Databricks Data Intelligence Platform 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.

Databricks Data Intelligence Platform Reviews

8 Reviews

Engineering

One Stop Shop for Data Professionals.

Rating: 10 out of 10

Incentivized

December 9, 2024

Use Cases and Deployment Scope

Databricks is the primary data platform where we land, standardize, clean, transform, and clean our data sources. We utilize the Workflows feature to automate reoccurring tasks and have built internal applications around the reusable workflows. We use the dashboard feature internally to allow customer success teams and business analysts to keep tabs on the performance and outputs of our products. The workloads are orchestrated in Databricks but executed within our own AWS accounts, allowing us to stay compliant with our stringent security requirements.

Pros

Thoughtful application of AI assistants during the coding and analysis steps.
Intuitive UI for users of varying skill sets.
Frequently updated documentation.

Cons

Greater support for non spark workloads.
Ability to host JAR files on serverless endpoints.

Likelihood to Recommend

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Austin Franchino

Senior Data and Security Engineer in Engineering at Lumos Data (Financial Services, 11-50 employees)

Vetted Review

3 years of experience

View profile

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

Databricks Lakehouse Platform: A 2-year user review

Rating: 9 out of 10

Incentivized

March 9, 2023

Use Cases and Deployment Scope

I use Databricks Lakehouse Platform to build a data-science based solutions that adress many problems in my business. This includes: increment our data in the lake house and use Databricks Lakehouse Platform computational capabilities to analyze and feature engineer our data, build different machine learning model and track different experiment and finally register our trained model that can be used by the business.

Pros

MLFLOW Experiment
MLFLOW Registry
Databricks Lakehouse Platform Notebook

Cons

Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
Visualization in MLFLOW experiment can be enhanced

Likelihood to Recommend

Well Suited: Dealing with big data and being able to train different models that address many problems in my business. In addition to its computational capabilities, using Databricks Lakehouse Platform allowed us to do all development in one platform. Less Appropriate: Having a small dataset that doesn't need parallel processing. Local development is easier to develop and track so if no parallelization is needed (data is not big or parallelized computations is not required), I prefer local development.

Verified User

Employee in Engineering (Retail company, 10,001+ employees)

Vetted Review

2 years of experience

Databricks Lakehouse Platform for all your analytics requirements

Rating: 8 out of 10

Incentivized

May 15, 2022

Use Cases and Deployment Scope

We used Databricks Lakehouse platform for running all our Machine Learning workloads as well as storing large amounts of data in our data lake backend. The data stored in the databricks lakehouse was used to train state-of-the-art ML and Deep Learning models on text and image datasets. Databricks' Spark jobs as well as Delta Lake Lakehouse backend is well equipped for these kinds of tasks.

Pros

Very well optimized Spark Jobs Execution Engine.
Time travel in Databricks Lakehouse Platform allows you to version your datasets.
Newly integrated Analytics feature allows you to build visualization dashboards.
Native integration with managed MLflow service.

Cons

Running MLflow jobs remotely is extremely cluttered and needs to be simplified.
All the runnable code has to stay in Notebooks which are not very production-friendly.
File management on DBFS can be improved.

Likelihood to Recommend

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Verified User

Engineer in Engineering (Computer Software company, 1001-5000 employees)

Vetted Review

2 years of experience

Great for both ad-hoc analyzes and scheduled jobs

Rating: 8 out of 10

Incentivized

May 15, 2021

Use Cases and Deployment Scope

We use Databricks Lakehouse Platform to transform IoT data and build data models for BI tools. It is being used by engineering and IT teams. We use it with a data lake platform, read the raw data and transform it to a suitable format for analytics tools. We run daily/hourly jobs to create BI models and save the resulting models back to data lake or SQL tables.

Pros

Ready-2-use Spark environment with zero configuration required
Interactive analysis with notebook-style coding
Variety of language options (R, Scala, Python, SQL, Java)
Scheduled jobs

Cons

Random task failures
Hard to debug code
Hard to profile code

Likelihood to Recommend

It is great for both ad-hoc analyzes and scheduled jobs. It supports most of the cloud storage technologies and provides an easy to use API to connect with them. Clusters can be auto scaled with the load, and you can also create temporary clusters for job runs, which cost less compared to all purpose clusters.

Ender Ortak

Senior Data Analyst in Engineering at Ford Otosan (Automotive, 10,001+ employees)

Vetted Review

3 years of experience

Databricks for modern day ETL

Rating: 9 out of 10

Incentivized

January 31, 2019

Use Cases and Deployment Scope

Data from APIs is streamed into our One Lake environment. This one lake is S3 on AWS.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.

Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.

Pros

Process raw data in One Lake (S3) env to relational tables and views
Share notebooks with our business analysts so that they can use the queries and generate value out of the data
Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

Cons

Databricks should come with a fine grained access control mechanism. If I have tables or views created then access mechanism should be able to restrict access to certain tables or columns based on the logged in user
There should be improved graphing and dash boarding provided from within Databricks
Better integration with AWS could help me code jobs in Databricks and run them in AWS EMR more easily using better devops pipelines

Likelihood to Recommend

Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.

Verified User

Team Lead in Engineering (Financial Services company, 10,001+ employees)

Vetted Review

2 years of experience

Databricks provides a cost effective end to end solution for Enterprise analytics

Rating: 9 out of 10

Incentivized

August 22, 2018

Use Cases and Deployment Scope

It's being used for:

Ingestion and cleansing of data
Interactive Analysis of data
Development of Analytic Services
Production Environment Customer Facing Analytic Services

Pros

Collaborative Development Environment using Notebooks.
Stable and Secure Cloud Development Environment requiring minimum DevOPs support
Fast with excellent scalability reduces time to market
Open source library support

Cons

Automation of Machine Learning Development
Optimization of GPU usage

Likelihood to Recommend

Great end to end analytics solution on AWS or Azure. Databricks continues to grow based on customer feedback. Just like everyone in the industry, they are focused on Machine Learning, but they also understand a complete solution is needed.

Verified User

Strategist in Engineering (Computer Hardware company, 10,001+ employees)

Vetted Review

2 years of experience

Databricks Review

Rating: 6 out of 10

Incentivized

September 15, 2017

Use Cases and Deployment Scope

Across whole organization.

[It's] Used by self-service analysts to quickly do analysis

Pros

Very simplified infrastructure initialization
Seamless and automated optimization of job execution
Simple tool to get used to

Cons

Visualization - Great area of improvement
Integration with Git
COST

Likelihood to Recommend

When you have analysts that are not cloud-savvy, this tool helps them quickly run code and not be overwhelmed by infrastructure and optimization. [It's] Less appropriate in production deployments.

Verified User

Director in Engineering (Financial Services company, 10,001+ employees)

Vetted Review

1 year of experience

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