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Databricks Unified Analytics Platform Reviews

16 Ratings
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Score 8.9 out of 101

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Reviews (1-5 of 5)

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January 31, 2019

Databricks for modern day ETL

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
Data from APIs is streamed into our One Lake environment. This one lake is S3 on AWS.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.

Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.
  • 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
  • Databricks should come with a fine grained access control mechanism. If I have tables or views created then access mechanism should be able to restrict access to certain tables or columns based on the logged in user
  • There should be improved graphing and dash boarding provided from within Databricks
  • Better integration with AWS could help me code jobs in Databricks and run them in AWS EMR more easily using better devops pipelines
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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August 22, 2018

Databricks Review

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
We leverage Databricks (DB) to run Big Data workloads. Primarily we build a Jar and attach to DB. We do not leverage the notebooks except for prototyping.
  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.
  • 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.
  • DB generally fits 95% of what you need to do
  • Primarily the ability to transform data and or do ad-hoc DS work
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August 22, 2018

Databricks provides a cost effective end to end solution for Enterprise analytics

Verified User
Score 9 out of 10
Vetted Review
Verified User
Review Source
It's being used for:

  • Ingestion and cleansing of data
  • Interactive Analysis of data
  • Development of Analytic Services
  • Production Environment Customer Facing Analytic Services
  • Collaborative Development Environment using Notebooks.
  • Stable and Secure Cloud Development Environment requiring minimum DevOPs support
  • Fast with excellent scalability reduces time to market
  • Open source library support
  • Automation of Machine Learning Development
  • Optimization of GPU usage
Great end to end analytics solution on AWS or Azure. Databricks continues to grow based on customer feedback. Just like everyone in the industry, they are focused on Machine Learning, but they also understand a complete solution is needed.
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Ann Le profile photo
March 28, 2018

If you want to be an effective ML learner, use Databricks

Ann Le
Score 7 out of 10
Vetted Review
Verified User
Review Source
I actually use Databricks for experiments and research for my master's program. I mostly use it to implement Python codes and testing the viability of the programs that I write. Many individuals in the Computer Information System department are using this software platform to implement programs. It is a good tool for us to learn [and] includes a community forum that is rather helpful if you are self-learning and have questions.
  • There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
  • There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.
  • The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
  • Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.
Right now, I am learning about Spark ML and general machine learning concepts. It is a good practice space to run different Spark ML codes. Databricks does provide valid errors and detailed descriptions of where I can fix my code. And to run a set of codes is very easy to maneuver around. If you do not know how to code, it might be less appropriate to use Databricks. But then again, they do have a large community where help can be found.
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September 15, 2017

Databricks Review

Verified User
Score 6 out of 10
Vetted Review
Verified User
Review Source
Across whole organization.

[It's] Used by self-service analysts to quickly do analysis
  • Very simplified infrastructure initialization
  • Seamless and automated optimization of job execution
  • Simple tool to get used to
  • Visualization - Great area of improvement
  • Integration with Git
  • COST
When you have analysts that are not cloud-savvy, this tool helps them quickly run code and not be overwhelmed by infrastructure and optimization. [It's] Less appropriate in production deployments.
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Databricks Unified Analytics Platform Scorecard Summary

Likelihood to Recommend (16)
8.9
Usability (1)
9.0

Feature Scorecard Summary

Connect to Multiple Data Sources (1)
9
Extend Existing Data Sources (1)
9
Automatic Data Format Detection (1)
7
Visualization (1)
6
Interactive Data Analysis (1)
6
Interactive Data Cleaning and Enrichment (1)
8
Data Transformations (1)
9
Data Encryption (1)
7
Built-in Processors (1)
8
Multiple Model Development Languages and Tools (1)
9
Automated Machine Learning (1)
8
Single platform for multiple model development (1)
9
Self-Service Model Delivery (1)
7
Flexible Model Publishing Options (1)
7
Security, Governance, and Cost Controls (1)
8

About Databricks Unified Analytics Platform

Databricks in San Francisco offers the Databricks Unified Analytics Platform, a data science platform and Apache Spark cluster manager.
Categories:  Data Warehouse,  Data Science

Databricks Unified Analytics Platform Technical Details

Operating Systems: Unspecified
Mobile Application:No