Data Engineering Part1

What is Data Engineering in Microsoft Fabric?

It enables users to design, build, and maintain infrastructures and systems for collecting, storing, processing, and analyzing large volumes of data.

What is the purpose of a Lakehouse in Microsoft Fabric?

Lakehouses are data architectures that allow organizations to store and manage structured and unstructured data in a single location.

What is a Spark Job Definition?

A set of instructions that define how to execute a job on a Spark cluster, including input and output data sources, transformations, and configuration settings.

What are Notebooks used for in Microsoft Fabric?

Notebooks are interactive computing environments that allow users to create and share documents containing live code, equations, visualizations, and narrative text for data ingestion, preparation, analysis, and more.

What are Data Pipelines?

A series of steps that collect, process, and transform data from its raw form to a format suitable for analysis and decision-making.

How does the SQL analytics endpoint work in a Lakehouse?

It provides direct querying capabilities on Delta tables without importing data, ensuring real-time data access.

What is the difference between Lakehouse and Data Warehouse?

Lakehouse integrates capabilities for handling both structured and unstructured data, while Data Warehouse focuses primarily on structured data with transactional support.

What are the features of Notebooks in Fabric?

They support multiple programming languages, allow for collaborative editing, and enable code execution for data tasks.

What is the role of Dataflows Gen 2 in Microsoft Fabric?

Dataflows Gen 2 is used for ingesting and transforming data before publishing it into the Lakehouse.

What capabilities does Microsoft Fabric offer for collaboration?

Fabric allows multiple users to work together in shared workspaces, enabling collaboration on data projects and insights.

What is the key benefit of using Spark in Data Engineering?

Spark enables distributed computing, allowing for parallel processing of large data sets efficiently.

What does high concurrency mean in the context of Fabric Notebooks?

It refers to the ability to support multiple simultaneous users accessing and editing notebooks without performance degradation.

Define the term 'Data Pipeline' as used in Microsoft Fabric.

A Data Pipeline involves a series of processes to collect, transform, and deliver data from source to destination effectively.

What is the significance of the V-Order feature in Delta Lake tables?

V-Order optimizes write operations for better performance and compression in Delta Lake tables.

What type of data can be stored in a Lakehouse?

Both structured and unstructured data can be stored and managed within a Lakehouse.

What is the function of the Livy API in Fabric?

It allows users to submit and manage Spark jobs programmatically via REST API calls.

What is required to create a new Lakehouse in Microsoft Fabric?

Users must sign in to the Microsoft Fabric portal and select the option to create a new Lakehouse.

How can users integrate existing data into a Lakehouse?

Through methods like upload, data pipeline copy activities, and by connecting existing external data sources.

What is a key feature of the automatic table discovery in Lakehouses?

It allows new data files to be automatically validated and registered in the metastore with metadata.

Describe the advantages of using Delta format within a Lakehouse.

Delta format supports ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

What is the primary goal of Microsoft Fabric's Data Engineering capabilities?

To enable organizations to efficiently collect, process, and analyze large volumes of diverse data.

Which tools can be used for data transformation in Fabric?

Users can utilize Notebooks, Dataflows Gen 2, or Apache Spark job definitions for data transformation tasks.

What is the benefit of using a Copy Data activity in Fabric?

It enables users to efficiently move data from various sources into a Lakehouse with minimal coding efforts.