The Heroku Platform, now from Salesforce, is a platform-as-a-service based on
a managed container system, with integrated data services and ecosystem for deploying modern apps. It takes an app-centric
approach for software delivery, integrated with developer tools and
workflows. It’s three main tool are: Heroku Developer Experience (DX), Heroku
Operational Experience (OpEx), and Heroku Runtime.
Heroku Developer Experience (DX)
Developers deploy directly from tools like…
$85
per month
Snowflake
Score 9.0 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.
Heroku is very well suited for startups looking to get a server stack up and running quickly. There is little to no overhead when managing your instances. However, you'll need a background in basic DevOps or system management to make sure everything is set up correctly. In addition, it's easy to accidentally go crazy on pricing. Make sure you're only creating the server instances you need to run the base application and set up an auto-scaler plugin to handle peaks.
I am over our HR data, and we use Workday for our HR management system. I have a script in place that runs reports on Workday and saves the results as CSVs. I can then use stages in Snowflake to insert these CSVs into Snowflake, then I can insert or truncate and replace these staged tables into a final schema. Then once these are in a schema I can reference them and build out my data models. In addition to ingesting CSVs, Snowflake has the ability to write a CSV file to our Amazon S3 bucket. Ingesting these CSVs, transforming the data, then delivering it to a destination would've involved so much more coding than my current process if we were on any other platform.
Heroku has a very simple deployment model, making it easy to get your application up-and-running with minimal effort. We can focus on our efforts the unique aspects of our application.
The robust add-on marketplace makes it easy to try out new approaches with minimal effort and investment -- and when we settle on a solution, we can easily scale it.
Heroku's support is quite good -- their staff is quite technical and willing to get into the weeds to diagnose even complicated problems.
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
Large price jumps between certain resource tiers (2x Dyno for $50 per month versus Performance Dyno for $250). Free Postgres next jumps to $50 per month.
Marketing/Branding to non-technical stakeholders. As the years pass, I've had to fight more to convince stakeholders on the value of Heroku over AWS.
Improve Buildpack documentation. This is one area where Heroku's documentation is fairly confusing.
This tool is very much technical and proper knowledge is required, so mostly you have to hire an IT team.
I wish if various videos could be available for basic quires like its initiation, then I think it would act as a guideline and would help the beginners a lot.
Heroku is easy to use, services a ton of functions for you out of the box, and provides a means to get a software product off the ground and managed quickly and easily. The tools provide allows a small to medium size org to move very quickly. The CLI tools provided make managing an entire technical infrastructure simple.
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
Easy to use web based console and easy to use command line tools; deployment is done directly from a GIT repository. What more could you ask for? The one thing that keeps me from giving it a 10 is that custom build packs are almost incomprehensible. We used one for a while because we needed cairo graphics processing. Fortunately, I was able to figure out a different way to do what we needed so that we could get off the custom build pack.
The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
Heroku availability correlates pretty strongly to AWS US EAST availability. We had a couple of times where there was a Heroku-specific issue but not for the last 7-8 months.
I've used it for many years without facing any major problem. It's not hard at all to get used to it, it's documentation is outstanding and simple. We are close to 2020 and I don't think most of the existing companies or startups should still face old problems such as wasting time deploying code and calculate computing resources.
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.
Be ready to pay a bit more than expected in the beginning if you're migrating from a big server. The application is probably not ready for the change and you have to keep improving it with time.
It's also important to consider that you can't save anything to the disc as it will be lost when your application restarts, so you have to think about using something like S3.
Heroku is the more expensive option for hosting compared to some of the cloud platforms we investigated, but it's worth it for us because of the plug-and-play nature of Heroku deployment. We can be up and running in a few minutes and know with precision how much it will cost us each month to run the application, unlike Amazon Web Services where you have to go to great pains to configure it correctly or else you might end up with a shocking monthly bill. Overall, spending the time to configure Amazon Web Services or one of its competitors is likely the more affordable and powerful choice, because you have control over so many specifics of the configuration. But it also requires the burden of continuing to maintain and update your AWS instance, whereas with Heroku they take care of security fixes and platform upgrades. It's a great service and we are happy to pay the extra cost for the value-adds Heroku provides.
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.
Positive impact: we use Snowflake to track our subscription and payment charges, which we use for internal and investor reporting
Positive impact: 3 times faster query speed compared to Treasure Data means that answers to stakeholders can be delivered quicker by analysts
Positive impact: recommender systems now source their data from Snowflake rather than Spark clusters, improving development speed, and no longer require maintainence of Spark clusters.
