Synapse POC playbook: Data warehousing with dedicated SQL pool in Azure Synapse Analytics

This article presents a high-level methodology for preparing and running an effective Azure Synapse Analytics proof of concept (POC) project for dedicated SQL pool.

Prepare for the POC

Before deciding on your Azure Synapse POC goals, we recommend that you first read the Azure Synapse SQL architecture article to familiarize yourself with how a dedicated SQL pool separates compute and storage to provide industry-leading performance.

Identify sponsors and potential blockers

Once you're familiar with Azure Synapse, it's time to make sure that your POC has the necessary support and won't hit any roadblocks. You should:

• Identify any restrictions or policies that your organization has about moving data to, and storing data in, the cloud.
• Identify executive and business sponsorship for a cloud-based data warehouse project.
• Verify that your workload is appropriate for Azure Synapse. For more information, see Dedicated SQL pool architecture in Azure Synapse Analytics.

Set the timeline

A POC is a scoped, time-bounded exercise with specific, measurable goals and metrics that define success. Ideally, it should have some basis in business reality so that the results are meaningful.

POCs have the best outcome when they're timeboxed. Timeboxing allocates a fixed and maximum unit of time to an activity. In our experience, two weeks provides enough time to complete the work without the burden of too many use cases or complex test matrices. Working within this fixed time period, we suggest that you follow this timeline:

1. Data loading: Three days or less
2. Querying: Five days or less
3. Value added tests: Two days or less

Here are some tips:

Once you've determined that there aren't any immediate obstacles and you've set the timeline, you can scope a high-level architecture.

Create a high-level scoped architecture

A high-level future architecture likely contains many data sources and data consumers, big data components, and possibly machine learning and AI data consumers. To keep your POC goals achievable (and within the bounds of your set timeline), decide which of these components will form part of the POC and which will be excluded.

Additionally, if you're already using Azure, identify the following:

• Any existing Azure resources that you can use during the POC. For example, resources can include Microsoft Entra ID, or Azure ExpressRoute.
• What Azure region(s) your organization prefers.
• A subscription you can use for non-production POC work.
• The throughput of your network connection to Azure.

  Important

  Be sure to check that your POC can consume some of that throughput without having an adverse effect on production solutions.

Apply migration options

If you're migrating from a legacy data warehouse system to Azure Synapse, here are some questions to consider:

• Are you migrating and want to make as few changes to existing Extract, Transform, and Load (ETL) processes and data warehouse consumption as possible?
• Are you migrating but want to do some extensive improvements along the way?
• Are you building an entirely new data analytics environment (sometimes called a greenfield project)?

Next, you need to consider your pain points.

Identify current pain points

Your POC should contain use cases to prove potential solutions to address your current pain points. Here are some questions to consider:

• What gaps in your current implementation do you expect Azure Synapse to fill?
• What new business needs are you required to support?
• What service level agreements (SLAs) are you required to meet?
• What will be the workloads (for example, ETL, batch queries, analytics, reporting queries, or interactive queries)?

Next, you need to set your POC success criteria.

Set POC success criteria

Identify why you're doing a POC and be sure to define clear goals. It's also important to know what outputs you want from your POC and what you plan to do with them.

Keep in mind that a POC should be a short and focused effort to quickly prove or test a limited set of concepts. If you have a long list of items to prove, you may want to dive them into multiple POCs. POCs can have gates between them so you can determine whether to proceed to the next POC.

Here are some example POC goals:

• We need to know that the query performance for our big complex reporting queries will meet our new SLAs.
• We need to know the query performance for our interactive users.
• We need to know whether our existing ETL processes are a good fit and where improvements need to be made.
• We need to know whether we can shorten our ETL runtimes and by how much.
• We need to know that Synapse Analytics has sufficient security capabilities to adequately secure our data.

Next, you need to create a test plan.

Create a test plan

Using your goals, identify specific tests to run in order to support those goals and provide your identified outputs. It's important to make sure that you have at least one test for each goal and the expected output. Identify specific queries, reports, ETL and other processes that you will run to provide quantifiable results.

