Databricks Data Intelligence Platform vs. SAP Adaptive Server Enterprise (ASE)

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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We use this for an inbuilt security management system, where it performs well in a scaled setup with a large volume of live data with high availability. Also, the performance is up to the mark for the large statement flow. From a DBA perspective, a lot of parameters need to be fine-tuned for the specific environment needs, which can cause overhead. Expertise is limited, and the learning curve is steep for the SAP ASE.

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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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• High availability and reliability.
• High performance.
• Monitoring of threshold based events.
• Security at multiple levels.
• Query plan optimization.
• Lock management.
• Access to HANA.

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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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• A quick button for devs to switch instances into a dev mode (eliminating logs, and such).
• Licensing is far too complicated. Simplify pricing.

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Our licenses are perpetual. It is the support that we will be renewing. We will renew because we continue to use and receive value from the product.

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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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Well-suited in the security domain, high performance, and low latency of the DBMS. In terms of the DBA perspective, a dedicated monitoring tool (Cockpit) helps a lot in terms of managing the database, which helps in identifying bottlenecks during performance issues. Also, it helps us to send custom alerts related to Database activities.

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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.

Incredibly responsive, saving us countless hours in troubleshooting.

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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

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

• 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 ROI.
• Ease of use with minimal training required to write high performance SQL (assumes a knowledge of SQL already).
• Ease of database administration.
• Sometimes upgrades can be difficult.
• Changing platforms can be difficult (changing say from SPARC to Intel).
• Changing operating systems can be difficult (changing from Solaris to linux).

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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