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154 Ratings
29 Ratings
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Score 8.3 out of 100
Top Rated
154 Ratings
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Score 8.5 out of 100

Likelihood to Recommend

Databricks Lakehouse 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.
Anonymous | TrustRadius Reviewer

Amazon Redshift

If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for)
Arthur Zubarev | TrustRadius Reviewer

Pros

Databricks Lakehouse Platform

  • It supports all data science programming languages like Python and R
  • it takes very few minutes to deploy models into production
  • it has tools that ensures collaborations between developers
Kofi Joshua | TrustRadius Reviewer

Amazon Redshift

  • Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms.
  • Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster.
  • Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box.
  • Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.
Gavin Hackeling | TrustRadius Reviewer

Cons

Databricks Lakehouse Platform

  • 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.
Anonymous | TrustRadius Reviewer

Amazon Redshift

  • It could benefit from adding data integrity and programming tools common to other database management systems.
  • Amazon Redshift is based on PostgreSQL 8.0.2. That version of PostgreSQL was released in December 2006. While PostgreSQL was much improved since then, the new features were not implemented in Redshift. Many basic features are missing from it.
  • Primary keys can be declared but not enforced. Referential integrity (foreign keys) can be declared but not enforced. UNIQUE and CHECK constraints are not supported and cannot be declared.
  • IDENTITY can be declared on a column, and Redshift will put unique values into it. However: IDENTITY values in the newly inserted rows won’t be incremental or sequential. To implement a sequential number, you need to write your own custom code.
  • There are no stored procedures in Redshift. We are writing SQL script files, and then parsing and running them one statement at a time from a Python program. This also enabled us to implement execution-time error logging.
  • In SQL scripts, to check for the row count of affected rows, a complicated join query against some system tables or views has to be executed.
  • Data Control Language (DCL) does not exist. No statements like IF, WHILE, DO, RAISERROR, etc.
  • On performance of views… Views do not “pass-through” a query parameter which is a potential problem for performance.
  • When selecting against a view with the WHERE clause outside of the view, the inner query of the view will be executed first without consideration for the WHERE clause, and only then the WHERE clause will be applied.
  • Certain clauses of SQL work many times faster than other clauses. So be careful and test your statements for performance earlier rather than later, especially if working with a large data set.
  • There was a situation when DELETE FROM JOIN was unacceptably slow. Replacing JOIN with the USING clause made DELETE instantaneous.
Michael Romm | TrustRadius Reviewer

Usability

Databricks Lakehouse Platform

Databricks Lakehouse Platform 9.0
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
Anonymous | TrustRadius Reviewer

Amazon Redshift

Amazon Redshift 8.5
Based on 8 answers
Just very happy with the product, it fits our needs perfectly. Amazon pioneered the cloud and we have had a positive experience using RedShift. Really cool to be able to see your data housed and to be able to query and perform administrative tasks with ease.
Brendan McKenna | TrustRadius Reviewer

Support Rating

Databricks Lakehouse Platform

No score
No answers yet
No answers on this topic

Amazon Redshift

Amazon Redshift 7.6
Based on 11 answers
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
Anonymous | TrustRadius Reviewer

Alternatives Considered

Databricks Lakehouse Platform

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.
Ann Le | TrustRadius Reviewer

Amazon Redshift

Than Vertica: Redshift is cheaper and AWS integrated (which was a plus because the whole company was on AWS).
Than BigQuery: Redshift has a standard SQL interface, though recently I heard good things about BigQuery and would try it out again.
Than Hive: Hive is great if you are in the PB+ range, but latencies tend to be much slower than Redshift and it is not suited for ad-hoc applications.
Anonymous | TrustRadius Reviewer

Return on Investment

Databricks Lakehouse Platform

  • Rapid growth of analytics within our company.
  • Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
  • Platform is easy to learn and Databricks provides excellent support and training.
  • Platform does not require a large DevOPs investment
Anonymous | TrustRadius Reviewer

Amazon Redshift

  • Redshift has had a very positive impact on our business. It has been used to provide analytics on marketing campaigns to boost revenue.
  • Redshift is instrumental in our payment collection business processes. It powers everything from who gets called to who gets sent collection emails.
Seth Goldberg | TrustRadius Reviewer

Pricing Details

Databricks Lakehouse Platform

General

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

Databricks Lakehouse Platform Editions & Modules

Edition
Standard$0.071
Premium$0.101
Enterprise$0.131
  1. Per DBU
Additional Pricing Details

Amazon Redshift

General

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

Amazon Redshift Editions & Modules

Edition
Current Generation$0.25 - $13.041
Previous Generation$0.25 - $4.081
Redshift Spectrum$5.002
Redshift Managed Storage$0.243
  1. per hour
  2. per terabyte of data scanned
  3. per GB per month
Additional Pricing Details

Add comparison