Databricks Data Intelligence Platform vs. Mapbox

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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For services that require maps and basic geo-functionality in production, Mapbox is one of the greatest choices out there. They're free, provide much more refined/modern productions compared to Google maps, and have very good support on different platforms. For services that require higher-computation products, like matrix routing, optimization, etc..., the prices can get quite high very quickly, and you should consider moving those services to an on-premise server at that point.

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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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• Documentation is clean, easy to read, and very well organized which makes it easy to get going or learn something new
• The online user interface makes it quick and easy to edit maps, upload new datasets, view analytics, or get API keys
• Great integrations with Tableau and easy to develop for Android, iOS, or Web

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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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• The product is not always that easy to use, aimed at professionals understanding colors and palettes etc.
• Being a full cloud product it can be complicated working with the interface when your connectivity is not great.
• Tutorials and samples could be more visual and could cover more ideas on how to use Mapbox.

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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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It is a good tool to use. We can perform various customisations; I always end up exploring and finding a new feature that can be used in my work somewhere. And one good thing is that is actually quite reasonable in terms of cost, with the free tier being quite adequate

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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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One feature that made me go in favor of Mapbox was its stellar documentation. Google Maps and Bing Maps are the other alternatives I considered, but the learning curve with both of them is steeper than it is with Mapbox. Also, Mapbox Studio gives newbies a very simple, clean and easy to use environment to make and store maps online

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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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• Mapbox is the only service that has all the products we need to release our product to the market. Without Mapbox, we would've spent far more time integrating multiple different map/geo services like Mapbox and HERE maps together.
• Mapbox was sometimes expensive in the testing period, and we would've definitely moved some of the services on-premise to save money if we had the time.
• Mapbox has functionality for traffic-aware routing in many countries, as well as matrix-structured routing data, which is what enables our service to function. Having all of this integrated within an API allows us to easily scale our service to multiple different cities/countries in a matter of days.

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