Databricks Exam Guidebook PDF

Section 1: Databricks Lakehouse Platform 1. Describe the relationship between the data lakehouse and the data warehouse. In Databricks, the relationship between a data lakehouse and a data warehouse is quite synergistic, combining the strengths of both architectures to provide a more...

Section 1: Databricks Lakehouse Platform 1. Describe the relationship between the data lakehouse and the data warehouse. In Databricks, the relationship between a data lakehouse and a data warehouse is quite synergistic, combining the strengths of both architectures to provide a more flexible and efficient data management solution. Data Warehouse A data warehouse is designed for structured data and is optimized for fast SQL queries and analytics. It typically involves: Structured Data: Data is organized into tables with predefined schemas. ETL Process: Data is extracted, transformed, and loaded (ETL) into the warehouse. High Performance: Optimized for complex queries and reporting. Examples: Amazon Redshift, Google BigQuery, Snowflake. Data Lake A data lake is more flexible and can handle both structured and unstructured data. It is often used for: Raw Data Storage: Stores data in its native format. ELT Process: Data is extracted, loaded, and then transformed (ELT). Variety of Data: Can store structured, semi-structured, and unstructured data. Examples: Hadoop, Amazon S3, Azure Data Lake. Data Lakehouse A data lakehouse combines the best features of both data lakes and data warehouses: Unified Architecture: Integrates the data management capabilities of a warehouse with the flexibility and cost-efficiency of a lake. Single Source of Truth: Allows for both raw and structured data to coexist, enabling diverse analytics and machine learning workloads. Cost Efficiency: Utilizes low-cost storage solutions while providing high- performance query capabilities.  Examples: Databricks Lakehouse, Delta Lake. In Databricks, the lakehouse architecture is implemented using Delta Lake, which provides: ACID Transactions: Ensures data reliability and consistency. Schema Enforcement: Maintains data quality by enforcing schemas. Unified Data Management: Supports both batch and streaming data. Scalability: Efficiently scales to handle large volumes of data. By leveraging a lakehouse, organizations can streamline their data architecture, reduce costs, and enhance their ability to perform advanced analytics and machine learning123. 2. Identify the improvement in data quality in the data lakehouse over the data lake. The data lakehouse architecture offers several improvements in data quality over a traditional data lake: 1. ACID Transactions Data Lakehouse: Supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring data reliability and consistency during read and write operations. Data Lake: Typically lacks ACID transaction support, which can lead to data inconsistencies and corruption. 2. Schema Enforcement and Evolution Data Lakehouse: Enforces schemas on write, ensuring that data adheres to predefined structures. It also supports schema evolution, allowing changes without breaking existing processes. Data Lake: Often stores raw data without strict schema enforcement, leading to potential data quality issues and difficulties in data integration. 3. Data Lineage and Governance Data Lakehouse: Provides robust data lineage and governance features, enabling better tracking of data origins, transformations, and usage.  Data Lake: Generally lacks comprehensive data lineage and governance capabilities, making it harder to trace data sources and transformations. 4. Data Validation and Quality Checks Data Lakehouse: Incorporates data validation and quality checks as part of the data ingestion and processing workflows, ensuring higher data quality. Data Lake: May not have built-in mechanisms for data validation, leading to potential quality issues. 5. Unified Data Management Data Lakehouse: Combines the management of both structured and unstructured data, providing a single platform for diverse data types and improving overall data quality. Data Lake: Primarily focuses on storing raw data, often requiring additional tools and processes to manage and ensure data quality. 6. Performance and Scalability Data Lakehouse: Optimized for both batch and real-time processing, ensuring timely and accurate data availability. Data Lake: May struggle with performance issues, especially with large-scale data processing, impacting data quality and usability. 3. Compare and contrast silver and gold tables, which workloads will use a bronze table as a source, which workloads will use a gold table as a source. Silver vs. Gold Tables in a Data Lakehouse Silver Tables: Purpose: Silver tables contain validated, cleansed, and conformed data. They serve as an intermediate layer where data is enriched and standardized. Data Quality: Data in silver tables is more reliable than in bronze tables but not as refined as in gold tables. Use Cases: Used for data validation, deduplication, and basic transformations. They provide an enterprise view of key business entities and transactions.  