Google Databricks
Author: Ronald Fung
Creation Date: 9 June 2023
Next Modified Date: 9 June 2024
A. Introduction
Google Cloud and Databricks share a common vision of open source, open data platforms, and an open cloud. With this shared philosophy, Google Cloud is delighted to announce our partnership with Databricks to bring their data analytics and machine learning solutions to Google Cloud. The partnership enables customers to accelerate Databricks implementations by simplifying their data access, by jointly giving them powerful ways to analyze their data, and by leveraging our combined AI and ML capabilities to impact business outcomes.
Databricks is tightly integrated with Google Cloud’s infrastructure and analytics capabilities with the security, elasticity and reliability that customers need.
By deploying on Google Cloud, Databricks users can build data lakes fast and integrate data science and ML workloads on their path towards production. Databricks’ capabilities in data engineering and analytics are complemented by Google Cloud’s global, secure network as well as the capabilities of BigQuery, Looker, AI Platform and our expertise in delivering applications in a containerized environment. All together, customers get an enterprise-ready cloud service with a Databricks experience that is reliable, scalable, secure and governed.
B. How is it used at Seagen
Seagen can use Google Cloud Databricks to run their data processing and machine learning workloads on a scalable, reliable, and collaborative platform. Here are some steps to get started with Google Cloud Databricks:
Create a Google Cloud account: Seagen can create a Google Cloud account in the Google Cloud Console. This will give them access to Google Cloud Databricks and other Google Cloud services.
Create a Databricks workspace: Seagen can create a Databricks workspace in the Google Cloud Console, which represents a collaborative environment for data processing and machine learning. They can specify the workspace settings, such as the region, network, and other workspace configurations.
Import the data: Seagen can import their data into the Databricks workspace using the appropriate data source, such as Google Cloud Storage or Hadoop File System. They can specify the data format, schema, and metadata.
Process the data: Seagen can process their data using Databricks notebooks, which provide an interactive and collaborative environment for data processing and analysis. They can use Python, R, or SQL to perform data cleansing, transformation, and aggregation.
Train the model: Seagen can train their machine learning model using Databricks notebooks and the built-in machine learning libraries, such as MLlib, TensorFlow, or PyTorch. They can use supervised or unsupervised learning algorithms to build predictive models.
Evaluate the model: Seagen can evaluate their machine learning model using Databricks notebooks and the appropriate evaluation metrics, such as accuracy, precision, recall, or F1 score. They can use cross-validation or other techniques to validate the model performance.
Deploy the model: Seagen can deploy their machine learning model using Databricks notebooks and the appropriate deployment methods, such as REST API or batch processing. They can specify the deployment configuration, such as the model version, input/output format, and other settings.
Monitor the model: Seagen can monitor their machine learning model using Databricks notebooks and the appropriate monitoring tools, such as Google Cloud Monitoring. They can set up alerts, dashboards, and reports to track the model performance and detect anomalies.
Overall, by using Google Cloud Databricks, Seagen can run their data processing and machine learning workloads on a scalable, reliable, and collaborative platform, and focus on their core business instead of managing their own IT infrastructure. With its support for different data sources, powerful data processing and machine learning capabilities, and easy-to-use interface, Google Cloud Databricks is an excellent choice for businesses and individuals who need to process and analyze large amounts of data and build predictive models quickly and efficiently.
C. Features
Google Cloud Databricks is a collaborative data analytics and machine learning platform that provides a unified workspace for processing and analyzing large datasets. Here are some of the key features of Google Cloud Databricks:
Unified workspace: Google Cloud Databricks provides a unified workspace for data processing, analysis, and machine learning, with support for multiple programming languages, such as Python, R, and SQL. Users can collaborate in real-time using notebooks, dashboards, and reports.
Scalable processing: Google Cloud Databricks provides a scalable processing engine that can handle large datasets and complex workloads. It integrates with Google Cloud Storage, BigQuery, and other data sources, and supports distributed processing using Apache Spark.
Built-in machine learning: Google Cloud Databricks provides built-in machine learning libraries, such as MLlib, TensorFlow, and PyTorch, that allow users to build and train machine learning models easily. It also supports hyperparameter tuning, model evaluation, and deployment.
Interactive visualization: Google Cloud Databricks provides interactive visualization tools, such as Matplotlib, Plotly, and Bokeh, that allow users to create rich and dynamic visualizations of their data. It also supports integration with Google Data Studio and other data visualization tools.
Collaboration and security: Google Cloud Databricks provides collaboration and security features, such as version control, access control, and encryption, that allow users to work together securely and efficiently. It also supports integration with Google Cloud Identity and Access Management and other identity and access management tools.
