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Databricks
What do you like best about the product?
Good experience in Data Analytics Industry. Products are relevant and cutting edge
What do you dislike about the product?
Some integration issues and transparency in cost
What problems is the product solving and how is that benefiting you?
Good experience in Data Analytics Industry. Products are relevant and cutting edge
Improvement on performance and foundation for our AI journey
What do you like best about the product?
Speed and performance, our daily sync jobs are running much faster compared to previous systems
What do you dislike about the product?
Support for legacy apps on on prem, needs more work to bring it to Databricks
What problems is the product solving and how is that benefiting you?
Solving the problem of long running jobs and performance, working towards bringing near realtime
Unified data Analytics
What do you like best about the product?
Integration to 3rd party solutions. Cost and adoption
What do you dislike about the product?
No integration to redshift on AWS. But works with snowflake
What problems is the product solving and how is that benefiting you?
Integration and unification
Databricks makes it easy to leverage their platform
What do you like best about the product?
Ease of use and feature set to perform analytics on a large dataset.
What do you dislike about the product?
Easy to rack up a big bill. Need to put additional measures for cost checks.
What problems is the product solving and how is that benefiting you?
Making it easier to get analytical work done fast.
Powerful unified data platform that transformed our analytics workflow
What do you like best about the product?
What I appreciate most about Databricks is its unified approach to data engineering and data science. The platform eliminates the traditional silos between our data engineers and data scientists by providing a collaborative workspace where both teams can work on the same datasets using their preferred tools - whether that's Spark, Python, R, or SQL. The Delta Lake technology has been particularly valuable for ensuring data quality and reliability in our pipelines. The auto-scaling clusters mean we don't have to worry about infrastructure management, and the notebook interface makes it easy to document and share our work. MLflow integration for experiment tracking and model deployment has streamlined our machine learning lifecycle significantly
What do you dislike about the product?
The main challenges we've encountered are around the learning curve and cost management. For team members coming from traditional SQL backgrounds, the transition to Spark-based analytics requires significant upskilling. The pricing model can be complex to predict, especially with auto-scaling clusters, and costs can escalate quickly if not monitored carefully. The UI, while functional, can feel overwhelming for new users with so many features and options. We've also experienced occasional performance inconsistencies during peak usage times, and some of the more advanced features require deep technical knowledge to implement effectively. Documentation, while comprehensive, can be dense and assumes a high level of technical expertise.
What problems is the product solving and how is that benefiting you?
Slow Time-to-Insight: Our analytics queries that previously took hours now complete in minutes, enabling faster business decision-making.
Data Infrastructure Complexity: We've eliminated the need to manage separate systems for data processing, storage, and ML, reducing operational overhead and technical debt.
Cross-Team Collaboration Barriers: Data engineers and data scientists now work in the same environment, improving project velocity and reducing miscommunication.
Scalability Bottlenecks: The platform automatically scales to handle peak workloads without manual intervention, supporting our growing business needs.
ML Model Governance: MLflow provides proper versioning, tracking, and deployment capabilities for our machine learning initiatives, ensuring models can be reliably moved to production.
These solutions have resulted in measurable business impact including reduced operational costs, faster product development cycles, and more data-driven decision making across the organisation.
Data Infrastructure Complexity: We've eliminated the need to manage separate systems for data processing, storage, and ML, reducing operational overhead and technical debt.
Cross-Team Collaboration Barriers: Data engineers and data scientists now work in the same environment, improving project velocity and reducing miscommunication.
Scalability Bottlenecks: The platform automatically scales to handle peak workloads without manual intervention, supporting our growing business needs.
ML Model Governance: MLflow provides proper versioning, tracking, and deployment capabilities for our machine learning initiatives, ensuring models can be reliably moved to production.
These solutions have resulted in measurable business impact including reduced operational costs, faster product development cycles, and more data-driven decision making across the organisation.
Best user
What do you like best about the product?
The distributed processing power is so quite good for all of data process in enterprise company
What do you dislike about the product?
It's quite hard to learn people who is not familiar with tech deeply
What problems is the product solving and how is that benefiting you?
There are ton's of data in enterprise company, databricks help us to process it by very easy way
They have the most unified and simplified data ecosystem for data governance, Analytics, AI/BI
What do you like best about the product?
Unity Catalog and MLOps are unmatched products. Having your data, process, and models all in unity catalog is mind blowing. Oh, also, I have never loved ML model serving more
What do you dislike about the product?
The free edition isn’t that helpful since you have to use serveless. You can’t set up a custom environment/compute resources which limit the usefulness of databricks free edition
What problems is the product solving and how is that benefiting you?
Model serving
Data Intelligence Platform for your organization
What do you like best about the product?
One platform
Future ready for AI workloads
Extensive integrations
Future ready for AI workloads
Extensive integrations
What do you dislike about the product?
None that come to mind based on the current usage
What problems is the product solving and how is that benefiting you?
Organizational datalake for analytics and reporting
Amazing scale low latency analytics
What do you like best about the product?
Easy to setup low tco, ml ops ai ops databricks apps
What do you dislike about the product?
Apps support as bundles is not GA yet maybe soon
What problems is the product solving and how is that benefiting you?
Real time app interface using agents
Databricks: Unleashing the Power of Data and AI
What do you like best about the product?
Databricks is an excellent platform that seamlessly integrates data and AI, serving as a powerful ally for organizations seeking advanced analytics and intelligent automation.
What do you dislike about the product?
I do not dislike it, Its a great platform.
What problems is the product solving and how is that benefiting you?
Govern all the data and AI assets
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