Refine your tests by adding multiple testing scenarios to clarify any table structure questions that arise.

Good planning usually defines an effective POC execution. Make sure all stakeholders agree to a written test plan that ties each POC goal to a set of clearly stated test cases and measurements of success.

Most test plans revolve around performance and the expected user experience. What follows is an example of a test plan. It's important to customize your test plan to meet your business requirements. Clearly defining what you are testing will pay dividends later in this process.

Goal	Test	Expected outcomes
We need to know that the query performance for our big complex reporting queries will meet our new SLAs	- Sequential test of complex queries - Concurrency test of complex queries against stated SLAs	- Queries A, B, and C completed in 10, 13, and 21 seconds, respectively - With 10 concurrent users, queries A, B, and C completed in 11, 15, and 23 seconds, on average
We need to know the query performance for our interactive users	- Concurrency test of selected queries at an expected concurrency level of 50 users. - Run the preceding query with result set caching	- At 50 concurrent users, average execution time is expected to be under 10 seconds, and without result set caching - At 50 concurrent users, average execution time is expected to be under five seconds with result set caching
We need to know whether our existing ETL processes can run within the SLA	- Run one or two ETL processes to mimic production loads	- Loading incrementally into a core fact table must complete in less than 20 minutes (including staging and data cleansing) - Dimension processing needs to take less than five minutes
We need to know that the data warehouse has sufficient security capabilities to secure our data	- Review and enable network security (VNet and private endpoints), access control (row-level security, dynamic data masking)	- Prove that data never leaves our tenant. - Ensure that customer content is easily secured

Next, you need to identify and validate the POC dataset.

Identify and validate the POC dataset

Using the scoped tests, you can now identify the dataset required to execute those tests in Azure Synapse. Review your dataset by considering the following:

• Verify that the dataset adequately represents your production dataset in terms of content, complexity, and scale.
• Don't use a dataset that's too small (less than 1TB), as you might not achieve representative performance.
• Don't use a dataset that's too large, as the POC isn't intended to complete a full data migration.
• Identify the distribution pattern, indexing option, and partitioning for each table. If there are any questions regarding distribution, indexing, or partitioning, add tests to your POC to answer them. Bear in mind that you may want to test more than one distribution option or indexing option for some tables.
• Check with the business owners for any blockers for moving the POC dataset to the cloud.
• Identify any security or privacy concerns.

Next, you need to assemble the team of experts.

Assemble the team

Identify the team members and their commitment to support your POC. Team members should include:

• A project manager to run the POC project.
• A business representative to oversee requirements and results.
• An application data expert to source the data for the POC dataset.
• An Azure Synapse specialist.
• An expert advisor to optimize the POC tests.
• Any person who will be required for specific POC project tasks but who aren't required for its entire duration. These supporting resources could include network administrators, Azure administrators, or Microsoft Entra administrators.

Now that you are fully prepared, it's time to put your POC into practice.

Put the POC into practice

It's important to keep the following in mind:

• Implement your POC project with the discipline and rigor of any production project.
• Run the POC according to plan.
• Have a change request process in place to prevent your POC scope from growing or changing.

Before tests can start, you need to set up the test environment. It involves four stages:

1. Setup
2. Data loading
3. Querying
4. Value added tests

Setup

You can set up a POC on Azure Synapse by following these steps:

1. Use this quickstart to provision a Synapse workspace and set up storage and permissions according to the POC test plan.
2. Use this quickstart to add a dedicated SQL pool to the Synapse workspace.
3. Set up networking and security according to your requirements.
4. Grant appropriate access to POC team members. See this article about authentication and authorization for accessing dedicated SQL pools.

Data loading

Once you've set up the dedicated SQL pool, you can follow these steps to load data:

1. Load the data into Azure Blob Storage. For a POC, we recommend that you use a general-purpose V2 storage account with locally-redundant storage (LRS). While there are several tools for migrating data to Azure Blob Storage, the easiest way is to use Azure Storage Explorer, which can copy files into a storage container.
2. Load the data into the dedicated SQL pool. Azure Synapse supports two T-SQL loading methods: PolyBase and the COPY statement. You can use SSMS to connect to the dedicated SQL pool to use either method.

