(Databricks) Section 1: Databricks Lakehouse Platform
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Questions and Answers

Match the following terms with their descriptions:

Data Warehouse = Optimized for fast SQL queries and analytics Data Lake = Stores data in its native format Data Lakehouse = Combines features of data lakes and warehouses ETL Process = Data is extracted, transformed, and loaded

Match the following storage types with their characteristics:

Structured Data = Organized into tables with predefined schemas Unstructured Data = Raw data without a specific format Semi-structured Data = Data that does not conform to a rigid structure Raw Data Storage = Stores data in original formats without processing

Match the following databases with their types:

Amazon Redshift = Data Warehouse Hadoop = Data Lake Databricks Lakehouse = Data Lakehouse Amazon S3 = Data Lake

Match the following processes with their definitions:

<p>ELT Process = Data is extracted, loaded, and then transformed ACID Transactions = Ensures data reliability and consistency Schema Enforcement = Maintains data quality by enforcing schemas Unified Data Management = Supports both batch and streaming data</p> Signup and view all the answers

Match the following comments with their implications for data architecture:

<p>Cost Efficiency = Utilizes low-cost storage solutions High Performance = Optimized for complex queries Single Source of Truth = Raw and structured data can coexist Scalability = Efficiently handles large volumes of data</p> Signup and view all the answers

Match the following improvements in data quality with their descriptions:

<p>ACID Transactions = Provides reliability and consistency Schema Enforcement = Helps maintain data integrity Unified Data Management = Facilitates diverse analytics Streamlined Architecture = Reduces complexity in data handling</p> Signup and view all the answers

Match the following examples with their respective categories:

<p>Delta Lake = Data Lakehouse Google BigQuery = Data Warehouse Azure Data Lake = Data Lake Snowflake = Data Warehouse</p> Signup and view all the answers

Match the following components of the data lakehouse architecture with their functions:

<p>Flexible Data Storage = Handles both structured and unstructured data High-Performance Query Capabilities = Optimizes for analytics Integration of Lake and Warehouse = Provides a unified architecture Cost Management = Utilizes affordable storage solutions</p> Signup and view all the answers

Match the clusters with their primary characteristics:

<p>Job Clusters = Cost-effective as they optimize resource usage All-Purpose Clusters = Designed for multiple users and collaborative tasks</p> Signup and view all the answers

Match the cluster management actions with their descriptions:

<p>Manual Termination = User selects a cluster to end through UI Automatic Termination = Ends clusters after a period of inactivity Filtering by Permissions = Viewing clusters based on specific access rights Using Clusters API = Programmatically managing clusters and their settings</p> Signup and view all the answers

Match the following features with their relevance to clusters:

<p>Lifespan = Clusters are created and terminated with a job Flexibility = Configured for specific jobs for optimal performance Accessibility = Ensures resources are available for intended tasks Cost = Reduces expenses by minimizing idle resource time</p> Signup and view all the answers

Match the term with its description in Databricks:

<p>Permission to Attach = Required to access a specific cluster Compute Section = Area to view and filter accessible clusters Cluster Termination = Releasing cloud resources after usage Resource Release = Stopping charges for unused cloud resources</p> Signup and view all the answers

Match the benefits with cluster types:

<p>Job Clusters = Reduces costs with targeted resource deployment All-Purpose Clusters = Ideal for exploratory and interactive use cases</p> Signup and view all the answers

Match the process of cluster interaction with its detail:

<p>Navigate to Compute = Access to view all clusters available Filter by Permissions = Narrow down accessible clusters based on roles Select Cluster to Terminate = Manual termination through UI options Terminate Automatically = Scheduled ending based on inactivity duration</p> Signup and view all the answers

Match the following actions with their implications:

<p>Manual Termination = Directly stops resources from running Automatic Termination = Helps control costs during idleness Filtering Accessible Clusters = Identifies clusters relevant to user permissions Releasing Resources = Prevents ongoing charges by stopping allocated VMs</p> Signup and view all the answers

Match the feature with its impact on cluster management:

<p>Lifespan of Clusters = Directly affects resource allocation efficiency Targeted Flexibility = Ensures high performance for defined tasks Cost Management = Deals with reducing unnecessary resource spending Cluster Accessibility = Affects user engagement and collaboration potential</p> Signup and view all the answers

Match the following data storage outcomes with their descriptions:

