RameshChSr. BigData Architect at ITC Infotech
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The solution is better than average and some of the valuable features include efficiency and stability."
"The solution is very stable and reliable."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"Automation with Databricks is very easy when using the API."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."
"I work in the data science field and I found Databricks to be very useful."
"The time travel feature is the solution's most valuable aspect."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"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 integration features could be more interesting, more involved."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"It should have more compatible and more advanced visualization and machine learning libraries."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"It would be very helpful if Databricks could integrate with platforms in addition to Azure."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"Pricing is one of the things that could be improved."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."
"I do not exactly know the costs, but one of our clients pays between $100 USD and $200 USD monthly."
"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."
"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."
"The pricing depends on the usage itself."
"I am based in South Africa, where it is expensive adapting to the cloud, and then there is the price for the tool itself."
"The price is okay. It's competitive."
"Databricks uses a price-per-use model, where you can use as much compute as you need."
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
Databricks creates a Unified Analytics Platform that accelerates innovation by unifying data science, engineering, and business. It utilizes Apache Spark to help clients with cloud-based big data processing. It puts Spark on “autopilot” to significantly reduce operational complexity and management cost. The Databricks I/O module (DBIO) improves the read and write performance of Apache Spark in the cloud. An increase in productivity is ensured through Databricks’ collaborative workplace.
Apache Spark Streaming is ranked 9th in Streaming Analytics with 2 reviews while Databricks is ranked 1st in Streaming Analytics with 24 reviews. Apache Spark Streaming is rated 7.6, while Databricks is rated 8.0. The top reviewer of Apache Spark Streaming writes "Mature and stable with good scalability". On the other hand, the top reviewer of Databricks writes "Has a good feature set but it needs samples and templates to help invite users to see results". Apache Spark Streaming is most compared with Amazon Kinesis, Azure Stream Analytics, Spring Cloud Data Flow, Talend Data Streams and IBM Streams, whereas Databricks is most compared with Microsoft Azure Machine Learning Studio, Amazon SageMaker, Azure Stream Analytics, Alteryx and Dataiku Data Science Studio. See our Apache Spark Streaming vs. Databricks report.
See our list of best Streaming Analytics vendors.
We monitor all Streaming Analytics 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.