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Apache Spark Streaming vs Databricks comparison

 

Comparison Buyer's Guide

Executive Summary

Review summaries and opinions

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

Customer Service

No sentiment score available
Sentiment score
6.8
Databricks support is praised for technical expertise and engagement, but experiences vary due to response times and Microsoft partner handling.
 

Room For Improvement

No sentiment score available
Sentiment score
5.8
Databricks faces challenges with visualization, integration, costs, error clarity, libraries, interfaces, documentation, onboarding, automation, governance, and performance.
 

Scalability Issues

No sentiment score available
Sentiment score
8.2
Databricks offers significant, praised scalability from megabytes to petabytes, supporting vertical and horizontal scaling with auto-scaling features.
 

Setup Cost

No sentiment score available
Sentiment score
7.0
Databricks pricing varies greatly based on usage and cluster type, often considered expensive with additional cloud storage costs.
 

Stability Issues

No sentiment score available
Sentiment score
7.7
Databricks is highly stable and reliable, with minimal issues reported, especially during heavy processes, and receives high user ratings.
 

Valuable Features

No sentiment score available
Sentiment score
8.5
Databricks offers user-friendly large-scale analytics, seamless integration, versatile coding, collaborative tools, and efficient big data handling with extensive cloud support.
 

Categories and Ranking

Apache Spark Streaming
Ranking in Streaming Analytics
9th
Average Rating
8.0
Reviews Sentiment
6.2
Number of Reviews
10
Ranking in other categories
No ranking in other categories
Databricks
Ranking in Streaming Analytics
1st
Average Rating
8.2
Reviews Sentiment
7.4
Number of Reviews
84
Ranking in other categories
Data Science Platforms (1st)
 

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 3.7%, down from 4.7% compared to the previous year. The mindshare of Databricks is 14.0%, up from 9.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Streaming Analytics
 

Featured Reviews

Oscar Estorach - PeerSpot reviewer
Versatile and flexible when dealing with large-scale data streams
What I like about Spark is its versatility in supporting multiple languages and that makes it my preferred choice for building scalable and efficient systems, whether it is hooking databases with web applications or handling large-scale data transformations. Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows. It works well in the cloud, and you can structure data using Databricks or Spark, providing flexibility for different projects. Spark Streaming's flexibility shines when dealing with large-scale data streams. It caters to different needs, offering real-time insights for tasks like online sales analytics. The ability to prioritize data streams is valuable, especially for monitoring competitor prices online.
Dunstan Matekenya - PeerSpot reviewer
Process large-scale data sets and integrates with Apache Spark with notebook environment
Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.
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Top Industries

By visitors reading reviews
Financial Services Firm
22%
Computer Software Company
20%
University
6%
Manufacturing Company
6%
Financial Services Firm
16%
Computer Software Company
12%
Manufacturing Company
9%
Healthcare Company
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Apache Spark Streaming?
Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.
What needs improvement with Apache Spark Streaming?
The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of exp...
What is your primary use case for Apache Spark Streaming?
I've used it more for ETL. It's useful for creating data pipelines, streaming datasets, generating synthetic data, synchronizing data, creating data lakes, and loading and unloading data is fast an...
Which do you prefer - Databricks or Azure Machine Learning Studio?
Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...
How would you compare Databricks vs Amazon SageMaker?
We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...
Which would you choose - Databricks or Azure Stream Analytics?
Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...
 

Also Known As

Spark Streaming
Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash
 

Learn More

 

Overview

 

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware
Find out what your peers are saying about Apache Spark Streaming vs. Databricks and other solutions. Updated: October 2024.
816,660 professionals have used our research since 2012.