I have a requirement like this:
Source data will come from Azure SQL (master data) and Azure Cosmos Db (transactional data)
Create a data model in Azure Databricks using Delta Lake
Create visualizations in Power BI Embedded
I am very new to Delta Lake. So, pardon my noob questions.
My questions are:
How should we create the data model (DWH)
Should we use Direct Query or Import Data in Power BI to visualize the data
Related
i am currently working in Azure databricks. There i do use the AutoML functionality to predict on data we get from the Delta lake tables.
For visualization purposes the data in the delta lake tables are saved to Azure data explorer to visualize then with a dashboarding solution
How can i save now the prediction from AutoML to Azure data explorer?. Actually it should not be possible because ADX are not changeable so you can only append or delete entrys but you cannot add a new column with a new value to it.
I am using this stack so far to be able to do streaming analytics/ machinelearning.
An alternative i do see is to keep the test data in the delta lake, do the inference, save the prediction to delta lake aswell and push both together to azure data explorer.
What would be your preferred way on saving predictions?
If further information is needed, please let me know.
We have an Azure SQL database where we collect a large amount of sensor data and we regularly extract the data from it and transform it a bit with a python script. The end result is a pandas DataFrame file. We would like to store the transformed data in an Azure database and use it as a source of a power BI dashboard.
On the one hand, we want to show the "almost" real-time data on a dashboard (the latency due to the transformation etc. is acceptable, but the dashboard needs to refresh very frequently, let's say once a minute), but we also want to store the transformed data and query it later e.g. to visualize the data only for a given day.
Is it possible to convert the pandas DataFrame into SQL and store it on Data Lake and stream the data from there? I read that it is possible to store structured data on Data Lake and even query it, but I am unsure if this would be the best solution.
(My current task is to choose the best database for storing the transformed data to enable both streaming and querying it later. I am very new in Azure products and I don't have a sandbox account yet to even try around and identify possible pitfalls. I've just figured out that PowerBI does not support DirectQuery for DataLake and I feel like this can be an issue - meaning we would have to query the data on DataLake at first and store it somewhere if we wanted to visualize a subset, is that correct?)
Azure Datalake is not a database, just a store for the data both structured and unstructured, so as mentioned you can't direct query it unless you have some compute capacity (Databricks, Azure Synapse, Azure DataLake Analytics, Power BI Premium with enhanced compute)
Depending on your approach, it may be best to move from Azure SQL Database and Pandas, to Azure Databricks, that can ingest the streaming data, transform, and provide an outputted table that is stored in the data lake. You will then connect Power BI to the Databricks instance and query that. The data will only be available while the cluster is running.
Moving to Databricks, will involve rewriting your Panda code to Koalas, or preferably Pyspark.
You do have the option of using Databricks to write the items back to a Azure SQL Database table. Depending on what transformations you are doing you could keep it all in Azure SQL, or if it is sensor data streaming, take the data through Azure Event Hubs, to Azure Streaming Analytics (does transformations), to Azure SQL Database (store Realtime and historical).
I'm trying to setup a Demo Data Warehouse in Azure Synapse. I would like to extract data from a local MySQL database, transform and aggregate some data and store it in fact-/dimension tables in Azure Synapse Analytics.
Currently I have an instance of Azure SQL Data Warehouse and Data Factory. I created a connection to my MySQL database in Data Factory and my thought was, i can use this connector as input for a new Data Flow, which transforms the dataset and stores it to my destination dataset, which is linked to my Azure Synapse Data Warehouse.
The Problem is, Data Factory just support some Azure Services like Azure Data Lake or Azure SQL Database as Source for a new Data Flow.
What would be the best practice for solving this Problem? Create an Instance of Azure SQL Database, copy the Data from the local MySQL Database to the Azure SQL Database and use it then as Source for a new Data Flow?
Best practice here is to use the Copy Activity in an ADF pipeline to land the data from MySQL into Parquet in Blob or ADLS G2, then transform the data using Data Flows.
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.