102 Ratings
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Score 8.5 out of 101
12 Ratings
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Score 8.6 out of 101

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Likelihood to Recommend

Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Thomas Young profile photo

Databricks Unified Analytics Platform

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.
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Feature Rating Comparison

Platform Connectivity

Apache Spark
Databricks Unified Analytics Platform
8.3
Connect to Multiple Data Sources
Apache Spark
Databricks Unified Analytics Platform
9.0
Extend Existing Data Sources
Apache Spark
Databricks Unified Analytics Platform
9.0
Automatic Data Format Detection
Apache Spark
Databricks Unified Analytics Platform
7.0

Data Exploration

Apache Spark
Databricks Unified Analytics Platform
6.0
Visualization
Apache Spark
Databricks Unified Analytics Platform
6.0
Interactive Data Analysis
Apache Spark
Databricks Unified Analytics Platform
6.0

Data Preparation

Apache Spark
Databricks Unified Analytics Platform
8.0
Interactive Data Cleaning and Enrichment
Apache Spark
Databricks Unified Analytics Platform
8.0
Data Transformations
Apache Spark
Databricks Unified Analytics Platform
9.0
Data Encryption
Apache Spark
Databricks Unified Analytics Platform
7.0
Built-in Processors
Apache Spark
Databricks Unified Analytics Platform
8.0

Platform Data Modeling

Apache Spark
Databricks Unified Analytics Platform
8.3
Multiple Model Development Languages and Tools
Apache Spark
Databricks Unified Analytics Platform
9.0
Automated Machine Learning
Apache Spark
Databricks Unified Analytics Platform
8.0
Single platform for multiple model development
Apache Spark
Databricks Unified Analytics Platform
9.0
Self-Service Model Delivery
Apache Spark
Databricks Unified Analytics Platform
7.0

Model Deployment

Apache Spark
Databricks Unified Analytics Platform
7.5
Flexible Model Publishing Options
Apache Spark
Databricks Unified Analytics Platform
7.0
Security, Governance, and Cost Controls
Apache Spark
Databricks Unified Analytics Platform
8.0

Pros

  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
Nitin Pasumarthy profile photo
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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Cons

  • Documentation could be better as I usually end up going to other sites / blogs to understand the concepts better
  • More APIs are to be ported to MLlib as only very few algorithms are available at least in clustering segment
Nitin Pasumarthy profile photo
  • Better Localized Testing
  • When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
  • Graphing Support went non-existent; when it was one of their compelling general engine.
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Usability

No score
No answers yet
No answers on this topic
Databricks Unified Analytics Platform9.0
Based on 1 answer
This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent
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Alternatives Considered

I prefer Apache Spark compared to Hadoop, since in my experience Spark has more usability and comes equipped with simple APIs for Scala, Python, Java and Spark SQL, as well as provides feedback in REPL format on the commands. At the same time, Apache Spark seems to have the best performance in the processing of large data that works in memory and, therefore, more processes can be downloaded on Spark than on Hadoop, despite the fact that Hadoop is also a very useful tool.
Carla Borges profile photo
When we started using it, only the notebook experience was mature. However, DB was very helpful giving us direct support to get onto their platform. Really there was little in the way to compare to them at the time. AWS has services but not the same low-cost angle
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Return on Investment

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
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  • Quick adoption of cloud services by end users
  • Cost is high
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Pricing Details

Apache Spark

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Databricks Unified Analytics Platform

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details