We're using it to provide a unified development experience for all our data experts, including all data engineers, data scientists, and IT engineers. With the Databrick Platform we allows teams to collaborate easily towards building Data Science models for our clients. The development environment allows us to ingest data from various data sources, scale the data processing and expose them either trough API or through enriched datasets made available to web app or dashboard leveraging the serverless capacities of SQL warehouse endpoints.
Databricks Data Intelligence Platform
Databricks, Inc.External reviews
636 reviews
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External reviews are not included in the AWS star rating for the product.
Simple to set up, fast to deploy, and with regular product updates
What is our primary use case?
How has it helped my organization?
Databricks allowed us to offer an homogeneous development environment accross different accounts and domains, and also across different clouds. The upskilling of our employees is far more linear and faster, while removing the complexity of infrastructure management. This lead to an increased collaboration between domain thanks to a better onboarding experience, more performant pipelines and a smoother industrialization process. Overall client satisfaction has increased and the time to first insight has been reduced.
What is most valuable?
The shared experience of collaborative notebooks is probably the most useful aspect since, as an expert, it allows me to help my juniors debug their books and their code live. I can do some live coding with them or help them find the errors very efficiently.
It has become very simple to set up thanks to its official Terraform provider and the open-source modules made available on GitHub.
I love Databricks due to the fact that we can now deploy it in 15 minutes and it's ready to use. That's very nice since we often help our clients in deploying their first Data Platform with Databricks.
The solution is stable, with LTS Runtimes that have proven to remain stable over the years.
What needs improvement?
I would love to be able to declare my workflows as-code, in an Airflow-like way. This would help creating more robust ingestion python modules we can test, share and update within the company.
We would also love to have access to cluster metrics in a programmatic way, so that we can analyse hardware logs and identify potential bottlenecks to optimize.
Lastly, the latest VS Code extension has proven to be useful and appreciated by the community, as it allows to develop locally and benefits from traditional software best-practices tools like pre-commits for example.
For how long have I used the solution?
I've been using the solution for more than four years now, in the context of PoC to full end-to-end Data Platform deployment.
What do I think about the stability of the solution?
The product is very stable. I've been using it for three years now, and I have projects that have been running for three years without any big issues.
What do I think about the scalability of the solution?
It's very scalable. I have a project that started as a proof of concept on connected cars. We had 100 cars to track at first - just for the proof of concept. Now we have millions of cars that are being tracked. It scales very well. We have terabytes of data every day and it doesn't even flinch.
How are customer service and support?
I've had very good experiences with technical support where they answer me in a couple of hours. Sometimes it takes a bit longer. It's usually a matter of days, so it's very good overall.
Even if it took a bit of time, I got my answer. They never left me without an answer or a solution.
How would you rate customer service and support?
Positive
How was the initial setup?
The implementation is very simple to set up. That's why we choose it over many other tools. Its Terraform provider is our way-to-go for the initial setup has we are reusing templates to get a functional workspace in minutes.
Usually, we have two to five data engineers handling the maintenance and running of our solutions.
What about the implementation team?
We deploy it in-house.
What's my experience with pricing, setup cost, and licensing?
The solution is a bit expensive. That said, it's worth it. I see it as an Apple product. For example, the iPhone is very expensive, yet you get what you pay for.
The cost depends on the size of your data. If you have lots of data, it's going to be more expensive since your paper compute units will be more. My smallest project is around a hundred euros, and my most expensive is just under a thousand euros a week. That is based on terabytes of data processed each month.
Which other solutions did I evaluate?
We looked into Azure Synapse as an alternative, as well as Azure ML and Vertex on GCP. Vertex AI would be the main alternative.
Some people consider Snowflake a competitor; however, we can't deploy Snowflake ourselves just like we deploy Databricks ourselves. We use that as an advantage when we sell Databricks to our clients. We say, "If you go with us, we are going to deploy Databricks in your environment in 15 minutes," and they really like it.
Lately Fabric was released and can offer quite a similar product as Databricks. Yet, the user experience, the CI/CD capabilities and the frequent release cycle of Databricks remains a strong advantage.
What other advice do I have?
We're a partner.
We use the solution on various clouds. Mostly it is Aure. However, we also have Google and AWS as well.
One of the big advantages is that it works across domains. I'm responsible for a data engineering team. However, I work on the same platform with data scientists, and I'm very close to my IT team, who is in charge of the data access and data access control, and they can manage all the accesses from one point to all the data assets. It's very useful for me as a data engineer. I'm sure that my IT director would say it's very useful for him too. They managed to build a solution that can very easily cross responsibilities. It unifies all the challenges in one place and solves them all mostly.
I'd rate the solution nine out of ten.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Microsoft Azure
It's really useful for big datasets
What do you like best about the product?
