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The Databricks Lakehouse Platform, also known as the Unified Analytics Platform, has been widely used by multiple departments to address a …
Databricks Lakehouse PlatformFormerly Databricks Unified Analytics Platform
Starting at $0.07 Per DBU
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What is Databricks Lakehouse Platform?
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data…
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Tech Lead Data Engineer
Ekimetrics (Information Technology & Services, 201-500 employees)
Pricing
Standard
$0.07
Cloud
Per DBU
Premium
$0.10
Cloud
Per DBU
Enterprise
$0.13
Cloud
Per DBU
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
Product Details
- About
- Tech Details
- FAQs
What is Databricks Lakehouse Platform?
Databricks Lakehouse Platform Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.
Reviewers rate Usability highest, with a score of 9.4.
The most common users of Databricks Lakehouse Platform are from Enterprises (1,001+ employees).
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
- Business Problems Solved
- Pros
- Cons
- Recommendations
The Databricks Lakehouse Platform, also known as the Unified Analytics Platform, has been widely used by multiple departments to address a range of data engineering and analytics challenges. Users have leveraged the platform to initiate data warehousing, SQL analytics, real-time monitoring, and data governance. The versatility and openness of the platform have allowed users to save a significant amount of time and effectively manage cloud costs and human resources.
Customers have utilized the Databricks Lakehouse Platform for various use cases, including creating dashboards with tools like Tableau, Redash, and Qlik, as well as integrating with CRM systems like Salesforce and SAP. The platform has also been employed for developing chatbots in Knowledge Management and serving machine learning models behind API endpoints. Furthermore, it is extensively used for data science project development, facilitating tasks such as data analysis, wrangling, feature creation, training, model testing, validation, and deployment.
Databricks' integration capabilities, including Git integration and integration with Azure or AWS, enable users to leverage the power of integrated machine learning features. Additionally, the platform's reliability and excellent technical support make it a preferred choice for building data pipelines and solving big data engineering problems. It is widely used by engineering and IT teams to transform IoT data, build data models for business intelligence tools, and run daily/hourly jobs to create BI models.
Moreover, the Databricks Lakehouse Platform serves as an invaluable learning tool for individuals in the Computer Information System department. The community forum proves particularly helpful for self-learners with questions. Furthermore, the platform supports deep dive analysis on metrics by Data and Product teams, facilitates client reporting and analytics through data mining capabilities, replaces traditional RDBMS like Oracle for Big Batch ETL jobs on big data sets.
In summary, the Databricks Lakehouse Platform is employed across organizations to solve a variety of data engineering and analytics use cases. Its seamless integration with cloud platforms, support for different data formats, and scalability make it suitable for tasks such as data ingestion and cleansing, interactive analysis, and development of analytic services.
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.
Confusing Workspace Navigation: Several users have found the navigation to create a workspace in the Databricks Lakehouse Platform confusing and time-consuming, hindering their productivity. They have expressed frustration over the complex steps involved, resulting in wasted time.
Difficulty Locating Tables: Many reviewers have expressed difficulty in locating tables after they were created, often leading to the need for deletion and recreation. This issue has caused frustration and wasted time for users who struggle to find their data within the platform.
Random Task Failures: Some users have experienced random task failures while using the platform, making it challenging for them to debug and profile code effectively. These unexpected failures undermine confidence in the system's stability and result in delays as users attempt to identify and fix these issues.
Users highly recommend the Lakehouse platform for various data-related tasks, such as building cloud-native lakehouse platforms, ingesting and transforming big data batches/streams, and implementing medallion lakehouse architectures. They find the platform simple to use and appreciate its hassle-free administration and maintenance.
The Lakehouse platform is also highly recommended for setting up Hadoop clusters and dealing with big data, analytics, and machine learning workflows. Users believe that it provides a comprehensive and open solution for these tasks.
Users suggest exploring the features of the Lakehouse platform, such as partner connect, advanced analytics/MLOPS/Data science Auto-ML capabilities. They find these features useful and believe that they enhance the platform's salient functionalities.
Overall, users highly recommend the Lakehouse platform for its ease of use, support for major cloud providers (AWS, AZURE, GCP), and useful features like data sharing (Delta Sharing). However, users also recommend considering the level of reliance on proprietary technology versus industry standards like Spark, SQL, and dbt. It is advised to read through the documentation and gather firsthand experiences from individuals who have used the Lakehouse platform.
Attribute Ratings
Reviews
(1-7 of 7)Companies can't remove reviews or game the system. Here's why
July 26, 2023
Most collaborative Data Science & AI workspace !
Score 10 out of 10
Vetted Review
Verified User
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 !
* 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 !
- Enhanced Data Science & Data Engineering collaboration
- Complete Infrastructure-as-code Terraform provider
- Very easy streaming capabilities
- Multiple Git providers integration with merge assistant
- VsCode IDE support for local development
- Python SDK for Workflows
- Poetry support
March 09, 2023
Databricks Lakehouse Platform: A 2-year user review
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.
- MLFLOW Experiment
- MLFLOW Registry
- Databricks Lakehouse Platform Notebook
- 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
February 08, 2022
Best in the industry
Score 9 out of 10
Vetted Review
Verified User
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.
- 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.
- Visualization
- Collaboration
August 11, 2021
My Lakehouse experiences
We build all our data pipelines with Databricks Lakehouse technology. It is reliable and the tech support from Databricks is very good.
- Better performance through consolidating small files in delta tables
- ACID functionality on delta tables
- Live delta tables
- Make it easier to test features in public preview, like delta live tables.
July 12, 2021
Data for insights
[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.
- SQL
- User friendly
- Great development environment
- Errors are not explained
- No data back up feature
- Interface can be more intuitive
Score 8 out of 10
Vetted Review
Verified User
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.
- 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
- Random task failures
- Hard to debug code
- Hard to profile code
It's being used for:
- Ingestion and cleansing of data
- Interactive Analysis of data
- Development of Analytic Services
- Production Environment Customer Facing Analytic Services
- 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
- Automation of Machine Learning Development
- Optimization of GPU usage