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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| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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.
