Currently as part of our requirements we are working with the below Azure components
Azure Event Hub
Azure Stream Analytics
Azure Table Storage
Azure Sql DB
Basically with first 3 components, we will be building an Analytics and Reports platform.
Currently as we just started we analyze the data from Azure Table Storage and display it in the analytics dashboard.
Recently we came across a new Azure product Azure Data Lake . Doing some research on microsoft website , we could see we can easily migrate data from Azure Table Storage (with help of Azure Data Factory) to Azure Lake Store. Creating big data pipelines using Azure Data Lake and Azure Data Factory
As we go through the above link, it's mentioned that we need to create an Azure Data Lake Analytics pipeline to process the data.
So what am unclear is the where will be analytics output data will be saved. Do we need to save the analytics output to some DB ? or can we real-time analytics through a Http request ?
We have huge number rows of records in Azure Table Storage that will be moved to Azure Data Lake. For this scenario is it a good option or Can we go an analytics-based solution from Azure Table Storage itself.
Please share your thoughts
You can store your analytics output data on Azure Data Lake Store (a data repository that enables you to store all kinds of data in their raw format without defining schemas.) after processing it through Azure Data lake Analytics (An analytics service that enables you to run jobs on data sets without having to think about clusters.)
As you said "We have huge number rows of records in Azure Table Storage that will be moved to Azure Data Lake.", I think performing analytics on data placed on Azure data lake store is much more efficient because it offers unlimited storage with immediate read/write access to it and scaling the throughput you need for your workloads. It's also offers small writes at low latency for big data sets. So I believe it is better choice then Azure Table storage.
Related
How can we create the real-time data pipeline while data resides on Azure Data Lake Storage Gen2, and the analytics has to be done using Elastic Stack.
What can be the integration tool or technique for the completion of this design?
As #Nick.McDermaid mentioned in the comment that you need to reconsider your design. AFAIK there is no such tool available which can integrate Azure Data Lake Gen2 and Elastic Stack for real time analytics.
Alternatively, the better way to implement your requirement is by using the Azure products designed for real time analytics like Azure Stream Analytics, Azure Synapse Analytics, etc. You can also consider Azure Data Factory for data movement and transformation.
You can check out this page to know more about all the analytics products available in Azure. Choose the best which suits your requirement and try to implement using official document examples.
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 want to integrate Azure data lake storage with Grafana for visualization of time series data. I need to know what all the tools I can use to make it possible.
I used ADF to extract data from csv files stored in data lake and move to a table in Azure data explorer. After that, I used Azure data explorer plugin in grafana to visualize the same. It worked fine. But I need to know is there any other approach which may be better or cost-effective.
Integrating Grafana with Azure Data Lake is the best option when compared to others because the other options include data movements using ADF and additional cost for Azure SQL Datawarehouse along with the cost of PowerBI.
Reason:
Grafana is a leading open source software designed for visualizing time series analytics. It is an analytics and metrics platform that enables you to query and visualize data and create and share dashboards based on those visualizations. Combining Grafana’s beautiful visualizations with Azure Data Explorer’s snappy ad hoc queries over massive amounts of data, creates impressive usage potential.
The Grafana and Azure Data Explorer teams have created a dedicated plugin which enables you to connect to and visualize data from Azure Data Explorer using its intuitive and powerful Kusto Query Language. In just a few minutes, you can unlock the potential of your data and create your first Grafana dashboard with Azure Data Explorer.
For more details on visualizing data from Azure Data Explorer in Grafana please visit our documentation, “Visualize data from Azure Data Explorer in Grafana”.
Other options:
For Azure Data Lake Gen1:
You can use a mix of services to create visual representations of data stored in Data Lake Storage Gen1.
You can start by using Azure Data Factory to move data from Data Lake Storage Gen1 to Azure SQL Data Warehouse.
After that, you can integrate Power BI with Azure SQL Data Warehouse to create visual representation of the data.
For Azure Data Lake Gen2:
You can use a mix of services to create visual representations of data stored in Data Lake Storage Gen2.
You can start by using Azure Data Factory to move data from Data Lake Storage Gen2 to Azure SQL Data Warehouse.
After that, you can integrate Power BI with Azure SQL Data Warehouse to create visual representation of the data.
Hope this helps.
They just released a new guide. This is for Grafana 5.3
https://learn.microsoft.com/en-us/azure/data-explorer/grafana
you are able to test this by running Grafana in a Docker container (or for real, if you want). I followed the guide, and it is working almost exactly as expected. The only issue I am having is Grafana is concatenating the column name and the data in the column, making reading and formatting tricky.
I am using ADF to connect to sources and get data into Azure Data Lake store. After getting data into Data Lake Store, I want to do some transformation, aggregation and use that data in SSRS reports and also for creating Cubes.
Can anyone suggest me which will be the best option (Azure Data Lake Analytics or Azure SQL DW) ?
I am looking here to make a decision on to take which one after Data lake.
There are no more Azure SQL DW. What we have now are Azure Synapse (same as Azure DW) and Azure Synapse Analytics (instead of Azure Datalake analytics). Microsoft is stopping support (develop) USQL and Azure Datalake analytic. If volume of your data is huge and you want use Polybase technology the best choice is Azure Synapse and Azure Synapse Analytics. You can rich your ADF by using Databricks to do analytics stuff. By using Polybase you can do ELT instead of ETL.
Microsoft Azure is not anymore investing on Azure Data Lake Analytics (ADLA) , you can evidently see that number of enhancements /updates in last couple of years are almost none in ADLA. While on the other side Azure SQL Data Warehouse is their flagship service ( recently names as azure synapse analytics) and hence getting enhanced and updated very fast. Synapse is based on MPP architecture and provides all required capabilities of big data computing.
What is the size of your data? Azure Data Lake is more meant for petabyte size big data processing and Azure SQL Data Warehouse for large relational DWH solutions (starting from 250/500 GB and up).
With Azure Data Lake you can even have the data from a data lake feed a NoSQL database, a SSAS cube, a data mart, or go right into Power BI. With Azure SQL Datawarehouse you can have cubes, Power BI reports and SSRS
If you need SQL Server Reporting Services, Integration Services (and you have complex SSIS logic), and Analysis Services (SSAS), you may better consider an Azure SQL VM.
I've Two Custom code dll, for Image related to IP Cams.
dll-One : Extract image from IP cams and can be stored it to Azure data lake Store.
Like :
/adls/clinic1/patientimages
/adls/clinic2/patientimages
dll-two : Use those image and extract information from it and load data into RDBMS tables.
So for instance in RDBMS ,say there are entities dimpatient, dimclinic and factpatientVisit.
For start, a one time data can be exported to defined location in Azure data lake store.
Like:
/adls/dimpatient
/adls/dimclinic
/adls/factpatientVisit
Question :
How to push incremental data in same file or how we can handle this incremental load in Azure data Analytics?
This like implementing Warehouse in Azure Data Analytics.
Note: Azure SQL db or any other storage offered by Azure is not want to.
I mean why to spend in other Azure Services if one type of storage has capabilities to hold all types of data.
adls is name of my ADLS storage.
I am not sure I completely understand your question, but you can organize your data files in Azure Data Lake Store or your rows in partitioned U-SQL tables along a time dimension, so you can add new partitions/files for each increment. In general, we recommend that such increments are of substantial sizes though to preserve the ability to scale.