Cloudera Enterprise Data Hub vs. Google BigQuery

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Cloudera Enterprise Data Hub
Score 9.0 out of 10
N/A
The Cloudera Enterprise Data Hub powered by SDX is a multifunction analytics solution that supports a range of operational and analytic use cases for enterprises.N/A
Google BigQuery
Score 8.7 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)
Pricing
Cloudera Enterprise Data HubGoogle BigQuery
Editions & Modules
No answers on this topic
Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
Offerings
Pricing Offerings
Cloudera Enterprise Data HubGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
YesYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Cloudera Enterprise Data HubGoogle BigQuery
Features
Cloudera Enterprise Data HubGoogle BigQuery
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Cloudera Enterprise Data Hub
-
Ratings
Google BigQuery
8.4
73 Ratings
3% below category average
Automatic software patching00 Ratings8.017 Ratings
Database scalability00 Ratings9.172 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.866 Ratings
Monitoring and metrics00 Ratings8.268 Ratings
Automatic host deployment00 Ratings8.013 Ratings
Best Alternatives
Cloudera Enterprise Data HubGoogle BigQuery
Small Businesses
Google BigQuery
Google BigQuery
Score 8.7 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
Medium-sized Companies
Snowflake
Snowflake
Score 8.8 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
Enterprises
Oracle Exadata
Oracle Exadata
Score 9.6 out of 10
IBM Cloudant
IBM Cloudant
Score 7.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Cloudera Enterprise Data HubGoogle BigQuery
Likelihood to Recommend
9.0
(12 ratings)
8.8
(73 ratings)
Likelihood to Renew
8.2
(7 ratings)
7.9
(3 ratings)
Usability
-
(0 ratings)
7.7
(5 ratings)
Support Rating
-
(0 ratings)
7.4
(10 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
Cloudera Enterprise Data HubGoogle BigQuery
Likelihood to Recommend
Cloudera
Cloudera excels at seamless migrations and upgrades.



Cloudera supports self-healing and data center
replacement of failed cloud instances while maintaining the state.



Cloudera is essential to increase or decrease
capacity through the user interface or API.



Cloudera is great at simplifying big data analytics
by providing the technology and tools needed to gain insights from IoT and
connected devices to help monitor and condition our assets.



Cloudera's cybersecurity platform option offers
stronger anomaly detection, visibility, and prevention, as well as faster
behavioral analysis.



Cloudera is beneficial for enabling and utilizing
the platform's machine learning and ad-hoc queries while securely storing,
retrieving, and analyzing any volume of data at scale.
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Google
Google BigQuery is great for being the central datastore and entry point of data if you're on GCP. It seamlessly integrates with other Google products, meaning you can ingest data from other Google products with ease and little technical knowledge, and all of it is near real-time. Being serverless, BigQuery will scale with you, which means you don't have to worry about contention or spikes in demand/storage. This can, however, mean your costs can run away quickly or mount up at short notice.
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Pros
Cloudera
  • Excellent management capabilities via Cloudera Manager.
  • Open source and does not restrict our data to be bound by a proprietary format.
  • Offers excellent support for data governance and auditing.
  • Has all the components that would help us build a data hub.
  • Excellent platform support offered by Cloudera.
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Google
  • First and foremost - Google BigQuery is great at quickly analyzing large amounts of data, which helps us understand things like customer behavior or product performance without waiting for a long time.
  • It is very easy to use. Anyone in our team can easily ask questions about our data using simple language, like asking ChatGPT a question. This means everyone can find important information from our data without needing to be a data expert.
  • It plays nicely with other tools we use, so we can seamlessly connect it with things like Google Cloud Storage for storing data or Data Studio for creating visual reports. This makes our work smoother and helps us collaborate better across different tasks.
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Cons
Cloudera
  • Not fully Open Source, couple of components of the distributions are privately owned, meaning with public contributions are not welcome
  • Improvements to Cloudera manager can only be recommended. its very hard to get it done once recommended as the full control is with them.
  • Should make components more aligned to Open Source rather than making it closed sourced.
  • Custom Features of open source software tools supported only by Cloudera are tricky. Cant commit changes to tools like Hue.
  • Improvements to Cluster Management tool is required, which are already available to its competitors.
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Google
  • It is challenging to predict costs due to BigQuery's pay-per-query pricing model. User-friendly cost estimation tools, along with improved budget alerting features, could help users better manage and predict expenses.
  • The BigQuery interface is less intuitive. A more user-friendly interface, enhanced documentation, and built-in tutorial systems could make BigQuery more accessible to a broader audience.
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Likelihood to Renew
Cloudera
Likely to renew the use in case the requirements for Cloudera remain valid. The rapid change in customer requirements and solutions that must be validated, integrated or tested changes. As the maturity of the solution increases, the requirements to renew use decrease. From a solution feature perspective by itself would probably grade 10.
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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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Usability
Cloudera
No answers on this topic
Google
web UI is easy and convenient. Many RDBMS clients such as aqua data studio, Dbeaver data grid, and others connect. Range of well-documented APIs available. The range of features keeps expanding, increasing similar features to traditional RDBMS such as Oracle and DB2
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Support Rating
Cloudera
No answers on this topic
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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Alternatives Considered
Cloudera
Cloudera is
compatible with Windows operating systems, and Mac allows cloud-based
deployment, it is also very useful to configure data encryption, guarantee
protocols, and security policies. It also provides integrated auditing and
monitoring capabilities, as well as a control comprehensive data repository for
the enterprise, and ensures vendor compatibility through its open-source
architecture.
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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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Contract Terms and Pricing Model
Cloudera
No answers on this topic
Google
None so far. Very satisfied with the transparency on contract terms and pricing model.
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Professional Services
Cloudera
No answers on this topic
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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Return on Investment
Cloudera
  • Cloudera products are the most widely. It is more business friendly as data is more secure. The sensitive data that you operate on is local to you and your project rather than processing this data on Cloud.
  • Cloudera is definitely faster as wait time is reduced if on Cloud.
  • A lot range of products are covered. So it is definitely good for businesses and had good returns on investments.
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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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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.