models.
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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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Hitachi Vantara
  • Integrate and synchronize with big data easily
  • Import data from any sources and different databases
  • Managing data in on-premise, hybrid and cloud environments.
  • Compatibility and flexibility of the platform with any type of scenario and any business or industry
  • Various tools in the software suite to transformation of data
  • Simple interface appearance and creative UI graphics
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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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Hitachi Vantara
  • I think the relative obscurity of the tool is a downside, not as many developers, consultants or peers you can tap into.
  • Lack of a solid user community held us back, looking at Power BI and Qlik, they have huge user communities that help each other out. Would have liked that here.
  • Smaller company means smaller sales force, and the lack of a local presence made it hard to only interact online with the account rep. Other companies have someone local who often stops by with pre-sales developers to just pitch in free of charge when they have time.
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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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Hitachi Vantara
I will use Pentaho until I find a better tool with a better, easier to use report designer client. For now, Pentaho has been the most powerful reporting tool for our clients because of its ability to connect to Odoo, integrate in Odoo (reports are accessible in Odoo) and the flexibility in report design and parameter integration
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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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Hitachi Vantara
The Pentaho tools are designed so you can start playing around on your own. Of course, you will need guidance at some point, but the training teams are good at guiding new users, and the online documentation is usually pretty up-to-date.
Some of the tools, such as the Pentaho Data Integration tool and the Pentaho Server, are pretty self-explanatory. The other tools maybe are not so quickly and obvious to use, but again, with some documentation and some customer support, you can find your way around them.
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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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Hitachi Vantara
No answers on this topic
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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Hitachi Vantara
No answers on this topic
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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Hitachi Vantara
They were responsive to our questions when we raised issues. They gave us workarounds when required. They were quite knowledgeable when it came to issue analysis and providing fixes. They were forthright in informing us if a bug was not due for release soon.
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Online Training
Google
No answers on this topic
Hitachi Vantara
Course Taken: DI1000 Pentaho Data Integration Fundamentals Setup A week before your class started, the instructor will start sending out class material and lab setup instructions. This is helpful so that you understand how the environment is laid out and can start reviewing the content. Ultimately it saved about a 1/2 day trying to setup with 10 other people online which was great! The Course The 3-day course was laid out like many other technical classes with 15-30 minutes instruction and 15-60 minutes of lab exercises. The instructor was very knowledgeable with the functionality from version to version and answered questions as we went along. I was amazed at some of the functionality that was available that I was not using at the time and quickly implemented changes to many existing transformations and jobs. The novice users seemed to catch on quickly and more experienced users explained how some of the functionality was used in their home environments. Towards the end there was enough time so that we were able to ask very directed questions about our own environments. Overall, I really found the class to be informative and deliver enough information to be dangerous. My skills improved and I was able to design better and efficient transformations for the HIE. Course Description: https://training.pentaho.com/instructor-led-training/pentaho-data-integration-fundamentals-di1000
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Implementation Rating
Google
No answers on this topic
Hitachi Vantara
Get the right people in before starting implementation. Start small and build as you go approach is time consuming and involves lot of rework. Evangalize within the organization the capabilities and limitations equally so that correct delivery expectations are set. Set expectations with the Customer that the tool cannot replace proprietary software in terms of stability/usability and that timelines could change given the new ness of the product.
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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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Hitachi Vantara
Since the Pentaho platform offers a range of broad functionality across data preparation and advanced analytics, it also can be easily integrated to support many data sources and machine-learning frameworks. Based on that fact, we selected Pentaho to be used in our internal department. It also supports many of our BI use cases as required by company management or the business user. Last but not least, the Pentaho license is cheaper than their competitor.
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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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Hitachi Vantara
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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Hitachi Vantara
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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Hitachi Vantara
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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Hitachi Vantara
  • Pentaho has improved our overall business process.
  • Pentaho has helped the Managers and Directors to analyze the numbers going up and down from time to time.
  • We have a started a big project using Pentaho that is going to include all the business processes in the organization.
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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.