Apache Spark vs. Firebase

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Firebase
Score 8.6 out of 10
N/A
Google offers the Firebase suite of application development tools, available free or at cost for higher degree of usages, priced flexibly accorded to features needed. The suite includes A/B testing and Crashlytics, Cloud Messaging (FCM) and in-app messaging, cloud storage and NoSQL storage (Cloud Firestore and Firestore Realtime Database), and other features supporting developers with flexible mobile application development.
$0.01
Per Verification
Pricing
Apache SparkFirebase
Editions & Modules
No answers on this topic
Phone Authentication
$0.01
Per Verification
Stored Data
$0.18
Per GiB
Offerings
Pricing Offerings
Apache SparkFirebase
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Best Alternatives
Apache SparkFirebase
Small Businesses

No answers on this topic

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Score 9.3 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Zoho Creator
Zoho Creator
Score 9.3 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
Quickbase
Quickbase
Score 9.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkFirebase
Likelihood to Recommend
9.8
(24 ratings)
8.8
(27 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
9.5
(2 ratings)
Support Rating
8.7
(4 ratings)
7.3
(6 ratings)
User Testimonials
Apache SparkFirebase
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Google
Firebase should be your first choice if your platform is mobile first. Firebase's mobile platform support for client-side applications is second to none, and I cannot think of a comparable cross-platform toolkit. Firebase also integrates well with your server-side solution, meaning that you can plug Firebase into your existing app architecture with minimal effort.
Firebase lags behind on the desktop, however. Although macOS support is rapidly catching up, full Windows support is a glaring omission for most Firebase features. This means that if your platform targets Windows, you will need to implement the client functionality manually using Firebase's web APIs and wrappers, or look for another solution.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Google
  • Analytics wise, retention is extremely important to our app, therefore we take advantage of the cohort analysis to see the impact of our middle funnel (retargeting, push, email) efforts affect the percent of users that come back into the app. Firebase allows us to easily segment these this data and look at a running average based on certain dates.
  • When it comes to any mobile app, a deep linking strategy is essential to any apps success. With Firebase's Dynamic Links, we are able to share dynamic links (recognize user device) that are able to redirect to in-app content. These deep links allow users to share other deep-linked content with friends, that also have link preview assets.
  • Firebase allows users to effectively track events, funnels, and MAUs. With this simple event tracking feature, users can put organize these events into funnels of their main user flows (e.g., checkout flows, onboarding flows, etc.), and subsequently be able to understand where the drop-off is in the funnel and then prioritize areas of the funnel to fix. Also, MAU is important to be able to tell if you are bringing in new users and what's the active volume for each platform (Android, iOS).
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Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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Google
  • Attribution and specifically multi-touch attribution could be more robust such as Branch or Appsflyer but understand this isn't Firebases bread and butter.
  • More parameters. Firebase allows you to track tons of events (believe it's up to 50 or so) but the parameters of the events it only allows you to track 5 which is so messily and unbelievable. So you're able to get good high-level data but if you want to get granular with the events and actions are taken on your app to get real data insight you either have to go with a paid data analytics platform or bring on someone that's an expert in SQL to go through Big Query.
  • City-specific data instead of just country-specific data would have been a huge plus as well.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Google
No answers on this topic
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Google
It is simple to use overall, the console's main menu is divided into Develop, Quality, Analytics and Grow - which have further subdivisions by their set of features and tools. Develop and Quality are relevant for product and tech. Analytics is relevant for product, analytics and Grow is relevant for marketing. This makes the overall use very easy.
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Support Rating
Apache
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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Google
Our analytics folks handled the majority of the communication when it came to customer service, but as far as I was aware, the support we got was pretty good. When we had an issue, we were able to reach out and get support in a timely fashion. Firebase was easy to reach and reasonably available to assist when needed.
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Google
Before using Firebase, we exclusively used self hosted database services. Using Firebase has allowed us to reduce reliance on single points of failure and systems that are difficult to scale. Additionally, Firebase is much easier to set up and use than any sort of self hosted database. This simplicity has allowed us to try features that we might not have based on the amount of work they required in the past.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Google
  • Makes building real-time interfaces easy to do at scale with no backend involvement.
  • Very low pricing for small companies and green-fields projects.
  • Lack of support for more complicated queries needs to be managed by users and often forces strange architecture choices for data to enable it to be easily accessed.
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