Cancel
You must select at least 2 products to compare!
Apache Spark Logo
10,189 views|8,201 comparisons
QueryIO Logo
36 views|8 comparisons
Top Review
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: September 2021.
534,226 professionals have used our research since 2012.
Quotes From Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros
"The most valuable feature of this solution is its capacity for processing large amounts of data.""The solution is very stable.""I feel the streaming is its best feature.""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.""The processing time is very much improved over the data warehouse solution that we were using.""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.""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."

More Apache Spark Pros »

"Anyone who has even a little bit of knowledge of the solution can begin to create things. You don't have to be technical to use the solution."

More QueryIO Pros »

Cons
"When you first start using this solution, it is common to run into memory errors when you are dealing with large amounts of data.""The solution needs to optimize shuffling between workers.""When you want to extract data from your HDFS and other sources then it is kind of tricky because you have to connect with those sources.""We've had problems using a Python process to try to access something in a large volume of data. It crashes if somebody gives me the wrong code because it cannot handle a large volume of data.""We use big data manager but we cannot use it as conditional data so whenever we're trying to fetch the data, it takes a bit of time.""I would like to see integration with data science platforms to optimize the processing capability for these tasks.""The graphical user interface (UI) could be a bit more clear. It's very hard to figure out the execution logs and understand how long it takes to send everything. If an execution is lost, it's not so easy to understand why or where it went. I have to manually drill down on the data processes which takes a lot of time. Maybe there could be like a metrics monitor, or maybe the whole log analysis could be improved to make it easier to understand and navigate.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing."

More Apache Spark Cons »

"There needs to be some simplification of the user interface."

More QueryIO Cons »

Pricing and Cost Advice
"Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."

More Apache Spark Pricing and Cost Advice »

Information Not Available
report
Use our free recommendation engine to learn which Hadoop solutions are best for your needs.
534,226 professionals have used our research since 2012.
Questions from the Community
Top Answer: I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
Top Answer: The solution has been very stable.
Top Answer: We use the open-source version. It is free to use. However, you do need to have servers. We have three or four. they can be on-premises or in the cloud.
Ask a question

Earn 20 points

Ranking
1st
out of 22 in Hadoop
Views
10,189
Comparisons
8,201
Reviews
11
Average Words per Review
472
Rating
8.6
7th
out of 22 in Hadoop
Views
36
Comparisons
8
Reviews
1
Average Words per Review
1,164
Rating
8.0
Comparisons
Learn More
QueryIO
Video Not Available
Overview

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 across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

QueryIO is a Hadoop-based SQL and Big Data Analytics solution, used to store, structure, analyze and visualize vast amounts of structured and unstructured Big Data. It is especially well suited to enable users to process unstructured Big Data, give it a structure and support querying and analysis of this Big Data using standard SQL syntax. QueryIO enables you to leverage the vast and mature infrastructure built around SQL and relational databases and utilize it for your Big Data Analytics needs.
Offer
Learn more about Apache Spark
Learn more about QueryIO
Sample Customers
NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
Information Not Available
Top Industries
REVIEWERS
Financial Services Firm40%
Computer Software Company20%
Marketing Services Firm10%
Non Profit10%
VISITORS READING REVIEWS
Computer Software Company23%
Comms Service Provider19%
Financial Services Firm10%
Media Company9%
No Data Available
Company Size
REVIEWERS
Small Business38%
Midsize Enterprise21%
Large Enterprise41%
No Data Available
Find out what your peers are saying about Apache, Cloudera, IBM and others in Hadoop. Updated: September 2021.
534,226 professionals have used our research since 2012.

Apache Spark is ranked 1st in Hadoop with 11 reviews while QueryIO is ranked 7th in Hadoop with 1 review. Apache Spark is rated 8.6, while QueryIO is rated 8.0. The top reviewer of Apache Spark writes "Good Streaming features enable to enter data and analysis within Spark Stream". On the other hand, the top reviewer of QueryIO writes "Stable with good connectivity and good integration capabilities". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Batch, AWS Lambda and SAP HANA, whereas QueryIO is most compared with .

See our list of best Hadoop vendors.

We monitor all Hadoop reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.