Forum,
I am currently looking into Azure Synapse as an option for migrating our on-prem data architecture. I am excited by the functionality it offers - SQL Pools, Spark Pools, and the accompanying notebooks. I get that Synapse can function as a all in one data platform, where my data scientists and data analists can use its functionality to deliver insights at will. However, a large part of the work my team does is creating data products.
We currently have a kubernetes cluster with several stand-alone API's that perform data-science operations in the larger whole of our software. They can be thought of as microservices. Most of the ETL is done in our SQL-server, and the microservices in our K8S cluster (usually python + some python packages + FastAPI) typically get the required data from our SQL-server through some SQL-query with an ODBC connector.
Now my question is, how suitable is Synapse for such an architecture? Can I call upon the SQL-pool or spark-pool to do the heavy data-lifting from outside the azure environment, say from a kubernetes pod?
Unfortunately you can't integrate Azure Synapse Analytics with Kubernetes Services.
While Synapse SQL helps perform SQL queries, Apache Spark executes batch/stream processing on Big Data. SQL Pool is used to work with data stored in Dedicated SQL Pool while Spark SQL can be integrated with existing data preparation or data science projects that you may hold in Azure Databricks or Azure Machine Learning Services.
Also, as per this third-party document, Azure Synapse Analytics can't integrate with Kubernetes Services.
As a workaround, you can copy/move your data from Kubernetes to Azure Services like Azure Dedicated SQL Pool, Azure Blob Storage or Azure Data Lake Storage and then integrate it with Azure Synapse pipeline or Spark Pool.
Related
Difference between the dedicated sql pool and dedicated sql pool inside the azure synapse analytics?
While provision the azure synapse analytics we will use the Azure storage layer gen2 ,as per the msdn the data will be stored in the azure storage gen2 but azure gen2 will use the hdfs features.so how the dfs feature will use the syanpse analytics?
They both are the same thing. Either you can first create a Dedicated SQL pool and link it with Synapse Workspace, or you can first create the Synapse Workspace and then dedicated pool inside it.
A dedicated SQL pool offers T-SQL based compute and storage capabilities. After creating a dedicated SQL pool in your Synapse workspace, data can be loaded, modeled, processed, and delivered for faster analytic insight.
Apart from Dedicated SQL pool, Azure Synapse provide Serverless SQL and Apache Spark pools. Based on your requirement you can choose the appropriate.
Serverless SQL pool is a query service over the data in your data lake. It enables you to access your data through the following functionalities:
A familiar T-SQL syntax to query data in place without the need to copy or load data into a specialized store.
Integrated connectivity via the T-SQL interface that offers a wide range of business intelligence and ad-hoc querying tools, including the most popular drivers.
You will be directly passing the file path stored in Data Lake Gen2 in T-SQL statement. Refer example below:
select top 10 *
from openrowset(
bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.csv',
format = 'csv',
parser_version = '2.0',
firstrow = 2 ) as rows
For more related information, I recommend you to go through this document.
Is it possible to connect Azure Data Factory with Azure Databricks SQL Endpoints (Delta table and views) instead of interactive cluster. I tried with Azure delta lake connector but it has options for cluster and not Endpoints?
Unfortunately, you cannot connect Azure Databricks SQL endpoints with Azure Databricks using ADF.
Note: With compute option - you can connect Azure Databricks workspace with the below cluster options:
New Job cluster
Existing interactive cluster
Existing instance pool
Note: With Datastore option - Azure Databricks Delta Lake option you can connect only existing interactive clusters:
Appreciate if you could share the feedback on our feedback channel. Which would be open for the user community to upvote & comment on. This allows our product teams to effectively prioritize your request against our existing feature backlog and gives insight into the potential impact of implementing the suggested feature.
I am attempting to spin up an azure synapse pool in terraform. At present from the documentation found at: https://registry.terraform.io/providers/hashicorp/azurerm/latest/docs/resources/synapse_sql_pool, it appears you have to use a synapse workspace, which also includes a datafactory integration and powerbi, etc.
Right now we just want to datawarehouse not all the other bells and whistles. As you can see within the Azure Portal, you are free to spin up a synapse analytics DW with or without a workspace (see the right image in the box, "formerly SQL DW"):
When you spin that up, you simply have a standalone DW...
