I am a Databricks service partner, and my customers use Azure Databricks and Data Factory.
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.
Data Lake but combined with Datawarehouse benefits
What do you like best about the product?
It offers ACID transactions which is a massive suppport for data consistency, along with this, the leveraging features such as Time travel and schema evolution comes real handy while builidng a scalable solution. In addition of all above,it reduce data storage costs all while not compromising on powerful distributed programming.
What do you dislike about the product?
With all the features combines, it truly is a powerful tool however, it can be a real challange for new users to master it. For BI users, analysts, who arent skilled with programming, may find it difficult to understand the workflow. Moreover, the community for this tool is currently relatively small and hence minimzing community support.
What problems is the product solving and how is that benefiting you?
The business requires to keep to update powerBI dashboard reports which are ever increasing day by day. As a solution, we are utilizing the lakehouse's ACID, and features such as Schema evolution to clean and transform data and build BI dashboard using the now cleaned data.
This solution has eliminated dependency on our already saturated datawarehouse resources. This has also helped in debugging as all data is processed and resides in one place.Last but not the least, this has reduced costs of our datawarehouse by 20%
This solution has eliminated dependency on our already saturated datawarehouse resources. This has also helped in debugging as all data is processed and resides in one place.Last but not the least, this has reduced costs of our datawarehouse by 20%
Databricks Unleashed - Unlocking Data Insights and Streamlining Analytics with Databricks
What do you like best about the product?
Unified analytics platform for batch & stream data processing
Auto loader, schema evolution capabilities with CDC usage
Delta Live table Serverless Pipelines
Data Quality expectations
Databricks workflows
Databricks SQL warehouse - Photon SQL endpoints
Unity Catalog for data governance & security
Ease of use with partner connect & integartions
Auto loader, schema evolution capabilities with CDC usage
Delta Live table Serverless Pipelines
Data Quality expectations
Databricks workflows
Databricks SQL warehouse - Photon SQL endpoints
Unity Catalog for data governance & security
Ease of use with partner connect & integartions
What do you dislike about the product?
cost is more when we use allpurpose cluster compared to DLT pipeline which won't suite for all use-cases
vendor lock-in if we use more databricks specific delta features
Learning curve for pyspark related stuff not for SQL coding
vendor lock-in if we use more databricks specific delta features
Learning curve for pyspark related stuff not for SQL coding
What problems is the product solving and how is that benefiting you?
Unified lakehouse platform for batch & stream processing
Building the catalog for centralized goverannce
Workflow orchestration
Integrations with cloud & data storage layers
Data sharing with external customer through delta sharing & marketplace
Building the catalog for centralized goverannce
Workflow orchestration
Integrations with cloud & data storage layers
Data sharing with external customer through delta sharing & marketplace
A Tool Box to the Modern Big Data Data Scientist
What do you like best about the product?
The upscale in storing and retrieving large quantities of data with its sdk to s3. In addition, great resources allocation support and additional tools such as clearml.
What do you dislike about the product?
The compatibility to pandas is lacking due to the fact that it is mainly used by me with pyspark which didnt allow an optimal usage for the various pandas libraries.
What problems is the product solving and how is that benefiting you?
Retrieving and querying a very large data warehouse on s3 (several hunders of T'). Performing basic filtering and quering on the data and running a ML experiment on huge amounts of data.
Great tool for data exploration and development, no so much for production pipelines
What do you like best about the product?
Easy to set up processes and iterate.
Shareability
Shareability
What do you dislike about the product?
Not tailored for production integration
Hard to incorporate without being databricks aware, which leads to a vendor lock
Hard to incorporate without being databricks aware, which leads to a vendor lock
What problems is the product solving and how is that benefiting you?
Gaining data visibility
Developing spark jobs towards production
Developing spark jobs towards production
A powerful solution that is easily integrated into a variety of platforms
What is our primary use case?
What is most valuable?
It's very simple to use Databricks Apache Spark. It's really good for parallel execution to scale up the workload. In this context, the usage is more about virtual machines.
Using meta-stores like Hive was optional, and the solution is good for data science use cases. With the Authenticator Log, Databricks is good for data transformation and BI usage. We have a platform.
What needs improvement?
I would like more integration with SQL for using data in different workspaces. We use the user interface for some functionalities, while for others, we have to use SQL to create data sets and grant permissions. For example, when creating a cluster, we have to create it with some API or user interface. Creating a cluster with some properties using SQL grants the possibility of using SQL syntax. Integration with SQL will make Databricks easier to use by people who have experience with databases like Lakehouse, and they would be able to use the data lake and BI. More integration will help have one point of view for everyone using SQL syntax.
Integration with Kubernetes could also be good for minimizing the price because you can use Kubernetes instead of virtual machines. But that won't be easy.
For how long have I used the solution?
I have worked with the solution for four or five years, with some experience since 2016.
What do I think about the stability of the solution?
The solution is stable. The only problem with stability would be that people are not using it efficiently.
What do I think about the scalability of the solution?
The solution is good for scalability.
How was the initial setup?
When we have administration experience, the solution is not difficult to deploy. Technically, however, it's difficult because governance is more complex. For example, I have two warehouses on Databricks, which are clusters in this workspace, and we have to switch from workspace to workspace to have all this information. There is a system table that has all this, but I don't know if everyone can use these tables.
What's my experience with pricing, setup cost, and licensing?
Databricks are not costly when compared with other solutions' prices.
Which other solutions did I evaluate?
Databricks's functionalities are as good as solutions like Snowflake, BigQuery, and Redshift.
What other advice do I have?
