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A great platform to realize business value through DE, ML and analytics"
What do you like best about the product?
The platform is easy to use and collaborate with my colleagues. Deploying to production is simple and can be even easier if you choose the non-self-hosted option. Although Databricks does some of the heavy lifting, it's still open enough to allow teams to use their own flexibility and complex processes without too much configuration. "
What do you dislike about the product?
Platforms constantly change as they adapt, so staying on top of everything can be difficult. - If you don't have a CICD system in place, once you hit a certain number it starts to get difficult to manage.
What problems is the product solving and how is that benefiting you?
Databricks Lakehouse Platform is a data platform that helps organizations unlock the full potential of their data. It aims to unite data engineering and data science, making it easier for teams to collaborate and create a comprehensive data strategy. It allows users to easily store, organize, access, and analyze data from multiple sources, making it easy to gain insights from their data. By leveraging the platform, organizations can optimize their data-driven decisions, improve customer experience, and drive business growth. Additionally, the Databricks Lakehouse platform allows users to quickly and easily build data pipelines for real-time analytics, machine learning, and AI applications. It can help organizations quickly discover valuable insights from their data, shorten the time to market for new products and services and make more accurate forecasts.
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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.
Databricks Lakehouse: Some Pros and Cons
What do you like best about the product?
- Keep updating the notebook platform (e,g, keep adding new features such as local variables track).
- MLFLOW experiment and Model registry, where all trained models can be tracked and registered in one place
- MLFLOW experiment and Model registry, where all trained models can be tracked and registered in one place
What do you dislike about the product?
Connect my local code in Visual code to my Databricks 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 extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
What problems is the product solving and how is that benefiting you?
Dealing with big data and being able to train different models that address many problems in my business. In addition to its computational capabilities, using Databricks allowed us to do all development in one platform.
Databricks is a one stop solution for all Analytics, Data Engineering and ML related projects
What do you like best about the product?
I like the ease to switch between Python, Pyspark and sql in the same notebook.
What do you dislike about the product?
The spark cluster needs to get connected faster in community edition.
What problems is the product solving and how is that benefiting you?
Faster ingestion of big data
Host of file formats accepted
Processing of unstructured data
Ease of ML model monitoring and tracking
Ease of switch in language in the same notebook
Host of file formats accepted
Processing of unstructured data
Ease of ML model monitoring and tracking
Ease of switch in language in the same notebook
Databrick Lakehouse Review
What do you like best about the product?
One of the best analytical databases currently available in the market and can handle all formats of data ranging from structured, semi-structured, to unstructured.
What do you dislike about the product?
I don't have anything I particularly don't like. If there is, I would say the SPARK statistical modeling libraries are still quite limited comparing with the packages from R, SAS, or Python.
What problems is the product solving and how is that benefiting you?
Databricks help us solve the data integration and processing legacy issues and also can provide AI, ML, statistical modeling functionalities and enable my team to build predictive models
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.
Life-changing Product, simple and easy flow to do hard tasks
What do you like best about the product?
its powerful data analytics and machine learning capabilities. The platform includes built-in tools and libraries for data analysis, visualization, and machine learning, allowing users to perform complex data modeling and analysis tasks with ease.
ffers a collaborative and flexible working environment, with support for multiple programming languages and easy integration with popular development tools. This makes it an ideal choice for data teams and organizations of all sizes looking to streamline their data processing and analysis workflows.
ffers a collaborative and flexible working environment, with support for multiple programming languages and easy integration with popular development tools. This makes it an ideal choice for data teams and organizations of all sizes looking to streamline their data processing and analysis workflows.
What do you dislike about the product?
I don't like some of the documentation. Some of the features are not being maintained properly and some of the features that are mainly needed never get added. However, I don't think this is an issue with Databricks but rather an issue on MLFLow.
What problems is the product solving and how is that benefiting you?
Improve data processing efficiency: The platform enables organizations to process large volumes of data quickly and efficiently, with support for distributed processing and scalable data storage.
Increase data integrity and consistency: By unifying data lakes and data warehouses, the platform helps to maintain data consistency and integrity across different systems and data sources.
Streamline data analysis and modeling: With built-in data analytics and machine learning tools, the platform makes it easy for users to perform complex data analysis and modeling tasks, without the need for specialized expertise or custom code.
Increase data integrity and consistency: By unifying data lakes and data warehouses, the platform helps to maintain data consistency and integrity across different systems and data sources.
Streamline data analysis and modeling: With built-in data analytics and machine learning tools, the platform makes it easy for users to perform complex data analysis and modeling tasks, without the need for specialized expertise or custom code.
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.
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