I use Databricks for various purposes, including data engineering, MLOps, machine learning training and deployment, the entire ML cycle, and dashboards. It serves different purposes for different projects.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
637 reviews
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External reviews are not included in the AWS star rating for the product.
Simultaneous data flow management.
What do you like best about the product?
- It has an excellent connection with the MLFlow system which guarantees that our clients have access to creation, management, monitoring and progress in Machine Learning.
- It offers professional processes to manage the clients infrastructure and manage all the clusters, all this can be done from the cloud and saves time in collecting data from the clusters.
- We can link several data sources perfectly and simultaneously, this helps collect all the data of our clients in a safe and automated manner, without going through complex data registration process, we can collect a large volume of data easily.
- It offers professional processes to manage the clients infrastructure and manage all the clusters, all this can be done from the cloud and saves time in collecting data from the clusters.
- We can link several data sources perfectly and simultaneously, this helps collect all the data of our clients in a safe and automated manner, without going through complex data registration process, we can collect a large volume of data easily.
What do you dislike about the product?
Databricks never gave us any type of negative experience, at all times it was able to offer management, data storage and collection of large volumes of data. With Databricks, our MSP-type functions have improved and have never had any failures collecting all the data of our clients who access IT services.
What problems is the product solving and how is that benefiting you?
Databricks has allowed the data management of our entire company to be much more proportional, allowing us to work together and integrate this platform with various Apache and ML services. This platform has benefited me a lot, because it has allowed us to completely analyze the data and collecting a large volume of data and storing it in the cloud, collecting data from our clients and managing this data together and working together, has never been easier until it came to this platform. I'm satisfied with the results, since it has benefited data management in our MSP company.
Databricks is The data and AI company
What do you like best about the product?
Databricks lakehouse platform is unique in 3 ways-
Simple- Data only needs to exist once to support all your workloads.
Open- It is based on open standards to work with existing tools and avoid propritary formats.
Collaborative - DE, Analysts and DS can work together much more easily.
Simple- Data only needs to exist once to support all your workloads.
Open- It is based on open standards to work with existing tools and avoid propritary formats.
Collaborative - DE, Analysts and DS can work together much more easily.
What do you dislike about the product?
Speed of innovations and features released. Sometimes features rolled-out without enough support and documentations.
What problems is the product solving and how is that benefiting you?
It is helping us in many ways:
1) Faster data processing with optimized features provided via DBR which is additional performace incentives on top of open spark e.g. Dynamic file pruning, low shuffle merge, deletion vectors, AQE etc
2) Unified governance and security - Rise of multi cloud adoption where each cloud has a unique governance model that requires individual familiarty intoduce complexity. solutions are unity catalog and data sharing. It is helping us a lot providing centralized governance
1) Faster data processing with optimized features provided via DBR which is additional performace incentives on top of open spark e.g. Dynamic file pruning, low shuffle merge, deletion vectors, AQE etc
2) Unified governance and security - Rise of multi cloud adoption where each cloud has a unique governance model that requires individual familiarty intoduce complexity. solutions are unity catalog and data sharing. It is helping us a lot providing centralized governance
Unlocking Data Potential: A Comprehensive Review of the Databricks Data Intelligence Platform
What do you like best about the product?
I like how Databricks seamlessly integrates data engineering, science and machine learning, offering scalabilty, collaboration and efficient analytics in one platform.
What do you dislike about the product?
The main drawbacks of Databricks are its steep learning curve and complex pricing, which can be challenging for new users or smaller teams or organizations with limited budgets.
What problems is the product solving and how is that benefiting you?
Databricks solves problems releated to data processing at scale, simplifying the integration of data engineering, data science and machine learning workflows. it allows for faster data analysis, more efficient model building, and seamless collaboration across teams.
Unified platform simplifies end-to-end processes with intuitive data access solutions
What is our primary use case?
What is most valuable?
Unity Catalog is a feature I am currently using extensively. I am migrating many projects to Unity Catalog. MLflow, which I use for model registering and creating the lineage of models, is also valuable.
Additionally, Databricks serves as a single platform for conducting the entire end-to-end lifecycle of machine learning models or AI ops. I don't need to switch between various tools, making it an all-encompassing solution for development and research. I use the lake house and utilize features effectively.
What needs improvement?
There has been a significant evolution in databases. One area of improvement is the Databricks File System (DBFS), where command-line challenges arise when accessing files. Standardization of file paths on the system could help, as engineers sometimes struggle.
It would be beneficial to have utilities where code snippets are readily available. This would allow engineers to easily click a snippet and import it into the notebook, enabling quick modifications to variables or paths for fetching files, such as reading data from DBFS files. If I could right-click to copy absolute paths or to read files directly into a data frame, it would standardize and simplify the process.
For how long have I used the solution?
I have used the solution for five years plus.
What do I think about the stability of the solution?
I would rate stability seven to eight out of ten.
What do I think about the scalability of the solution?
I would rate scalability seven to eight out of ten.
How are customer service and support?
I do not have any issues that require support. Many resources are available online.
How would you rate customer service and support?
