Apache Spark Reviews & Insights

Score9.2 out of 10

161 Reviews and Ratings

Community insights

TrustRadius Insights for Apache Spark are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

Great Computing Engine: Apache Spark is praised by many users for its capabilities in handling complex transformative logic and sophisticated data processing tasks. Several reviewers have mentioned that it is a great computing engine, indicating its effectiveness in solving intricate problems.

Valuable Insights and Analysis: Many reviewers find Apache Spark to be useful for understanding data and performing data analytical work. They appreciate the valuable insights and analysis capabilities provided by the software, suggesting that it helps them gain deeper understanding of their data.

Extensive Set of Libraries and APIs: The extensive set of libraries and APIs offered by Apache Spark has been highly appreciated by users. It provides a wide range of tools and functionalities to solve various day-to-day problems, making it a versatile choice for different data processing needs.

Apache Spark Reviews

9 Reviews

Engineering

Apache Spark is still a valid DE tool

Rating: 9 out of 10

Incentivized

December 28, 2024

Use Cases and Deployment Scope

We use Apache Spark on a daily basis as the main computation engine for updating most critical and non-critical data pipelines. We mostly work with batch processing but there are instances for using Spark Streaming as well. The scope is for all analysis pipelines, machine learning datasets and several operational use cases.

Pros

Parallel processing
Configurability
Usage with other tools

Cons

More ready-to-use solutions for tweaking the Apache Spark configs
Reduce the creation of UDFs for Pyspark by implementing transformations directly

Likelihood to Recommend

Based on my personal experience, Apache Spark is great when you have the need for highly parallelized jobs and have the time and resources to adapt the configurations for your jobs: for this reason I would not recommend it for companies that do not have a strong group of data engineers that can support other data roles to process data in their company.

Verified User

Employee in Engineering (Consumer Services company, 1001-5000 employees)

Vetted Review

Apache Spark: Lightning-Fast Distributed Computing with a Learning Curve

Rating: 10 out of 10

Incentivized

August 18, 2023

Use Cases and Deployment Scope

If you are working on large and big scale data with analytics - don't go further without the use of Apache Spark! One of the projects that I was involved in using Apache Spark was a Recommendation Systems based project. My area or domain of research expertise is also Recommendation Systems. The deployment of a RecSys along with the use of Apache Spark - functionalities like scalability, flexibility of using various data sources along with fault-tolerant systems - are very easy. The built-in machine learning library MLlib is a boon to work. We don't require any other libraries.

Pros

Fault-tolerant systems: in most cases, no node fails. If it fails - the processing still continues.
Scalable to any extent.
Has built-in machine learning library called - MLlib
Very flexible - data from various data sources can be used. Usage with HDFS is very easy

Cons

Its fully not backward compatible.
It is memory-consuming for heavy and large workloads and datasets
Support for advanced analytics is not available - MLlib has minimalistic analytics.
Deployment is a complex task for beginners.

Likelihood to Recommend

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.

Ananth Gouri

Assistant Professor in Engineering at The National Institute of Engineering, Mysuru (Education Management, 501-1000 employees)

Vetted Review

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Want to save dollars, resources and time processing big data, switch to Apache Spark

Rating: 9 out of 10

Incentivized

March 27, 2019

Use Cases and Deployment Scope

We sold a data science product to one of the leading US-based e-commerce firms. Suddenly, their data started growing at a very fast rate. The product, at this stage, was based on R programming. With such huge data, the product started taking a lot of time. We then started thinking of an alternative to R, to process multiplying big data such as this client has. We eventually came across Apache Spark. With the permission of the client, we started switching the codes from R to Apache Spark. It took a very long time to learn and code in Spark, but it was worth the effort. The R codes, which were taking days of time to run, came down to a few hours.

Pros

Very good tool to process big datasets.
Inbuilt fault tolerance.
Supports multiple languages.
Supports advanced analytics.
A large number of libraries available -- GraphX, Spark SQL, Spark Streaming, etc.

Cons

Very slow with smaller amounts of data.
Expensive, as it stores data in memory.

Likelihood to Recommend

If your data is very huge, I recommend converting the underlying technology into Apache Spark. This will save you a lot of time and effort in the near future due to your growing data. The Apache Spark scalability feature also means it handles all the future data related processing.

Verified User

Engineer in Engineering (Management Consulting company, 51-200 employees)

Vetted Review

Very useful application for Big Data processing and excellent for large volume production workflows

Rating: 10 out of 10

Incentivized

August 28, 2018

Use Cases and Deployment Scope

Apache Spark is being used by the whole organization. It helps us a lot in the transmission of data, as it is 100 times faster than Hadoop MapReduce in memory and 10 times faster in disk, as we work with Java this application. It allows native links for Java programming languages, and as it is compatible with SQL, is completely adapted to the needs of our organization, because of the large amount of information that we use. We highly prefer Apache Spark since it supports in-memory processing to increase performance of big data analysis applications.

Pros

It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.

Cons

Increase the information and trainings that come with the application, especially for debugging since the process is difficult to understand.
It should be more attentive to users and make tutorials, to reduce the learning curve.
There should be more grouping algorithms.

Likelihood to Recommend

It is suitable for processing large amounts of data, as it is very easy to use and its syntax is simple and understandable. I also find it useful to use in a variety of applications without the need to integrate many other processing technologies, and it is very fast and has many machine learning algorithms that can be used for data problems. I find it less appropriate for data that is not so large, as it uses too many resources.

