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.
Intuitive and Powerful
What do you like best about the product?
As a frequent user of Databricks, it has made my life so much easier by simplifying processes and allowing me to develop proof-of-concept designs rapidly. The orchestration of notebooks via workflows provides excellent visualization and enables me to conduct real-time demos for members on the business side. In addition, the integration with Azure and AWS makes it so that Databricks does not operate in isolation and allows me and other engineering team members to transform large amounts of data that is ingested via our enterprise pipelines.
What do you dislike about the product?
There can sometimes be issues integrating Databricks workflows with open source frameworks, often requiring lots of debugging and trial and error. Additionally, I've been told that the platform can be pretty expensive.
What problems is the product solving and how is that benefiting you?
The Databricks Lakehouse Platform allows me to create and deploy workflows to orchestrate and test proof-of-concept ideas in our organization. This will enable us to validate ideas and develop presentations for the organization's business side.
Excellent solution to unlock data analytics full power
What do you like best about the product?
The infrastructure is pretty straightforward. I started out using the Community edition before switching to the premium version, but if you're a student or working on one-off projects, the Community edition should be more than sufficient.
What do you dislike about the product?
Finding some answers can be challenging at times because there aren't many Pyspark users, forums, or resources available.
What problems is the product solving and how is that benefiting you?
People who are not very proficient in coding can nonetheless gain useful insights from the data utilizing notebooks prepared by data scientists. I've been accustomed to Databricks and creating PySpark programs pretty easily. Databricks have a great ability to manipulate data and perform any desired action.
Excellent for all sorts of data analytics
What do you like best about the product?
The versatility and scalability are the best features for us. We currently use SQL, R, Python, SQL, Spark and Scala with Databricks. It's impressive how seamless this experience is for different teams with different use cases and skill sets. The interoperability across these languages and accessing data is a blessing and enables us to use a vast array of tools to solve problems.
What do you dislike about the product?
More insight into individual job costs would be a helpful feature that is currently lacking. Deploying code is also not as intuitive and the Git integration could be more powerful with an enhanced feature set.
What problems is the product solving and how is that benefiting you?
Easy integration across data sources and using a mixed bag of tools to perform advanced analytics. The mixed bag of tools available also means that we treat this as a one-stop solution and are able to serve analytics outputs to various consumers and stakeholders conveniently.
A lot easier to use than other tools I have used before.
What do you like best about the product?
Python notebooks that abstract away a lot of the complexity e.g. packages and infrastructure.
What do you dislike about the product?
The drop down menu/tool bar in the UI sometimes feels a bit clunky.
What problems is the product solving and how is that benefiting you?
Having multiple data pipelines that are able to be quickly developed and deployed, and then the output data sets made available to clients and internal teams
A great tool that simplifies your infrastructure
What do you like best about the product?
Opportunity to not manage Hadoop clusters.
What do you dislike about the product?
Cluster autoscaling doesn't always work as expected, and I would like to have more control over EC2 instances provisioning (availability to use multiple instance types in a single job/cluster, affinities, possibility to define some sort of topology, etc.). The whole experience is notebook focused.
What problems is the product solving and how is that benefiting you?
Enabling data-driven decisions by analyzing huge amounts of data
Easy, fast and Reliable
What do you like best about the product?
Ease of access to multiple data sources and we can change the code to python to SQL,Scala etc it is impressive.
What do you dislike about the product?
Not able to create interactive visualization
What problems is the product solving and how is that benefiting you?
Major problem solving is of data warehousing with its multilayer data models
Excellent experience so far!!!!
What do you like best about the product?
We employ Python, Spark, and SQL to develop ELT pipelines, and Databricks is the most reliable and user-friendly option available. Developers may concentrate on writing code, creating pipelines, and creating models rather than spending time setting up the environment because it is very simple to do so.
What do you dislike about the product?
Knowledge of the cost model and recommendation engine to reduce burned DBUs There is an Overwatch notebook that offers general statistics about the environment, but it isn't developed enough and it also doesn't show you the cost of the infrastructure used in the back cloud kitchen. Platform as a whole is excellent.
What problems is the product solving and how is that benefiting you?
Two things to mention here.
- It unites all data teams from around the organization on one platform, reducing the need to maintain multiple copies of the same data.
- Because computation and storage are no longer linked, resizing any kind of warehouse environment is no longer necessary to boost compute capacity.
- It unites all data teams from around the organization on one platform, reducing the need to maintain multiple copies of the same data.
- Because computation and storage are no longer linked, resizing any kind of warehouse environment is no longer necessary to boost compute capacity.
Works well in those grey areas of data management.
What do you like best about the product?
Easy to develop and maintain. Flexibility with transactional integrity.
What do you dislike about the product?
Can be more integrated with DW systems like Snowflake.
What problems is the product solving and how is that benefiting you?
Data Summary tables - I work with vast amounts of raw data. Being from the backend team, I do not need to ingest all of the data, but specific parts. Building summary tables by partly processing the data within the lakehouse framework is the most ideal solution I could find.
I am using databricks on my daily routine , experienced a wonderful experience
What do you like best about the product?
I like delta live table the most because of its working and the exposure it gave to the customer like data constraints and data quality check , that is best
What do you dislike about the product?
I dislike the python syntax and code to create the delta live tables , so confusing and need to be change the logic , sql syntax is best
What problems is the product solving and how is that benefiting you?
As Delta live tables comes into picture , we dont have to focused on the data quality just only focus is to read the file and all the work will be done by delta live tables pipelines
Swiss-Army Knife of Data Analytics
What do you like best about the product?
Databricks' versatility is its best feature. The range of languages and functionality afforded by Databricks is impressive. Thus far, I've written code in R, Python, SQL and Scala in Databricks. And im just getting started. But I've composed SQL code in both R and Python, executed in Databricks. And then we come to interoperability. Data written to SQL can be accessed by either R or Python. Parameters can be passed across SQL, R and Python via widgets or environmental variables. If you have an intractable data or analytics problem, Databricks would be my 'go to' to maximise the options as to how you could potentially code your way under, around or over the obstacles standing between your project and successful execution.
What do you dislike about the product?
The options for deployment of Databricks code from dev >> qa >> uat >> prod aren't as intuitive as I might like. This might have more to do with our current use of Azure Data Factory for orchestration. Setting up workflow natively in Databricks was quite straightforward. It seems to be accessing Databricks notebooks from Azure Data Factory in dev >> qa >> uat >> prod where we are perhaps creating problems for ourselves. Perhaps not a shortcoming in Databricks at all. Curious as to how Databricks would operate with AWS rather than Azure. Perhaps a better experience?
What problems is the product solving and how is that benefiting you?
Data migration, data modelling & reporting.
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