We use the solution for data analytics of industrial data.

We extensively use the product’s notebooks, jobs, and triggers. We can create activities. Wherever translation is required, we use Databricks. The product fulfills our customer requirements. It is a cost-effective solution.

The product should provide more advanced features in future releases.

I have been using the solution for six months.

Our data was not too huge. It worked well. It is easily adaptable.

The tool is scalable. We can make it available for a larger audience.

The initial setup is not that difficult. I rate the ease of setup a seven out of ten. The solution is cloud-based. We use native services like Data Factory for orchestration. Sometimes, the customers require us to use Amazon as the cloud provider instead of Azure.

The pricing is average.

There are many services which are coming up. They are still in the preview stage. Overall, I rate the product an eight out of ten.

The product has helped in data fabrication. 

Databricks has helped us have a good presence in data. 

The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice. 

I have been using the product for more than six months. 

I rate Databricks' an eight out of ten. 

I rate the tool's scalability an eight out of ten. 

The transition to Databricks was smooth. 

Databricks' price is high. 

I rate the solution a nine out of ten. 

I am a Databricks service partner, and my customers use Azure Databricks and Data Factory.

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.

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.

I have worked with the solution for four or five years, with some experience since 2016.

The solution is stable. The only problem with stability would be that people are not using it efficiently.

The solution is good for scalability.

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.

Databricks are not costly when compared with other solutions' prices.

Databricks's functionalities are as good as solutions like Snowflake, BigQuery, and Redshift.

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.

It's mainly used for data science, data analytics, visualization, and industrial analytics.

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.

There is room for improvement in visualization.

I used it for two years. I worked with the latest update. 

I would rate the stability a nine out of ten. I didn't face performance drops.

I would rate the scalability an eight out of ten.

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.

Positive

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.

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.

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.

Overall, I would rate the solution a eight out of ten. I would definitely recommend this solution for small organizations. 

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.

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.

Scalability is an area with certain shortcomings. The solution's scalability needs improvement.

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.

It is a stable solution. Stability-wise, I rate the solution an eight to nine out of ten.

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.

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.

Positive

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.

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.

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.

Our primary use case is in our project; we are dealing with Duo Special Data, where we need a lot of computing resources. Here, the traditional warehouse cannot handle the amount of data we are using, and this is where Databricks comes into the picture. 

The processing capacity is tremendous in the database. We are dealing with Azure as storage, so we have not faced any challenges. And also the connectors to different data sources. Moreover, it is not a language-dependent tool. Therefore, development also takes place faster. It is one of the best features of Databricks.

There is room for improvement in the documentation of processes and how it works. I was trying to get one of the certifications, so I saw an area of improvement there. 

I have been using Databricks for eight to nine months.

It is a stable product for us. We didn't see any challenges. 

There are around 30 to 35 users in our organization. 

The initial setup was easy because the third-party team made the clusters for us. 

A third-party team enabled the cluster to make the setup easy for us. 

I would advise using it based on the use case because it easily handles big data. It is your go-to tool if you are dealing with massive data. 

Overall, I would rate the solution a nine out of ten. The tool performs well in various use cases, availability of documentation online, and compatibility with big data systems like GCP, Azure, or AWS.