Databricks Reviews

Our company uses the solution for series-based and panel-based migrations. We collect and store user requirements, use apps to fetch data, and provide customers with better data for business reports. There are 30 to 40 users in our company.

The solution is an impressive tool for data migration and integration. 

The run time is very quick.

The solution has some scalability and integration limitations when consolidating legacy systems.

I have been using the solution for two years. 

The solution is stable. 

It is not really scalability but more about the combination of the structure, consolidation, and different formats we can split and merge. We do a lot of things while storing the target operational model. Snowflake is more flexible and scalable in that regard. 

We have contacted technical support a lot about replicating values in PDF files. So far, they have not been able to provide a viable solution. 

The setup is of average difficulty but tougher than Snowflake. 

Deployment is easy and run time is quick. 

We implemented the solution in-house.

One resource manages services for end-to-end monitoring and maintenance activities. 

The solution is based on a licensing model. Updates occur automatically by the task base. 

Snowflake is quite impressive in comparison to the solution because there is flexibility in the way you consolidate. In contrast, the solution has some scalability and integration limitations when consolidating legacy systems. Tool wise, Snowflake is easy from the technical perspective because connectors are included.  

We are evaluating options for one particular use case. The customer wants to replicate values from PDFs and enter them in the data model. We contacted the solution's technical support but do not yet have a viable answer. There are gaps in what we do and how we capture. The only option right now is for the customer to manually upload values that we integrate using Synapse to consolidate report data. We haven't yet found another tool that maps to meet our customer's requirement. 

I rate the solution a seven out of ten. 

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.

I work in a project where I build data pipelines using Azure Data Factory. I ingest data from on-premises to Azure Data Lake. After that, I perform transformations using Databricks notebooks and Spark, building the Databricks bronze, silver, and gold layers. We export reports from the gold layer.

Recently, we started using Databricks in our organization. It helps integrate data science and machine learning capabilities.

The Unity Catalog is a central governance for all data around the workspaces, and also Databricks' integration capabilities with cloud services like Azure Event Hub and Azure Data Factory. It is user-friendly for data processing, and Spark is a strong language for big data processing.

Performance could be improved. It is crucial to check coding, configure Spark correctly, implement caching, and monitor performance metrics to enhance performance.

I have used Databricks for over two years.

I would rate stability as eight out of ten. It is quite stable.

Databricks is perfect for scalability. It is easy to scale clusters.

I haven't faced any issues requiring customer support, so I don't have experience with their customer support.

Positive

We used Informatica before, which is perfect for data management solutions. We started using Databricks for its capabilities in data science and machine learning.

I would rate the initial setup as nine out of ten. It is quite easy for someone experienced with Spark.

For my company, it's okay to upgrade to Databricks because it's comparable in price to Informatica. It is not considered expensive for the company.

For machine learning, I used Python and its libraries manually. Prior to Databricks, there was no special tool used for these purposes.

If a company focuses on data science and machine learning, I recommend using Databricks. It's a great solution in this field. For data management needs, Informatica is advantageous due to its comprehensive tools.

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.

We use it for data analysis and testing of high volume web user behavioral data.

Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes.

I'm starting to build a solution using Delta Live Tables and Delta Live pipelines, and it is proving to be exceptionally easy to use. I have also been able to quickly implement a pipeline.

Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present.

I've been using Databricks for a year.

It is a stable and reliable solution. I'd rate stability at eight out of ten.

Databricks is absolutely scalable, and I'd rate scalability at eight out of ten. We probably have between 60 and 100 users in our organization, and we hope to increase usage in the future.

The technical support staff we have worked with have been amazing. They helped us initially with our Delta Live pipelines. I would give them a rating of ten out of ten.

Positive

I have previously worked with Apache Hadoop, and Databricks is definitely a better product. It's much easier to get data quickly in Databricks. As a result, a lot of the drudgery is taken away. Whereas with Hadoop, it's a bit more tricky to get data together.

We're charged on what the data throughput is and also what the compute time is.

I'd strongly recommend giving Databricks a try. We have found it to be a fantastic tool that has accelerated some of our solutions. We're an AI-heavy shop, and there are a lot of data scientists using the MLflow capabilities. I hear a lot of good things from that side as well. From a data analysis point of view, Databricks has been fantastic, and I would rate it at eight on a scale from one to ten.

I worked with Databricks pretty recently. The particular design processes involved in Databricks were also a part of that specific design/architectural process.

We have used the solution for the overall data foundation ecosystem for processing and storage on a Delta format. We have also seen use cases where we were trying to establish advanced analytics models and data sharing where we leverage the Delta Sharing capabilities from Databricks.

A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem.

There are some aspects of Databricks, like generative AI, where they are positioning things like DALL-E. They're a little bit late to the game, but I think there are some things that they are working on. Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster, and even though they are fast, I'm not sure how they'll catch up and get adopted because there are strong players in the market.

