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    which role is most likely to use azure data factory to define a data pipeline for an etl process?

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    Study with Quizlet and memorize flashcards containing terms like What three main types of workload can be found in a typical modern data warehouse?, A ____________________ is a continuous flow of information, where continuous does not necessarily mean regular or constant., __________________________ focuses on moving and transforming data at rest. and more.

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    4.8 (4 reviews) Term 1 / 204

    What three main types of workload can be found in a typical modern data warehouse?

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    Definition 1 / 204 - Streaming Data - Batch Data - Relational Data

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    Created by mcconnelljh

    Terms in this set (204)

    What three main types of workload can be found in a typical modern data warehouse?

    - Streaming Data - Batch Data - Relational Data

    A ____________________ is a continuous flow of information, where continuous does not necessarily mean regular or constant.

    data stream

    __________________________ focuses on moving and transforming data at rest.

    Batch processing

    This data is usually well organized and easy to understand. Data stored in relational databases is an example, where table rows and columns represent entities and their attributes.

    Structured Data

    This data usually does not come from relational stores, since even if it could have some sort of internal organization, it is not mandatory. Good examples are XML and JSON files.

    Semi-structured Data

    Data with no explicit data model falls in this category. Good examples include binary file formats (such as PDF, Word, MP3, and MP4), emails, and tweets.

    Unstructured Data

    What type of analysis answers the question "What happened?"

    Descriptive Analysis

    What type of analysis answers the question "Why did it happen?"

    Diagnostic Analysis

    What type of analysis answers the question "What will happen?"

    Predictive Analysis

    What type of analysis answers the question "How can we make it happen?"

    Prescriptive Analysis

    The two main kinds of workloads are ______________ and _________________.

    extract-transform-load (ETL)

    extract-load-transform (ELT)

    ______ is a traditional approach and has established best practices. It is more commonly found in on-premises environments since it was around before cloud platforms. It is a process that involves a lot o data movement, which is something you want to avoid on the cloud if possible due to its resource-intensive nature.

    ETL

    ________ seems similar to ETL at first glance but is better suited to big data scenarios since it leverages the scalability and flexibility of MPP engines like Azure Synapse Analytics, Azure Databricks, or Azure HDInsight.

    ELT

    _______________ is a cloud service that lets you implement, manage, and monitor a cluster for Hadoop, Spark, HBase, Kafka, Store, Hive LLAP, and ML Service in an easy and effective way.

    Azure HDInsight

    _____________ is a cloud service from the creators of Apache Spark, combined with a great integration with the Azure platform.

    Azure Databricks

    ____________ is the new name for Azure SQL Data Warehouse, but it extends it in many ways. It aims to be the comprehensive analytics platform, from data ingestion to presentation, bringing together one-click data exploration, robust pipelines, enterprise-grade database service, and report authoring.

    Azure Synapse Analytics

    A ___________ displays attribute members on rows and measures on columns. A simple ____________ is generally easy for users to understand, but it can quickly become difficult to read as the number of rows and columns increases.

    table

    A _____________ is a more sophisticated table. It allows for attributes also on columns and can auto-calculate subtotals.

    matrix

    Objects in which things about data should be captured and stored are called: ____________.

    A. tables B. entities C. rows D. columns B. entities

    You need to process data that is generated continuously and near real-time responses are required. You should use _________.

    A. batch processing

    B. scheduled data processing

    C. buffering and processing

    D. streaming data processing

    D. streaming data processing

    A. Extract, Transform, Load (ETL)

    B. Extract, Load, Transform (ELT)

    1. Optimize data privacy.

    2. Provide support for Azure Data Lake.

    1 - A 2 - B

    Extract, Transform, Load (ETL) is the correct approach when you need to filter sensitive data before loading the data into an analytical model. It is suitable for simple data models that do not require Azure Data Lake support. Extract, Load, Transform (ELT) is the correct approach because it supports Azure Data Lake as the data store and manages large volumes of data.

    The technique that provides recommended actions that you should take to achieve a goal or target is called _____________ analytics.

    A. descriptive B. diagnostic C. predictive D. prescriptive D. prescriptive A. Tables B. Indexes C. Views D. Keys

    1. Create relationships.

    2. Improve processing speed for data searches.

    3. Store instances of entities as rows.

    4. Display data from predefined queries.

    1 - D 2 - B 3 - A 4 - C

    The process of splitting an entity into more than one table to reduce data redundancy is called: _____________.

