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January 31, 2019
Data from APIs is streamed into our One Lake environment. This one lake is S3 on AWS.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.
Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.
Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.
- Process raw data in One Lake (S3) env to relational tables and views
- Share notebooks with our business analysts so that they can use the queries and generate value out of the data
- Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
- Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
- Databricks should come with a fine grained access control mechanism. If I have tables or views created then access mechanism should be able to restrict access to certain tables or columns based on the logged in user
- There should be improved graphing and dash boarding provided from within Databricks
- Better integration with AWS could help me code jobs in Databricks and run them in AWS EMR more easily using better devops pipelines
March 28, 2018
I actually use Databricks for experiments and research for my master's program. I mostly use it to implement Python codes and testing the viability of the programs that I write. Many individuals in the Computer Information System department are using this software platform to implement programs. It is a good tool for us to learn [and] includes a community forum that is rather helpful if you are self-learning and have questions.
- There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
- There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.
- The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
- Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.
We leverage Databricks (DB) to run Big Data workloads. Primarily we build a Jar and attach to DB. We do not leverage the notebooks except for prototyping.
- Extremely Flexible in Data Scenarios
- Fantastic Performance
- DB is always updating the system so we can have latest features.
- Better Localized Testing
- When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
- Graphing Support went non-existent; when it was one of their compelling general engine.
August 22, 2018
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
Across whole organization.
[It's] Used by self-service analysts to quickly do analysis
[It's] Used by self-service analysts to quickly do analysis
- Very simplified infrastructure initialization
- Seamless and automated optimization of job execution
- Simple tool to get used to
- Visualization - Great area of improvement
- Integration with Git
- COST
Databricks Unified Analytics Platform Scorecard Summary
Feature Scorecard Summary
What is Databricks Unified Analytics Platform?
Databricks in San Francisco offers the Databricks 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.
Databricks Unified Analytics Platform Technical Details
| Operating Systems: | Unspecified |
|---|---|
| Mobile Application: | No |