26. Which cloud service category does Microsoft’s Azure Databricks fall under?
Azure Databricks falls under the category of PaaS (Platform as a Service), providing an application development platform built on top of Microsoft Azure and Databricks.
27. Explain the difference between Azure Databricks and AWS Databricks.
| Aspect | Azure Databricks | AWS Databricks |
| Cloud Provider | Microsoft Azure | Amazon Web Services (AWS) |
| Integration | Deep integration with Azure services like ADLS, SQL DW, and more | Integration with AWS services like S3, Redshift, Glue, SageMaker, etc. |
| Security | Integrated with Azure Active Directory for authentication | Integrated with AWS IAM for authentication and access control |
| Machine Learning | Integration with Azure Machine Learning for ML workflows | Integration with ML tasks and model deployment in AWS |
| Analytics Tools | Integration with Azure Data Factory, Power BI, and more | Integration with AWS Glue, Athena, QuickSight, etc. |
| Marketplace Offerings | Azure Marketplace offers Databricks services | AWS Marketplace offers Databricks services |
28. Name the types of widgets used in Azure Databricks.
Widgets are essential components of notebooks and dashboards, simplifying parameter addition and modeling logic assessment.
There are four types of widgets available in Azure Databricks:
- Text Widgets: They make entering values into text fields easier.
- Dropdown Widgets: You can find a value from a list of preset values by using dropdown widgets.
- Combobox Widgets: Combobox widgets allow you to choose a value from a list or enter a value into the text field. They are a cross between dropdown and text widgets.
- Multiselect Widgets: Widgets that allow you to select numerous options from a list of values are known as multi select widgets.
29. What is the Delta table?
Any table containing data saved in the Delta format is referred to as a Delta table. On top of Apache Spark, these tables offer ACID (Atomicity, Consistency, Isolation, Durability) transaction capabilities. For analytics and machine learning, they offer an effective means of storing and managing structured and semi-structured data.
30. How does Azure Databricks handle schema evolution in Delta tables?
This is accomplished through automatic schema evolution. In its simplest form, automatic schema evolution simply implies that when new columns are inserted into the data, it does not need manual schema modifications. As the schema progresses, current queries run well, and virus-like operating scheme changes are easily handled. It increases the flexibility and speed of data pipelines while also allowing them to be customized to meet changing requirements.
31. What is the purpose of the command-line interface in Databricks?
The command-line interface (CLI) in Databricks serves as a powerful tool for developers and data engineers to interact with Databricks workspaces, clusters, jobs, and data. With scripting and command execution, it offers a method for managing resources, automating operations, and streamlining workflows. Using the Databricks CLI, users may run queries, plan jobs, upload and download files, manage clusters, and carry out a variety of administrative operations via the command line.
32. Explain the concept of table caching in Azure Databricks.
Table caching in Azure Databricks is like keeping a quick-access copy of a table or data in the computer’s memory. As a result, the data may be searched and analyzed much faster because it is not constantly read from storage. It’s like having a reference cheat sheet to speed up time-consuming tasks like sorting, finding, and performing calculations. These “cheat sheets” make working with the same data faster and easier by remaining in the computer’s memory. This makes it incredibly useful for handling large datasets or performing intricate computations, which speeds up and simplifies processes in Azure Databricks.
33. How do Azure Databricks facilitate collaboration and productivity among data engineers and data scientists working on data analytics projects?
Azure Databricks facilitates collaboration and productivity among data engineers and data scientists by providing a unified analytics platform that integrates with Azure Data Lakes. Through seamless integration with Azure Data Lakes, users can easily access and analyze large volumes of data stored in various formats. Additionally, Azure Databricks offers collaborative features such as shared notebooks, version control, and real-time collaboration, enabling teams to work together efficiently on data analytics projects. Moreover, with built-in support for popular programming languages and machine learning libraries, Azure Databricks empowers data engineers and data scientists to explore, analyze, and derive insights from data effectively, ultimately driving innovation and decision-making within organizations.
34. Explain Azure data lakehouse and data lake.
An Azure data lake house combines the features of a data lake with the capabilities of a data warehouse. It offers a single, scalable platform for storing and managing both structured and unstructured data, bridging the gap between traditional data warehouses and data lakes. Features like ACID (Atomicity, Consistency, Isolation, Durability) transactions, indexing, schema enforcement, and enhanced query performance are all available with Azure Data Lake House.
Designed for big data analytics applications, Azure Data Lake is a highly scalable and secure data storage solution offered by Microsoft Azure. Large volumes of both organized and unstructured data can be stored by businesses in their original format. High productivity, smooth access control, and interaction with several analytics and data processing services are just a few of the benefits offered by Azure Data Lake.
