What is the difference (if any) between azure table storage and azure easy tables?
As far as I know, there are totally different things.
Azure easy table which is used by the azure mobile app Node.js backend.
It is an easy way to CRUD the data to the sql database/azure storage account like EF.
We couldn't directly use it without binding the azure sql database and azure storage account.
Azure table storage is a service that stores structured NoSQL data in the cloud.
In my opinion, azure easy table is a tool to CRUD the data to the database.
Azure table storage is a database to store the structured NoSQL data.
Related
I'm trying to increase the performance tier of storage account from standard to premium and migrate Azure Table Storage Data to premium storage account. Premium storage accounts do not support Table storage account.
So what I can do now and which premium account type I should select?
If you want to use Table Storage, you have two options:
Azure Table Storage
Azure Cosmos DB Table API
In short: using Table Storage is the entry-level table storage. Cosmos DB is the premium version of Azure Table Storage. Although only calling it the premium version doesn't do Cosmos DB justice.
The nice thing is also that the Azure Tables client library can seamlessly target either Azure Table storage or Azure Cosmos DB table service endpoints with no code changes.
This is at least the case for the .NET client, but since the API's are compatible I would assume this to be the case for other libraries, too.
For an overview of the differences between the services, see Introduction to Azure Cosmos DB: Table API - Table offerings
More relevant sources:
The concepts of Azure Table storage also apply to the newer Azure Cosmos DB Table API. The Cosmos DB Table API offers higher performance and availability, global distribution, and automatic secondary indexes.
Source: What is Azure Table Storage
and
Azure Cosmos DB Table API and Azure Table storage share the same table data model and expose the same create, delete, update, and query operations through their SDKs.
Source: Developing with Azure Cosmos DB Table API and Azure Table storage
Which Azure pipeline and Data Storage you would prefer for a MongoDB migration?
I know there exists the functionality of an Azure Migration Service where you can shift MongoDB data directly to an Azure CosmosDB. Azure Migration Services seems to be available only for specific licenses. Using Cosmos DB it is also necessary to take care of costs.
Another possibility is to use Stitch to shift MongoDB directly into Azure.
Since we don't want to use an additional tool, we want to use Azure Data Factory to shift the MongoDB data into an Azure Data Storage. We want to use the Data Lake Storage Gen2, as it combines the advantages of the Blob Storage and the Data Lake Storage Gen1.
Which pipeline you would prefer? Any experiences with storing the MongoDB data in Azure Data Lake Storage Gen2?
Please see the following Azure Data Factory document pertaining to Pipelines and Activities, which details the source and target data endpoints that are currently supported.
Copy data to or from Azure Data Lake Storage Gen1 using Azure Data Factory
Copy and transform data in Azure Data Lake Storage Gen2 using Azure Data Factory
Copy data from MongoDB using Azure Data Factory
Using the MongoDB connector as a source and Azure Data Lake Storage Gen2 as a sink, you can then perform any transformation and finally, migrate the data to Azure Cosmos DB...if desired.
Copy and transform data in Azure Cosmos DB (SQL API) by using Azure Data Factory
Copy data to or from Azure Cosmos DB's API for MongoDB by using Azure Data Factory
If you experience any issues with the migration of data to Azure Cosmos DB, if that is the goal of the migration, then consider the following direct migration paths: Options to migrate your on-premises or cloud data to Azure Cosmos DB
We are working on implementing a new project in Azure. The idea is to move out of on-premise systems into the cloud as we have our vendors, partners and clients moving into the cloud. The option we are trying out is to use Azure Data Share and have Azure SQL Database subscribe to the data.
The thing we are now trying to explore is once a new data snapshot is created how do we import this data into Azure SQL Database?
For instance we have Partner information and this information is made available via Azure Data Share and new data snapshot is created daily.
The part that I am not sure of is how to synchronize this data between Azure Data Share and Azure SQL Database.
Also, Is there an api available to expose this data out to external vendors, partners or clients from Azure SQL Database after we have data sync to Azure SQL Database from Azure Data Share?
Azure Data Share -> Azure SQL Database
Yes, Azure SQL Database is a supported.
Azure Data Share -> SQL Server Database (on-prem)? Is this option supported?
No, SQL Server Database (on-prem) is not supported.
Is there an api that could be consumed to read data?
Unfortunately, there is no such API that could be consumed to read data.
Azure Data Share enables organizations to simply and securely share data with multiple customers and partners. In just a few clicks, you can provision a new data share account, add datasets, and invite your customers and partners to your data share. Data providers are always in control of the data that they have shared. Azure Data Share makes it simple to manage and monitor what data was shared, when and by whom.
Azure Data Share helps enhance insights by making it easy to combine data from third parties to enrich analytics and AI scenarios. Easily use the power of Azure analytics tools to prepare, process, and analyze data shared using Azure Data Share.
Which Azure data stores does Data Share support?
Data Share supports data sharing to and from Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Storage, Azure Blob Storage, and Azure Data Explorer. Data Share will support more Azure data stores in the future.
The below table details the supported data sources for Azure Data Share.
How to synchronize this data between Azure Data Share and Azure SQL Database.
You need to choose “Snapshot setting” to refresh data automatically.
A data provider can configure a data share with a snapshot setting. This allows incremental updates to be received on a regular schedule, either daily or hourly. Once configured, the data consumer has the option to enable the schedule.
I am looking for a best programmatic way to extract data from Azure Data Lake to MSSQL database, which is installed on a VM within Azure.
Currently I am considering following options:
Azure Data Factory
SSIS (Using Azure Data Lake Store Connection Manager)
User-Defined Outputter Example1, Example2
Custom C# code that reads Azure Data Lake data and inserts it into SQL Server DB
Any other good ways I am missing?
Data factory v2 (currently in public preview), also supports hosting SSIS to give you a data factory AND ssis option.
And not necessarily a good idea for many scenarios, but Azure Logic Apps has both a data lake store connector and SQL Server connector, which could be useful in scenarios such as writing lots of small files on a schedule or trigger.
You also may not need to go full on c# and instead use PowerShell, there are powershell modules for both data lake store and sql server.
I am a new to azure.Could any one help me what is table storage in Azure and how can I do table storage deployment through VSTS?Please share your thoughts and what steps involved in this and which plugin/task I can use in VSTS to perform this?
About Azure Table storage, you can refer to this article: Azure Table storage overview.
Regarding Azure table storage with VSTS, you can manage azure tables and table entities through Azure PowerShell task.
Azure Table storage stores large amounts of structured data. The service is a NoSQL datastore which accepts authenticated calls from inside and outside the Azure cloud. Azure tables are ideal for storing structured, non-relational data. Common uses of Table storage include:
Storing TBs of structured data capable of serving web scale
applications
Storing datasets that don't require complex joins, foreign keys, or
stored procedures and can be denormalized for fast access
Quickly querying data using a clustered index
Accessing data using the OData protocol and LINQ queries with WCF
Data Service .NET Libraries
You can use Table storage to store and query huge sets of structured, non-relational data, and your tables will scale as demand increases.
You’ll have to install Azure Storage Client Library for .NET to work with Azure Storage.
For more details, refer to the documentations Get started with Azure Table storage using .NET and Get started with Azure table storage and Visual Studio Connected Services (ASP.NET) incase if you haven't checked earlier.