TrustRadius: an HG Insights company

Databricks Data Intelligence Platform Reviews & Insights

Score8.5 out of 10

90 Reviews and Ratings

Get a Demo

Community insights

TrustRadius Insights for Databricks Data Intelligence Platform are summaries of user sentiment data from TrustRadius reviews and, when necessary, third party data sources.

Pros

User-Friendly SQL: Users have found the SQL in Databricks to be user-friendly, allowing them to easily write and execute queries. Several reviewers have praised the intuitive nature of the SQL interface, making it accessible for users of different skill levels.

Enhanced Collaboration: The enhanced collaboration between data science and data engineering teams is seen as a positive feature by many users. They appreciate how Databricks facilitates seamless communication and knowledge sharing among team members, ultimately leading to improved productivity and efficiency.

Versatile Integration: The integration with multiple Git providers and the merge assistant is highly valued by users. This feature allows for smooth version control and simplifies the collaborative development process. With this capability, developers can easily manage their codebase, track changes, resolve conflicts, and ensure a streamlined workflow.

Databricks Data Intelligence Platform Reviews

6 Reviews
Mid-sized Companies (51-1,000 employees)

Most collaborative Data Science & AI workspace !

Rating: 10 out of 10
Incentivized

Use Cases and Deployment Scope

I use Databricks Lakehouse Platform in my Data Scienc & AI consulting company to help various business entities with data-driven solutions. The platform can handle large and complex data sets and enable us to build and deploy applications using the latest technologies. The opennness of Databricks allows us to seamlessly integrate and adapt to our clients requirements :
* Creating dashboards with Tableau, Redash, Qlik,
* Feed their CRM tool like Salesforce, SAP,
* developing chatbots for Knowledge Management
* Serve ML models behind API endpoints.
Databricks Lakehouse Platform is a versatile and open product that saves us a lot of time, help us control cloud cost and human resources energy !

Pros

  • Enhanced Data Science & Data Engineering collaboration
  • Complete Infrastructure-as-code Terraform provider
  • Very easy streaming capabilities
  • Multiple Git providers integration with merge assistant

Cons

  • VsCode IDE support for local development
  • Python SDK for Workflows
  • Poetry support

Likelihood to Recommend

Databricks shines when you are working with a growing team of multiple data professions. By providing an easy to instantiate common workspace for Data Engineers, Data Scientist, ML Engineers and Data Analyst, fully integrated with Active Directory security, it makes your data projects more likely to go to production. No need to switch between tools, to transfer the data, the Unity Catalog will centralize all the assets and all your data citizens will find it in a second and can benefit from the Spark engine whatever language they use.

It would be less appropriate for very small data projects as the entry cost may be high. Yet, if the data is meant to grow, Databricks will horizontally scale without requiring a re-write of your codebase

Alternatives

Azure Synapse Analytics and Snowflake
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities.
It works out-of-the-box but still allows you intricate customisation of the environment.
I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
AR
Axel Richier
Tech Lead Data Engineer in Engineering at Ekimetrics (Information Technology & Services, 201-500 employees)
Vetted Review
Databricks Data Intelligence Platform
4 years of experience
View profile

The wonders of all your data analysis in one place

Rating: 8 out of 10
Incentivized

Use Cases and Deployment Scope

It is currently used by our Data and Product teams in order to perform deep dives analysis on how our current metrics are performing (KPIs, OKRs), to develop tools for metric predictions based on data models in languages such as SQL and Python while mixing them and giving to the entire company visibility of the results with graphs via shared workspaces

Pros

  • Cross company shared workspaces for unified comprehension of the data
  • Combining different languages such as SQL and Python in one single space in order to make data analysis
  • Quick execution of highly complex queries

Cons

  • How graphs are created, it requires a certain level of expertise in the platform and it could be more intuitive and user friendly
  • More guidance on the basics, since some of the new users come from different platforms expecting a similar UI
  • An option where all the tables are shown with their respective fields, when a DB is selected for a query

Likelihood to Recommend

I reckon is an amazing platform for users with a certain level of expertise for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, also it is very useful when it comes to cross company shared workspaces for unified comprehension of the data.

it is less appropriate for users who don't have full knowledge of the tables they are going to query on and need more support on the data, since the platform doesn't give an option to see what are the fields in a table before even querying it
VU
Verified User
Manager in Product Management (Financial Services company, 201-500 employees)
Vetted Review
Databricks Data Intelligence Platform
1 year of experience

My Lakehouse experiences

Rating: 10 out of 10
Incentivized

Use Cases and Deployment Scope

We build all our data pipelines with Databricks Lakehouse technology. It is reliable and the tech support from Databricks is very good.