<p>Data in local storage = Lost upon termination of the cluster Data in external storage = Remains unaffected after termination In-memory data = Lost during interrupted jobs or sessions Cluster configuration = Retained for 30 days after termination</p> Signup and view all the answers

Match the following scenarios with their corresponding actions when restarting a cluster:

<p>Applying Configuration Changes = Ensures new settings take effect Resolving Performance Issues = Clears memory leaks and contention Refreshing the Environment = Provides a clean slate for notebooks Updating Libraries = Loads new library versions for use</p> Signup and view all the answers

Match the job impact scenarios with the consequences of terminating a cluster:

<p>Running jobs = Will fail on the terminated cluster Scheduled jobs = Require cluster restart to continue Interactive sessions = Get interrupted without recovery Cluster pinning = Prevents configuration deletion after 30 days</p> Signup and view all the answers

Match the conditions for restarting a cluster with their benefits:

<p>Applying configuration changes = Ensures updates are effective Recovering from failures = Resumes operations smoothly Updating libraries = Uses the latest versions instantly Performance degradation = Restores optimal functioning</p> Signup and view all the answers

Match the terminology used in Databricks to its description:

<p>Cluster termination = Loss of in-memory data External storage = S3 and ADLS data retention Job failure = Occurs on terminated clusters Configuration retention = Access for 30 days post-termination</p> Signup and view all the answers

Match the types of issues you might face with their required actions:

<p>Performance degradation = Consider restarting the cluster Unexpected errors = Restart to clear transient issues Library updates = Restart for new versions availability Cluster failures = Restart for operational recovery</p> Signup and view all the answers

Match the group of languages with their usage in Databricks notebooks:

<p>Python = Data analysis and visualization SQL = Database querying Scala = Big data processing R = Statistical computing</p> Signup and view all the answers

Match the statements about cluster actions with their results:

<p>Restarting the cluster = Applies configuration changes Terminating the cluster = Loses in-memory data Scheduling a job = Requires active cluster Pinning a configuration = Preserves it beyond 30 days</p> Signup and view all the answers

Match the example usage with the corresponding programming language:

<p>print('This is a Python cell') = Python SELECT * FROM my_table = SQL val data = spark.read.json('path/to/json') = Scala summary(my_data_frame) = R</p> Signup and view all the answers

Match the benefits of using multiple languages in a notebook:

<p>Flexibility = Leverage strengths of different languages for different tasks Collaboration = Teams can use their preferred language Efficiency = Streamline workflows between languages Simplicity = Makes it easier to manage different tools</p> Signup and view all the answers

Match the notebook inclusion methods with their characteristics:

<p>%run command = Variables and functions are directly accessible dbutils.notebook.run() = Cannot return values directly</p> Signup and view all the answers

Match the parts of a notebook cell with their descriptions:

<p>Magic command = Begins the cell with language specification Variable = Stores data for use in the notebook Function = Reusable code block within the notebook Cell = Unit of code execution in the notebook</p> Signup and view all the answers

Match the programming languages to their typical usage:

<p>Python = Data manipulation R = Statistical analysis SQL = Database queries Scala = Big data processing</p> Signup and view all the answers

Match the commands with their execution contexts:

<p>%run = Current notebook context %sql = Switching to SQL context %python = Switching to Python context %scala = Switching to Scala context</p> Signup and view all the answers

Match the concept to its definition in the context of notebooks:

<p>Magic commands = Syntax for switching languages Variables = Named storage for data Functions = Code block that performs an operation Cells = Sections of code or text in a notebook</p> Signup and view all the answers

Match the following features of Databricks Repos with their descriptions:

<p>Git Integration = Clone Git repositories directly into Databricks workspace Branch Management = Isolate development work and facilitate code reviews Automated Testing = Ensure code quality by running tests on code changes Deployment Automation = Package your code for deployment to different environments</p> Signup and view all the answers

Match the following CI/CD tools with their respective functions:

<p>GitHub Actions = Trigger automated tests on code changes Azure DevOps = Provide a platform for managing CI/CD workflows Databricks Asset Bundles = Package code for deployment Databricks CLI = Control deployment processes from the command line</p> Signup and view all the answers

Match the following CI/CD process components with their roles:

<p>Continuous Integration = Automate the process of code merging and testing Continuous Deployment = Deploy code updates efficiently to production Code Reviews = Facilitate collaboration among team members Branching = Enable focus on specific features or fixes during development</p> Signup and view all the answers

