Apache Spark vs. Redis™*

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Redis™*
Score 9.0 out of 10
N/A
Redis is an open source in-memory data structure server and NoSQL database.
$388
per month
Pricing
Apache SparkRedis™*
Editions & Modules
No answers on this topic
Cloud
$388.00
per month
Offerings
Pricing Offerings
Apache SparkRedis™*
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Features
Apache SparkRedis™*
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
Apache Spark
-
Ratings
Redis™*
9.2
69 Ratings
5% above category average
Performance00 Ratings10.069 Ratings
Availability00 Ratings9.069 Ratings
Concurrency00 Ratings9.068 Ratings
Security00 Ratings8.063 Ratings
Scalability00 Ratings9.469 Ratings
Data model flexibility00 Ratings9.962 Ratings
Deployment model flexibility00 Ratings9.362 Ratings
Best Alternatives
Apache SparkRedis™*
Small Businesses

No answers on this topic

IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
IBM Cloudant
IBM Cloudant
Score 8.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkRedis™*
Likelihood to Recommend
9.9
(24 ratings)
7.8
(76 ratings)
Likelihood to Renew
10.0
(1 ratings)
8.7
(12 ratings)
Usability
10.0
(3 ratings)
8.2
(5 ratings)
Support Rating
8.7
(4 ratings)
8.7
(5 ratings)
Implementation Rating
-
(0 ratings)
7.3
(1 ratings)
User Testimonials
Apache SparkRedis™*
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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Redis Labs
Redis has been a great investment for our organization as we needed a solution for high speed data caching. The ramp up and integration was quite easy. Redis handles automatic failover internally, so no crashes provides high availability. On the fly scaling scale to more/less cores and memory as and when needed.
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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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Redis Labs
  • Easy for developers to understand. Unlike Riak, which I've used in the past, it's fast without having to worry about eventual consistency.
  • Reliable. With a proper multi-node configuration, it can handle failover instantly.
  • Configurable. We primarily still use Memcache for caching but one of the teams uses Redis for both long-term storage and temporary expiry keys without taking on another external dependency.
  • Fast. We process tens of thousands of RPS and it doesn't skip a beat.
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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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Redis Labs
  • We had some difficulty scaling Redis without it becoming prohibitively expensive.
  • Redis has very simple search capabilities, which means its not suitable for all use cases.
  • Redis doesn't have good native support for storing data in object form and many libraries built over it return data as a string, meaning you need build your own serialization layer over it.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Redis Labs
We will definitely continue using Redis because: 1. It is free and open source. 2. We already use it in so many applications, it will be hard for us to let go. 3. There isn't another competitive product that we know of that gives a better performance. 4. We never had any major issues with Redis, so no point turning our backs.
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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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Redis Labs
It is quite simple to set up for the purpose of managing user sessions in the backend. It can be easily integrated with other products or technologies, such as Spring in Java. If you need to actually display the data stored in Redis in your application this is a bit difficult to understand initially but is possible.
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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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Redis Labs
The support team has always been excellent in handling our mostly questions, rarely problems. They are responsive, find the solution and get us moving forward again. I have never had to escalate a case with them. They have always solved our problems in a very timely manner. I highly commend the support team.
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Implementation Rating
Apache
No answers on this topic
Redis Labs
Whitelisting of the AWS lambda functions.
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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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Redis Labs
We are big users of MySQL and PostgreSQL. We were looking at replacing our aging web page caching technology and found that we could do it in SQL, but there was a NoSQL movement happening at the time. We dabbled a bit in the NoSQL scene just to get an idea of what it was about and whether it was for us. We tried a bunch, but I can only seem to remember Mongo and Couch. Mongo had big issues early on that drove us to Redis and we couldn't quite figure out how to deploy couch.
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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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Redis Labs
  • Redis has helped us increase our throughput and server data to a growing amount of traffic while keeping our app fast. We couldn't have grown without the ability to easily cache data that Redis provides.
  • Redis has helped us decrease the load on our database. By being able to scale up and cache important data, we reduce the load on our database reducing costs and infra issues.
  • Running a Redis node on something like AWS can be costly, but it is often a requirement for scaling a company. If you need data quickly and your business is already a positive ROI, Redis is worth the investment.
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ScreenShots

Redis™* Screenshots

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