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.
$4
per 100 slots
SingleStore
Score 9.3 out of 10
N/A
SingleStore aims to deliver the world’s fastest distributed SQL database for data-intensive applications: SingleStoreDB, which combines transactional + analytical workloads in a single platform.
SingleStore provides a solution for working with larger amount of data (vs. MySQL) with better performance (vs. BigQuery) without having to preprocess the data (vs. MongoDB), so basically it does better for specific use cases.
One of the most important aspects while working with data warehousing solutions and analytics is the ability to handle large datasets. Google BigQuery is the best in business for that particular aspect. It is ridiculously fast while handling large data sets. Another aspect where it is well suited is the ability to integrate it with data visualization tools like Data Studio. It is fast, easy to use, and very reliable. The only aspect where I feel it is less appropriate where you have to pay more of inefficient scripts and that can hamper the growth of the company a bit.
Our workload is 100% analytical. We also have to ingest a lot of data each month. SingleStore is a perfect match for our needs because it has fast pipelines for data ingestion and great performance, even in large and complex queries. We need fast response times for our user interface and great performance in our ETL processes, which are rather complicated. SingleStore handles all of this very well.
One issue with Google Cloud Storage is its price. For one to have that premium Google Cloud Storage, for the purpose of massive storage, he/she must have adequate cash. Otherwise, Google Cloud Storage is a safe and perfect online storage platform.
The only thing that can come to mind that would be annoying with this software was that sometimes when trying to share files on the Cloud with coworkers, it would just not share at all, or there would be a massive delay in when I shared them and when they received them. Other than that though, everything is perfect with this.
We wish the product had better support for High Availability of the aggregator. Currently the indexes generated by the two different aggregators are not in the same sequential space and so our apps have more burden to deal with HA.
More tools for debugging issues such as high memory usage would be good.
The price was the one that kept us away from purchasing for the first few years. Now we are able to afford due to a promotion that gives it at 25% of the list price. Not sure if we'll continue after the promotion offer expires in another 2 years.
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
[Until it is] supported on AWS ECS containers, I will reserve a higher rating for SingleStore. Right now it works well on EC2 and serves our current purpose, [but] would look forward to seeing SingleStore respond to our urge of feature in a shorter time period with high quality and security.
SingleSore can perform transactions and operational analytics together in order to utilize their data and transform their business. SingleStore delivers a database that performs both functions. Before using SingleStore, we had different systems for OLTP queries and for OLAP analyses, and a number of ETL packages to bring data from the OLTP system to Reporting database.
It’s Google, they’re big and well organized, the documentation is abundant and the scalability is amazing. The UX is good too, considering it’s a professional tool expected to be used by people with a specific technical background. Overall, it makes me feels good and secure that we know where to store the data, how to use that data and that the data is handled with utmost security and performance practices.
Very responsive to trouble tickets - Often, I think, the SingleStore's monitoring systems have already alerted the engineers by the time I get around to writing a ticket (about 10 - 20 mins after we see a problem). I feel like things are escalated nicely and SingleStore takes resolving trouble tickets seriously. Also SingleStore follows up after incidents to with a post mortem and actionable takaways to improve the product. Very satisfied here.
We allowed 2-3 months for a thorough evaluation. We saw pretty quickly that we were likely to pick SingleStore, so we ported some of our stored procedures to SingleStore in order to take a deeper look. Two SingleStore people worked closely with us to ensure that we did not have any blocking problems. It all went remarkably smoothly.
Spinning up, provisioning, maintaining and debugging a Hadoop solution can be non-trivial, painful. I'm talking about both GCE based or HDInsight clusters. It requires expertise (+ employee hire, costs). With BigQuery if someone has a good SQL knowledge (and maybe a little programming), can already start to test and develop. All of the infrastructure and platform services are taken care of. Google BigQuery is a magnitudes simpler to use than Hadoop, but you have to evaluate the costs. BigQuery billing is dependent on your data size and how much data your query touches.
Vertica, Snowflake, SQL Server, Azure Data Warehouse, PowerBI, Aerospike, etc. From what I've seen MemSQL is well worth the cost when latency and data freshness needs are high, i.e. you need a lot of queries to run with UI latency (the query itself takes less than a second or so), with very fresh streaming fact and dimensional data. It will be more expensive per "unit of performance" but if you need that performance then it'll get the job done.
On-prem Vertica (note, not Eon) provides more knobs for optimizing a particular data set and set of queries against it and performs as well or better in a single table, fact table queries. It will also scale to data size more cheaply due to its on-disk model. For large queries against large data sets where data freshness isn't as important (and latency either is or isn't), I'd take Vertica, although if you need to do a lot of joins that will struggle). However, as they still are exclusively columnar, dimension table updates, and recalls based on them, can only be tuned to happen so fast (we could do much better than 10 seconds with 10-100 updates per second for raw replication, and Vertica's joins are always slow so recalls were worse).
Snowflake suffers similarly to Vertica in the data freshness, replication, and re-calc area; SF also doesn't give as many knobs to turn as Vertica for data set optimization but seems to be better at joins. If you have a lot of queries to run against a lot of data and joins are limited, you need query latency low and consistent but you don't need a ton of freshness, I'd stick with Vertica. If joins matter more, or you can accept notably-but-not-terribly worse performance, then Snowflake is fine and cheaper from what we've seen. (Again, I can't speak to SF vs Vertica Eon).
SQL Server and ADW we couldn't get to perform as well as the other options, but I'll say we didn't try that hard on those.
Aerospike is amazing as a KV store; however for OLAP use cases where you want to balance performance against the flexibility of queries against general event (time series) data (i.e. be able to roll up to different grains) then KV becomes challenging.
PBI is great if you want an integrated BI tool, but if you want an OLAP solution to build against, with some particular scale or performance needs to be mentioned above, I'd go with one of these other solutions. It really can be great for letting non-tech folks build relatively small data sets and quick insights for customers (internal or external), great leverage in that case.
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.
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.
As the overall performance and functionality were expanded, we are able to deliver our data much faster than before, which increases the demand for data.
Metadata is available in the platform by default, like metadata on the pipelines. Also, the information schema has lots of metadata, making it easy to load our assets to the data catalog.
