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What is Databricks Data Intelligence Platform?
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Overall experience with Databricks Data Intelligence Platform
“Adopting Databricks Shifts Data Integration Approach but Increases Learning Demands”
“Platform Offers Robust Features but Presents Steep Learning Curve for Beginners”
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Databricks is a global company focusing on data and AI. At the core of Databricks is the Databricks Data Intelligence Platform which allows entire organizations to use data and AI to power a wide range of business use cases. It's built on a lakehouse to provide an open, unified foundation for all data and governance and is powered by a Data Intelligence Engine that understands the uniqueness of the organizations’ data. Databricks simplifies and accelerates enterprises' data and AI goals by unifying data, analytics and AI on one platform. Its key mission is to assist data teams in addressing some of the world's most challenging problems.
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Databricks Data Intelligence Platform Likes & Dislikes
What I really like about Databricks is that it is constantly innovating. The UI is constantly evolving along with its features. I believe I heard a quote that within a year over a hundred different features are released, and not only are features released but they continue to build on them so its not like once a new feature is released old ones are then removed. Secondly I really like its ability to be able to basically ingest any form of modern data (json, parquet, csv, xls, xml you name it) and be able to ingest it and start to derive insights right away during ingestion. The last thing that I really like about Databricks is the fact that it is truly trying to become a hub for all things data including data governance, analysis, data science, machine learning and a dash of reporting.
Speed and performance, it is quick to query and start up a warehouse. The Unity catalog makes it easy to see what kind of data is available across the enterprise.
What stands most is the unified lake house architecture that combines data engineering, analytics and machine learning within a single ecosystem. The collaborative notebook environment makes it easy for teams to prototype, test transformations and validate results in real time. Integration with Spark enables scalable data processing. Built in support for delta lake improves data reliability and versioning. Integration with Azure services and Devops pipelines supports CI/CD workflows . The ability to handle both batch and streaming data is valuable for modern enterprise use cases. The platform also promotes better collaboration between technical and non-technical stakeholders as notebooks provide transparency into transformation logic and outputs.
In the same breath though I do admit there is a bit of a steep learning curve given the fact that there are so many new features constantly being released. Its a real challenge keeping up with all the innovative features, things are moved around and names are changed (delta live tables to now Lakeflow Declarative Pipelines). A second thing that can be frustrating is when the odd chance there is some sort of error that it can be very difficult to pin down what the real source of the error is since there are so many moving pieces (is it a package, is it the data lake, is it code logic issue etc). The last thing that can be frustrating is that since this is a tool that can be hosted on multiple cloud providers at times reading up on documentation it can be confusing as to whether or not the article is applicable to my particular provider.
It was hard to get started with it, training for non-technical and business users was scattered and there are still features being built (like Lakeflow Designer) for us.
One challenge is the operational complexity that can arise in larger implementations. Cluster management , runtime version compatibility and environment configuration require careful oversight. If not managed properly , costs can increase due to inefficient cluster utilization. Native testing frameworks are limited. teams often need to build custom data validation or automated test frameworks.
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Peer Discussions
Databricks Data Intelligence Platform Reviews and Ratings
- Data EngineerGov't/PS/EdEducationReview Source
Adopting Databricks Shifts Data Integration Approach but Increases Learning Demands
My organization up until just before 2021 was by and large an on premise shop. So most of our ETL tools, reporting and business intelligence tech stack was all based on premise. That all changed in the last few years as there has been a major shift towards embracing more of what cloud providers can enable our organization. Our journey began with Azure and talking about reporting, data science, machine learning and ELT/ETL you have to look at Azure Databricks. Databricks can seem a bit daunting at first as there is a lot of features packed into it but after using it for a month you will definitely get the hang of things and how to find things. Its definitely been a tool that has become our go-to when looking towards any sort of data integration or analysis - Sdet10B+ USDBankingReview Source
Scalable and Collaborative Data Platform with Strong Governance Needs in Enterprise Enviroments
Overall, our experience with the Databricks Data Intelligence Platform has been strong , particularly for large scale data engineering , analytics, and machine learning workloads in a cloud environment. The platform provides a unified workspace that enables collaboration between data engineers, developers, analysts, and testing teams. From a development and quality engineering perspective , it has helped streamline data pipeline development and improve scalability . That said the platform requires disciplined governance and structured adoption. Its flexibility is powerful , but without proper standards around data validation, CI/CD and environment management, complexity can increase as usage grows. When implemented with clear guidelines , it significantly improves productivity and cross team collaboration. - It Services Associate10B+ USDIT ServicesReview Source
Databricks Unifies Data Engineering and ML Workloads with Collaborative Features
Databricks has been a strong platform for unifying our data engineering, analytics and ML workloads. It also helps to improve collaboration, simplified governance and easily for large scale. - Senior Digital Analytics Manager1B-10B USDRetailReview Source
A Powerful and Well-Designed data Platform That Has Delivered Strong Results for Our Organisation
We don't usually give top scores lightly, but our experience with Databricks has been genuinely very strong. From the initial implementation through to ongoing support, they've been reliable, easy to work with, and clearly invested in making the platform successful for us, not just selling the technology. - Data Analyst<50M USDSoftwareReview Source
Difficulties With GPU Nodes and High Performance Storage Affect Workflow Productivity
learning curve for the product is pretty steep. It really should come with some easily accessible tutorials. I've been able to use the product effectively.