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
628 reviews
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Best unified platform for AI and data analytics
What do you like best about the product?
It stands out as it seamlessly integrates data engineering, analytics and AI/ML workloads. Their Lakehouse architecture is a game changer as it combines the best of data lakes and warehouse, eliminating then need for complex ETL pipelines.
What do you dislike about the product?
Cluster startup is slow and time consuming. Price of products are very high if it is not used to its fully utilizing capabilities.
What problems is the product solving and how is that benefiting you?
We use for big data processing and real time analytics and AI driven insights. It has significantly improved data governance, performance and collaboration across teams. By replacing our legacy ETL workflow we have reduced processing time by half and improved model deployment efficiency.
The Tool for data analysis is Phenomenal
What do you like best about the product?
Offers interactive notebooks where different users can collaborate on data projects in real time.
What do you dislike about the product?
The disadvantage is that depending on the cluster, it takes a long time to pull the database.
What problems is the product solving and how is that benefiting you?
It helps to process large volumes of data, understand and optimize the problem, as through analysis we can have a more assertive decision-making process.
Databricks -Data transformation and pipeline Use
What do you like best about the product?
Databricks is very user friendly platform whuch supports multiple database and scripting languages at same place.
Pyspark and delta feature enhance the speed of data extraction
AI assostant auto complete the code and optimize when needed to do that.
Lots of paid connectors are available to use.
Pyspark and delta feature enhance the speed of data extraction
AI assostant auto complete the code and optimize when needed to do that.
Lots of paid connectors are available to use.
What do you dislike about the product?
Transformation from sql to pandas can be very time consuming.
Estimated runtime should be available so use can optimize the pipeline or notebook.
Estimated runtime should be available so use can optimize the pipeline or notebook.
What problems is the product solving and how is that benefiting you?
Simple platform to transform or aggrefate the data to create business data views/table.
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.
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