Databricks Data Intelligence Platform
Databricks, Inc.External reviews
638 reviews
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Databricks Lakehouse Solved lot of things
What do you like best about the product?
Data governance and security of data within the organization and provide dashboarding.
We can use choose the storage that is the most beautiful thing.
We can use it for SQL engine as well
We can use choose the storage that is the most beautiful thing.
We can use it for SQL engine as well
What do you dislike about the product?
nothing to say, I haven't seen any feature that is bothering me.
What problems is the product solving and how is that benefiting you?
It is cost-efficient and scales data lakes with the data management and ACID transactions of data warehouses.
It has new query engine designs providing high-performance SQL execution on data lakes.
It has new query engine designs providing high-performance SQL execution on data lakes.
Databricks has been absolute productivity tool
What do you like best about the product?
Databricks come with everything in one place, and that is the best thing about the platform. Now I don't have to go to EMR page to create an ERM and then create a notebook. All these can be done with just one click and one page. One gets to jump from notebooks to Query service and to AI in just one click. You get data store views, auto type suggestions based on table and schema context. All these makes the experience very smooth and fast.
What do you dislike about the product?
Queries get refreshed once in a while, where if some queries are not saved explicitly get deleted.
There is no dark mode, hence have to use a white theme.
Sometimes your queries get pushed to long queues which takes a lot of time and can be little frustrating.
There is no dark mode, hence have to use a white theme.
Sometimes your queries get pushed to long queues which takes a lot of time and can be little frustrating.
What problems is the product solving and how is that benefiting you?
- Adhoc queries on datalake, data profiling on various schema and tables.
- Creating and testing data pipelines using notebooks with various language support.
- Quick, easy, and on-demand resource creation and termination.
- Creating and testing data pipelines using notebooks with various language support.
- Quick, easy, and on-demand resource creation and termination.
Databricks Lakehouse a new data warehouse
What do you like best about the product?
Offers low cost storage of data with efficient schema structure and analytics. Support for ACID transactions. Quick and easy data accessibility. Good data governance.
What do you dislike about the product?
I should hold an active account with AWS or Azure to use Databricks Lakehouse platform or else I can't access it. More guidance on data quality tools is needed.
What problems is the product solving and how is that benefiting you?
The speed of processing the data is lagging sometimes. Databricks Lakehouse focusing to slove lagging issue helps me in smooth functioning of the tasks and saves sometime.
I like working on Databricks
What do you like best about the product?
Databricks allow us to access data via pyspark, python and sql.
The interface is easy to use and most of my work is spent there.
The interface is easy to use and most of my work is spent there.
What do you dislike about the product?
I was told the model training part is more costly in Databricks than in Azure.
So some of the jobs need to be done in databricks and some of the jobs need to be done on Azure.
It will be good if cost is not an issue when choosing platforms.
So some of the jobs need to be done in databricks and some of the jobs need to be done on Azure.
It will be good if cost is not an issue when choosing platforms.
What problems is the product solving and how is that benefiting you?
move of ETL and analysis are done in databircks for me.
Powerful and user-friendly interface for data scientists and engineers to work!
What do you like best about the product?
Its ability to seamlessly integrate data processing, analytics, and machine learning workflows, its scalability and performance, and its support for a wide range of data sources and programming languages. I like that it get UI updates on the notebooks.
What do you dislike about the product?
The learning curve for new users can be steep. There are limited documentation on markdowns in notebooks and it can be faster. I would like to see it faster and improved.
What problems is the product solving and how is that benefiting you?
Can help solve for organizations include simplifying and streamlining complex data processing and analysis workflows, improving scalability and performance for large datasets and machine learning workloads, and enabling collaboration among data scientists and engineers across teams and geographies. These benefits can lead to more efficient and effective data-driven decision making, improved product development, and enhanced customer experiences.
It just works
What do you like best about the product?
It's fully managed, and gives us lots of processing power with very little effort.
What do you dislike about the product?
There are lots of areas to it, so understanding all of it at any depth takes time.
What problems is the product solving and how is that benefiting you?
It's a single place for all our data, and the compute is separated from the storage, meaning we can use it for reporting and more comprehensive analytics without performance impact.
Key to modern data management platform
What do you like best about the product?
One of the key advantages of Databricks Lakehouse Platform is its unified approach to data management, which allows organizations to manage all types of data, including structured, semi-structured, and unstructured, in a single location. This simplifies data management and provides a unified view of all data, enabling better decision-making.