People sometimes do not use the solution efficiently. They misunderstand databases, the usage of tables, and the performance. Many data engineers are very junior and don't have skills in that. Stability is more a customer problem than a problem with the product itself. One possible problem with the product is that there's no method to pause the usage of something. For example, we have to use the meta server or the data catalog in Synapse. But in Databricks, we have a choice to use a catalog or not, or Hive, which is always integrated, but we have to choose whether to use it or not. Many customers directly use the passes on Databricks, which causes performance and governance problems.
I can offer a lot of advice on Databricks, and one is to use meta stores like Unity Catalog or Hive Metastore. For incoming use cases, it's better to use Unity Catalog.
I rate Databricks a nine out of ten.
Processes large data for data science and data analytics purposes
What is our primary use case?
It's mainly used for data science, data analytics, visualization, and industrial analytics.
What is most valuable?
Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours.
So that's why it's quite convenient to use for data science, for training machine learning models. By using more computing power, you can make it even faster.
What needs improvement?
There is room for improvement in visualization.
For how long have I used the solution?
I used it for two years. I worked with the latest update.
What do I think about the stability of the solution?
I would rate the stability a nine out of ten. I didn't face performance drops.
What do I think about the scalability of the solution?
I would rate the scalability an eight out of ten.
How are customer service and support?
Databrick's support is great. If we need any support, they are very quick with it. And they genuinely want you to use Databricks. So, whatever we ask them, they come up with multiple solutions to problem statements. That's really good.
Overall, the customer service and support are very good.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
I personally prefer using Databricks. However, we also considered using Snowflake, but the pricing was different. It's price per query.
So, as per your storage, a data scientist or a data analytics team needs to query again and again, which does not suit a data-heavy organization.
What was our ROI?
It's a good return on investment for Databricks from a delivery perspective. Delivered multiple dashboards. So, it's quite a good return on investment. And being a small organization, everyone can use Databricks, and cost-wise, it's also good for small organizations.
Which other solutions did I evaluate?
If the company is a startup, Databricks might be suitable. If a big company needs a lot of storage, Teradata might be best for them. It depends on the situation.
What other advice do I have?
Overall, I would rate the solution a eight out of ten. I would definitely recommend this solution for small organizations.
Which deployment model are you using for this solution?
Private Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Data + AI should be unified
What do you like best about the product?
Before exploring Databricks I had worked on many tools & their integration to make Data flow simpler but Databricks is doing wonders in this space by unifying Data + AI at one platform & also giving smooth UI to Intereatc & get your job done in a simplified manner
What do you dislike about the product?
Although my experience is really good but what I have observed is that data bricks is doing wonders with Azure most of the time, I think they introduce Databricks as PAAS with other platforms like AWS especially.
What problems is the product solving and how is that benefiting you?
I think overall the Platform is really good but I loved their Data Governance support using Catalog is really a solution to most of the Data Governance problems.
An easy to setup tool that provides its users with an insight into the metadata of the data they process
What is our primary use case?
My company uses Databricks to process real-time and batch data with its streaming analytics part. We use Databricks' Unified Data Analytics Platform, for which we have Azure as a solution to bring the unified architecture on top of that to handle the streaming load for our platform.
What is most valuable?
The most valuable feature of the solution stems from the fact that it is quite fast, especially regarding features like its computation and atomicity parts of reading data on any solution. We have a storage account, and we can read the data on the go and use that since we now have the unity catalog in Databricks, which is quite good for giving you an insight into the metadata of the data you're going to process. There are a lot of things that are quite nice with Databricks.
What needs improvement?
Scalability is an area with certain shortcomings. The solution's scalability needs improvement.
For how long have I used the solution?
I have been using Databricks for a few years. I use the solution's latest version. Though currently my company is a user of the solution, we are planning to enter into a partnership with Databricks.
What do I think about the stability of the solution?
It is a stable solution. Stability-wise, I rate the solution an eight to nine out of ten.
What do I think about the scalability of the solution?
It is a scalable solution. Scalability-wise, I rate the solution an eight to nine out of ten.
My company has a team of 50 to 60 people who use the solution.
How are customer service and support?
Sometimes, my company does need support from the technical team of Databricks. The technical team of Databricks has been good and helpful. I rate the technical support an eight out of ten.
How would you rate customer service and support?
Positive
How was the initial setup?
The initial setup phase of Databricks was good. You can spin up clusters and integrate those with DevOps as well. Databricks it's quite nice owing to its user-friendly UI, DPP, and workspaces.
The solution is deployed on the cloud.
The time taken for the deployment depends on the workload.
What's my experience with pricing, setup cost, and licensing?
I cannot judge whether the product is expensive or cheap since I am unaware of the prices of the other products, which are competitors of Databricks. The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts.
What other advice do I have?
It is a state-of-the-art product revolutionizing data analytics and machine learning workspaces. Databricks are a complete solution when it comes to working with data.
I rate the overall product an eight out of ten.
Most advanced data platform
What do you like best about the product?
Databricks has been developing an excellent product with new features being added constantly. ETL/ELT, dbt integration, integration with external sources, and many more features make it a product that is versatile and highly effective. Managment of permissions is also extremely easy.
What do you dislike about the product?
Some automated recommendations on optimizing jobs are lacking, so you could end up spending quite a lot of money without the best performance. The platform is also not for small scale use-cases.
What problems is the product solving and how is that benefiting you?
Databricks is providing the fundamental building block of our data warehouse + data lake + data processing platform from which other systems receive analytics data.
It is a one stop solution to collaborate for all Data Engineering and Data Science activities
What do you like best about the product?
Collaboration,Feature store,MLOps,scheduling
What do you dislike about the product?
Don't have a way to put dependencies between workflows
What problems is the product solving and how is that benefiting you?
It has helped me build the datalake for business intelligence usecases
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