Does Azure Databricks use the query acceleration functions in Azure Data Lake Storage gen2? In documentation we can see that spark can benefit from this functionality.
I'm wondering if, in the case where I only use the delta format, I'm profiting from this functionality and whether to include it in the pricing in Azure Calculator under the Storage Account section?
From the docs
Query acceleration supports CSV and JSON formatted data as input to each request.
So it doesn't work with Parquet or Delta - because it is fundamentally a row based accelerator, and Parquet is a columnar format.
Related
Even after going through many resources, I have failed to understand what constitutes a lakehouse, hence my question below.
If we have Azure Gen 2 Storage, ADF, and Azure Databricks with the possibility of converting the incoming CSV files into Delta tables can that be called a "Lakehouse" architecture or is it called a "Delta Lake"?
Or is it the "SQL analytics" engine over and above the Delta Lake layer that makes it a "Lakehouse"?
Please clarify.
At a high level a Lakehouse must contain the following properties:
Open direct access data formats (Apache Parquet, Delta Lake etc.)
First class support for machine learning and data science workloads
state of the art performance
Databricks is the first Lakehouse because it meets the above three properties. Specifically, if you are using Databricks with ADLS and converting all your data (json, csv, parquet, messages etc.) into Delta tables that are available within Databricks. Then that is the making of a Lakehouse, but it still needs to be built and supported. The Databricks platform allows us to satisfy points 2 and 3 above and Delta Lake satisfies 1 ad 3 (performance relies on the engine and the storage which is why 3 is mentioned twice).
Leveraging Databricks and accessing data stored in Delta is a Lakehouse. By adding Databricks SQL (formally SQL Analytics) we allow more users to access and use the Lakehouse. In Databricks SQL users are using the same compute and data as the data engineer does in Databricks, they just have a different UI that they are familiar with. Additionally, Databricks SQL is optimized for SQL and BI workloads while the notebook environment is better for engineering and data science
As a fun read you should check our the Lakehouse whitepaper.
We have a Datafactory pipeline in Azure to move a on-premise SQL table to Azure blob storage Gen2 in parquet format. I think the majority cost would come from the Azure storage, right?
Now we want to move those data to BigQuery. Due to our security policy, we still need the Datafactory pipeline to read from SQL table. So we create a databrick notebook to read the parquet file and move to BigQuery using the Spark BigQuery connector. Now I need to estimate the total cost. On top of the Azure storage, do we have to pay some kind of egress cost to move data out of Azure storage? And does google would charge us some kind of ingress cost to move data to BQ?
All inbound or ingress data transfers to Azure data centers from on-premises environments are free. However, outbound data transfers incur charges.
Data migration from other platforms into BigQuery is free.
To estimate the cost of Google Cloud Platform services, you can use the Google Cloud Pricing Calculator.
Complementing #Ismail's answer:
The migration from other platforms is free when the BigQuery Data Transfer service is used; however, this is not the case if the data is moved to BigQuery using the Spark BigQuery connector.
The connector writes data to BigQuery by writing it first to Cloud Storage (GCS) and then loading it into BigQuery, as mentioned here:
Notice that the process writes the data first to GCS and then loads it to BigQuery, a GCS bucket must be configured to indicate the temporary data location.
Cloud Storage princing depends on the Storage class used and the location of the bucket; so, asuming a Standard class, your migration process will generate charges for:
Data storage; and
Operations
Loading the data from Cloud Storage to BigQuery is free; however, there might be network egress fees if the bucket location is not on the same region/multi-region than the dataset.
Finally, once your data is in BigQuery it will be subject to the BigQuery Storage pricing.
I suggest to check both the Storage and BigQuery complete pricing documentation to check for details, limitations and some examples on how the pricing work.
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've created a DataFrame which I would like to write / export next to my Azure DataLake Gen2 in Tables (need to create new Table for this).
In the future I will also need to update this Azure DL Gen2 Table with new DataFrames.
In Azure Databricks I've created a connection Azure Databricks -> Azure DataLake to see my my files:
Appreciate help how to write it in spark / pyspark.
Thank you!
I would suggest instead of writing data in parquet format, go for Delta format which internally uses Parquet format but provide other features like ACID transaction.The syntax would be
df.write.format("delta").save(path)
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.