    A. deduplication B. denormalization C. normalization D. optimization C. normalization

    Azure SQL Database is an example of ________________ -as-a-service.

    A. platform B. infrastructure

    स्रोत : quizlet.com

    Top 20 Azure Data Factory interview Questions & Answers 2022

    Looking for Azure Data Factory interview questions? Here is the list of Azure Data Factory (ADF) Interview Questions and Answers for freshers & experienced Data Engineers.

    Azure Data Factory Interview Questions

    Updated on June 15, 2022

    In this Azure Data Factory interview questions blog, you will learn about Data Factory to clear your job interview. You will also find questions related to steps for the ETL process, integration Runtime, Data lake storage, and more in this Azure Data factory interview questions. Learn Azure Data Factory in Intellipaat Azure Data Factory training and excel in your career.

    Become a Certified Professional

    Azure Data Factory Interview Questions and Answer

    Azure Data Factory is a cloud-based Microsoft tool that collects raw business data and further transforms it into usable information. It is a data integration ETL (extract, transform, and load) service that automates the transformation of the given raw data. This Azure Data Factory Interview Questions blog includes the most-probable questions asked during Azure job interviews. Following are the questions that you must prepare for:

    Q1. Why do we need Azure Data Factory?

    Q2. What is Azure Data Factory?

    Q3. What is the integration runtime?

    Q4. What is the limit on the number of integration runtime?

    Q5. What is the difference between Azure Data Lake and Azure Data Warehouse?

    Q6. What is blob storage in Azure?

    Q7. What is the difference between Azure Data Lake Store and Blob storage?

    Q8. What are the steps for creating ETL process in Azure Data Factory?

    Q9. What is the difference between HDinsight & Azure Data Lake Analytics?

    Q10. What are the top-level concepts of Azure Data Factory?

    These Azure Data Factory interview questions are classified into the following parts:

    1. Basic 2. Intermediate 3. Advanced

    Check out this video on Azure Data Factory Tutorial by Intellipaat:

    Basic Interview Questions

    1. Why do we need Azure Data Factory?

    The amount of data generated these days is huge and this data comes from different sources. When we move this particular data to the cloud, there are a few things needed to be taken care of.

    Data can be in any form as it comes from different sources and these different sources will transfer or channelize the data in different ways and it can be in a different format. When we bring this data to the cloud or particular storage we need to make sure that this data is well managed. i.e you need to transform the data, delete unnecessary parts. As per moving the data is concerned, we need to make sure that data is picked from different sources and bring it at one common place then store it and if required we should transform into more meaningful.

    This can be also done by a traditional data warehouse as well but there are certain disadvantages. Sometimes we are forced to go ahead and have custom applications that deal with all these processes individually which is time-consuming and integrating all these sources is a huge pain. we need to figure out a way to automate this process or create proper workflows.

    Data factory helps to orchestrate this complete process into a more manageable or organizable manner.

    2. What is Azure Data Factory?

    Cloud-based integration service that allows creating data-driven workflows in the cloud for orchestrating and automating data movement and data transformation.

    Using Azure data factory, you can create and schedule the data-driven workflows(called pipelines) that can ingest data from disparate data stores.

    It can process and transform the data by using compute services such as HDInsight Hadoop, Spark, Azure Data Lake Analytics, and Azure Machine Learning.

    3. What is the integration runtime?

    The integration runtime is the compute infrastructure that Azure Data Factory uses to provide the following data integration capabilities across various network environments.

    3 Types of integration runtimes:

    Azure Integration Run Time: Azure Integration Run Time can copy data between cloud data stores and it can dispatch the activity to a variety of compute services such as Azure HDinsight or SQL server where the transformation takes placeSelf Hosted Integration Run Time: Self Hosted Integration Run Time is software with essentially the same code as Azure Integration Run Time. But you install it on an on-premise machine or a virtual machine in a virtual network. A Self Hosted IR can run copy activities between a public cloud data store and a data store in a private network. It can also dispatch transformation activities against compute resources in a private network. We use Self Hosted IR because the Data factory will not be able to directly access on-primitive data sources as they sit behind a firewall. It is sometimes possible to establish a direct connection between Azure and on-premises data sources by configuring the Azure firewall in a specific way if we do that we don’t need to use a self-hosted IR.Azure SSIS Integration Run Time: With SSIS Integration Run Time, you can natively execute SSIS packages in a managed environment. So when we lift and shift the SSIS packages to the data factory, we use Azure SSIS Integration Run Time.Learn more about the concept by reading from the blog regarding SSIS by Intellipaat.

