We performed a comparison between Apache Spark Streaming and Databricks based on real PeerSpot user reviews.
Find out in this report how the two Streaming Analytics solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution is better than average and some of the valuable features include efficiency and stability."
"The solution is very stable and reliable."
"As an open-source solution, using it is basically free."
"It's the fastest solution on the market with low latency data on data transformations."
"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."
"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."
"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."
"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"Databricks integrates well with other solutions."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"Its lightweight and fast processing are valuable."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"The integration with Python and the notebooks really helps."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"The solution is very easy to use."
"It was resource-intensive, even for small-scale applications."
"The initial setup is quite complex."
"We would like to have the ability to do arbitrary stateful functions in Python."
"In terms of improvement, the UI could be better."
"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."
"The solution itself could be easier to use."
"The cost and load-related optimizations are areas where the tool lacks and needs improvement."
"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."
"In the next release, I would like to see more optimization features."
"The ability to customize our own pipelines would enhance the product, similar to what's possible using ML files in Microsoft Azure DevOps."
"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."
"The Databricks cluster can be improved."
"There is room for improvement in visualization."
"It should have more compatible and more advanced visualization and machine learning libraries."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"A lot of people are required to manage this solution."
Apache Spark Streaming is ranked 8th in Streaming Analytics with 8 reviews while Databricks is ranked 1st in Streaming Analytics with 78 reviews. Apache Spark Streaming is rated 8.0, while Databricks is rated 8.2. The top reviewer of Apache Spark Streaming writes "Easy integration, beneficial auto-scaling, and good open-sourced support community". On the other hand, the top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". Apache Spark Streaming is most compared with Amazon Kinesis, Azure Stream Analytics, Spring Cloud Data Flow, Confluent and Amazon MSK, whereas Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio. See our Apache Spark Streaming vs. Databricks report.
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