Carla Borges

Consultor Tecnico - Java Developer and Php Developer. in Engineering at Consultec-TI (Computer Software, 51-200 employees)

Vetted Review

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Apache Spark: One stop shop for distributed data processing, machine learning and graph processing

Rating: 10 out of 10

Incentivized

July 20, 2018

Use Cases and Deployment Scope

We use Apache Spark across all analytics departments in the company. We primarily use it for distributed data processing and data preparation for machine learning models. We also use it while running distributed CRON jobs for various analytical workloads. I am familiar with a story where we contributed an algorithm to Spark open source which is on Random Walks in Large Graphs - https://databricks.com/session/random-walks-on-large-scale-graphs-with-apache-spark

Pros

Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
Faster in execution times compare to Hadoop and PIG Latin
Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
Interoperability between SQL and Scala / Python style of munging data

Cons

Documentation could be better as I usually end up going to other sites / blogs to understand the concepts better
More APIs are to be ported to MLlib as only very few algorithms are available at least in clustering segment

Likelihood to Recommend

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.

Nitin Pasumarthy

Software Engineer in Engineering at LinkedIn (Internet, 5001-10,000 employees)

Vetted Review

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Apache Spark, the be all End All.

Rating: 9 out of 10

Incentivized

March 27, 2018

Use Cases and Deployment Scope

Spark was/is being used in myriad of ways. With Kafka, using Spark Streams to grab data from kafka queue into our hdfs environment. SparkSQL used for analysis of data for those not familiar with spark. Using Spark for data analysis as well and for main workflow process. Using spark over mapreduce. Using Spark for some machine learning algo's with the data.

Pros

Machine Learning.
Data Analysis
WorkFlow process (faster than MapReduce).
SQL connector to multiple data sources

Cons

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

Likelihood to Recommend

Spark is great as a workflow process and extract transform layer process tool. Is really good for machine learning especially for large datasets that can be processed in split file paralallelization.
Spark streaming is scalable for close to real-time data workflow process.
what it's not good for, is smaller subset of data processing.

Anson Abraham

Data Czar in Engineering at Envisagenics, Inc. (Marketing and Advertising, 51-200 employees)

Vetted Review

View profile

Use Apache Spark to Speed Up Cluster Computing

Rating: 7 out of 10

Incentivized

January 23, 2018

Use Cases and Deployment Scope

In our company, we used Spark for a healthcare analytical project, where we need to do large-scale data processing in a Hadoop environment. The project is about building an enterprise data lake where we bring data from multiple products and consolidate. Further, in the downstream, we will develop some business reports.

Pros

We used to make our batch processing faster. Spark is faster in batch processing than MapReduce with it in memory computing
Spark will run along with other tools in the Hadoop ecosystem including Hive and Pig
Spark supports both batch and real-time processing
Apache Spark has Machine Learning Algorithms support

Cons

Consumes more memory
Difficult to address issues around memory utilization
Expensive - In-memory processing is expensive when we look for a cost-efficient processing of big data

Likelihood to Recommend

Well suited:
1. Data can be integrated from several sources including click stream, logs, transactional systems
2. Real-time ingestion through Kafka, Kinesis, and other streaming platforms

Verified User

Employee in Engineering (Hospital & Health Care company, 501-1000 employees)

Vetted Review

Apache Spark Should Spark Your Interest

Rating: 9 out of 10

Incentivized

December 13, 2017

Use Cases and Deployment Scope

At my current company, we are using Spark in a variety of ways ranging from batch processing to data analysis to machine learning techniques. It has become our main driver for any distributed processing applications. It has gained quick adoption across the organization for its ease of use, integration into the Hadoop stack, and for its support in a variety of languages.

Pros

Ease of use, the Spark API allows for minimal boilerplate and can be written in a variety of languages including Python, Scala, and Java.
Performance, for most applications we have found that jobs are more performant running via Spark than other distributed processing technologies like Map-Reduce, Hive, and Pig.
Flexibility, the frameworks comes with support for streaming, batch processing, sql queries, machine learning, etc. It can be used in a variety of applications without needing to integrate a lot of other distributed processing technologies.

Cons

Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.

Likelihood to Recommend

If you are running a distributed environment and are running applications that make use of batch processing, analytics, streaming, machine learning, or graphing then I cannot recommend Spark enough. It is easy to get going, simple to learn (relative to similar technologies), and can be used in a variety of use cases. All while giving you great performance.

Verified User

Engineer in Engineering (Computer Software company, 51-200 employees)

Vetted Review

Apache Spark if great for high volume production workflows

Rating: 10 out of 10

Incentivized

August 2, 2017

Use Cases and Deployment Scope

We use it primarily in our department as part of a machine learning and data processing platform to build enterprise scale predictive applications.

Pros

Great APIs and tools.
Scale.
Speed for iterative algorithms.

Cons

No true streaming.
Lack of strongly typed yet convenient APIs.

Likelihood to Recommend

Well suited for batch and near-real time data processing tasks as well as production deployments of machine learning, especially at large scale. Not well suited for general analytics workflows for small and medium sized data sets; SQL based data warehouses like Redshift, Vertica, and etc. are better for those use cases.

Verified User

Director in Engineering (Computer Software company, 10,001+ employees)

Vetted Review

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Very useful application for Big Data processing and excellent for large volume production workflows

Rating: 10 out of 10

Incentivized

August 28, 2018

Use Cases and Deployment Scope

Pros

It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.

Cons

Increase the information and trainings that come with the application, especially for debugging since the process is difficult to understand.
It should be more attentive to users and make tutorials, to reduce the learning curve.
There should be more grouping algorithms.

Likelihood to Recommend

Carla Borges

Consultor Tecnico - Java Developer and Php Developer. in Engineering at Consultec-TI (Computer Software, 51-200 employees)

Vetted Review

View profile