Lesser Running time, handling big datsets, user-friendly platform
What do you dislike about the product?
Cluster active time is less, active time should be increased when not in use
What problems is the product solving and how is that benefiting you?
Helps in Data Warehousing on big datasets, building data engineering pipeline and ML models end to end
Best conference in data and analytics now
What do you like best about the product?
The extensive detailed content that is not shy from being deeply technical and in the same time industry-focused to depth. The amount of information covered here is incredible from data & analytics to GPUs to K8s to industry discussions
What do you dislike about the product?
there is no on-demand hands-on labs. and we cannot download all slides for all sessions. The timing of the sessions was also a challenge.
I didn't like the scheduling features too.
I didn't like the scheduling features too.
What problems is the product solving and how is that benefiting you?
Mainly it solves the ability to ask questions against the data regardless of their nature (streaming and batch); without the need to move the data around to other platforms etc.
Recommendations to others considering the product:
Follow the guidance
Fantastic Data Engineering and Data Science platform
What do you like best about the product?
Best Data Engineering features. Love it.
What do you dislike about the product?
Very expensive. Wish it would cost less.
What problems is the product solving and how is that benefiting you?
It is solving data ingestion problems and dataset preparation problems. This is benefitting me by making automated Data Engineering easy to implement by myself.
Build fast and reliable data pipelines batch/streaming supporting unstructured and structured data
What do you like best about the product?
Data Governance and Simplified Schema.
Support for unstructured along with structured data enabling support for any use cases to build machine learning, business intelligence, and streaming features. Also support Streaming Live Tables which is a new feature in latest version.
Support for unstructured along with structured data enabling support for any use cases to build machine learning, business intelligence, and streaming features. Also support Streaming Live Tables which is a new feature in latest version.
What do you dislike about the product?
performance benchmark needs to be verified with other competitors like Snowflake. Looks like(as per the documentation) the latest version is blazing fast.
What problems is the product solving and how is that benefiting you?
Unstructured and Structured data in a unified repository to build Machine Learning and BI capabilities.
Recommendations to others considering the product:
How simple to deliver BigData applications. Hassle-free administration and maintenance.
The Future of Data is Databricks Lakehouse!
What do you like best about the product?
Databricks support services needs a special nod, they have come to the table day in and day out with solutions and ideas to better our enterprise environment for any channel we were working with. Also, the support within the platform multi-format data, github support, and an open-source experience are what I love best about the platform.
What do you dislike about the product?
I'd like a better more seamless way to integrate development teams into our environment. This does feel a bit challenging at times with some minor re-work, but I think there could be progress made in this area. Particularly speaking to how you manage workloads for teams with multiple workspaces.
What problems is the product solving and how is that benefiting you?
It's solving many problems but mostly eliminating siloed development environments and centralization of code as a service for global development and product creation. We have in the past had many areas of sources of truth in data and now we are building a more streamlined ecosystem of data services with a centralized solution of truth for Data Opreations.
Data bricks review
What do you like best about the product?
Data bricks Lake House architecture seems promising
What do you dislike about the product?
I don't see much video tutorials. If we have tutorials for free we can learn more
What problems is the product solving and how is that benefiting you?
We are building etl platform where we use data bricks
I love the intuitive user interface.
What do you like best about the product?
Easy to use for a SQL only technical person.
What do you dislike about the product?
I would like SQL query designer to make the text
What problems is the product solving and how is that benefiting you?
Geocoding data natively with Precisely's Geocoding SDK.
Recommendations to others considering the product:
The Lakehouse is an easy to use platform.
Free yourself from the DataWarehouse
What do you like best about the product?
Your data is not trapped with a propietary tool or platform.
You can access your data thru an SQL interface or interface it with Python, Scala or R API.
Much faster than other DWs.
You can access your data thru an SQL interface or interface it with Python, Scala or R API.
Much faster than other DWs.
What do you dislike about the product?
The notebook editor could be better for copying and pasting.
Also it needs better Find lookup function for really long cells.
The new feature identity column feature for tables, needs better support to Create or Alter a table from pyschark commands.
Also it needs better Find lookup function for really long cells.
The new feature identity column feature for tables, needs better support to Create or Alter a table from pyschark commands.
What problems is the product solving and how is that benefiting you?
One central repository single point of truth for all your data lake, and lakehouse governance across companies, customers, departments etc.
One real single point of truth!
One real single point of truth!
Recommendations to others considering the product:
look no further switch
The Databricks Lakehouse Platform provides a seamless experience for the user community.
What do you like best about the product?
The platform integrates the best of the warehouse and lake.
What do you dislike about the product?
As of now, there is nothing to report on disadvantages.
What problems is the product solving and how is that benefiting you?
Being on the Azure stack allows for a seamless integration across the platform of databricks to Power BI.
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