Match the following types of code change actions with their descriptions:

<p>Clone = Create a local copy of a repository Commit = Save changes to the local repository Push = Upload local changes to the remote repository Merge = Combine changes from different branches</p> Signup and view all the answers

Match the following Databricks features with their purposes:

<p>Comments = Facilitate discussion and feedback in notebooks YAML files = Define workflows and dependencies for deployment Feature Branches = Allow separate areas for development work Automated Tests = Run checks to maintain code quality</p> Signup and view all the answers

Match the following Git providers with their features:

<p>GitHub = Popular choice for open-source projects GitLab = Offers built-in CI/CD pipelines Bitbucket = Supports both Git and Mercurial repositories Azure DevOps = Integration with Azure services for DevOps</p> Signup and view all the answers

Match the following collaboration strategies with their benefits:

<p>Git-based Workflows = Enhanced collaboration among developers Code Reviews = Improve code quality through peer feedback Branching Strategies = Manage different features or fixes simultaneously Automated Deployments = Minimize human error during releases</p> Signup and view all the answers

Match the following automated testing concepts with their functions:

<p>Test Triggers = Activate tests when code changes occur Test Reporting = Communicate results back to the development team Continuous Testing = Run tests regularly throughout the development cycle Mock Testing = Simulate conditions for testing without dependencies</p> Signup and view all the answers

What is the primary purpose of all-purpose clusters in Databricks?

<p>To provide resources for interactive and collaborative use</p> Signup and view all the answers

Which of the following components is NOT part of the control plane in Databricks?

<p>Compute resources</p> Signup and view all the answers

What is a key characteristic of job clusters in Databricks?

<p>They are used for running automated jobs and batch processes.</p> Signup and view all the answers

Which statement best describes the significance of the data plane in Databricks architecture?

<p>It houses compute resources and data storage solutions in the customer's cloud account.</p> Signup and view all the answers

What is a potential disadvantage of using all-purpose clusters in Databricks?

<p>They may lead to higher costs due to shared user access and persistence.</p> Signup and view all the answers

What is a primary feature that distinguishes a Data Lakehouse from a Data Lake?

<p>Data Lakehouses support ACID transactions.</p> Signup and view all the answers

Which of the following best describes the capability of schema enforcement in a Data Lakehouse?

<p>It enforces schemas on write and supports schema evolution.</p> Signup and view all the answers

What is a significant limitation of Data Lakes in terms of data management?

<p>They lack robust data lineage and governance features.</p> Signup and view all the answers

Which process does a Data Lakehouse utilize to ensure higher data quality during ingestion?

<p>Data Lakehouses include data validation and quality checks.</p> Signup and view all the answers

What processing capabilities does a Data Lakehouse optimize for?

<p>Both batch and real-time processing for timely data availability.</p> Signup and view all the answers

What is a primary characteristic of job clusters in Databricks?

<p>Optimized for automated and single-purpose tasks.</p> Signup and view all the answers

How can users filter clusters to view those accessible to them?

<p>By filtering based on user permissions.</p> Signup and view all the answers

Which of the following statements accurately describes the lifespan of clusters in Databricks?

<p>Clusters are ephemeral, created at job start and terminated upon completion.</p> Signup and view all the answers

What is the impact of manually terminating a cluster in Databricks?

<p>It releases cloud resources and reduces costs.</p> Signup and view all the answers

What is a key benefit of using automatic termination for clusters?

<p>It helps manage costs by preventing idle clusters.</p> Signup and view all the answers

What happens when a cluster is terminated due to inactivity?

<p>Resources allocated to the cluster are released.</p> Signup and view all the answers

What is the primary task for which all-purpose clusters are designed?

<p>To facilitate interactive and collaborative tasks for multiple users.</p> Signup and view all the answers

Which of the following is a method to programmatically manage clusters in Databricks?

<p>Using the Clusters API for scripted operations.</p> Signup and view all the answers

What term describes the resources used by a cluster while it is active?

<p>Allocated cloud resources.</p> Signup and view all the answers

Which statement is true regarding the configurations of job clusters?

<p>They guarantee optimal resource allocation for specified jobs.</p> Signup and view all the answers

Data Lakehouses typically lack support for ACID transactions.

<p>False</p> Signup and view all the answers

Data Lakes enforce strict schemas on write, promoting data quality.