Pros

  • Better performance through consolidating small files in delta tables
  • ACID functionality on delta tables
  • Live delta tables

Cons

  • Make it easier to test features in public preview, like delta live tables.

Likelihood to Recommend

We can run data pipelines and use SQL Analytics to build dynamic dashboards for clients. The same platform can be used for running ML pipelines.
SP
Stefan Panayotov
Lead Data Engineer in Information Technology at Cadent (Broadcast Media, 201-500 employees)
Vetted Review
Databricks Data Intelligence Platform
6 years of experience
View profile

Databricks--a good all-rounder

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

We use Databricks Lakehouse Platform (Unified Analytics Platform) in our ETL process (data loading) to perform transformations and to implement the toughest loading strategies on huge datasets. It is very easy to understand and it can connect to almost all the modern data formats like Avro, Parquet, and JSON. It supports almost every popular cloud platform, like Azure and AWS, and offers better performance in terms of data processing speed.

Pros

  • Complex transformations
  • Supports major data sources
  • Great performance

Cons

  • User interface to connect data sources
  • Pricing
  • Community support

Likelihood to Recommend

Databricks Lakehouse Platform (Unified Analytics Platform) can be used to process raw data from any system like IoT, structured, and unstructured data sources. Since it supports Pyspark and Scala to do data processing, it can do any complex business transformation very easily. Also, the Databricks Lakehouse Platform (Unified Analytics Platform) architecture is very similar to Big Data; it can process huge datasets from Hadoop systems and machine learning models in minutes.
VU
Verified User
Professional in Information Technology (Information Technology & Services company, 201-500 employees)
Vetted Review
Databricks Data Intelligence Platform
2 years of experience

Databricks Review

Rating: 9 out of 10
Incentivized

Use Cases and Deployment Scope

We leverage Databricks (DB) to run Big Data workloads. Primarily we build a Jar and attach to DB. We do not leverage the notebooks except for prototyping.

Pros

  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.

Cons

  • Better Localized Testing
  • When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
  • Graphing Support went non-existent; when it was one of their compelling general engine.

Likelihood to Recommend

  • DB generally fits 95% of what you need to do
  • Primarily the ability to transform data and or do ad-hoc DS work

Alternatives

Cloudera Data Science Workbench
When we started using it, only the notebook experience was mature. However, DB was very helpful giving us direct support to get onto their platform. Really there was little in the way to compare to them at the time. AWS has services but not the same low-cost angle.
VU
Verified User
Director in Information Technology (Financial Services company, 201-500 employees)
Vetted Review
Databricks Data Intelligence Platform
2 years of experience

If you want to be an effective ML learner, use Databricks

Rating: 7 out of 10
Incentivized

Use Cases and Deployment Scope

I actually use Databricks for experiments and research for my master's program. I mostly use it to implement Python codes and testing the viability of the programs that I write. Many individuals in the Computer Information System department are using this software platform to implement programs. It is a good tool for us to learn [and] includes a community forum that is rather helpful if you are self-learning and have questions.

Pros

  • There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
  • There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.

Cons

  • The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
  • Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.

Likelihood to Recommend

Right now, I am learning about Spark ML and general machine learning concepts. It is a good practice space to run different Spark ML codes. Databricks does provide valid errors and detailed descriptions of where I can fix my code. And to run a set of codes is very easy to maneuver around. If you do not know how to code, it might be less appropriate to use Databricks. But then again, they do have a large community where help can be found.

Alternatives

Azure ML
I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.
AL
Ann Le
Freelance Translator in Corporate at ZOO Digital Group plc (Entertainment, 501-1000 employees)
Vetted Review
Databricks Data Intelligence Platform
1 year of experience
View profile