    4. What is the limit on the number of integration runtimes?

    There is no hard limit on the number of integration runtime instances you can have in a data factory. There is, however, a limit on the number of VM cores that the integration runtime can use per subscription for SSIS package execution.

    स्रोत : intellipaat.com

    Extract, transform, and load (ETL)

    Learn about extract-transform-load (ETL) and extract-load-transform (ELT) data transformation pipelines, and how to use control flows and data flows.

    Extract, transform, and load (ETL)

    Synapse Analytics Data Factory

    A common problem that organizations face is how to gather data from multiple sources, in multiple formats. Then you'd need to move it to one or more data stores. The destination might not be the same type of data store as the source. Often the format is different, or the data needs to be shaped or cleaned before loading it into its final destination.

    Various tools, services, and processes have been developed over the years to help address these challenges. No matter the process used, there's a common need to coordinate the work and apply some level of data transformation within the data pipeline. The following sections highlight the common methods used to perform these tasks.

    Extract, transform, and load (ETL) process

    Extract, transform, and load (ETL) is a data pipeline used to collect data from various sources. It then transforms the data according to business rules, and it loads the data into a destination data store. The transformation work in ETL takes place in a specialized engine, and it often involves using staging tables to temporarily hold data as it is being transformed and ultimately loaded to its destination.

    The data transformation that takes place usually involves various operations, such as filtering, sorting, aggregating, joining data, cleaning data, deduplicating, and validating data.

    Often, the three ETL phases are run in parallel to save time. For example, while data is being extracted, a transformation process could be working on data already received and prepare it for loading, and a loading process can begin working on the prepared data, rather than waiting for the entire extraction process to complete.

    Relevant Azure service:

    Azure Data Factory & Azure Synapse Pipelines

    Other tools:

    SQL Server Integration Services (SSIS)

    Extract, load, and transform (ELT)

    Extract, load, and transform (ELT) differs from ETL solely in where the transformation takes place. In the ELT pipeline, the transformation occurs in the target data store. Instead of using a separate transformation engine, the processing capabilities of the target data store are used to transform data. This simplifies the architecture by removing the transformation engine from the pipeline. Another benefit to this approach is that scaling the target data store also scales the ELT pipeline performance. However, ELT only works well when the target system is powerful enough to transform the data efficiently.

    Typical use cases for ELT fall within the big data realm. For example, you might start by extracting all of the source data to flat files in scalable storage, such as a Hadoop distributed file system, an Azure blob store, or Azure Data Lake gen 2 (or a combination). Technologies, such as Spark, Hive, or Polybase, can then be used to query the source data. The key point with ELT is that the data store used to perform the transformation is the same data store where the data is ultimately consumed. This data store reads directly from the scalable storage, instead of loading the data into its own proprietary storage. This approach skips the data copy step present in ETL, which often can be a time consuming operation for large data sets.

    In practice, the target data store is a data warehouse using either a Hadoop cluster (using Hive or Spark) or a SQL dedicated pools on Azure Synapse Analytics. In general, a schema is overlaid on the flat file data at query time and stored as a table, enabling the data to be queried like any other table in the data store. These are referred to as external tables because the data does not reside in storage managed by the data store itself, but on some external scalable storage such as Azure data lake store or Azure blob storage.

    The data store only manages the schema of the data and applies the schema on read. For example, a Hadoop cluster using Hive would describe a Hive table where the data source is effectively a path to a set of files in HDFS. In Azure Synapse, PolyBase can achieve the same result — creating a table against data stored externally to the database itself. Once the source data is loaded, the data present in the external tables can be processed using the capabilities of the data store. In big data scenarios, this means the data store must be capable of massively parallel processing (MPP), which breaks the data into smaller chunks and distributes processing of the chunks across multiple nodes in parallel.

    The final phase of the ELT pipeline is typically to transform the source data into a final format that is more efficient for the types of queries that need to be supported. For example, the data may be partitioned. Also, ELT might use optimized storage formats like Parquet, which stores row-oriented data in a columnar fashion and provides optimized indexing.

    Relevant Azure service:

    SQL dedicated pools on Azure Synapse Analytics

    SQL Serverless pools on Azure Synapse Analytics

    HDInsight with Hive Azure Data Factory Other tools:

    SQL Server Integration Services (SSIS)

    स्रोत : learn.microsoft.com

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