Databricks Data Intelligence Platform vs. Google Assistant for Developers

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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I'm in a Me vs. The World environment rather often. I can connect to my outer realm when heading to live meetings. Auditions, job assignments all via my assistant. I like having the ability to capture the moment and rewrite it as well. This is a primary driver for me. Sometimes branching out or when collaborating, I think I work a little harder in the moment than Google Assistant might but that is moreso my limitations and not the feature so much. I catch this scene when I'm in a group environment or at times having to create and respond to a larger scale event. Not a deal breaker for me however.

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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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• To-do lists and task boards so I can work on it better, and can ask quickly on what I need to do.
• Saves time and increases efficiency - I can ask and can answer relevant answers
• Set-up meetings - quickly scheduling and checking for time which suits all people

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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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• I think newer, complementary ideas are a bit sharper than Google Assistant especially in a Q&A environment or when seeking some depth to a subject. That enhancement is to be expected I feel. And Google Assistant is not so self limiting so I don't have a lot of improvement needs because I use this for what I've become accustomed to and for the ability overall.
• It is always important to do your best around hectic places, in bad tower signal areas or even if trying to do something new while using Google Assistant. Have patience in the setting. It pays off.

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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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I feel this can be adjusted and after some trial and error you sort of start knowing what will work and how. And I have to say the overall impact becomes personal and we are all different. I'm small scale and as I've said, it works.

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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.

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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I chose this because it was easier for me and can be accessed via mobile and laptop too because it enables cross device support because it helps in adding more depth to my life, and can help me save tons of time.

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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.

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• positive because it saves my time and improves productivity
• I can do quick research based on my thoughts and even asking it to write notes

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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