35. In Azure Databricks, what are collaborative workspaces?
Collaborative workspaces in Azure Databricks offer a unified environment where data engineers, data scientists, and business analysts can seamlessly collaborate on big dataprojects. This shared workspace simplifies collaboration by enabling the real-time sharing of notebooks, data, and models. It ensures that everyone involved has access to the most up-to-date data, models, and insights, facilitating smoother and more efficient teamwork on complex data-driven initiatives.
36. What is serverless database processing?
The term “serverless database processing” describes a technique that allows users to interact with databases and execute operations without handling the infrastructure that supports them. Under this architecture, users may concentrate only on the data and queries as the cloud provider manages the provisioning, scaling, and maintenance of the database resources automatically.
Users are charged according to the resources utilized for query execution or data processing while using serverless database processing. Services like Google BigQuery, Amazon Athena, Azure Synapse Analytics on-demand SQL pools, and Snowflake’s Snowflake Data Cloud are well-known instances of serverless database processing.
37. How can large data be processed in Azure Databricks?
Azure Databricks is ideal for processing large data sets. It starts by configuring your cluster with the right VM types for your workload. Then store data in Azure Blob Storage, or ADLS, and mount it to Databricks using DBFS.
After that, it ingests data using tools like Azure Data Factory or Kafka for streaming. Then use Databricks notebooks for ETL jobs and optimize with caching. It monitors performance with Azure Monitor and explores advanced features like MLlib for machine learning or Spark GraphX for graph processing. Consider pricing based on cluster size and storage usage.
38. What are Databricks secrets?
Databricks secret is a key-value pair made up of a distinct key name enclosed in a secret context that may help in maintaining secret content. 1000 secrets are allotted to each scope. Its size must be more than 128 KB.
39. What are PySpark DataFrames?
In Apache Spark, PySpark DataFrames are distributed collections of data organized into named columns, similar to traditional tables in databases or spreadsheets. They allow you to work with large datasets efficiently across multiple computers (nodes) in a cluster.
Some of the key characteristics include:
- Distributed: Data is stored and processed in parallel across multiple nodes, enabling you to handle massive datasets that wouldn’t fit on a single machine.
- Structured: Data is organized with rows and named columns, each containing a specific data type (e.g., integer, string, date). This structure makes it easier to manipulate and analyze data.
- Lazy Evaluation: Operations on DataFrames are not immediately executed but rather defined in a logical plan. When an “action” (like displaying results) is triggered, the plan is optimized and executed efficiently.
40. How can data be imported into Delta Lake?
Azure Databricks uses the Delta Lake data storage format. Data can be imported from a number of formats, including CSV, JSON, and other data warehouses. PySpark has routines that can read information from many sources and write it to Delta Lake. It functions similarly to copying and pasting data into a designated Databricks container.
41. How is the code for Databricks managed?
Git and Team Foundation Server (TFS) are two version control systems that we use to manage your Databricks notebooks and code. These systems facilitate cooperation, keep track of modifications, and guard against duplicate efforts. It acts as a collaborative work environment where all members may view and modify the same document.
42. What is the procedure for revoking a private access token?
A private access token is like a key that grants access to your Databricks account. If you no longer want someone to have access, revoke the token. You can do this in the Databricks security settings. It’s like changing the locks on your house to prevent someone with an old key from entering.
43. Describe the advantages of using Delta Lake.
The advantages of using Delta Lake are as follows:
- Reliability: Data is automatically repaired in cases of corruption.
- Time Travel: You can access older versions of your data, like going back in time.
- Schema Enforcement: It ensures your data structure is consistent.
- ACID Transactions: Guarantees data consistency during updates.
44. What are ‘Dedicated SQL pools’?
Dedicated SQL pools are a separate compute resource for running SQL queries in Azure Databricks. They are useful for queries that don’t require the full power of a Databricks cluster. We can imagine that we have a dedicated computer specifically for running calculations, so it doesn’t slow down other tasks.
45. What are some best practices for organizing and managing notebooks in a Databricks workspace?
Below are some of the best practices:
- Use folders and notebooks to categorize your work.
- Add comments and documentation to explain your code.
- Consider using libraries and notebooks from shared locations for reusability.
- Keep your workspace tidy, with folders for projects and clear instructions within notebooks.
46. Describe the process of creating a Databricks workspace in Azure.
- Click the workspaces tile in your account console.
- Select the Quickstart option when you click the Create workspace dropdown.
47. Enter the following on the Let’s Set Up Your Workplace page:
- A Workspace name that’s easy to use.
- The AWS region in which you wish to host your Databricks workspace.
- Click on Start Quickstart and enter the password for your Databricks account.
- To view the workspace being generated, click Create Stack to open the databricks-workspace-stack page.
- Once the databricks-workspace-stack status indicates CREATE_COMPLETE, go back to the Workspaces dashboard in the Databricks account console to access your newly created workspace.
- To start your workspace, click Open next to the newly created workspace.