Users: Data engineers and data analysts who need a clean and consistent dataset for further processing and analysis. Gold Tables: Purpose: Gold tables contain highly refined, aggregated, and business-ready data. They are optimized for analytics and reporting. Data Quality: Data in gold tables is of the highest quality, ready for consumption by business intelligence (BI) tools and machine learning models. Use Cases: Used for advanced analytics, machine learning, and production applications. They support complex queries and reporting. Users: Business analysts, data scientists, and decision-makers who require high- quality data for strategic insights and decision-making. Workloads Using Bronze Tables as a Source Data Ingestion: Raw data from various sources is ingested into bronze tables. This includes batch and streaming data. Historical Data Storage: Bronze tables store the raw, unprocessed history of datasets, which can be used for reprocessing if needed. Initial Data Processing: Basic transformations and metadata additions are performed before moving data to silver tables. Workloads Using Gold Tables as a Source Business Intelligence (BI): BI tools use gold tables for generating reports and dashboards. Advanced Analytics: Data scientists use gold tables for building and training machine learning models. Production Applications: Applications that require high-quality, aggregated data for real-time decision-making and operational processes. 4. Identify elements of the Databricks Platform Architecture, such as what is located in the data plane versus the control plane and what resides in the customer’s cloud account Databricks Platform Architecture The Databricks platform architecture is divided into two main components: the Control Plane and the Data Plane. Control Plane Location: Managed by Databricks within their cloud account. Components: o Backend Services: Includes the web application, REST APIs, job scheduling, and cluster management. o User Interface: The Databricks workspace interface where users interact with notebooks, jobs, and other resources. o Metadata Management: Manages metadata for clusters, jobs, and other resources. o Security and Governance: Handles authentication, authorization, and auditing. Data Plane Location: Resides in the customer’s cloud account. Components: o Compute Resources: Includes clusters and jobs that process data. These resources are provisioned within the customer’s virtual network. o Data Storage: Data is stored in the customer’s cloud storage (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage). o Networking: Network configurations and security groups that control access to the compute resources and data. Customer’s Cloud Account Data Plane: All compute resources (clusters, jobs) and data storage are located here. Workspace Storage Bucket: Contains workspace system data, such as notebook revisions, job run details, and Spark logs. DBFS (Databricks File System): A distributed file system accessible within Databricks environments, stored in the customer’s cloud storage. Summary Control Plane: Managed by Databricks, includes backend services, user interface, and metadata management. Data Plane: Located in the customer’s cloud account, includes compute resources, data storage, and networking. This architecture ensures that sensitive data remains within the customer’s control while leveraging Databricks’ managed services for operational efficiency123. 5. Differentiate between all-purpose clusters and jobs clusters. All-Purpose Clusters vs. Job Clusters in Databricks All-Purpose Clusters: Usage: Designed for interactive and collaborative use. Ideal for ad hoc analysis, data exploration, development, and interactive workloads. Accessibility: Multiple users can share these clusters simultaneously, making them suitable for team-based projects. Lifespan: Typically long-lived, as they are manually started and stopped by users. Flexibility: Users can run various types of workloads, including notebooks, scripts, and interactive queries. Cost: May incur higher costs due to their persistent nature and shared usage. Job Clusters: Usage: Specifically created for running automated jobs. Ideal for scheduled tasks, ETL processes, and batch jobs. Accessibility: Dedicated to a single job or workflow, ensuring resources are fully available for that task. Lifespan: Ephemeral; they are automatically created when a job starts and terminated once the job completes, optimizing resource usage and cost. Flexibility: Configured to run specific jobs, ensuring optimal performance and resource allocation for those tasks. Cost: More cost-effective as they only run for the duration of the job, reducing idle time and resource wastage. Summary All-Purpose Clusters: Best for interactive, exploratory, and collaborative tasks with multiple users. Job Clusters: Best for automated, scheduled, and single-purpose tasks with optimized resource usage. 7. Identify how clusters can be filtered to view those that are accessible by the user. To filter and view clusters that are accessible to you in Databricks, you can use the Compute section in the workspace. Here’s how you can do it: 1. Navigate to Compute: In the Databricks workspace sidebar, click on Compute. 