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    what should you do to an existing azure storage account in order to support a data lake for azure synapse analytics?

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    Tutorial load data from Azure Data Lake Storage

    Use the COPY statement to load data from Azure Data Lake Storage for dedicated SQL pools.

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    Microsoft Ignite

    October 12-14, 2022 Register now

    Load data from Azure Data Lake Storage into dedicated SQL pools in Azure Synapse Analytics

    Article 09/02/2022 4 minutes to read

    This guide outlines how to use the COPY statement to load data from Azure Data Lake Storage. For quick examples on using the COPY statement across all authentication methods, visit the following documentation: Securely load data using dedicated SQL pools.

    Note

    To provide feedback or report issues on the COPY statement, send an email to the following distribution list: [email protected]

    Create the target table to load data from Azure Data Lake Storage.

    Create the COPY statement to load data into the data warehouse.

    If you don't have an Azure subscription, create a free account before you begin.

    Before you begin

    Before you begin this tutorial, download and install the newest version of SQL Server Management Studio (SSMS).

    To run this tutorial, you need:

    A dedicated SQL pool. See Create a dedicated SQL pool and query data.

    A Data Lake Storage account. See Get started with Azure Data Lake Storage. For this storage account, you will need to configure or specify one of the following credentials to load: A storage account key, shared access signature (SAS) key, an Azure Directory Application user, or an Azure AD user that has the appropriate Azure role to the storage account.

    Currently, ingesting data using the COPY command into an Azure Storage account that is using the new Azure Storage DNS partition feature results in an error. Provision a storage account in a subscription that does not use DNS partitioning for this tutorial.

    Create the target table

    Connect to your dedicated SQL pool and create the target table you will load to. In this example, we are creating a product dimension table.

    -- A: Create the target table

    -- DimProduct

    CREATE TABLE [dbo].[DimProduct]

    (

    [ProductKey] [int] NOT NULL,

    [ProductLabel] [nvarchar](255) NULL,

    [ProductName] [nvarchar](500) NULL

    ) WITH (

    DISTRIBUTION = HASH([ProductKey]),

    CLUSTERED COLUMNSTORE INDEX

    --HEAP );

    Create the COPY statement

    Connect to your SQL dedicated pool and run the COPY statement. For a complete list of examples, visit the following documentation: Securely load data using dedicated SQL pools.

    -- B: Create and execute the COPY statement

    COPY INTO [dbo].[DimProduct]

    --The column list allows you map, omit, or reorder input file columns to target table columns.

    --You can also specify the default value when there is a NULL value in the file.

    --When the column list is not specified, columns will be mapped based on source and target ordinality

    (

    ProductKey default -1 1,

    ProductLabel default 'myStringDefaultWhenNull' 2,

    ProductName default 'myStringDefaultWhenNull' 3

    )

    --The storage account location where you data is staged

    FROM 'https://storageaccount.blob.core.windows.net/container/directory/'

    WITH (

    --CREDENTIAL: Specifies the authentication method and credential access your storage account

    CREDENTIAL = (IDENTITY = '', SECRET = ''),

    --FILE_TYPE: Specifies the file type in your storage account location

    FILE_TYPE = 'CSV',

    --FIELD_TERMINATOR: Marks the end of each field (column) in a delimited text (CSV) file

    FIELDTERMINATOR = '|',

    --ROWTERMINATOR: Marks the end of a record in the file

    ROWTERMINATOR = '0x0A',

    --FIELDQUOTE: Specifies the delimiter for data of type string in a delimited text (CSV) file

    FIELDQUOTE = '', ENCODING = 'UTF8', DATEFORMAT = 'ymd',

    --MAXERRORS: Maximum number of reject rows allowed in the load before the COPY operation is canceled

    MAXERRORS = 10,

    --ERRORFILE: Specifies the directory where the rejected rows and the corresponding error reason should be written

    ERRORFILE = '/errorsfolder',

    ) OPTION (LABEL = 'COPY: ADLS tutorial');

    Optimize columnstore compression

    By default, tables are defined as a clustered columnstore index. After a load completes, some of the data rows might not be compressed into the columnstore. There's a variety of reasons why this can happen. To learn more, see manage columnstore indexes.

    To optimize query performance and columnstore compression after a load, rebuild the table to force the columnstore index to compress all the rows.

