One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast.
Senior Solutions Architect at a retailer with 10,001+ employees
Real User
2021-03-27T15:39:24Z
Mar 27, 2021
I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.
Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica.
Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark.
AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.
The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.
I love every core functionality of Apache Spark Initially they have only provided RDD basic interface to process the data across distributed cluster. Then it evolved to dataframe and dataset interface with optimised execution engine and more flexibility for developers to perform querying on the data.
Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function...
The product’s most valuable features are lazy evaluation and workload distribution.
The deployment of the product is easy.
We use Spark to process data from different data sources.
We use it for ETL purposes as well as for implementing the full transformation pipelines.
The most crucial feature for us is the streaming capability. It serves as a fundamental aspect that allows us to exert control over our operations.
The most valuable feature of Apache Spark is its flexibility.
The data processing framework is good.
The distribution of tasks, like the seamless map-reduce functionality, is quite impressive.
Provides a lot of good documentation compared to other solutions.
There's a lot of functionality.
The most valuable feature of Apache Spark is its ease of use.
One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast.
Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.
Spark can handle small to huge data and is suitable for any size of company.
Apache Spark can do large volume interactive data analysis.
The solution has been very stable.
I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.
Its scalability and speed are very valuable. You can scale it a lot. It is a great technology for big data. It is definitely better than a lot of earlier warehouse or pipeline solutions, such as Informatica.
Spark SQL is very compliant with normal SQL that we have been using over the years. This makes it easy to code in Spark. It is just like using normal SQL. You can use the APIs of Spark or you can directly write SQL code and run it. This is something that I feel is useful in Spark.
AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.
The memory processing engine is the solution's most valuable aspect. It processes everything extremely fast, and it's in the cluster itself. It acts as a memory engine and is very effective in processing data correctly.
I love every core functionality of Apache Spark Initially they have only provided RDD basic interface to process the data across distributed cluster. Then it evolved to dataframe and dataset interface with optimised execution engine and more flexibility for developers to perform querying on the data.
The processing time is very much improved over the data warehouse solution that we were using.
The features we find most valuable are the machine learning, data learning, and Spark Analytics.
The main feature that we find valuable is that it is very fast.
I feel the streaming is its best feature.
The solution is very stable.
The most valuable feature of this solution is its capacity for processing large amounts of data.
The scalability has been the most valuable aspect of the solution.
I found the solution stable. We haven't had any problems with it.
Features include machine learning, real time streaming, and data processing.