- Click Finish after making your principal use case selection.
48. How can I record live data in Azure? Where can I find instructions?
Azure offers various services for capturing live data streams, like Event Hubs or IoT Hubs. There is documentation available on the Azure website to guide you through the setup process. Search for “Azure Event Hubs documentation” or “Azure IoT Hub documentation” for specific instructions.
49. How can you scale up and down clusters automatically based on workload in Azure Databricks?
Databricks has built-in features for scaling clusters (groups of computers) up or down based on workload. You can set minimum and maximum worker numbers for your cluster. When there’s more work to do, Databricks automatically adds workers (scales up). When it’s less busy, it removes workers (scales down) to save costs.
50. What are the different applications of table storage in Microsoft Azure?
- Keeping Structured Data: Table storage can be used to store structured data without requiring a fixed schema, such as user preferences, product catalogs, or customer information.
- Web Applications: Ideal for web applications that need quick and easy access to large volumes of data, such as user behavior tracking, session data, or user profiles.
- Internet of Things (IoT) and Sensor Data: Suitable for managing sensor and IoT data, including temperature sensor readings, GPS locations, and other device data.
- Analytics and Logging: Enables analysis of large datasets and logging of data, including application logs, website visitors, and metrics.
- Backup and Disaster Recovery: Can be used to store backup copies of critical data, ensuring data availability in case of unexpected events.
51.How does Azure handle redundant storage of data?
Azure ensures data availability and accessibility by maintaining multiple copies of data stored at different levels. Several data redundancy techniques are available:
- Locally Redundant Storage (LRS): Copies data across multiple storage nodes within the same data center to ensure high availability.
- Zone Redundant Storage (ZRS): Replicates storage data across three different availability zones (AZs) within the primary zone for recovery in case of zone failure.
- Geographically Redundant Storage (GRS): Maintains data copies at two or more sites in different geographic areas to ensure data availability in the event of a regional outage. Requires geo-failover for accessing data from secondary locations.
- Read Access Geo Redundant Storage (RA-GRS): Ensures data accessibility from secondary regions when the primary region is unavailable.
52.Which kind of consistency models are supported by Cosmos DB?
Consistency levels in Azure Cosmos DB include:
- Strong: Ensures every read operation gets the most recent write, guaranteeing absolute data freshness.
- Bounded Staleness: Guarantees that reads reflect recent changes within a specified time or number of updates.
- Session: Provides consistency within a session, ensuring a client sees its updates immediately.
- Consistent Prefix: Reads reflect a linear sequence of writes, maintaining order across operations.
- Eventual: Guarantees that all replicas eventually catch up to the last write, allowing for eventual consistency across distributed systems.
53. How CI/CD is achieved in the case of Azure Databricks?
Continuous Integration/Continuous Deployment (CI/CD) in Azure Databricks involves:
- Version Control: Developers track changes to their code and notebooks using version control systems like Git.
- Automated Pipelines: Automated pipelines and Databricks Jobs are used for Continuous Integration, executing automatically on changes.
- Integration Testing: Pipelines incorporate tests such as integrity checks and data validation to ensure data quality.
- Deployment Pipelines: Databricks Notebooks and Jobs are deployed within deployment pipelines after passing integration testing, coordinated by CI/CD platforms like Azure DevOps.
54. What does ‘mapping data flows’ mean? Explain.
Mapping data flows involves visualizing how data moves within a system or organization, akin to drawing a data roadmap. It illustrates the source, path, and destination of data, helping understand data usage patterns, authorized users, and security levels.
By creating data flow maps, businesses can identify hardships, improve processes, ensure compliance, and enhance data security and integrity.
55. Is it possible for us to prevent Databricks from connecting to the internet?
Yes, it’s possible to prevent Databricks clusters from connecting to the internet by configuring network security settings such as Virtual Private Clouds (VPCs) or Network Security Groups (NSGs) to restrict internet access.
56. Define partition in PySpark. In how many ways does PySpark support partitioning?
PySpark partitioning is the process of dividing a DataFrame into smaller datasets while writing to disk. PySpark supports partitioning in two ways: Memory Partitioning (using coalesce() or repartition()) and Disk Partitioning (using partitionBy()).
57. How is the trigger execution functionality used by Azure Data Factory?
Azure Data Factory triggers can start pipeline executions automatically based on external events or schedules. Types of triggers include Schedule Trigger, Tumbling Window Trigger, and Event-Based Trigger.
58. Does Delta Lake provide access controls for governance and security?
Yes, Delta Lake provides access control capabilities to manage user access to workspaces, notebooks, experiments, and files, enhancing security and governance.
59. How is data encryption handled by the ADLS Gen2?
ADLS Gen2 ensures data security through multiple layers of protection, including authentication mechanisms, ACLs, network separation, and encryption during transfer over HTTPS.
