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Unified Still Federated Lakehouse Platform
What do you like best about the product?
A great platform to focus on industry challenges around data and ai. Good part is solutions for those challenges are quickly built, tested and released. Participation during private preview also make sure that these solutions are fit for purpose to industry challenges.
Best features floats around combining data lake and datawarehouse capability to help reduce cost and deliver faster with improved security
Best features floats around combining data lake and datawarehouse capability to help reduce cost and deliver faster with improved security
What do you dislike about the product?
Its integration with native cloud services is still weak, out of the box integration & use with org identify federation is still not mature. Along with capability to integrate with enterprise catalog and buillding a unified metric system for organization.
What problems is the product solving and how is that benefiting you?
Unified view for all our data sources, easy sharing of data with our product team, easy platform for data owners to democratise their data. and central place to apply security and governance.
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One of the best cloud data warehousing solution
What do you like best about the product?
Databricks Lakehouse Platform impressively unifies data lakes and data warehouses, empowering seamless data access and analysis. Its Apache Spark-powered engine ensures lightning-fast processing, while advanced analytics and machine learning capabilities drive data-driven insights. With robust security, auto-scaling, and managed services, Databricks simplifies data management and boosts collaboration among data teams. An extensive range of integrations further enhances its versatility, making it a game-changer for data-driven organizations.
What do you dislike about the product?
Learning curve - for users unfamiliar with Apache Spark there may be a learning curve to fully utilize platform capabilities.
Complexity - managing and optimizing large-scale data workflows can be comples, requiring skilled data engineers and administrators,
Sometimes new features are not fully tested, which may cause some problems in the future - but honestly it's not a big disadvantage.
Complexity - managing and optimizing large-scale data workflows can be comples, requiring skilled data engineers and administrators,
Sometimes new features are not fully tested, which may cause some problems in the future - but honestly it's not a big disadvantage.
What problems is the product solving and how is that benefiting you?
Keeping everything as all in one product for creating ETL pipelines and data governance solutions. Also it lets you simply scale your workload if it's really needed.
Easy integration for Data from Marketplaces
What do you like best about the product?
- Easily integrate data from the marketplace into your own ML models and data pipelines
- Open source nature allows for easy integration into existing tools
- Open source nature allows for easy integration into existing tools
What do you dislike about the product?
- Can be difficult to configure clusters for cost and performance optimizations
What problems is the product solving and how is that benefiting you?
We are able to process Billions of records of data each day. This would not have been possible without the Databricks platform.
Great product
I've been using Databricks for over a year and it's really easy to work with files sitting on the Data Lake and building the "data warehouse" right there, with the possibility to do analysis and ML within the same platform.
Databricks is the user Friendly
What do you like best about the product?
Everything is integrated into one tool. New features are very helpful. Optimization looks promising.
What do you dislike about the product?
We can leverage new features to work out any flaws.
What problems is the product solving and how is that benefiting you?
Processing huge files.
Always excited about Databricks
What do you like best about the product?
The upsides of Databricks are it accelerates the ML/AI for me.
What do you dislike about the product?
I like all everything about Databricks and dislike none.
What problems is the product solving and how is that benefiting you?
Especially accelerating analytics, curation of data, compatibility with Spark.
Had a blast!
What do you like best about the product?
Training sessions helped us prepare for our use cases.
What do you dislike about the product?
The food was not as great the first day. The speaker blocked the screens.
What problems is the product solving and how is that benefiting you?
Learn from inference models on generative ai.
Experience with Databricks
What do you like best about the product?
The usability and the ability to allow so many differs users to access data from 1 platform
What do you dislike about the product?
Nothing that I can't think of at this point in time
What problems is the product solving and how is that benefiting you?
Centralised data access. Different user personas on the same place
Holistic, E2E, and Intuitive
What do you like best about the product?
A one stop place for holistic teams - engineers, data scientists, architects, and administrators - to collaborate and have transparent visibility to our data, access, and insights
What do you dislike about the product?
There is a learning curve with the syntax, interface, and jargon. Need to ensure all personas and stakeholders are onboarding and adopting at a concerted pace and sequence in order to make adoption work.
What problems is the product solving and how is that benefiting you?
Data access and lineage visibility; EDA, Analysis, and visualization all in one place. More productive and confident in the data I am working with.
It's great
What do you like best about the product?
I have been using jupyter notebooks to run models on databricks and I haven't explored everything else.
What do you dislike about the product?
I haven't explored everything yet so I don't have anything specific in mind.
What problems is the product solving and how is that benefiting you?
It helps run machine learning models on large scale. I'm benefiting the most from parallel processing.
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