<p>False</p> Signup and view all the answers

Data Lakehouses provide robust data lineage and governance features.

<p>True</p> Signup and view all the answers

Silver tables in a Data Lakehouse contain unvalidated, raw data.

<p>False</p> Signup and view all the answers

Data Lakehouses are optimized solely for batch processing, without real-time capabilities.

<p>False</p> Signup and view all the answers

Data in silver tables is more reliable than in gold tables.

<p>False</p> Signup and view all the answers

Users of gold tables include data engineers and data analysts.

<p>False</p> Signup and view all the answers

Bronze tables store raw, unprocessed data from various sources.

<p>True</p> Signup and view all the answers

Gold tables are optimized for data validation and deduplication.

<p>False</p> Signup and view all the answers

The Control Plane in Databricks is managed within the customer's cloud account.

<p>False</p> Signup and view all the answers

Raw data is ingested into bronze tables through both batch and streaming methods.

<p>True</p> Signup and view all the answers

Business intelligence tools utilize gold tables for generating insights and reports.

<p>True</p> Signup and view all the answers

Databricks Repos allows you to manage branches and commit changes from within Databricks.

<p>True</p> Signup and view all the answers

Automated testing is not supported in Databricks Repos CI/CD workflows.

<p>False</p> Signup and view all the answers

Databricks Repos supports deployment automation through tools like Databricks Asset Bundles and the Databricks CLI.

<p>True</p> Signup and view all the answers

GitHub Actions and Azure DevOps cannot be used to trigger automated tests in Databricks Repos.

<p>False</p> Signup and view all the answers

By using Git-based workflows, teams can collaborate less effectively in Databricks.

<p>False</p> Signup and view all the answers

The dbutils.notebook.run() function allows you to run another notebook in the same context, sharing all variables and functions.

<p>False</p> Signup and view all the answers

In Databricks, permissions for notebooks can be managed individually or inherited from folder-level permissions.

<p>True</p> Signup and view all the answers

The CAN EDIT permission level allows a user to view, run, and make changes to a shared notebook.

<p>True</p> Signup and view all the answers

Using dbutils.notebook.run() is best suited for tasks where variables need to be shared between notebooks.

<p>False</p> Signup and view all the answers

The dbutils.notebook.run() function can return values from the called notebook back to the caller.

<p>True</p> Signup and view all the answers

To share a notebook in Databricks, you need to provide an external link to the user.

<p>False</p> Signup and view all the answers

The CAN RUN permission level allows a user to edit the contents of the notebook.

<p>False</p> Signup and view all the answers

The %run command is preferable for modularizing code when variable sharing is necessary.

<p>True</p> Signup and view all the answers

In Databricks, to set permissions for multiple notebooks, you must manage them individually without folder-level permissions.

<p>False</p> Signup and view all the answers

The arguments parameter in dbutils.notebook.run() can include multiple key-value pairs.

<p>True</p> Signup and view all the answers

Study Notes

Data Lakehouse and Data Warehouse

  • Data Warehouse: Focused on structured data, optimized for fast SQL queries and analytics.
    • Utilizes ETL process (Extract, Transform, Load) for data management.
    • Examples include Amazon Redshift, Google BigQuery, Snowflake.
  • Data Lake: Stores both structured and unstructured data in its native format.
    • Uses ELT process (Extract, Load, Transform).
    • Examples include Hadoop, Amazon S3, Azure Data Lake.
  • Data Lakehouse: Combines the strengths of data lakes and data warehouses.
    • Enables unified data management for diverse analytics and machine learning workloads.
    • Offers cost efficiency with the use of low-cost storage solutions while maintaining high-performance query capabilities.
    • Databricks Lakehouse architecture utilizes Delta Lake for ACID Transactions, schema enforcement, unified data management, and scalability.

Data Quality Improvement in Data Lakehouse

  • The data lakehouse architecture provides improved data quality over traditional data lakes.
  • Enforces Schemas: Guarantees data quality through schema enforcement which results in consistent and reliable data.
  • ACID Transactions: Ensures data reliability and consistency by using ACID transactions, preventing inconsistencies and data loss.

Databricks Clusters

  • Databricks clusters can be categorized as All-Purpose Clusters or Job Clusters.
  • All-Purpose Clusters: Suitable for interactive, exploratory, and collaborative tasks with multiple users.
  • Job Clusters: Best suited for automated, scheduled, and single-purpose tasks, optimizing resource usage.
  • Ephemeral Clusters: Created when a job starts and automatically terminated upon completion, which optimizes resources and cost.

