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    how can you enable globally distributed users to work with their own local replica of a cosmos db database?

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    Distribute data globally with Azure Cosmos DB

    Learn about planet-scale geo-replication, multi-region writes, failover, and data recovery using global databases from Azure Cosmos DB, a globally distributed, multi-model database service.

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    Distribute your data globally with Azure Cosmos DB

    Article 06/05/2022 4 minutes to read

    APPLIES TO: SQL API Cassandra API Gremlin API Table API Azure Cosmos DB API for MongoDB

    Today's applications are required to be highly responsive and always online. To achieve low latency and high availability, instances of these applications need to be deployed in datacenters that are close to their users. These applications are typically deployed in multiple datacenters and are called globally distributed. Globally distributed applications need a globally distributed database that can transparently replicate the data anywhere in the world to enable the applications to operate on a copy of the data that's close to its users.

    Azure Cosmos DB is a globally distributed database system that allows you to read and write data from the local replicas of your database. Azure Cosmos DB transparently replicates the data to all the regions associated with your Cosmos account. Azure Cosmos DB is a globally distributed database service that's designed to provide low latency, elastic scalability of throughput, well-defined semantics for data consistency, and high availability. In short, if your application needs fast response time anywhere in the world, if it's required to be always online, and needs unlimited and elastic scalability of throughput and storage, you should build your application on Azure Cosmos DB.

    You can configure your databases to be globally distributed and available in any of the Azure regions. To lower the latency, place the data close to where your users are. Choosing the required regions depends on the global reach of your application and where your users are located. Cosmos DB transparently replicates the data to all the regions associated with your Cosmos account. It provides a single system image of your globally distributed Azure Cosmos database and containers that your application can read and write to locally.

    With Azure Cosmos DB, you can add or remove the regions associated with your account at any time. Your application doesn't need to be paused or redeployed to add or remove a region. Cosmos DB is available in all five distinct Azure cloud environments available to customers:

    Azure public cloud, which is available globally.Azure China 21Vianet is available through a unique partnership between Microsoft and 21Vianet, one of the country’s largest internet providers in China.Azure Germany provides services under a data trustee model, which ensures that customer data remains in Germany under the control of T-Systems International GmbH, a subsidiary of Deutsche Telekom, acting as the German data trustee.Azure Government is available in four regions in the United States to US government agencies and their partners.Azure Government for Department of Defense (DoD) is available in two regions in the United States to the US Department of Defense.

    Key benefits of global distribution

    Build global active-active apps. With its novel multi-region writes replication protocol, every region supports both writes and reads. The multi-region writes capability also enables:

    Unlimited elastic write and read scalability.

    99.999% read and write availability all around the world.

    Guaranteed reads and writes served in less than 10 milliseconds at the 99th percentile.

    As you add and remove regions to and from your Azure Cosmos account, your application does not need to be redeployed or paused, it continues to be highly available at all times.

    Build highly responsive apps. Your application can perform near real-time reads and writes against all the regions you chose for your database. Azure Cosmos DB internally handles the data replication between regions with consistency level guarantees of the level you've selected.Build highly available apps. Running a database in multiple regions worldwide increases the availability of a database. If one region is unavailable, other regions automatically handle application requests. Azure Cosmos DB offers 99.999% read and write availability for multi-region databases.Maintain business continuity during regional outages. Azure Cosmos DB supports service-managed failover during a regional outage. During a regional outage, Azure Cosmos DB continues to maintain its latency, availability, consistency, and throughput SLAs. To help make sure that your entire application is highly available, Cosmos DB offers a manual failover API to simulate a regional outage. By using this API, you can carry out regular business continuity drills.Scale read and write throughput globally. You can enable every region to be writable and elastically scale reads and writes all around the world. The throughput that your application configures on an Azure Cosmos database or a container is provisioned across all regions associated with your Azure Cosmos account. The provisioned throughput is guaranteed up by financially backed SLAs.

    स्रोत : learn.microsoft.com

    Azure Cosmos DB Globally Distributed Databases to Replicate Data

    Learn about Azure Cosmos DB globally distributed databases for data replication, low latency, high throughput, and data consistency.

    Learn about Azure Cosmos DB Globally Distributed Databases

    By: Rajendra Gupta   |   Updated: 2022-02-22   |   Comments   |   Related: > Azure Cosmos DB

    Problem

    Azure Cosmos DB supports a globally distributed database for reading and writing local database replicas. In this tutorial, we will explore the purpose and benefits of a globally distributed database and the way to configure it using the Azure portal.

    Solution

    In today's digital world, applications require high responsiveness and availability. The applications can be social media platforms, e-commerce websites, international financial systems, etc. Users should get the required data with low latency, consistency, and availability. It means the application response should not change with the location wherever it may be in the world.

    Azure Cosmos DB globally distributed database feature replicates data to associated regions. It is designed to meet the requirement of low latency, high throughput, and data consistency. It also supports elastic scalability of the storage and throughput as per the application workload.

    The Cosmos DB administrator can configure the globally distributed database in any supported Azure region. Once we configure it, Cosmos DB replicates a single system image of the globally distributed database for the application to read and write locally. Another benefit of Cosmos DB is that you do not need to pause or redeploy applications to configure (add/remove) a region.

    You can browse products available by region to get a supported Azure Cosmos DB regions list.

    The benefit of a global distribution database

    The global distribution database offers the following benefits:

    Build global active-active apps: Azure Cosmos DB supports read and writes in every region using the novel multi-region write replication protocol. The multi-region write enables unlimited write and reads scalability, 99% read\write availability, and guaranteed read-write with less than 10ms latency.

    Image Reference: Microsoft docs

    Build highly responsive and available applications: You can build applications with multi-region writes capabilities, guaranteed data consistency level, and 99.999% highly available database. If a region is down, another region automatically handles the database requests.

    The following image shows that the container data is distributed in the following ways:

    Within a region Across region

    Automatic and Manual failovers for business continuity: Azure Cosmos DB automatically performs the failover during a regional outage. It ensures the minimum latency, consistency, and throughput SLAs. You also get manual failover for performing disaster recovery (business continuity) drills.Scaling read and write throughput: You can configure auto-scaling reads and writes throughput in all Azure regions. Azure uses financially backed SLAs for the guaranteed provisioned throughput. You can refer to SLA for Azure Cosmos DB for more details.

    How do we configure Azure Cosmos DB regions?

    In this section, we will configure the globally distributed Azure Cosmos DB. Before proceeding, you can follow earlier tips to implement the Azure Cosmos database.

    As shown below, my demo database has the following configurations:

    Read Locations: East US 2

    Write Locations: East US 2

    Currently, my Cosmos DB account has no read regions. Click on the Replicate Data Globally option from the Azure Cosmos DB account. As shown below, by default, multi-region writes are disabled.

    Click on the +Add Region option to configure a region for reads, writes, and availability zone. Alternatively, you can click on the hexagons on the map to select your desired Azure region. Add the region and click on save for configuring it.

    It displays a message at the top – when you add a region to your account, you will be billed for the additional RU/s and storage copied to the region.

    Click on Save to configure the Azure regions for the Cosmos DB account.

    After the configuration, the map shows the two regions for the Azure Cosmos DB account.

    Write region: East US 2

    Read region: North Europe

    It highlights the regions in the map with a tick configured for the reading and writes regions in Azure Cosmos DB.

    स्रोत : www.mssqltips.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

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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

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