Google's Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads with up to 99.999% availability.
$0.03
per month
OpenText Vertica
Score 10.0 out of 10
N/A
The Vertica Analytics Platform supplies enterprise data warehouses with big data analytics capabilities and modernization. Vertica is owned and supported by OpenText.
N/A
Pricing
Google Cloud BigTable
OpenText Vertica
Editions & Modules
Backup Storage
$0.026
per month per GB
HDD storage
$0.026
per month per GB
SSD storage
$0.17
per month per GB
Nodes
$0.65/hour
per month per node (minimum 1 nodes)
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Offerings
Pricing Offerings
Cloud BigTable
OpenText Vertica
Free Trial
No
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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More Pricing Information
Community Pulse
Google Cloud BigTable
OpenText Vertica
Features
Google Cloud BigTable
OpenText Vertica
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
It works great for any use case that requires high throughput for reads and writes, for key-value kinda data. This enables you to use it for usecases where you need to retain data for a long term or even short term but then use it in pipelines to enrich data, sync states between backend and frontend or to quickly lookup say a user.
Vertica as a data warehouse to deliver analytics in-house and even to your client base on scale is not rivaled anywhere in the market. Frankly, in my experience it is not even close to equaled. Because it is such a powerful data warehouse, some people attempt to use it as a transactional database. It certainly is not one of those. Individual row inserts are slow and do not perform well. Deletes are a whole other story. RDBMS it is definitely not. OLAP it rocks.
Analytics: is at Google's heart. No on can beat Google in this space and BigTable is one of its implementation of this. The insights you gain from BigTable are simply usable in your day to day activities and can help you make real difference.
Speed: Processing TBs and PBs of data under minutes needs real efficient platform which is capable of doing much more than just processing data. All this data cannot be processed by a single machine, but rather huge pairs of machines working in conjuction with each other. BigTable's implementation is one of the finest and allows you achieve great speeds!
Interface: is great. Google has segregated required task under logically placed buttons which takes no time by users to understand and get habituated.
Could use some work on better integrating with cloud providers and open source technologies. For AWS you will find an AMI in the marketplace and recently a connector for loading data from S3 directly was created. With last release, integration with Kafka was added that can help.
Managing large workloads (concurrent queries) is a bit challenging.
Having a way to provide an estimate on the duration for currently executing queries / etc. can be helpful. Vertica provides some counters for the query execution engine that are helpful but some may find confusing.
Unloading data over JDBC is very slow. We've had to come up with alternatives based on vsql, etc. Not a very clean, official on how to unload data.
For big IT firms like us, data is very important and it only holds its value if it can make sense to us. Therefore, Bigtable's usability is priceless when it comes to decision making based on data.
I haven't had any recent opportunity to reach out to Vertica support. From what I remember, I believe whenever I reached out to them the experience was smooth.
As a hosted solution, that can be managed easily from the Google cloud platform, it enables the teams to think about business use cases vs thinking about the management of the database itself. It is easily to scale it up when you need more throughput or storage. This keeps the developers and the product folks happy.
Vertica performs well when the query has good stats and is tuned well. Options for GUI clients are ugly and outdated. IO optimized: it's a columnar store with no indexing structures to maintain like traditional databases. The indexing is achieved by storing the data sorted on disk, which itself is run transparently as a background process.
