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Best all-in-one Data Platform environment
What do you like best about the product?
By providing a fully-managed, highly performant and customizable Spark environment along with regular top-tier productivity features, Databricks is undeniably the most complete Data Platform in the market. My favorites features would be :
* Unity Catalog that provides colum-level lineage and a centralized access right management service
* Delta Live Table that provides easy to implement Data Quality control and monitoring
* Extensive CLIs that gives freedom in chosing your CI/CD platform
* Clusters policies that allows a better cost control
* Unity Catalog that provides colum-level lineage and a centralized access right management service
* Delta Live Table that provides easy to implement Data Quality control and monitoring
* Extensive CLIs that gives freedom in chosing your CI/CD platform
* Clusters policies that allows a better cost control
What do you dislike about the product?
The offline development can be improved. The recently release of a VsCode Extension and also Spark connect are steps towards this but still needs more features to be satisfying.
What problems is the product solving and how is that benefiting you?
Databricks helps us develop and deploy Data Products and train Machine Learning models more successfully in production, in very short amount of time, a secured environment and with controlled costs management.
Its compatibility with infrastructure-as-code tools such as Terraform also allows us to easily reproduce our standards accross multiple instances.
The integration with Azure Active Directory also eases the adoption and Access Control of resources and data assets.
Its compatibility with infrastructure-as-code tools such as Terraform also allows us to easily reproduce our standards accross multiple instances.
The integration with Azure Active Directory also eases the adoption and Access Control of resources and data assets.
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Best implementation of Lakehouse Architecture
What do you like best about the product?
It provides a brilliant view of the entire data journey. It is a one-stop solution or a unified platform for all related Data Science and Analytics activities. Do not see any alternative product in the market offering such capabilities.
What do you dislike about the product?
Need a much larger Databricks community for discussions and clarifications.
What problems is the product solving and how is that benefiting you?
It helps in Model experimentation and bringing out the best accurate model for usage. It is also supporting the models to be deployed for serving clients. The turnaround time for the requests has been significantly less, which has increased the performance of our system.
Streamlining Data Management: My Experience with Databricks Lakehouse Platform
What do you like best about the product?
One of the primary benefits of Databricks Lakehouse Platform is its ability to unify data warehousing, data lakes, and streaming into a single, comprehensive platform. This allows users to easily access and analyze data from multiple sources in one place, improving efficiency and reducing complexity.
Another advantage is the platform's scalability and flexibility, which allows organizations to handle large volumes of data and adjust to changing business needs. Databricks Lakehouse Platform also offers powerful analytics and machine learning capabilities that enable users to gain deeper insights and make data-driven decisions.
In addition, the platform provides collaborative features that allow teams to work together on data-related projects, facilitating knowledge sharing and improving productivity. The platform's security and compliance features also ensure that data is protected and meets regulatory requirements.
Overall, Databricks Lakehouse Platform is a powerful tool that offers many benefits to organizations, including unified data management, scalability, flexibility, analytics, machine learning, collaboration, and security.
Another advantage is the platform's scalability and flexibility, which allows organizations to handle large volumes of data and adjust to changing business needs. Databricks Lakehouse Platform also offers powerful analytics and machine learning capabilities that enable users to gain deeper insights and make data-driven decisions.
In addition, the platform provides collaborative features that allow teams to work together on data-related projects, facilitating knowledge sharing and improving productivity. The platform's security and compliance features also ensure that data is protected and meets regulatory requirements.
Overall, Databricks Lakehouse Platform is a powerful tool that offers many benefits to organizations, including unified data management, scalability, flexibility, analytics, machine learning, collaboration, and security.
What do you dislike about the product?
For example, some users may find that the platform can be complex and may require a certain level of technical expertise to fully utilize its capabilities
What problems is the product solving and how is that benefiting you?
One of the key problems that Databricks Lakehouse Platform addresses is the challenge of managing and analyzing large volumes of data from various sources. The platform unifies data warehousing, data lakes, and streaming into a single, comprehensive platform, making it easier for organizations to access and analyze data from different sources in one place.
Another problem that Databricks Lakehouse Platform solves is the difficulty of integrating different data management and analytics tools. By providing a unified platform for data management, analytics, and machine learning, Databricks Lakehouse Platform eliminates the need for multiple tools and simplifies the integration process.
Another problem that Databricks Lakehouse Platform solves is the difficulty of integrating different data management and analytics tools. By providing a unified platform for data management, analytics, and machine learning, Databricks Lakehouse Platform eliminates the need for multiple tools and simplifies the integration process.
Databricks Review
What do you like best about the product?
Best for Analytics, we also got started off first using Fivetran and it was the easiest destination for us to use.
What do you dislike about the product?
Getting setup wasnt the easiest thing to do, also the UI feels a little old.
What problems is the product solving and how is that benefiting you?
Our use case is a little different, first we needed a destination when we were using Fivetran for connectors so we needed a place to put data in from the connectors we had setup using Fivetran and Databricks was the best solution for that.
We were also building analytics solutions and so Databricks also capture that well for us as well.
We were also building analytics solutions and so Databricks also capture that well for us as well.
One stop solution for Multi-cloud architecture
What do you like best about the product?
It is the best datalake even when compared to the offerings by AWS, GCP or Azure due to its proprietary delta lake technology
What do you dislike about the product?
No offering for developers to debug their code line by line
What problems is the product solving and how is that benefiting you?
It served as a data warehouse for all our unstructured data with super fast retrieval
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.
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
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.
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