Databricks Data Intelligence Platform Professional, Scientific, and Technical Services Reviews & Insights

Score8.5 out of 10

90 Reviews and Ratings

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.

Reviews

3 Reviews

Professional, Scientific, and Technical Services

Most collaborative Data Science & AI workspace !

Rating: 10 out of 10

Incentivized

July 26, 2023

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

Axel Richier

Tech Lead Data Engineer in Engineering at Ekimetrics (Information Technology & Services, 201-500 employees)

Vetted Review

4 years of experience

View profile

Positive review for Databricks Lakehouse Platform

Rating: 9 out of 10

Incentivized

August 13, 2021

Use Cases and Deployment Scope

We currently use the Databricks Lakehouse Platform for a client. My team specifically uses it to data-mine, create reports and analytics for the client. Depending on where the data is stored, various Analytics teams in my company use different platforms - GCP, AWS, Databricks, etc.

Pros

Scheduling jobs to automate queries
User friendly - a new user can easily navigate through SQL/Python queries
Options to code in multiple languages (SQL, Python, Scala, R) and easy to switch with the use of the % operator

Cons

Errors can be difficult to understand at times
Session resets automatically at times, which leads to the temporary tables being wiped out from memory
Git connections are dicey
Very inconsistent with job success/failure notification emails

Likelihood to Recommend

Databricks is great for beginner as well as advanced coders. The interface is extremely user-friendly and the learning curve is quite short. It is well suited for automation where we can have scripts running late at night when the load is less and wake up to an email notification of success or failure. It is also well suited for writing codes that require the use of multiple languages (in some cases of data modeling)

The ability to store temporary/permanent tables on data lakes is a fabulous feature as well. PySpark is an excellent language to learn and it works really fast with large datasets.

Verified User

Analyst in Marketing (Marketing & Advertising company, 5001-10,000 employees)

Vetted Review

2 years of experience

Databricks--a good all-rounder

Rating: 9 out of 10

Incentivized

May 28, 2021

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.

Verified User

Professional in Information Technology (Information Technology & Services company, 201-500 employees)

Vetted Review

2 years of experience

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