Compare Apache Beam and Hadoop HDFS 
- Users report that Hadoop HDFS excels in data distribution with a score of 9.1, making it a preferred choice for enterprises needing robust data management solutions. In contrast, Apache Beam scores lower at 8.0, which may limit its effectiveness in large-scale data distribution scenarios.
- Reviewers mention that Hadoop HDFS offers superior real-time data collection capabilities, scoring 8.5, which is crucial for businesses that rely on immediate data insights. Apache Beam, while still competent, scores 7.7, indicating it may not be as effective for real-time applications.
- G2 users highlight that Hadoop HDFS has strong integration capabilities, particularly with Hadoop itself, scoring 9.1. This is a significant advantage for organizations already using Hadoop ecosystems. Conversely, Apache Beam's integration with Hadoop is rated at 6.0, suggesting it may not be as seamless for users in that environment.
- Users on G2 report that both platforms have similar scores in machine scaling, with Hadoop HDFS at 8.4 and Apache Beam at 8.3. However, users mention that Hadoop HDFS's scaling capabilities are more mature, making it a more reliable choice for enterprises with fluctuating workloads.
- Reviewers say that while both products offer data preparation features, Hadoop HDFS scores slightly higher at 8.6 compared to Apache Beam's 8.1. This indicates that Hadoop HDFS may provide more comprehensive tools for preparing data for analysis.
- Users report that the quality of support for Hadoop HDFS is rated at 7.8, slightly better than Apache Beam's 7.6. This suggests that users may find more responsive and effective support when using Hadoop HDFS, which can be critical for troubleshooting and implementation challenges.
I have used Apache Beam to design my pipeline on Google cloud platform. My experience was seamless. I have designed for both batch and streaming data.
deployment and production instructions and case studies should be improved
1.Storing of file in sequential format, using key value pair.- Stores file as key and content as value and encrypts them. 2.128 mb block size.- Previously it was 64 which was less. But still some people like the old block size. 3.storing multiple copies...
Inflexible, data needs to be copied to HDFS from other places, one cannot do real-time access from HDFS. This survey is not well-written if its primarily HDFS that you need feedback on.
I have used Apache Beam to design my pipeline on Google cloud platform. My experience was seamless. I have designed for both batch and streaming data.
1.Storing of file in sequential format, using key value pair.- Stores file as key and content as value and encrypts them. 2.128 mb block size.- Previously it was 64 which was less. But still some people like the old block size. 3.storing multiple copies...
deployment and production instructions and case studies should be improved
Inflexible, data needs to be copied to HDFS from other places, one cannot do real-time access from HDFS. This survey is not well-written if its primarily HDFS that you need feedback on.
