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)
Agentforce Sales
Score 8.7 out of 10
N/A
Salesforce' Agentforce Sales (formerly Salesforce Sales Cloud) is the company's flagship CRM platform. The AI CRM for Sales features data built right in.
$25
per month
Snowflake
Score 8.7 out of 10
N/A
The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
I have used Snowflake and DataGrip for data retrieval as well as Google BigQuery and can say that all these tools compete for head to head. It is very difficult to say which is better than the other but some features provided by Google BigQuery give it an edge over the others. …
Google BigQuery is less expensive to run and offers free storage of up to the first 10 GB of data. Google BigQuery is also easier (and faster) to get up and running. Unlike Snowflake, Google BigQuery does not require any manual scaling or performance tuning. Scaling is …
Google BigQuery is cheaper and much faster as compared to both. While as compared to Snowflake , we tested it was faster and cheaper by 30%, that is after Snowflake tweaked their environment, if not for that it would have been 90% cheaper than Snowflake. Redshift is not easy …
We actually use Snowflake and BigQuery in tandem because they both currently meet various needs. Redshift, however, has barely been used since our migration away from it. In the case of both Snowflake and BigQuery, they beat Redshift by a long shot. The main reasons are their …
Fully serverless. We don’t manage clusters or warehouses. Requires us to manage virtual warehouses. BigQuery is cheaper for exploratory heavy queries; Snowflake is more predictable for sustained workloads. BigQuery is unbeatable if you’re deep in Google’s ecosystem; Snowflake …
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 …
Compared to every other analytics DB solution I've used, Google BigQuery was by far the easiest to set up and maintain, and scale. The price was also much lower for our use case (internal data analysis).
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big …
Treasure Data is more for the marketer rather than a developer audience, so depending on who your main users will be for the machine learning you can decide which tool is better. In our case we went with Treasure Data because it was more for a marketer and less for the …
At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.
Both BigQuery and Redshift are two comparable fully managed petabyte-scale cloud data warehouses. They’re similar in many ways, but you should consider their unique features and how they can contribute to an organization’s data analytics infrastructure. When considering which …
BigQuery by far the best solution in all angles compared to other ones: Especially scalability, ease of use, performance and there is no need to manage any cluster of servers. Also it's ABSOLUTELY pay as you go! No one in market currently provide such service that can compete …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Accommodates future data types such as JSON and XML. Scalability is another advantage. Pay per use is beneficial for organizations like yours. Direct connectors with AWS help us to go with it. No limit on user creation and clone data not eating up extra disk space are a few …
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
More flexible and faster compared to Redshift, more functionality compared to BigQuery e.g. - per minute billing, instant spin up of warehouse. Overall, the cost and time savings swayed us in favor of Snowflake.
Event-based data can be captured seamlessly from our data layers (and exported to Google BigQuery). When events like page-views, clicks, add-to-cart are tracked, Google BigQuery can help efficiently with running queries to observe patterns in user behaviour. That intermediate step of trying to "untangle" event data is resolved by Google BigQuery. A scenario where it could possibly be less appropriate is when analysing "granular" details (like small changes to a database happening very frequently).
Obviously, for any business, there are two main areas to focus on — the sales path and the service path. Sales Cloud wouldn’t be suited for a company that’s primarily into support services. For those kinds of companies, Salesforce has a different product — Service Cloud. So, for anyone in the support or service space, Sales Cloud isn’t the right fit.
Snowflake is well suited when you have to store your data and you want easy scalability and increase or decrease the storage per your requirement. You can also control the computing cost, and if your computing cost is less than or equal to 10% of your storage cost, then you don't have to pay for computing, which makes it cost-effective as well.
GSheet data can be linked to a BigQuery table and the data in that sheet is ingested in realtime into BigQuery. It's a live 'sync' which means it supports insertions, deletions, and alterations. The only limitation here is the schema'; this remains static once the table is created.
Seamless integration with other GCP products.
A simple pipeline might look like this:-
GForms -> GSheets -> BigQuery -> Looker
It all links up really well and with ease.
One instance holds many projects.
Separating data into datamarts or datameshes is really easy in BigQuery, since one BigQuery instance can hold multiple projects; which are isolated collections of datasets.
