Amazon Comprehend vs. Databricks Lakehouse Platform

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Amazon Comprehend
Score 7.6 out of 10
N/A
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend uses machine learning to help uncover insights and relationships in unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text…
$0
per unit
Databricks Lakehouse Platform
Score 8.3 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Pricing
Amazon ComprehendDatabricks Lakehouse Platform
Editions & Modules
Syntax Analysis
$0.00005
per unit
Key Phrase Extraction
$0.0001
per unit
Sentiment Analysis
$0.0001
per unit
Entity Recognition
$0.0001
per unit
Language Detection
$0.0001
per unit
Pll Detection
$0.0001
per unit
Event Detection Per Event Type
$0.003
per unit
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
Offerings
Pricing Offerings
Amazon ComprehendDatabricks Lakehouse Platform
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Amazon ComprehendDatabricks Lakehouse Platform
Top Pros

No answers on this topic

Top Cons
Best Alternatives
Amazon ComprehendDatabricks Lakehouse Platform
Small Businesses
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10

No answers on this topic

Medium-sized Companies
Posit
Posit
Score 9.1 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
Enterprises
IBM SPSS Modeler
IBM SPSS Modeler
Score 7.8 out of 10
Snowflake
Snowflake
Score 9.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Amazon ComprehendDatabricks Lakehouse Platform
Likelihood to Recommend
10.0
(1 ratings)
8.4
(17 ratings)
Usability
-
(0 ratings)
9.4
(3 ratings)
Support Rating
-
(0 ratings)
8.6
(2 ratings)
Contract Terms and Pricing Model
-
(0 ratings)
8.0
(1 ratings)
Professional Services
-
(0 ratings)
10.0
(1 ratings)
User Testimonials
Amazon ComprehendDatabricks Lakehouse Platform
Likelihood to Recommend
Amazon AWS
Specifically, it starts processing millions of documents in minutes by leveraging the power of machine learning without having trained models from scratch. If any of the content contains personally identifiable information not only can Amazon Comprehend locate it but it will also redact or mask it. Using NLP techniques Amazon Comprehend goes well beyond keyword search or rules-based tagging to accurately classify documents. For my task or development, I cannot find any difficulties with Amazon Comprehend.
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Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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Pros
Amazon AWS
  • Amazon Comprehend identifies the language of the text and extracts Key-phrases, places, people, brands or events.
  • It can build a custom set of entities or text classification models that are tailored uniquely to the organisation's need
  • Amazon Comprehend's medical can be used to identify medical conditions, medications, dosages, strength and frequencies from sources like doctor's notes, clinical trial reports and patient health records. This service is very good and with well an accuracy or confidence score.
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Databricks
  • 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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Cons
Amazon AWS
  • It will be great if Amazon Comprehend provide support specifically for litigation or related text documents to extract insights from it.
  • For REST API support using JAVA SDK, it will be great for developers if they provide support for testing without any credentials or account details.
  • Setting up for REST API integration can be as simple as possible.
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Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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Usability
Amazon AWS
No answers on this topic
Databricks
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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Support Rating
Amazon AWS
No answers on this topic
Databricks
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.
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Alternatives Considered
Amazon AWS
For natural language processing tasks or techniques, there are many service providers out there in the market such as Azure Cloud Services, IBM Watson and Google Cloud Platform (GCP), but compared with them, Amazon Comprehend is the best service provider in contents of accuracy, speed of processing multilingual text, supporting SDK for most of the languages and well documented.
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Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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Return on Investment
Amazon AWS
  • It supports better and accurately as compared with our existing or old implementations. So, we fulfil our needs as per clients' requirements and it will help to grow or improve client satisfaction.
  • For these specific requirements, we do not require any machine learning engineers or related professionals to hire in our organisation.
  • None of any negative sides can be affected our business or distract existing clients.
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Databricks
  • 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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ScreenShots