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.
Related
I am trying to load the data (tabular data in tables, in a schema named 'x' from a spark pool in Azure Synapse. I can't seem to find how to do that. Until now i have only linked synapse and my pool to the ML studio. How can I do that?
The Lake Database contents are stored as Parquet files and exposed via your Serverless SQL endpoint as External Tables, so you can technically just query them via the endpoint. This is true for any tool or service that can connect to SQL, like Power BI, SSMS, Azure Machine Learning, etc.
WARNING, HERE THERE BE DRAGONS: Due to the manner in which the serverless engine allocates memory for text queries, using this approach may result in significant performance issues, up to and including service interruption. Speaking from personal experience, this approach is NOT recommended. I recommend that you limit use of the Lake Database for Spark workloads or very limited investigation in the SQL pool. Fortunately there are a couple ways to sidestep these problems.
Approach 1: Read directly from your Lake Database's storage location. This will be in your workspace's root container (declared at creation time) under the following path structure:
synapse/workspaces/{workspacename}/warehouse/{databasename}.db/{tablename}/
These are just Parquet files, so there are no special rules about accessing them directly.
Approach 2: You can also create Views over your Lake Database (External Table) in a serverless database and use the WITH clause to explicitly assign properly sized schemas. Similarly, you can ignore the External Table altogether and use OPENROWSET over the same storage mentioned above. I recommend this approach if you need to access your Lake Database via the SQL Endpoint.
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.
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.
Has anybody ever moved Google Analytics data into Azure? I have seen a handful of ways to do it but I am not sure what I am getting myself into. The Google Analytics data is becoming quite large and I am wondering if it is best suited to leave it in google storage and access it from Azure or move it to something like HDInsight or Data Lake. I need to join the data across several disparate data stores, SQL Azure, Blob, and Table Storage. I was also looking into Apache Drill and Presto as a possible solution to unify the data access. Just looking to see if anybody out there has dealt with this same issue and has any experience to share. Thanks!
Preface
I don't have experience with Presto so I can only comment on the feasibility of doing this with Drill. Also I have not used Azure services so my advice is theoretical.
Drill Storage Plugins
Drill will allow you to perform any SQL queries you want on data originating from different sources, provided that each data source has a storage plugin. A storage plugin is simply a piece of code in Drill that allows you to interface with a data source. Since you are concerned with performing queries on 3 data sources, we need to determine if each of those 3 data sources have a Storage plugin.
SQL Azure
I assume SQL Azure has a jdbc driver for java. If so then Drill can be configured to use SQL Azure by following these instructions.
Azure Blob
Azure Blob storage has an implementation of the hadoop filesystem api which Drill uses to read data from file systems. So you could theoretically add the hadoop-azure jar and its dependencies https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-azure/2.7.0 to Drill's class path and configure Drill's DFS storage plugin to use it.
Additionally the data in Azure Blob would have to be stored in a supported file format like: json, parquet, csv, or hadoop sequence files.
Azure Table
This looks like Microsoft's custom NoSQL database. Currently Drill does not support it.
Conclusion
With a bit of work you could use Drill to query data on both Azure SQL and Blob, but not Azure Table.