Databricks Cluster Access and Permissions

  • Users can filter and view clusters with access permissions in Databricks by navigating to the Compute section.
  • Clusters can be filtered based on permissions. Users need at least "CAN ATTACH TO" permission to access a cluster.
  • The Clusters API allows programmatic listing and filtering of clusters based on access permissions.

Databricks Cluster Termination and its Impact

  • Databricks clusters can be terminated manually or automatically.
  • Manual Cluster Termination: Users can terminate a cluster through the Databricks UI or using the Clusters API.
  • Automatic Cluster Termination: Clusters can be configured to terminate automatically after a period of inactivity.
  • Impact of Cluster Termination: Releases the cloud resources allocated to the cluster, and any data stored locally on the cluster is lost. However, data stored externally remains unaffected. Running jobs or interactive sessions are interrupted, and in-memory data is lost.
  • The cluster configuration is retained for 30 days, allowing for restarting with the same settings. After 30 days, it is permanently deleted unless the cluster is pinned.
  • Scheduled jobs running on a terminated cluster will fail and need to be restarted or configured to use a different cluster.

Restarting a Databricks Cluster

  • Restarting a Databricks cluster can be useful:
    • To apply configuration changes, such as updating instance types, adding libraries, or modifying environment variables.
    • To resolve performance issues caused by memory leaks, resource contention, or other unforeseen factors.
    • To refresh the environment and clear any transient issues causing unexpected errors or instability.
    • To update libraries or install new ones.
    • To recover from cluster failures or crashes.

Using Multiple Languages in a Databricks Notebook

  • Databricks notebooks can utilize multiple languages through magic commands.
  • Default Language: Each notebook has a default language set at creation.
  • Magic Commands: Used to switch between languages within a notebook, for example:
    • %python for Python
    • %r for R
    • %scala for Scala
    • %sql for SQL

Running One Notebook from Another in Databricks

  • Databricks allows running one notebook from another using the %run command and the dbutils.notebook.run() function.
  • %run Command: Includes another notebook within the current notebook, running it in the same context.
  • dbutils.notebook.run() Function: Enables running a different notebook as a separate process.

Sharing Databricks Notebooks

  • Databricks offers collaboration features:
    • Sharing: Sharing notebooks with different levels of access, including view, edit, and owner permissions.
    • Comments: Users can add comments to specific cells in the notebook for discussions and feedback.

Databricks Repos and CI/CD Workflows

  • Databricks Repos enables CI/CD workflows by integrating with Git repositories, providing tools for code management, automated testing, and deployment.
  • Git Integration: Supports cloning Git repositories directly into Databricks workspaces.
  • Branch Management: Facilitates collaborative development by allowing developers to work on separate branches, make changes, and commit them. Branches can be merged through the Git UI in Databricks.
  • Automated Testing: Automated tests can be set up to run on code files when changes are pushed to the repository.
  • Deployment Automation: Supports deployment automation through Databricks Asset Bundles and the Databricks CLI.
  • Collaboration and Code Reviews: Facilitates collaboration with Git-based workflows, enabling code reviews and tracking changes.

Data Lakehouse vs. Data Lake

  • Data Lakehouse supports ACID 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.

Schema Enforcement and Evolution

  • Data Lakehouse enforces schemas on write, ensuring that data adheres to predefined structures.
  • Data Lakehouse 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.

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.

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.

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.

Performance and Scalability

  • Data Lakehouse is 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.

Silver vs. Gold Tables

  • Silver tables contain validated, cleansed, and conformed data.
  • Silver tables serve as an intermediate layer where data is enriched and standardized.
  • Gold tables are optimized for reporting and analytics.
  • Gold tables typically use a silver table as a source.
  • Workloads that require validated and cleansed data will use silver tables.
  • Workloads requiring standardized and optimized data for analytics and reporting will use gold tables.

Databricks Architecture

  • The control plane manages backend services, user interface, and metadata management.
  • The data plane includes compute resources, data storage, and networking.
  • The data plane is located in the customer’s cloud account.
  • The workspace storage bucket contains workspace system data, such as notebook revisions, job run details, and Spark logs.
  • DBFS (Databricks File System) is a distributed file system accessible within Databricks environments, stored in the customer’s cloud storage.

