Codacy vs. Databricks Data Intelligence Platform

I recommend it for companies that use several programming languages, as it supports several languages ​​such as Java, Javascript, Python, among others. However, for companies that use only one programming language, there are specific tools for each language that can be more complete in this scenario. I do not recommend it for companies that only use open source software, in which case there are other tools available.

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Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

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• Seamless and easy integrations with GitHub for fantastic pipeline of viewing errors.
• Intuitive UI which is easy to customize and built-in patterns recommendations and security checks helps to fix issues faster.
• Metrics and duplication and complexity easily identify areas that need attention to fix it easy.
• Great for open-source projects.

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• 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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• There should be customization to get code quality for your own projects if standards are provided.
• Offline or a standalone application is much needed from Codacy to get local support.
• Spots a lot of errors and small ones that don't affect much about quality and are de-facto standards.

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• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

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Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

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Great company and support team!

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

Even though it is paid while SonarQube is free, we chose Codacy because it is simpler to configure and maintain the implemented rules. In addition, it offers support for the main programming languages ​​on the market, ensuring that we can continue to use it if we want to use other languages ​​in new products.

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The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

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Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• The integration of Codacy with new code base is quick and easy.
• Improves our code.
• Easy to detect errors easily.
• Ability to check duplicate codes.

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Independently Invited by TrustRadius

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• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info