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.
$0.04
HCL Actian Data Platform
Score 8.0 out of 10
Mid-Size Companies (51-1,000 employees)
The HCL Actian Data Platform (formerly Actian Avalanche) hybrid cloud data warehouse is a fully managed service that aims to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a lower cost than alternative solutions. Avalanche has built-in self-service data integration that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling users to migrate or offload applications and data to…
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Pricing
Google BigQuery
HCL Actian Data Platform
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Standard edition
$0.04 / slot hour
Enterprise edition
$0.06 / slot hour
Enterprise Plus edition
$0.10 / slot hour
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Google BigQuery
HCL Actian Data Platform
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Yes
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No
Premium Consulting/Integration Services
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No
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Google BigQuery
HCL Actian Data Platform
Features
Google BigQuery
HCL Actian Data Platform
Database-as-a-Service
Comparison of Database-as-a-Service features of Product A and Product B
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.
VectorWise is suitable to be a departmental data mart database or an operational data store (ODS). It is not suitable for enterprise data warehouse database.
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.
The support community was not as robust as you would find in a Mulesoft or Informatica environment. Given time and growth, it’s possible it will blossom, but for now it is minimal.
Training is always a big thing for us, and the tool was not expansive enough for us to implement our own internal training program. There was some online training, and we acquired an expert when we brought on the new company, but some additional training tools would have helped the tool grown its user base internally.
Not a lot to set it apart from the competition. Most of the features are available with other more established tools, but for a small company that maybe grew too quickly and needs to get its arms around many different data sources, I can see the appeal. Not really geared for larger firms.
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.
As I said before, more training or greater visibility to training tools/options would be a plus. It’s easy to publish YouTube videos these days, I think they should make more of them.
Differentiation would help, there’s not a lot out there to drive you to buy the product if you are well informed in the market. If you know the market, you steer towards the large or trendy products. It’s a good product, but lost in the noise of the field I think.
Hitching the wagon to a major software brand (like Mule did to Salesforce) would help grow the user base, and thus increase the activity in the support community. More users also translates into product champions.
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.
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
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.
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.
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.
Google BigQuery of course collects a much much larger array of raw data and can handle (practically) an unlimited amount of data. For a large enterprise like ours that relies on large-scale analytics, this is absolutely imperative. Google BigQuery can also combine GA4 data with external sources (like CRM tools), so our analytics can be unified. Due to our heavy reliance on GA4, Google BigQuery is the natural choice since it is a Google product and has better integration.
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The included hundreds of pre-built connectors gave us lots of options, but in the end, we were just too large of a company to rely on the product and needed a big-name player to address our wide-ranging needs. Powerful for its size, but not sized enough to address big businesses.
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.
In some places, Google BigQuery has helped us save some money by avoiding the need for expensive infrastructure and reducing some of the operational costs.
Scalability is up-to-date and really helpful in multiple places.
Knowledge transfer is easy as it is very user-friendly, so the learning curve has been reduced.
Also, it gives us more insights from our data, helping us make smarter decisions for our business.