All-Purpose Clusters vs. Job Clusters

  • All-purpose clusters are designed for interactive and collaborative use.
  • Job clusters are specifically created for running automated jobs.
  • All-purpose clusters are ideal for ad hoc analysis, data exploration, development, and interactive workloads.
  • Job clusters are ideal for scheduled tasks, ETL processes, and batch jobs.
  • All-purpose clusters can be shared by multiple users, making them suitable for team-based projects.
  • Job clusters are dedicated to a single job or workflow, ensuring resources are fully available for that task.
  • All-purpose clusters are typically long-lived, as they are manually started and stopped by users.
  • Job clusters are ephemeral; they are automatically created when a job starts and terminated once the job completes, optimizing resource usage and cost

Finding Accessible Clusters

  • To filter and view clusters accessible in Databricks, navigate to the Compute section in the workspace sidebar.
  • Clusters accessible to you will be listed.
  • You can filter clusters based on your permissions.
  • Clusters where you have at least the CAN ATTACH TO permission will be accessible.

Terminating and Restarting Clusters

  • Clusters can be terminated either manually or automatically.

  • Manually terminating a cluster releases the cloud resources allocated to it, reducing costs.

  • Data stored in the cluster’s local storage is lost upon termination.

  • Data stored in external storage systems remains unaffected.

  • Running jobs or interactive sessions are interrupted, and any in-memory data is lost.

  • Cluster configurations are retained for 30 days after termination.

  • Scheduled jobs that were running on the terminated cluster will fail.

  • Restarting a cluster can be useful for applying configuration changes.

  • Restarting a cluster can help resolve performance issues.

  • Restarting a cluster can provide a clean slate, clearing any transient issues.

  • Restarting a cluster can ensure new libraries are loaded and available.

  • Restarting a cluster can help recover from failures.

Using Multiple Languages in Databricks Notebooks

  • Each code cell can have a specific language.
  • The cell magic command %%% is used to indicate the desired language for the cell.

Git Operations in Databricks

  • Git is a version control system used to track and manage changes made to code and files.
  • Common Git operations include:
    • Commit: Saves changes to the local repository.
    • Push: Uploads changes to a remote repository.
    • Pull: Downloads changes from a remote repository.
    • Branch: Creates a new branch to work on a specific feature.
    • Merge: Combines changes from different branches.
    • Rebase: Re-applies commits to a different base branch.
    • Git Reset: Undo changes by resetting the current branch to a previous state.
    • Sparse Checkout: Clone only specific subdirectories of a repository.

Databricks Notebooks Version Control

  • Databricks Notebooks have limitations in version control functionality compared to Databricks Repos.
  • Version control in notebooks is less granular. Changes are tracked at the notebook level.
  • Branching and merging are not natively supported within the notebook interface.
  • Limited integration with CI/CD pipelines.
  • Resolving conflicts in notebooks can be cumbersome.
  • Databricks Repos provide more granular version control, tracking changes at the file level.
  • Databricks Repos offer full Git support, including branching, merging, and pull requests.
  • Databricks Repos have direct integration with CI/CD tools, enabling automated testing, deployment, and continuous integration workflows.

Data Lakehouse vs Data Lake

  • Data Lakehouses: Provide ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring data reliability and consistency during read and write operations.
  • Data Lakehouses: Enforce schemas on write, ensuring data adheres to predefined structures. They also support schema evolution, allowing changes without breaking existing processes.
  • Data Lakehouses: Offer robust data lineage and governance features, enabling better tracking of data origins, transformations, and usage.
  • Data Lakehouses: Incorporate data validation and quality checks as part of the data ingestion and processing workflows, ensuring higher data quality.
  • Data Lakehouses: Combine the management of both structured and unstructured data, providing a single platform for diverse data types and improving overall data quality.
  • Data Lakehouses: Optimized for both batch and real-time processing, ensuring timely and accurate data availability.
  • Data Lakes: Typically lack ACID transaction support, which can lead to data inconsistencies and corruption.
  • Data Lakes: Often store raw data without strict schema enforcement, leading to potential data quality issues and difficulties in data integration.
  • Data Lakes: Generally lack comprehensive data lineage and governance capabilities, making it harder to trace data sources and transformations.
  • Data Lakes: May not have built-in mechanisms for data validation, leading to potential quality issues.
  • Data Lakes: Primarily focuses on storing raw data, often requiring additional tools and processes to manage and ensure data quality.
  • Data Lakes: May struggle with performance issues, especially with large-scale data processing, impacting data quality and usability.

