Have anyone performed benchmark test using polybase with adl, I want to know if I am having a data file which is having 4milion rows, will polybase be helpful in fetching those rows to the data warehouse. Can anyone post any articles where I Can learn about these things.
Yes Microsoft have conducted some trials, for example:
Load 1 TB into Azure SQL Data Warehouse under 15 minutes with Data Factory
https://learn.microsoft.com/en-us/azure/data-factory/data-factory-load-sql-data-warehouse
This is using Data Factory but it's really Polybase under the hood doing the heavy lifting. Now, it was using Polybase with Blob Storage (not Data Lake) but you get the idea. As an experiment, why don't you set this up, run it, then convert it to use Data Lake and report back?
Related
Azure Synapse Analytics is the datawarehouse solution from Azure.
There are 3 ways to load the data into the warehouse:
COPY statement
PolyBase
Bulk insert
The fastest and most scalable way to load data is through the COPY statement or the PolyBase.
However now it is also possible to load the data through Synapse Links. Which allows near-real time data.
But I do not see any documentation referring to Synapse Links being used in a traditional Data Warehouse for analytics.
The use cases in the documentation are:
Supply chain analytics, forecasting & reporting
Real-time personalization
Predictive maintenance, anomaly detection in IOT scenarios
Which are use cases that need real time data.
I do not need near real time data. Therefore I assume "Synapse Link" has some disadvantages for a traditional data warehouse solution.
Could someone please tell me their knowledge about using "Synapse Link" in a traditional analytics data warehouse ?
Thanks in advance
With "Traditional datawarehouse solution" I assume you have ETL processes, that load/refresh your DWH say once a day.
The Synapse Link is a very convenient way to import Cosmos DB or Dataverse Data into a Data Lake connected to Synapse. The "real time" part of it shouldn't bother you, because you can always use batch jobs (dataflows) to load the data periodically from the lake into your datawarehouse.
With the Synapse Link you save time and development effort to bring the data properly from the Cosmos DB or Dataverse into your analytical environment. It works great for us.
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 need to read data from azure data from azure data lake and apply some joins in sql and show in Web UI.
Data is around 300 gb and migrating data from azure data factory to azure sql database is happening at the speed of 4Mbps.
I have also tried to use sql server 2019 which has polybase support but that is also taking 12-13 hours to copy data.
Also tried cosmos db for storing data from lake but seems it is taking large amount of time.
Any other way we can read data from lake.
One way can be azure data warehouse,but that is too costly and support only 128 concurrent transactions.
Can databricks be used,but its a computation engine and we need it to be available 24*7 for UI Queries
I still suggest you using Azure Data Factory. As you said, your data is around 300 gb.
Here's the Copy performance and scalability achievable using ADF:
I agree with David Makogon. The performance of your Data Factory is very slowly( 4Mbps). Please reference this document Copy activity performance and scalability guide.
It will help you improve the Data Factory data copy performance, give more suggestions about Data Factory settings or Database settings.
Hope this helps.
I had a very similar situation, just more data +-900GB.
If you need to show it in ui, you will still need to load data to Azure SQL, as DWH is not very good at handling parallel load and its costy.
We ended up using bulk insert from blob storage.
I created sp to call bulk insert with parameters (source file, target table) and ADF to orchestrate and run in parallel.
Could not find anything faster than that.
https://learn.microsoft.com/en-us/sql/relational-databases/import-export/examples-of-bulk-access-to-data-in-azure-blob-storage?view=sql-server-ver15
I'm in a position where we're reading from our Azure Data Lake using external tables in Azure Data Warehouse.
This enables us to read from the data lake, using well known SQL.
However, another option is using Data Lake Analytics, or some variation of HDInsight.
Performance wise, I'm not seeing much difference. I assume Data Warehouse is running some form of distributed query in the background, converting to U-SQL(?), and so why would we use Data Lake Analytics with the slightly different syntax of U-SQL?
With python script also available in SQL, I feel I'm missing a key purpose of Data Lake Analytics, other than the cost (pay per batch job, rather than constant up time of a database).
If your main purpose is to query data stored in the Azure Data Warehouse (ADW) then there is not real benefit to using Azure Data Lake Analytics (ADLA). But as soon as you have other (un)structured data stored in ADLS, like json documents or csv files for example, the benefit of ADLA becomes clear as U-Sql allows you to join your relational data stored in ADW with the (un)structured / nosql data stored in ADLS.
Also, it enables you to use U-Sql to prepare this other data for direct import in ADW, so Azure Data Factory is not longer required to get the data into you data warehouse. See this blogpost for more information:
A common use case for ADLS and SQL DW is the following. Raw data is ingested into ADLS from a variety of sources. Then ADL Analytics is used to clean and process the data into a loading ready format. From there, the high value data can be imported into Azure SQL DW via PolyBase.
..
You can import data stored in ORC, RC, Parquet, or Delimited Text file formats directly into SQL DW using the Create Table As Select (CTAS) statement over an external table.
Please note that the SQL statement in SQL Data Warehouse is currently NOT generating U-SQL behind the scenes. Also, the use cases between ADLA/U-SQL and SDW are different.
ADLA is giving you an processing engine to do batch data preparation/cooking to generate your data to build a data mart/warehouse that you then can read interactively with SQL DW. In your example above, you seem to be mainly doing the second part. Adding "Views" on top on these EXTERNAL tables to do transformations in SQL DW will quickly run into scalability limits if you operating on big data (and not just a few 100k rows).
What would cause Polybase performance to degrade when querying larger datasets in order to insert records into Azure Data Warehouse from Blob storage?
For example, a few thousand compressed (.gz) CSV files with headers partitioned by a few hours per day across 6 months worth of data. Querying these files from an external table in SSMS is not exactly optimial and it's extremely slow.
Objectively, I'm loading data into Polybase in order to transfer data into Azure Data Warehouse. Except, it seems with large datasets, Polybase is pretty slow.
What options are available to optimize Polybase here? Wait out the query or load the data after each upload to blob storage incrementally?
In your scenario, Polybase has to connect to the files in the external source, uncompress them, then ensure they fit your external table definition (schema) and then allow the contents to be targeted by the query. When you are processing large amounts of text files in a one-off import fashion, there is nothing to really cache either, since it is dealing with new content every time. In short, your scenario is compute heavy.
Azure Blob Storage will (currently) max out at around 1,250MB/sec, so if your throughput is not near maxing this, then the best way to improve performance is to upgrade your DWU on your SQL data warehouse. In the background, this will spread your workload over a bigger cluster (more servers). SQL Data Warehouse DWU can be scaled either up and down in a matter of minutes.
If you have huge volumes and are maxing the storage, then use multiple storage accounts to spread the load.
Other alternatives include relieving Polybase of the unzip work as part of your upload or staging process. Do this from within Azure where the network bandwidth within a data center is lightning fast.
You could also consider using Azure Data Factory to do the work. See here for supported file formats. GZip is supported. Use the Copy Activity to copy from the Blob storage in to SQL DW.
Also look in to:
CTAS (Create Table as Select), the fastest way to move data from external tables in to internal storage in Azure Data Warehouse.
Creating statistics for your external tables if you are going to query them repeatedly. SQL Data Warehouse does not create statistics automatically like SQL Server and you need to do this yourself.