2. View Clusters: You will see a list of clusters. The clusters you can access will be listed here. 3. Filter by Permissions: You can filter clusters based on your permissions. Clusters you have access to will be those where you have at least the CAN ATTACH TO permission1. Additionally, you can use the Clusters API to programmatically list clusters and filter them based on your access permissions1. 8. Describe how clusters are terminated and the impact of terminating a cluster. n Databricks, clusters can be terminated either manually or automatically. Here’s how it works and the impact of termination: Terminating Clusters 1. Manual Termination: o You can manually terminate a cluster through the Databricks UI by navigating to the Compute section, selecting the cluster, and clicking Terminate. o You can also use the Clusters API to programmatically terminate a cluster. 2. Automatic Termination: o Clusters can be configured to terminate automatically after a specified period of inactivity. This helps in managing costs by ensuring that idle clusters do not continue to run unnecessarily1. Impact of Terminating a Cluster 1. Resource Release: o Terminating a cluster releases the cloud resources (such as VMs) that were allocated to it. This helps in reducing costs as you are no longer billed for those resources. 2. Data and State: o Any data stored in the cluster’s local storage is lost upon termination. However, data stored in external storage systems (like S3, ADLS, etc.) remains unaffected. o Running jobs or interactive sessions are interrupted, and any in-memory data is lost. 3. Cluster Configuration: o The cluster configuration is retained for 30 days after termination, allowing you to restart the cluster with the same settings if needed. After 30 days, the configuration is permanently deleted unless the cluster is pinned1. 4. Job Impact: o Scheduled jobs that were running on the terminated cluster will fail. You need to restart the cluster or configure the job to use a different cluster. Would you like more details on configuring automatic termination or managing cluster configurations? 9. Identify a scenario in which restarting the cluster will be useful. Restarting a cluster in Databricks can be particularly useful in several scenarios. Here are a few examples: 1. Applying Configuration Changes If you have made changes to the cluster configuration, such as updating the instance type, adding libraries, or modifying environment variables, restarting the cluster ensures that these changes take effect. 2. Resolving Performance Issues  Sometimes, clusters may experience performance degradation due to memory leaks, resource contention, or other issues. Restarting the cluster can help clear these issues and restore optimal performance. 3. Refreshing the Environment If you encounter unexpected errors or instability in your notebooks or jobs, restarting the cluster can provide a clean slate, clearing any transient issues that might be affecting your work. 4. Updating Libraries When you need to update or install new libraries, restarting the cluster ensures that the new versions are loaded and available for use in your notebooks and jobs. 5. Recovering from Failures In case of a cluster failure or crash, restarting the cluster can help recover from the failure and resume operations without needing to create a new cluster from scratch. Would you like more details on any of these scenarios or guidance on how to restart a cluster? 10. In Databricks, Describe how to use multiple languages within the same notebook Steps to Use Multiple Languages 1. Default Language: o Each notebook has a default language, which is typically set when the notebook is created. This default language is used for all cells unless specified otherwise. 2. Magic Commands: o To switch languages within a notebook, you use magic commands at the beginning of a cell. The available magic commands are: ▪ %python for Python ▪ %r for R ▪ %scala for Scala  ▪ %sql for SQL 3. Example: o Here’s an example of how you can use different languages in the same notebook: Python # Python cell %python print("This is a Python cell") # SQL cell %sql SELECT * FROM my_table # Scala cell %scala val data = spark.read.json("path/to/json") # R cell %r summary(my_data_frame) AI-generated code. Review and use carefully. More info on FAQ. Benefits of Using Multiple Languages Flexibility: You can leverage the strengths of different languages for different tasks. For example, you might use SQL for querying data, Python for data manipulation, and R for statistical analysis. Collaboration: Teams with diverse skill sets can work together in the same notebook, each using their preferred language.  Efficiency: Switching languages within the same notebook can streamline workflows and reduce the need to switch between different tools or environments. Would you like more details on any specific language or how to integrate them in your workflows? 