Another advantage is its scalability and performance. Databricks Lakehouse Platform is designed to handle large volumes of data and can scale horizontally as well as vertically. It also provides high-speed data processing and query performance, thanks to its distributed architecture and optimized computing engines.
The platform's built-in capabilities for machine learning and AI is another advantage. This allows organizations to easily integrate machine learning and AI into their data workflows and derive insights and value from their data.
Another advantage is its scalability and performance. Databricks Lakehouse Platform is designed to handle large volumes of data and can scale horizontally as well as vertically. It also provides high-speed data processing and query performance, thanks to its distributed architecture and optimized computing engines.
The platform's built-in capabilities for machine learning and AI is another advantage. This allows organizations to easily integrate machine learning and AI into their data workflows and derive insights and value from their data.
What do you dislike about the product?
One potential challenge is the learning curve associated with the platform. Databricks Lakehouse Platform requires a certain level of technical expertise and familiarity with the tools and technologies used in the platform, such as Apache Spark, SQL, and Python. This can make it challenging for some organizations to adopt the platform, especially if they lack the necessary expertise.
Another potential limitation is the cost associated with the platform. Databricks Lakehouse Platform is a commercial product, and as such, it requires a subscription or licensing fee. This can be a barrier to entry for some organizations, especially smaller ones with limited budgets.
Another potential limitation is the cost associated with the platform. Databricks Lakehouse Platform is a commercial product, and as such, it requires a subscription or licensing fee. This can be a barrier to entry for some organizations, especially smaller ones with limited budgets.
What problems is the product solving and how is that benefiting you?
Data Silos: With traditional data management approaches, data is often stored in separate silos, making it difficult to access and integrate data from different sources. Databricks Lakehouse Platform provides a unified approach to data management, allowing organizations to manage all types of data in a single location and providing a unified view of all data.
Scalability and Performance: As data volumes continue to grow, traditional data management approaches may struggle to handle the volume and complexity of data. Databricks Lakehouse Platform is designed to scale horizontally and vertically, allowing organizations to handle large volumes of data and providing high-speed data processing and query performance.
Security and Governance: With data privacy regulations becoming increasingly stringent, organizations need to ensure that their data is secure and compliant with regulations. Databricks Lakehouse Platform provides robust security and governance features, including access control, auditing, and compliance monitoring.
AI and Machine Learning Integration: As organizations look to derive insights and value from their data, machine learning and AI have become essential tools. Databricks Lakehouse Platform provides built-in capabilities for machine learning and AI, allowing organizations to easily integrate these tools into their data workflows.
Scalability and Performance: As data volumes continue to grow, traditional data management approaches may struggle to handle the volume and complexity of data. Databricks Lakehouse Platform is designed to scale horizontally and vertically, allowing organizations to handle large volumes of data and providing high-speed data processing and query performance.
Security and Governance: With data privacy regulations becoming increasingly stringent, organizations need to ensure that their data is secure and compliant with regulations. Databricks Lakehouse Platform provides robust security and governance features, including access control, auditing, and compliance monitoring.
AI and Machine Learning Integration: As organizations look to derive insights and value from their data, machine learning and AI have become essential tools. Databricks Lakehouse Platform provides built-in capabilities for machine learning and AI, allowing organizations to easily integrate these tools into their data workflows.
One stop shop for (almost) all your analytics needs
What do you like best about the product?
The flexibility of working with notebooks that combine python and sql
What do you dislike about the product?
The visualization tools are nice but very basic and not really helpful
What problems is the product solving and how is that benefiting you?
Super fast sql engine reduces time to results from hours to seconds at a reasonable cost
Really useful tool
What do you like best about the product?
Ease of use, really optimised platform, lots of good integrations, good customer support.
What do you dislike about the product?
The platform has some glitches that have been lying around for a while now I feel. The SQL dashboards are very very slow and the screen gets stuck often.
What problems is the product solving and how is that benefiting you?
It helps me greatly in big data analytics. The recent feature upgrades about auto-completion like VS code have been great additions. The platform is generally pretty fast.
Abstraction from non core work makes core work much easier
What do you like best about the product?
The platform allows us to quickly start developing and prototyping without worrying much about setting up workspaces, the runtimes, connectors etc. The best part is that it is really powerful to move up from basic prototyping to production ready codebase maintenance
What do you dislike about the product?
The editor could be better. I have had some poor but expected experiences with managing / writing code.
There should be better support for accessing same functionalities from CLI
There should be better support for accessing same functionalities from CLI
What problems is the product solving and how is that benefiting you?
It is giving the ability to start working immediately without worrying much about setting up / managing runtimes or workspaces. This is really helpful when you want to develop production products
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