Silver vs Gold Tables

  • **Silver Tables:**Contain validated, cleansed, and conformed data. They serve as an intermediate layer where data is enriched and standardized.
  • Silver Tables: Data in silver tables is more reliable than in bronze tables but not as refined as in gold tables. They are used for data validation, deduplication, and basic transformations. They provide an enterprise view of key business entities and transactions.
  • Gold Tables: Contain highly refined, aggregated, and business-ready data. They are optimized for analytics and reporting.
  • Gold Tables: Data in gold tables is of the highest quality, ready for consumption by business intelligence (BI) tools and machine learning models. Used for advanced analytics, machine learning, and production applications. They support complex queries and reporting.

Workloads Using Bronze, Silver and Gold Tables

  • Bronze Tables: Workloads using bronze tables include data ingestion, historical data storage, and initial data processing.
  • Silver Tables: Data engineers and data analysts use silver tables for further processing and analysis.
  • Gold Tables: Business analysts, data scientists, and decision-makers use gold tables for strategic insights and decision-making.

Databricks Architecture: Control Plane and Data Plane

  • Control Plane: Managed by Databricks within their cloud account. This encompasses backend services, web applications, REST APIs, job scheduling, and cluster management.
  • Data Plane: Resides within the customer's cloud account, typically on a cloud provider like AWS, Azure, or GCP. This component includes Databricks clusters, storage, and runtime environments.

Using Databricks Notebooks: %run vs dbutils.notebook.run()

  • %run: Designed for modularizing code, allowing you to share functions and variables across notebooks. It is ideal for situations where you need consistent data access across various notebooks. Provides a simple and efficient way to reuse code within the same workspace.

  • dbutils.notebook.run(): Best for orchestrating complex workflows, passing parameters, and handling dependencies between notebooks. You can pass parameters, handle dependencies between notebooks, and control execution workflow across notebooks.

Sharing Notebooks in Databricks

  • Sharing: Click the Share button at the top of the notebook.
  • Permissions: Determine who to share the notebook with and what level of access they should have: CAN READ, CAN RUN, CAN EDIT, and CAN MANAGE.
  • Folder Permissions: Manage permissions at the folder level to organize and manage access to multiple notebooks. Notebooks within a folder inherit the permissions set for that folder.
  • Comments: Add comments to specific cells in the notebook to facilitate discussions and feedback.

Databricks Repos and CI/CD Workflows

  • Databricks Repos: Enables CI/CD workflows in Databricks by integrating with Git repositories and providing tools to manage code changes, automate testing, and deploy updates.
  • Git Integration: Allows you to clone Git repositories directly into your Databricks workspace, manage branches, commit changes, and push updates to your remote repository from within Databricks.
  • Branch Management: Enables developers to work on feature branches, make changes, commit them, and merge branches using the Git UI within Databricks, ensuring changes are integrated smoothly.
  • Automated Testing: Uses CI tools (like GitHub Actions) to trigger automated tests whenever changes are pushed. Test results are reported back, and any issues are addressed.
  • Deployment Automation: Supports deployment automation using Databricks Asset Bundles and the Databricks CLI. These tools help package your code and deploy it to different environments seamlessly, ensuring consistent and repeatable deployments.
  • Collaboration and Code Reviews: Facilitates collaboration through Git-based workflows. Pull requests and code reviews can be managed through the Git provider, ensuring that all changes are reviewed and approved before being merged.

Git Operations in Databricks Repos

  • Clone a Repository: Allows you to clone a remote Git repository into your Databricks workspace, enabling you to work with the repository's contents directly within Databricks.
  • Branch Management: Lets you create new branches, switch between branches, merge branches, and rebase branches to integrate changes efficiently.
  • Commit and Push Changes: Lets you save changes to the local repository and push them to the remote repository.
  • Pull Changes: Fetches and integrates changes from the remote repository into your local branch.
  • Resolve Conflicts: Helps resolve conflicts that may arise during merging or rebasing.

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Related Documents

Databricks Exam Guidebook PDF

Description

Test your knowledge on data lakehouses, warehouses, and their key differences. This quiz covers the fundamental concepts, architecture, and data management strategies essential for efficient analytics. Explore how these technologies integrate and their implications for data quality improvement.

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