Databricks is coming up with a few good things in terms of integration. But I have to put one point forward that covers multiple aspects, which is the ease of use for the end user while operating this particular tool. For example, a tool like ADS gives you a GUI-based development, which is good for the end user who does development or maintenance. Looking at the complexities of data integration, a GUI might not be easy, but Databricks should embrace something on the graphical user development front because it is currently notebook-driven. Also, in terms of accessing the data for the end user, Databricks has an SQL interface, similar to earlier tools like SQL Management Studio. Since people are mostly comfortable with SSMS already or not, Databricks can build integration to known tools for data access, and that also helps, apart from what they're doing. I would like to see improvements with respect to user enablement, which is a good part of enterprise strategy. I would like to see their integration with a broader ecosystem of products. If you have to do data governance in tools like Microsoft Purview, it's manual and difficult. Now, I'm unsure if that momentum must be from Databricks or Microsoft. But it would be good if Databricks had some open interfaces to share metadata, which could be viewed in tools enabling data governance like Collibra, Purview, or Informatica. The improvement has to do with user and metadata integration for tools.

I've worked with Databricks for over five or six years, but it's been on and off.

The solution is scalable. In this particular ecosystem, there is no one else who can catch up with Databricks for now.

Databricks' customer support is very good. They have a lot of ways in which they interact with vendors and service partners across the globe. They have periodic touch-up sessions with vendors, where their engineers answer your questions.

The implementation is not challenging because the solution integrates well with the platforms on which they are established, whether it's Azure, AWS, or GCP. The solution is not difficult to set up, but you'd probably need a technical user to operate it.

It's the same story with maintenance, where you'd need a technically proficient person with programming knowledge to maintain it.

Databricks integrates many enterprise processes because data processing and AIML are a small part of a larger ecosystem. Databricks has been a part of other platforms, and they are trying to establish their platform, which is a good direction.

Most of the capabilities of the underlying platform can be leveraged there. But the setup isn't difficult if the database lacks some capability, you can't find it in the database, or you're not comfortable with a certain feature in the database. It integrates well with the underlying platform. For example, with scheduling, let's say you are uncomfortable with workflow management. You can utilize integrations with EDA for any other tool and probably perform scheduling. Even if what you're trying to do is not easy, it is enabled with integration. Either they build a required feature in their tool later on, like a GUI, or you perform integrations to make the features possible.

We did evaluate licensing costs, but it had more to do with the Azure ecosystem pricing since whatever we are doing has more to do with Azure Databricks. Many optimizations are recommended, but we haven't exercised those for now. But considering that the processing is a bit more efficient, the overall price won't be much different from what it could be for any other similar component or technology. We haven't had specific discussions with Databricks' folks on pricing.

My advice to users who would like to start working with Databricks is that it is a good solution to work with for data integration and machine learning. Databricks is maturing for other use cases, so there are two points to be considered. One is that you need to evaluate how they will mature, which will be on a case-to-case basis. Second, how will it align with the overall platform story? There will be many overlapping aspects over there as Databricks expands its capabilities. In that case, it must be considered that if those capabilities overlap, how will the underlying platform vendors handle it? How would that interplay happen if many of Databricks' new capabilities align with Microsoft Fabric? That has to be very carefully considered. Otherwise, if you utilize those new capabilities, there might be a discontinuity where you cannot use Databricks because the platform does not support that.

If I specifically talk about Spark-based processing transformations, the data integration story, and advanced stability, I would rate Databricks around eight out of ten. However, with respect to new capabilities like cataloging, data governance, and security integration, I rate Databricks around five because it has to establish these features. And since Databricks integrates with platforms, we must see the interplay with the platforms' capabilities.

I overall rate Databricks a seven 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.

We use the solution for data engineering. 

The tool helps us manage large amounts of data. 

The simplicity of development is the most valuable feature. 

Databricks has a lack of debuggers, and it would be good to see more components. 

Another issue is that the D4 data format keeps changing on our cluster. This doesn't affect me much because I use functions to define it, but it is very frustrating for some more casual users. One day the output will be in a particular format, and then it becomes an object without us changing the cluster configuration. As a small team, we don't have the capacity to dig deeply into the issue, which has been frustrating.

We have been using the solution for three years. 

The solution's stability is good. 

The product is scalable. We're a small organization with 12 users, and we don't currently have any plans to increase our usage.

We see an ROI from Databricks. 

I would rate the solution seven out of ten. 

It's a good solution and more for handling large amounts of data. Databricks is better as a batch processing system than as an interactive system. The performance is a little disappointing because the memory processing is supposed to be excellent, but it's not as competitive as some other solutions out there in this regard. Even classical databases can respond and process faster.