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

11 Reviews

Enterprises (1,001+ employees)

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

Best in the industry

Rating: 9 out of 10

Incentivized

February 8, 2022

Use Cases and Deployment Scope

This product is used for Data Science project development, from data analysis/wrangling to feature creation, to training, to finetuning and to model test and validation, and finally to deployment. While Databricks is used by many users, we also use GitHub and code Q/A to promote a code in production. This is one of the advantages of Databricks is the integration part, not only Git but whether you use it on Azure or AWS, you can also leverage the power of the integrated Machine Learning in those platforms, such as auto ml or Azure ML.

Pros

Data Science code agnostic (SQL, R, Pyton, Pyspark, Scala)
Customer Service with REAL support from data eng. and data scientist
Integration with many technology : Tableau, Azure, AWS, Spark, etc.

Cons

Visualization
Collaboration

Likelihood to Recommend

Currently the best Data Science tool for a large-scale company that needs strong tech support once and a while. The performance and the connectivity/integration with a large bread of tools and platform is also important when you don't want to change all your stack. DataBricks is a great non-drage and drops tool for real Data Scientist that knows their things.

Jonatan Bouchard

Director Data Science in Information Technology at CN (Transportation/Trucking/Railroad, 10,001+ employees)

Vetted Review

5 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 is Great Platform for Data Virtualization based on Delta Lake

Rating: 10 out of 10

Incentivized

August 9, 2021

Use Cases and Deployment Scope

We use Databricks to replace traditional RDBMS like Oracle. We have Big Batch ETL, Ingestion and Extraction Job for Big data ran across different products where we leverage Lakehouse platform to put our raw data in Data Lake and Create Delta Lake platform based on high performing Parquet.
It is kind of proposed to use across the whole organization and different BU's. Databricks will be our key main virtualized platform.
It addresses very fast data ingestion, reduces the overall ETL window. Integrated different datasource and also helps to perform Machine Learning jobs to run and scale. Idea is to reduce overall computation time to save cost on onprem.

Pros

Data Virtualization
Spark Real time and Batch streaming
Notebook to run Jobs
integrate Python and Apache Spark SQL
SQL Analytics

Cons

SQL Analytics Performance
Help migration for RDBMS sources
To make Transactional OLTP aspects faster

Likelihood to Recommend

Delta Share, Data virtualization , Open Data Integration with Other data sources, parquet ingestion

Verified User

Director in Information Technology (Hospitality company, 10,001+ employees)

Vetted Review

3 years of experience

Data for insights

Rating: 7 out of 10

Incentivized

July 12, 2021

Use Cases and Deployment Scope

[Databricks Lakehouse Platform (Unified Analytics Platform) is] used by a few departments to start off with data warehousing. SQL analytics, real time monitoring and data governance.

Pros

SQL
User friendly
Great development environment

Cons

Errors are not explained
No data back up feature
Interface can be more intuitive

Likelihood to Recommend

[Databricks Lakehouse Platform (Unified Analytics Platform)] makes the power of Spark accessible. Databricks's proactive and customer-centric service. It is a highly adaptable solution for data engineering, data science, and AI. Load times are not consistent and no ability to restrict data access to specific users or groups.

Gilrod Maerina

Analyst in Sales at Riverbed Technology (Internet, 1001-5000 employees)

Vetted Review

1 year of experience

Databricks Lakehouse is modern solutions for current big data problems

Rating: 9 out of 10

Incentivized

July 7, 2021

Use Cases and Deployment Scope

Databricks Lakehouse platform is used across all departments in my current organization.
It is used as part of solving different data engineering and data analytics use cases in different teams.
Databricks Lakehouse platform provides seamless integration with Azure cloud in Maersk. Databricks Lakehouse platform uses spark, mlops, delta for slovong the recent big data engineering problems.

Pros

Seamless integration with Azure cloud platform services like Azure Data Lake Storage, Blobstorage , Azure Data Factory, Azure DevOps.
Databricks lakehouse platform in backed uses Apache Spark for all the computation to be faster and distributed. It helps to complete data pipelines to process huge amounts [of] big data in lesser time with low cost.
Databricks Lakehouse solves the problems data lake, by introducing Delta Lake concept. It provides support for updates, deletes, schema evaluation.

Cons

Databricks Lakehouse platform can provide better platform for managing, and monitoring the cluster performance, utilization, optimization suggestions. It helps developers to leverage those insights for building better data pipelines.
Databricks Lakehouse platform can provide GUI version to create spark jobs by click, drag and drop. That reduces the significant amount of time to develop code.
Databricks Lakehouse platform can provide better insights and details regarding the jobs failures and resources consumption

Likelihood to Recommend

Databricks Lakehouse platform is well suited for below use cases :
1. Process different types of data sources like structured data, semi structured data and unstructured data.
2. Process data different data sources like RDBMS, REST APIs, File servers, IoT sensors.
3. Provide support for Updates, Deletes, schema evaluation

Databricks Lakehouse platform is not well suited for below usecases :
1. Less data volume and doesn't have analytics requirements

2. Developers doesn't have skill set on spark and Hive

Surendranatha Reddy Chappidi

Senior Data Engineer in Information Technology at A.P. Moller - Maersk (Logistics & Supply Chain, 10,001+ employees)

Vetted Review

1 year of experience

View profile

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

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Databricks Lakehouse is modern solutions for current big data problems

Rating: 9 out of 10

Incentivized

July 7, 2021

Use Cases and Deployment Scope

Pros

Seamless integration with Azure cloud platform services like Azure Data Lake Storage, Blobstorage , Azure Data Factory, Azure DevOps.
Databricks lakehouse platform in backed uses Apache Spark for all the computation to be faster and distributed. It helps to complete data pipelines to process huge amounts [of] big data in lesser time with low cost.
Databricks Lakehouse solves the problems data lake, by introducing Delta Lake concept. It provides support for updates, deletes, schema evaluation.

Cons

Databricks Lakehouse platform can provide better platform for managing, and monitoring the cluster performance, utilization, optimization suggestions. It helps developers to leverage those insights for building better data pipelines.
Databricks Lakehouse platform can provide GUI version to create spark jobs by click, drag and drop. That reduces the significant amount of time to develop code.
Databricks Lakehouse platform can provide better insights and details regarding the jobs failures and resources consumption

Likelihood to Recommend

Databricks Lakehouse platform is not well suited for below usecases :
1. Less data volume and doesn't have analytics requirements

2. Developers doesn't have skill set on spark and Hive

Surendranatha Reddy Chappidi

Senior Data Engineer in Information Technology at A.P. Moller - Maersk (Logistics & Supply Chain, 10,001+ employees)

Vetted Review

1 year of experience

View profile