    ALTER INDEX ALL ON [dbo].[DimProduct] REBUILD;

    Optimize statistics

    It is best to create single-column statistics immediately after a load. There are some choices for statistics. For example, if you create single-column statistics on every column it might take a long time to rebuild all the statistics. If you know certain columns are not going to be in query predicates, you can skip creating statistics on those columns.

    स्रोत : learn.microsoft.com

    How to query blob storage with SQL using Azure Synapse

    How do you include blob storage in reports? In this article Dennes Torres explains how to query blob storage with SQL using Azure Synapse.

    Dennes Torres 01 July 2021

    19001 views

    9 0

    How to query blob storage with SQL using Azure Synapse

    How do you include blob storage in reports? In this article Dennes Torres explains how to query blob storage with SQL using Azure Synapse.

    The series so far:

    How to query blob storage with SQL using Azure Synapse

    How to query private blob storage with SQL and Azure Synapse

    Performance of querying blob storage with SQL

    When you deal with Big Data in Azure, you will store lots of files on Azure Storage. From time to time, you may need to investigate these files. Wouldn’t it be cool if you could just open a window and execute a SQL Query over the storage? In this article, I explain how to query blob storage with SQL using Azure Synapse.

    Synapse Analytics makes this not only possible but also very affordable since you pay as you go. At the time of this writing, it estimates only $5.00 USD for each terabyte processed. Synapse Analytics makes it easy to query your storage and analyse files using the SQL language.Disclaimer: This is only the price for data processing on the Synapse Serverless SQL pool. It doesn’t include the storage price and other services used. This price may change anytime. Please, check the pricing on the Azure website for more precise information.

    Next, I’ll show you how to implement Azure Synapse and dig into the details to show you how it works.

    Provisioning the workspace

    I will not duplicate the good Microsoft documentation. You can follow this QuickStart to provision the workspace. Use the option to create a new storage account together with the workspace.

    When the workspace is provisioned, only the SQL On Demand pool is created. It’s the most affordable one, and you can stick to that: use the SQL On Demand pool to query your blob storage.

    The Synapse Workspace name needs to be unique because It can become a public URL. In fact, it is one by default. I will call my Synapse Workspace MaltaLake. You need to create your own custom name for your Synapse Workspace. Every time I refer to MaltaLake, replace it with your workspace name.

    Provisioning a storage account

    The storage account you created in the previous step will be used by Synapse Analytics itself for things like metadata. In order to make a demonstration of a small data lake, you will need another storage account.

    During the example, I will call the storage LakeDemo. This is the name for my storage, which also needs to be unique. You will need to create a unique name for your storage and use it every time you run an example that uses LakeDemo by adjusting the scripts, replacing LakeDemo with your storage account name.

    You can use a PowerShell script to provision the storage account and upload demo files to it. You can download the scripts and demos and execute the file Install.bat. It will ask you some questions and create the environment on your Azure account.

    This is what the script will do for you:

    Create a resource group or use the existing resource group you provide

    Create a storage account or use an existing storage account you provide

    Upload the files to the storage account

    If you would like, you can also follow these steps to provision a new storage account. You can still run the script after this, providing the name of the resource group and the storage account. The script will create the containers and upload the file.

    Once again, if you would like, you can create the containers and upload the files by yourself. You will need two containers for the demonstrations, one private and one with public access: DataLake and OpenDataLake. In order to upload the files, you can use Azure Storage Explorer if you chose not to use the script.

    Querying the blob storage data

    Azure provides a nice environment, Synapse Studio, for running queries against your storage. Next, I’ll show you how to get started.

    Opening the environment

    Follow these steps to learn the details of Synapse Studio and how you will run these queries.

    Open Azure Synapse Studio. You will find it under Getting Started on the Overview tab of the MaltaLake workspace

    Synapse studio may ask you to authenticate again; you can use your Azure account.

    Open the Develop tab. It’s the 3rd icon from the top on the left side of the Synapse Studio window

    Create a new SQL Script

    On the Develop window, click the “+” sign

    Click the SQL Script item on the menu

    The script will be connected to BuiltIn, meaning the SQL On Demand. There are two databases, master and default. You can’t create objects in either of them.

    A side note about scripts

    You can save your scripts in Synapse. The button Publish on the image above is used to save your scripts. It is recommended that you change the name of your script before publishing because many scripts called SQL Script X can become confusing very fast.

    स्रोत : www.red-gate.com

    DP

    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.

    DP-900

    4.8 (4 reviews) Term 1 / 204

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

    Click the card to flip 👆

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

    Click the card to flip 👆

    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

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