Neutral
How was the initial setup?
I use infrastructure as code on the cloud to deploy the infrastructure. I have all the Git repositories and code repositories for deploying the code and models in the workspace. My setup includes a shared workspace, shared clusters, and integration with Unity Catalog.
What about the implementation team?
I have a team of 100 engineers working with me, and I head the Center of Excellence (COE).
What was our ROI?
I believe it is competitive across clouds. When it comes to big data processing, I prefer Databricks over other solutions. Cost-wise, it is very competitive. The setup process is straightforward, thanks to the use of Spark clusters. This allows for faster turnaround times with Databricks.
What other advice do I have?
The product rating is nine out of ten.
Databricks serves as a single platform that can handle numerous end-to-end machine learning tasks. The configuration is simple, scalability is excellent, and monitoring cluster utilization facilitates informed business decisions.
It's easy to schedule jobs, pipelines, and handle multiple use cases in parallel, providing countless benefits.
Which deployment model are you using for this solution?
Hybrid Cloud
Boosting efficiency with the unified data and AI ecosystem
What do you like best about the product?
Databricks scalability features allowed us to process millions of rows of customer data efficiently. We use utilized Databricks to build a customer segmentation model for an e-commerce platform. Here we identified targeted groups by analyzing purchase history, browsing behavior and other related data. The huge amount of data can be visualized using in-built visualization tool in Databricks, which was incredibly helpful in exploring patterns and presenting insights.
What do you dislike about the product?
I personally reached out to support when debugging data pipeline issues. But the issue got resolved in no time, preventing delays in the project.
What problems is the product solving and how is that benefiting you?
The platform has helped us to automate data aggregation and pre processing, which results in cutting down the time spent on repetitive task by nearly 30%. Also, it's support for real time data updates insured that the result were always based on the most current data.
A fantastic serverless data warehousing platform is Databrick data intelligence
What do you like best about the product?
Databricks data intelligence is really fast and can handle large amounts of data easily, you can run complex Sql quries on huge datasets in seconds without needing to worry about managing serves or infrastructure, everthing is taken care of for you, like maintance and backups, so you don't have to think about that either. It also works smoothly with other parts of the databricks, making it easier to build software workflows and data piprlines for analyzing and managing your data.
What do you dislike about the product?
Databricks can be hard for beginners, if they don't know Sql. it can also get expensive and complicated for many users.
What problems is the product solving and how is that benefiting you?
Managing data from different places like on premises the cloud or third party services can be tricky, but with databricks, it makes combing and analyzzing all that data easy. This saves time amd reduce a lot of complexity.
Faculte using the platform
What do you like best about the product?
The practice and how simple is to use the platform.
What do you dislike about the product?
About the platform, I didn't dislike nothing.
What problems is the product solving and how is that benefiting you?
Using the DIP my work time was optimized and the solutions came more faster.
Databricks Intelligence Change the data perception
What do you like best about the product?
its unified approach to data management, allowing seamless integration of data warehousing, data lakes, and machine learning capabilities on a single platform, making it easy to work with diverse data types while maintaining high performance and scalability, all while offering a user-friendly interface with natural language features for simplified data exploration and analysis.
What do you dislike about the product?
its relatively high cost, especially for smaller organizations, potential complexity in managing cluster configurations, challenges with identifying error sources within large data pipelines, and occasional difficulties with integrating with other tools, particularly for data visualization purposes.
What problems is the product solving and how is that benefiting you?
reducing costs, improving data quality, and accelerating decision-making processes across an organization
All in all Data Intelligence software
What do you like best about the product?
I liked the lakehouse architecture and performance of data warehouse helps to analyse data in large scale and it can be integrated with AWS, gcloud , azure , and MLflow integration feature was good.it also usable with tablue and database sql.
What do you dislike about the product?
Prize is high and not suitable for small companies
What problems is the product solving and how is that benefiting you?
We used this tool for analyzing data in large scale for that this tool was efficient
Ideal for large-scale data processing and collaboration
What do you like best about the product?
I like the way Databricks does data management, all in one place. What it does is it unites the data engineers and data scientists on the same platform to collaborate and solve problems quickly. Scaling became effortless thanks to the integration with tools like AWS, as well as keeping up with the progress in the notes continues to keep us all on the same page in the notes. It’s helped remove communication issues and it’s helped take care of things faster.
What do you dislike about the product?
Databricks has one downside and that is the learning curve, especially for people that want to get started with a more complex configuration. We spent some time troubleshooting the setup, and it’s not the easiest one to begin with. The pricing model is also a little unclear, so it isn’t as easy to predict cost as your usage gets bigger. At times that has led to some unforeseen expenses that we might have cut if we had better cost visibility.
What problems is the product solving and how is that benefiting you?
We’ve seen Databricks drastically improve our efficiency. Now we can manage large datasets, run machine learning models, and work in real time but without worrying about working with different tools. It’s allowed us to simplify our data pipeline and speed up decision making, which has been a big win for the team. At the same time, this has allowed faster product development cycles all the way to shorter time to market for new features.
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