The customizations - We have an organization that operates differently from most companies, so we’ve had to implement quite a few customizations — and Salesforce allows us to do that quite quickly. Most of the time, delays come from dependencies on other internal parties rather than the system itself.
From my perspective as a consultant, one of the biggest advantages is that everything is in Salesforce — all the details, all in one place. The ability to customize it easily is a big plus; there’s really a lot you can do with it.
Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
Snowflake manages concurrency better in our trials than other premium competitors
Snowflake has little to no setup and ramp up time
Snowflake offers online training for various employee types
Please expand the availability of documentation, tutorials, and community forums to provide developers with comprehensive support and guidance on using Google BigQuery effectively for their projects.
If possible, simplify the pricing model and provide clearer cost breakdowns to help users understand and plan for expenses when using Google BigQuery. Also, some cost reduction is welcome.
It still misses the process of importing data into Google BigQuery. Probably, by improving compatibility with different data formats and sources and reducing the complexity of data ingestion workflows, it can be made to work.
We still need to include the production part. We started using Salesforce to sell the seeds — our inventory is in SAP — and from there we handle sales and track the process of planting, harvesting, selling, and then collecting payments. But we don’t yet manage the earlier production processes, like production planning. We handle allocation, but not full production planning, and that’s an area where we still have room for improvement.
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
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.
There are days when I wish we hadn't switched, but I know that if we put in the time, we will get to where we want to be with the software and that it has many more capabilities than anything else we looked at. However, the amount of time and onboarding we need to do is also far greater than we realized/were told when we originally bought the product. They told us we should hire onboarding support, but at the end, after we had already reached our budget maximum for this, so it's been slower than we had hoped.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
I think overall it is easy to use. I haven't done anything from the development side but an more of an end user of reporting tables built in Google BigQuery. I connect data visualization tools like Tableau or Power BI to the BigQuery reporting tables to analyze trends and create complex dashboards.
Because I think it could be easier. We have different standards today since we’re used to interacting with consumer apps like Starbucks, where all you do is scan your card. Then, when you use Sales Cloud, there are still a lot of manual inputs. So my mission with AI is really about figuring out how to make that easier.
Because the fact that you can query tons of data in a few seconds is incredible, it also gives you a lot of functions to format and transform data right in your query, which is ideal when building data models in BI tools like Power BI, it is available as a connector in the most used BI tools worldwide.
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.
Salesforce is always available securely from any internet-capable device anywhere in the world, UNLESS you choose to set security measures so that ONLY trusted IP ranges may access the system at certain times of the day. It's all about choice and flexibility with Salesforce products.
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.
Salesforce performance in general is excellent. "The cloud infrastructure beneath Force.com has been fine-tuned over the past 10 years. It powers nearly 100,000+ businesses running more than 185,000 applications that 3 million users count on every day." Points per Salesforce - 1) Multitenant kernel - With a multitenant platform, each business that uses the app doesn’t have its own copy. Instead, all businesses share a single copy and then customize it for their specific needs. 2) ISO 27001 certified security - You can’t compromise when it comes to enterprise-level security. Force.com is road-tested and trusted by nearly 100,000+ companies, including many of the world’s most security-conscious organizations, such as banks and health care providers. 3) Proven reliability - All Force.com apps run on world-class data centers with backup, failover, and disaster-recovery facilities. Force.com has had a proven 99.9 percent uptime record for years. 4) Proven, real-time scalability - Force.com is used by many of the world's largest enterprises, including Cisco, Japan Post Network, and Symantec. Applications can automatically scale from a few users to millions of page views, as needed. 5) Real-time query optimizer - You need fast access to your data. The Force.com query optimizer delivers under 300ms response time, at a massive scale. 6) Real-time transparent system status - You can always see real-time system performance, availability, and security information at trust.salesforce.com. 7) Real-time upgrades - Unlike traditional software platforms, our upgrades never break your customizations, code, or integrations. We upgrade the platform for you 3 to 4 times each year. As a result, you’re always on the latest version, with access to the latest features, performance, and security enhancements. 8) Real-time sandbox environments - With a single click, you can create copies of your applications, configuration, and data in separate environments for development, testing, and training. 9) Three global production data centers and disaster recovery - Force.com runs on three geographically dispersed, mirrored data centers with built-in replication, disaster recovery, a redundant network backbone, and no single points of failure
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.