Integration with Google Cloud: Google Cloud Databricks integrates with other Google Cloud services, such as Google Cloud Storage, BigQuery, and Dataflow, allowing users to process and analyze their data seamlessly across different services. It also supports integration with Google Cloud AI Platform and other machine learning and artificial intelligence tools.
Overall, Google Cloud Databricks is a powerful and flexible platform that allows users to process and analyze their data at scale, build and train machine learning models easily, and collaborate securely and efficiently. With its support for multiple programming languages, interactive visualization, and integration with other Google Cloud services, Google Cloud Databricks is an excellent choice for businesses and individuals who need to work with large datasets and build predictive models quickly and efficiently.
D. Where Implemented
E. How it is tested
Testing Google Cloud Databricks involves ensuring that the data processing and machine learning workloads are running correctly, that the cluster is scalable and reliable, and that the security and collaboration controls are properly configured. Here are some steps to test Google Cloud Databricks:
Create a test dataset: Create a test dataset that mimics the production dataset as closely as possible, including the data format, schema, and metadata.
Create a test Databricks workspace: Create a test Databricks workspace that mimics the production workspace as closely as possible, including the cluster size, region, and other workspace configurations.
Import the test data: Import the test dataset into the Databricks workspace using the appropriate data source, such as Google Cloud Storage or Hadoop File System. Specify the data format, schema, and metadata.
Process the test data: Process the test dataset using Databricks notebooks, which provide an interactive and collaborative environment for data processing and analysis. Use Python, R, or SQL to perform data cleansing, transformation, and aggregation.
Train the test model: Train a test machine learning model using Databricks notebooks and the built-in machine learning libraries, such as MLlib, TensorFlow, or PyTorch. Use supervised or unsupervised learning algorithms to build predictive models.
Evaluate the test model: Evaluate the test machine learning model using Databricks notebooks and the appropriate evaluation metrics, such as accuracy, precision, recall, or F1 score. Use cross-validation or other techniques to validate the model performance.
Deploy the test model: Deploy the test machine learning model using Databricks notebooks and the appropriate deployment methods, such as REST API or batch processing. Specify the deployment configuration, such as the model version, input/output format, and other settings.
Monitor the test model: Monitor the test machine learning model using Databricks notebooks and the appropriate monitoring tools, such as Google Cloud Monitoring. Set up alerts, dashboards, and reports to track the model performance and detect anomalies.
Overall, by thoroughly testing Google Cloud Databricks, users can ensure that their data processing and machine learning workloads are reliable, scalable, and secure, and that they are complying with industry and regulatory standards. Additionally, users can reach out to Google Cloud support for help with any technical challenges they may encounter.
F. 2023 Roadmap
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G. 2024 Roadmap
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H. Known Issues
While Google Cloud Databricks is a reliable and powerful platform for data processing and machine learning workloads, there are some known issues that users may encounter. Here are some of the known issues for Google Cloud Databricks:
Performance issues: Users may encounter performance issues with Databricks, such as slow data processing times or high resource utilization. These issues can often be resolved by optimizing the cluster configuration, such as using the appropriate machine types or adjusting the cluster settings.
Availability issues: Users may encounter availability issues with Databricks, such as downtime or service disruptions. These issues can often be resolved by configuring the appropriate high availability and fault tolerance mechanisms, such as auto-scaling, multi-zone deployment, and data replication.
Security issues: Users may encounter security issues with Databricks, such as data breaches or unauthorized access. These issues can often be resolved by implementing the appropriate security and compliance controls, such as encryption, access control, and auditing.
Integration issues: Users may encounter integration issues with Databricks, such as interoperability issues or compatibility issues with other systems. These issues can often be resolved by using the appropriate integration standards, such as REST APIs or messaging protocols, and ensuring that the data and models are compatible with other systems.
Cost issues: Users may encounter cost issues with Databricks, such as unexpected charges or inefficient resource utilization. These issues can often be resolved by optimizing the cluster configuration, such as using the appropriate machine types, storage options, and pricing models.
Overall, while these issues may impact some users, Google Cloud Databricks remains a reliable and powerful platform for data processing and machine learning workloads that is widely used by businesses and organizations around the world. By monitoring their Databricks usage and reviewing their usage reports and logs, users can ensure that their workloads are secure, scalable, and cost-effective, and that they are complying with industry and regulatory standards. Additionally, users can reach out to Google Cloud support for help with any known issues or other technical challenges they may encounter.
[x] Reviewed by Enterprise Architecture
[x] Reviewed by Application Development
[x] Reviewed by Data Architecture