11. In Databricks, Identify how to run one notebook from within another notebook. In Databricks, you can run one notebook from within another using two primary methods: the %run command and the dbutils.notebook.run() function. Here’s how each method works: 1. Using %run Command The %run command allows you to include another notebook within your current notebook. This method runs the included notebook in the same context, meaning any variables or functions defined in the included notebook become available in the current notebook. Example: Python # Include another notebook %run /path/to/your/notebook AI-generated code. Review and use carefully. More info on FAQ. Pros: Simple to use, variables and functions are directly accessible. Cons: Cannot pass parameters or get return values. 2. Using dbutils.notebook.run() Function The dbutils.notebook.run() function allows you to run another notebook as a separate job. This method is more flexible as it allows you to pass parameters to the called notebook and retrieve return values. Example: Python # Run another notebook and pass parameters result = dbutils.notebook.run("/path/to/your/notebook", timeout_seconds=60, arguments={"param1": "value1"}) # Print the result returned by the notebook print(result) AI-generated code. Review and use carefully. More info on FAQ. Pros: Can pass parameters and get return values, supports error handling and conditional workflows. Cons: Runs in a separate context, so variables and functions are not shared. When to Use Each Method %run: Best for modularizing code where you need to share functions and variables across notebooks. dbutils.notebook.run(): Ideal for orchestrating complex workflows, passing parameters, and handling dependencies between notebooks. Would you like more details on any of these methods or examples of specific use cases? 12. Identify how notebooks can be shared with others In Databricks, you can share notebooks with others to collaborate effectively. Here’s how you can do it: Sharing a Notebook 1. Open the Notebook: o Navigate to the notebook you want to share. 2. Click the Share Button: o At the top of the notebook, click the Share button (usually represented by an icon of a person with a plus sign). 3. Set Permissions: o In the sharing dialog that opens, you can specify who to share the notebook with and what level of access they should have. The available permission levels are:  ▪ CAN READ: The user can view the notebook but cannot make any changes. ▪ CAN RUN: The user can run the notebook but cannot edit it. ▪ CAN EDIT: The user can both run and edit the notebook. ▪ CAN MANAGE: The user has full control, including the ability to change permissions1. Managing Permissions Folder Permissions: o You can also manage permissions at the folder level. Notebooks within a folder inherit the permissions set for that folder. This is useful for organizing and managing access to multiple notebooks at once1. Collaborative Features Comments: o You can add comments to specific cells in the notebook to facilitate discussions and feedback. To add a comment, highlight the text in the cell and click the comment bubble icon1. Would you like more details on any specific aspect of sharing notebooks or managing permissions? 13. Describe how Databricks Repos enables CI/CD workflows in Databricks. Databricks Repos enables Continuous Integration and Continuous Deployment (CI/CD) workflows by integrating with Git repositories and providing tools to manage code changes, automate testing, and deploy updates. Here’s how it works: Key Features of Databricks Repos for CI/CD 1. Git Integration: o Databricks Repos allows you to clone Git repositories directly into your Databricks workspace. This integration supports popular Git providers like GitHub, GitLab, and Bitbucket1. o You can manage branches, commit changes, and push updates to your remote repository from within Databricks1.  2. Branch Management: o Developers can work on feature branches, make changes, and commit them. This helps in isolating development work and facilitates code reviews and collaboration1. o Branches can be merged using the Git UI in Databricks, ensuring that changes are integrated smoothly1. 3. Automated Testing: o CI/CD workflows often include automated testing to ensure code quality. You can set up automated tests to run on your notebooks and other code files whenever changes are pushed to the repository2. o Tools like GitHub Actions or Azure DevOps can be used to trigger these tests and report results back to the development team3. 4. Deployment Automation: o Databricks Repos supports deployment automation through tools like Databricks Asset Bundles and the Databricks CLI. These tools help package your code and deploy it to different environments (e.g., development, staging, production) seamlessly23. o You can define workflows in YAML files to specify the sequence of tasks and dependencies, ensuring that deployments are consistent and repeatable3. 5. Collaboration and Code Reviews: o By using Git-based workflows, teams can collaborate more effectively. Pull requests and code reviews can be managed through the Git provider, ensuring that all changes are reviewed and approved before being merged1. Example Workflow 1. Development: o Clone the repository into Databricks Repos. o Create a feature branch and make changes. o Commit and push changes to the remote repository. 