Databricks Data Intelligence Platform Finance and Insurance 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

5 Reviews

Finance and InsuranceFinancial Services5

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

The wonders of all your data analysis in one place

Rating: 8 out of 10

Incentivized

November 9, 2021

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

Verified User

Manager in Product Management (Financial Services company, 201-500 employees)

Vetted Review

1 year 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 Review

Rating: 9 out of 10

Incentivized

August 22, 2018

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

Verified User

Director in Information Technology (Financial Services company, 201-500 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