Cloudera DataFlow is a scalable data integration platform offering high performance through native connections with Cloudera ecosystems like Hive, Impala, and Spark, facilitating robust data management and analytics.
| Product | Mindshare (%) |
|---|---|
| Cloudera DataFlow | 2.0% |
| Apache Flink | 8.9% |
| Databricks | 8.1% |
| Other | 81.0% |
Cloudera DataFlow excels in delivering comprehensive data analysis with end-to-end workflow scheduling and stands out for its high throughput and effective integration capabilities. However, users note areas needing improvement, such as transformation coding complexity, limited language support, and memory handling. While it plays an essential ETL or ELT role in Cloudera's data pipeline, providing seamless data ingestion, transformation, and warehousing, the platform's restriction to its environment and the setup's complexity remain points of user concern.
What are the key features of Cloudera DataFlow?Industries use Cloudera DataFlow for applications like sentiment analysis, fraud detection, and product royalty analysis. It is widely deployed for stream analytics and module development in telecommunications, functioning as a critical tool for data ingestion and transformation, ensuring efficient operational tasks.
Cloudera DataFlow was previously known as CDF, Hortonworks DataFlow, HDF.
Clearsense
| Author info | Rating | Review Summary |
|---|---|---|
| Senior Data Architect at Teradata Corporation | 4.0 | I use Cloudera DataFlow as an ETL solution for data ingestion and transformation within Cloudera's ecosystem, appreciating its native connectivity with components like Hive and Spark. Although the UI and memory handling need improvement, its integration benefits outweigh alternatives like Informatica. |
| Consultant at a government with 10,001+ employees | 2.5 | I find Cloudera DataFlow's performance satisfactory. However, it feels restrictive as it requires keeping data within its closed environment, with no options for working with external virtual data. I haven't explored other solutions or cloud providers yet. |
| Manager at a tech services company with 201-500 employees | 4.0 | I use Cloudera DataFlow for analysis and fraud detection. It's scalable and robust, improving data processing. However, it requires significant transformation coding. Support is fair, and I recommend ROI analysis before committing. I rate it an 8. |
| CEO at AM-BITS LLC | 4.5 | I use Cloudera DataFlow primarily for stream analytics. The most effective features are data management and analytics, though I believe the setup process could be simpler. I haven't considered or used any alternative solutions or cloud providers. |
| Data Scientist at Orys | 3.5 | I use Cloudera DataFlow to develop quality modules for telecommunication companies. I utilize all features for data analysis, though not machine learning. Improvement is needed in using the R language as it's challenging and not ideal for machine learning. |
