Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Aerospike
Score 9.7 out of 10
N/A
The Aerospike Real-time Data Platform aims to enable organizations to act instantly across billions of transactions while reducing server footprint up to 80%. The vendor states Aerospike multi-cloud platform powers real-time applications with predictable sub-millisecond performance up to petabyte scale with five-nines uptime with globally distributed, consistent data. Aerospike boasts customers such as Airtel, Experian, European Central Bank, Nielsen, PayPal, Snap, Verizon Media and Wayfair.N/A
Google BigQuery
Score 8.6 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
AerospikeGoogle 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
AerospikeGoogle BigQuery
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
AerospikeGoogle BigQuery
Top Pros
Top Cons
Features
AerospikeGoogle BigQuery
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Aerospike
9.1
2 Ratings
4% above category average
Google BigQuery
-
Ratings
Performance9.92 Ratings00 Ratings
Availability8.12 Ratings00 Ratings
Concurrency9.12 Ratings00 Ratings
Security9.01 Ratings00 Ratings
Scalability9.82 Ratings00 Ratings
Data model flexibility9.92 Ratings00 Ratings
Deployment model flexibility8.02 Ratings00 Ratings
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
Aerospike
-
Ratings
Google BigQuery
8.4
50 Ratings
4% below category average
Automatic software patching00 Ratings8.117 Ratings
Database scalability00 Ratings8.850 Ratings
Automated backups00 Ratings8.524 Ratings
Database security provisions00 Ratings8.743 Ratings
Monitoring and metrics00 Ratings8.445 Ratings
Automatic host deployment00 Ratings8.113 Ratings
Best Alternatives
AerospikeGoogle BigQuery
Small Businesses
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
Medium-sized Companies
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
Enterprises
IBM Cloudant
IBM Cloudant
Score 8.4 out of 10
SingleStore
SingleStore
Score 9.7 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
AerospikeGoogle BigQuery
Likelihood to Recommend
8.7
(3 ratings)
8.6
(50 ratings)
Likelihood to Renew
8.0
(1 ratings)
7.0
(1 ratings)
Usability
9.8
(2 ratings)
9.4
(3 ratings)
Support Rating
8.9
(2 ratings)
10.0
(9 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
10.0
(1 ratings)
Professional Services
-
(0 ratings)
8.2
(2 ratings)
User Testimonials
AerospikeGoogle BigQuery
Likelihood to Recommend
Aerospike Inc
We were developing an advertisement time auction application, where we had to store the client's personal details, advertisement-related details, location, and many other details. Moreover, we required a promotion, cookies, and a few more details from the front end. All this information is heavy in terms of size and cannot be lost if the server crash. So, we required an extremely fast disk database with high scalability and low throughput.
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Google
For organizations looking to avoid the overhead of managing infrastructure, BigQuery's server-less architecture allows teams to focus on analyzing data without worrying about server maintenance or capacity planning. Small projects or startups with limited data analysis needs and tight budgets might find other solutions more cost-effective. Also, it is not suitable for OLTP systems.
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Pros
Aerospike Inc
  • Low latency
  • Stable
  • Highly configurable
  • increasing features
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Google
  • Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
  • Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
  • Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
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Cons
Aerospike Inc
  • Load balancing per network segments.
  • Reduction in price.
  • Cross datacenter replication usage isn't so straightforward. Sometimes cross dc replication can have issues of bad data..
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Google
  • Can't use it out of Google's cloud platform which is a minus point if you want a local setup.
  • Can be a little expensive to manage.
  • A little difficult to manage someone with less technical expertise as it requires you to have SQL knowledge of joins, CTEs etc.
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Likelihood to Renew
Aerospike Inc
If money isn't an issue, and you're not on the cloud, then I'd go with Aerospike. If you're the cloud ie, aws or azure, then i'd stick with dynamoDB or Cosmos then. Aerospike is definitely not something you want to put into the cloud. It doesn't work well w/ cross regions. If cross DC, you'll have to write some stuff for data integrity checks.
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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
Aerospike Inc
We were in dilemma in deciding the database and it was the first time we were using Aerospike. Eventually, everything went as expected and resolved the client's requirement along with positive feedback and appreciation
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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
Aerospike Inc
You pay for the level of support.
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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
Aerospike Inc
Aerospike is much more performant than MongoDB, however there is much greater community adoption and support for mongo
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Google
Google's Firebase isn't a competitor but we had to use Google's BigQuery because Google's Firebase's database is limited compared to Google's BigQuery. Linking your Firebase project to BigQuery lets you access your raw, unsampled event data along with all of your parameters and user properties. Highly recommend connecting the two if you have a mobile app.
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Contract Terms and Pricing Model
Aerospike Inc
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
Aerospike Inc
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
Aerospike Inc
  • increased response time
  • minimal managerial resource required
  • developer can start using with shallow learning curve
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Google
  • Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
  • Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
  • Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.
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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.