The overall support has been good. More and more features are being released quite frequently. Very small features are also making big difference in how the tool can be adapted and used better. If there is anything we need or are stuck, the support team sets up a call and helps in resolving the issue/provides workarounds.
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
I attended two training sessions. I would rate them a 4 as an advanced user. It was very basic – great for someone new – would give 8+ for new person.
I had 3 years of experience at the time. I skipped basic and went onto advanced and still not helpful. A lot of it was best practices that didn’t feel relevant for our business
I have gone through multiple. The content that’s delivered is quite basic – I wish they had more advanced training.
We are grandfathered into premium support plus training. We get unlimited access to instructor led and online training for free. We have taken advantage of this
Just from an organizational standpoint - we standardized our data prior to moving to Salesforce. But we essentially standardized it wrong. That's created a big disgusting mess for us know that I'll have to deal with as the Admin. Be sure you think through use cases prior to doing something like that - seek outside opinions on how the data will work best, especially depending on what else you're going to integrate with Salesforce.
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.
So I've evaluated, implemented Microsoft Dynamics in the past. I've used Oracle CRM solutions. I've used Daylight, which is a very niche CRM system the last couple of years. And I've evaluated a variety from Legacy Microsoft Ones to Zoho and Sugar when making implementation decisions at other companies. But usually I've gone with Salesforce. I'd say it's better than most. The only one that I generally prefer, and last time I chose an implementation from scratch, I did Microsoft Dynamics. And the reason is for small mid-size organization, Microsoft Dynamics, if you already have Microsoft Office products, it's much better integrated to all of the Excel, Word, OneNote, Outlook email than what you get from Salesforce. And so that's the only one that if someone's a Microsoft organization and small sized company, it'll save a lot of integration things, a lot of security, a lot of login and access and IT management by just sticking within the Microsoft ecosystem. But outside of that, if you don't use Microsoft or if you're a large organization or have other needs that you want, Salesforce I'd say is better than all of the other CRM offerings out there. It's the easiest to use and the most robust and the most vendors and products for the ecosystem.
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system to store a large amount of data. All these functionalities give the better edge to Snowflake.
Salesforce is the most widely used CRM system. Professionalism tends to increase when things go wrong for market leaders. Salesforce considers us as users because they own the market. Having all of our data in one place and all of our teams working within Salesforce. Anyone who uses Salesforce is impacted by it, even if they don't.
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.
It's very scalable as it has a ton of features (but you do need an admin who understands how to leverage these features). Because of the various features, we've also needed to host onboarding sessions with our users so that they can familiarize themselves with the platform, which isn't always super user-friendly or intuitive.
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.
Using Salesforce.com has made my daily routines more efficient and simplified the manual tasks I had to perform independently. I can now access data from any device, online or offline, and provide better guidance to my team about the forecasts provided by the built-in artificial intelligence (AI). A chat with a Salesforce support specialist would be great. The knowledge base has a community forum where Salesforce users can ask questions and learn more about the product.
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.
It allows me to keep a close eye on all of my performance metrics through the Dashboard Reporting, ie what my sales pipeline looks like, how much it's changed in the last 60 days, new opportunities created in the last 7 days, # of emails sent for the week, etc. The ease of the design and output make it really easy to check my progress throughout the day to find where I have holes and am falling short on my personal and work goals. It's resulted in greater transparency with my Mgmt Team and shorter 1-on-1 mtgs with my boss as he can see exactly where I am at all times (to be fair, I'm a senior sales rep, so he pretty much lets me do my job completely unfettered), but it does prove that I am continually producing which recently resulted in a raise I didn't even ask for.
The SF repository is so detailed that I don't have to spend tons of time finding frequently used websites attached to a client or see what all interactions with the company look like. Even though I don't use SF for my bulk emails and email sequences, SF provides me with an email to use in the bcc of these emails which links everything back to SF. I find that extremely helpful. This really impacts my efficiency and I can honestly say that once I started using all the functionality of data management, it saved me about 20% of my time/week that I could then allocate towards other revenue-generating tasks like prospecting and account management. The more time I have for those, the better. My year-over-year on accounts 1 year and older just grew by 17% this last year.