Any insight on just getting the datawarehouse as you can in the portal without the workspace and realted?
I am not a Terraform guy. As for Synapse, you are referring to the new one that is in preview. The new one has the workspace which supports SQL pools, Sparks clusters and Pipelines. Although they are supported, they are not created when you deploy a Synapse workspace.
So you can go ahead and created the workspace and one SQL Pool and you will get what you're looking for: the data warehouse engine, named SQL Pool.
Some extra notes: there are 2 types of SQL data warehouse in Synapse Analytics: SQL Pools and SQL on demand. The first one is provisioned computing and is the traditional one with all the features. SQL on demand is still in preview, doesn't have all the features and is charged by the terabyte processed by your queries.
Happy data crunching!
Objective
I'm storing data as Delta Lake format at ADLS gen2. Also they are available through Hive catalog.
It's important to notice that we're currently using PowerBI, but in future we may switch to Excel over AAS.
Question
What is the best way (or hack) to connect AAS to my ADLS gen2 data in Delta Lake format?
The issue
There are no Databricks/Hive among AAS supported sources. AAS supports ADLS gen2 through Blob connector, but AFAIK, it doesn't support Delta Lake format, only parquet.
Possible solution
From this article I see that the issue may be potentially solved with PowerBI on-premise API gateway:
One example is the integration between Azure Analysis Services (AAS)
and Databricks; Power BI has a native connector to Databricks, but
this connector hasn’t yet made it to AAS. To compensate for this, we
had to deploy a Virtual Machine with the Power BI Data Gateway and
install Spark drivers in order to make the connection to Databricks
from AAS. This wasn’t a show stopper, but we’ll be happy when AAS has
a more native Databricks connection.
The issue with this solution is that we're planning to stop using PowerBI. I don't quite understand how it works, what PBI license and implementation/maintenance efforts it requires. Could you please provide deeper insight on how it'll work?
UPD, 26 Dec 2020
Now, when Azure Synapse Analytics is GA, it has full support of SQL on-demand. That means that serverless Synapse may theoretically be used as a glue between AAS and Delta Lake. See "Direct Query Databricks' Delta Lake from Azure Synapse".
In the same time, is that possible to query Databricks Catalog (internal/external) from Synapse on-demand using ODBC? Synapse supports ODBC as external source.
Power BI Dataflows now supports Parquet files, so you can load from those files to Power BI, however the standard design pattern is to use Azure SQL Data Warehouse to load the file then layer Azure Analysis Service (AAS) over that. AAS does not support parquet, you would have to create a CSV version of the final table, or load it to a SQL Database.
As mentioned the typical architecture, is to have Databricks do some or all of the ETL, then have Azure SQL DW sit over it.
Azure SQL DW has now morphed into Azure Synapse, but this has the benefit of that a Databricks/Spark database now has a shadow copy but accessible by the SQL on Demand functionality. SQL on Demand doesn't require to to have an instance of the data warehouse component of Azure Synapse, it runs on demand, and you per per TB of query. A good outline of how it can help is here. The other option is to have Azure Synapse load the data from external table into that service then connect AAS to that.
Can someone explain the distinct difference between these two products in all major aspects? As far as I am aware from reading the official documents, both could host database systems and provide data cleaning pipeline? Both are on cloud?
Databricks:
Azure Databricks is an Apache Spark-based analytics platform optimized
for the Microsoft Azure cloud services platform. Designed with the
founders of Apache Spark, Databricks is integrated with Azure to
provide one-click setup, streamlined workflows, and an interactive
workspace that enables collaboration between data scientists, data
engineers, and business analysts.
Synapse Analytics:
Azure Synapse is a limitless analytics service that brings together
enterprise data warehousing and Big Data analytics. It gives you the
freedom to query data on your terms, using either serverless on-demand
or provisioned resources—at scale. Azure Synapse brings these two
worlds together with a unified experience to ingest, prepare, manage,
and serve data for immediate BI and machine learning needs
they do overlap to some extent, but they are not the same thing. Databricks is pretty much managed Apache Spark, whereas Synapse Analytics is managed SQL Data Warehouse.