Overview
What is Databricks Data Intelligence 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…
Most collaborative Data Science & AI workspace !
Databricks Lakehouse Platform: A 2-year user review
Databricks Lakehouse Platform for all your analytics requirements
Best in the industry
The wonders of all your data analysis in one place
Positive review for Databricks Lakehouse Platform
My Lakehouse experiences
Databricks is Great Platform for Data Virtualization based on Delta Lake
Data for insights
Databricks Lakehouse is modern solutions for current big data problems
It is used as part of solving different data …
Databricks--a good all-rounder
Great for both ad-hoc analyzes and scheduled jobs
Databricks for modern day ETL
Once this raw data is on S3, we use Databricks to …
Databricks provides a cost effective end to end solution for Enterprise analytics
- Ingestion and cleansing of data
- Interactive Analysis of data
- Development of Analytic Services
- Production Environment …
Databricks Review
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Reviewer Pros & Cons
Pricing
Standard
$0.07
Premium
$0.10
Enterprise
$0.13
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 Data Intelligence Platform?
Databricks Data Intelligence Platform Technical Details
Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
---|---|
Operating Systems | Unspecified |
Mobile Application | No |
Frequently Asked Questions
Comparisons
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Reviews and Ratings
(75)Community Insights
- 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
(1-17 of 17)Most collaborative Data Science & AI workspace !
It would be less appropriate for very small data projects as the entry cost may be high. Yet, if the data is meant to grow, Databricks will horizontally scale without requiring a re-write of your codebase
Databricks Lakehouse Platform: A 2-year user review
Best in the industry
The wonders of all your data analysis in one place
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
Positive review for Databricks Lakehouse Platform
The ability to store temporary/permanent tables on data lakes is a fabulous feature as well. PySpark is an excellent language to learn and it works really fast with large datasets.
My Lakehouse experiences
Data for insights
1. Process different types of data sources like structured data, semi structured data and unstructured data.
2. Process data different data sources like RDBMS, REST APIs, File servers, IoT sensors.
3. Provide support for Updates, Deletes, schema evaluation
Databricks Lakehouse platform is not well suited for below usecases :
1. Less data volume and doesn't have analytics requirements
Databricks--a good all-rounder
Databricks for modern day ETL
Databricks Review
- DB generally fits 95% of what you need to do
- Primarily the ability to transform data and or do ad-hoc DS work