2. Testing:  o Automated tests are triggered by CI tools (e.g., GitHub Actions) whenever changes are pushed. o Test results are reported back, and any issues are addressed. 3. Deployment: o Use Databricks Asset Bundles and the Databricks CLI to package and deploy the code. o Deploy to different environments as specified in the CI/CD pipeline. By leveraging these features, Databricks Repos enables robust CI/CD workflows, ensuring that code changes are tested, reviewed, and deployed efficiently. Would you like more details on setting up a specific CI/CD pipeline or using any particular tool within this workflow? : Databricks Repos Documentation 2: CI/CD Integration with Databricks Workflows 3: Run a 1 CI/CD workflow with a Databricks Asset Bundle and GitHub Actions 14. In Databricks, identify Git operations available via Databricks Repos. In Databricks Repos, you can perform a variety of Git operations to manage your code and collaborate with your team. Here are the key Git operations available: Common Git Operations 1. Clone a Repository: o You can clone a remote Git repository into your Databricks workspace. This allows you to work with the repository’s contents directly within Databricks1. 2. Branch Management: o Create a New Branch: Create a new branch for development work. o Switch Branches: Switch between different branches to work on various features or fixes. o Merge Branches: Merge changes from one branch into another. o Rebase Branches: Rebase a branch on top of another branch to integrate changes1. 3. Commit and Push Changes:  o Commit Changes: Save your changes to the local repository. o Push Changes: Push your committed changes to the remote repository1. 4. Pull Changes: o Pull Changes: Fetch and integrate changes from the remote repository into your local branch1. 5. Resolve Conflicts: o Merge Conflicts: Resolve conflicts that arise during merging or rebasing1. 6. Reset: o Git Reset: Undo changes by resetting the current branch to a previous state1. 7. Sparse Checkout: o Sparse Checkout: Clone only specific subdirectories of a repository, which is useful for large repositories1. Additional Features Visual Comparison: o Compare differences between commits visually to understand changes and resolve conflicts2. Collaboration: o Share Git folders with collaborators and manage permissions to control access2. These operations help you maintain version control, collaborate effectively, and integrate seamlessly with CI/CD workflows. Would you like more details on any specific Git operation or how to set up a Git workflow in Databricks 15. in Databricks, Identify limitations in Databricks Notebooks version control functionality relative to Repos. Databricks Notebooks have some limitations in version control functionality compared to Databricks Repos. Here are the key differences: Limitations in Databricks Notebooks Version Control  1. Granularity of Version Control: o Notebooks: Version control in notebooks is less granular. Changes are tracked at the notebook level, making it harder to manage and review individual changes within a notebook. o Repos: Repos provide more granular version control, tracking changes at the file level, which is more suitable for collaborative development and code reviews1. 2. Branching and Merging: o Notebooks: Branching and merging are not natively supported within the notebook interface. This makes it challenging to manage different versions of the notebook or collaborate on features. o Repos: Full Git support allows for branching, merging, and pull requests, facilitating better collaboration and version management1. 3. Integration with CI/CD: o Notebooks: Limited integration with CI/CD pipelines. While you can manually export notebooks and integrate them into CI/CD workflows, it is not as seamless. o Repos: Direct integration with CI/CD tools, enabling automated testing, deployment, and continuous integration workflows2. 4. Conflict Resolution: o Notebooks: Resolving conflicts in notebooks can be cumbersome, especially when multiple users are editing the same notebook. o Repos: Git-based conflict resolution tools make it easier to handle merge conflicts and ensure code integrity1. 5. History and Rollback: o Notebooks: Limited version history and rollback capabilities. While Databricks autosaves notebooks, it does not provide the same level of detailed history and rollback options as Git. o Repos: Comprehensive version history and the ability to roll back to any previous state, making it easier to manage changes and recover from errors3. Summary  Databricks Notebooks: Suitable for quick, interactive development but limited in collaborative and version control features. Databricks Repos: Provides robust version control, collaboration, and integration with CI/CD workflows, making it ideal for more structured and collaborative development. Would you like more details on how to set up Databricks Repos or integrate them into your CI/CD workflows? : Run Git operations on Databricks Git folders (Repos) 2: CI/CD Integration with Databricks 1 Workflows 3: Software engineering best practices for notebooks