When you load data into the dedicated SQL pool for the first time, you need to consider which distribution pattern and index option to use. While a dedicated SQL pool supports a variety of both, it's a best practice to rely on default settings. Default settings use round-robin distribution and a clustered columnstore index. If necessary, you can adjust these settings later, which is described later in this article.

The following example shows the COPY load method:

--Note when specifying the column list, input field numbers start from 1
COPY INTO
    test_1 (Col_1 default 'myStringDefault' 1, Col_2 default 1 3)
FROM
    'https://myaccount.blob.core.chinacloudapi.cn/myblobcontainer/folder1/'
WITH (
    FILE_TYPE = 'CSV',
    CREDENTIAL = (IDENTITY = 'Storage Account Key' SECRET = '<Your_Account_Key>'),
    FIELDQUOTE = '"',
    FIELDTERMINATOR = ',',
    ROWTERMINATOR = '0x0A',
    ENCODING = 'UTF8',
    FIRSTROW = 2
);

Querying

The primary purpose of an data warehouse is to perform analytics, which requires querying the data warehouse. Most POCs start by running a small number of representative queries against the data warehouse, at first sequentially and then concurrently. You should define both approaches in your test plan.

Sequential query tests

It's easy to run sequential query tests in SSMS. It's important to run these tests by using a user with a sufficiently large resource class. A resource class is a pre-determined resource limit in dedicated SQL pool that governs compute resources and concurrency for query execution. For simple queries, we recommend using the pre-defined staticrc20 resource class. For more complex queries, we recommend using the pre-defined staticrc40 resource class.

Notice that the following first query uses a query label to provide a mechanism to keep track of the query. The second query uses the sys.dm_pdw_exec_requests dynamic management view to search by the label.

/* Use the OPTION(LABEL = '') Syntax to add a query label to track the query in DMVs */
SELECT TOP (1000)
    *
FROM
    [dbo].[Date]
OPTION (LABEL = 'Test1');

/* Use sys.dm_pdw_exec_requests to determine query execution duration (ms) */
SELECT
    Total_elapsed_time AS [Elapsed_Time_ms],
    [label]
FROM
    sys.dm_pdw_exec_requests
WHERE
    [label] = 'Test1';

Concurrent query tests

After recording sequential query performance, you can then run multiple queries concurrently. That way, you can simulate a business intelligence workload running against the dedicated SQL pool. The easiest way to run this test is to download a stress testing tool. The most popular tool is Apache JMeter, which is a third-party open source tool.

The tool reports on minimum, maximum, and median query durations for a given concurrency level. For example, suppose that you want to simulate a business intelligence workload that generates 100 concurrent queries. You can setup JMeter to run those 100 concurrent queries in a loop and then review the steady state execution. It can be done with result set caching on or off to evaluate the suitability of that feature.

Be sure to document your results. Here's an example of some results:

Concurrency	# Queries run	DWU	Min duration(s)	Max duration(S)	Median duration(s)
100	1,000	5,000	3	10	5
50	5,000	5,000	3	6	4

Mixed workload tests

Mixed workload testing is an extension of the concurrent query tests. By adding a data loading process into the workload mix, the workload will better simulate a real production workload.

Optimize the data

Depending on the query workload running on Azure Synapse, you may need to optimize your data warehouse's distributions and indexes and rerun the tests.

The most common mistakes seen during setup are:

• Large queries run with a resource class that's too low.
• The dedicated SQL pool service level DWUs are too low for the workload.
• Large tables require hash distribution.

To improve query performance, you can:

• Create materialized views, which can accelerate queries involving common aggregations.
• Replicate tables, especially for small dimension tables.
• Hash distribute large fact tables that are joined or aggregated.

Value added tests

Once query performance testing is complete, it's a good time to test specific features to verify that they satisfy your intended use cases. These features include:

• Row-level security
• Column-level security
• Dynamic data masking
• Intra-cluster scaling via workload isolation

Finally, you need to interpret your POC results.

Interpret the POC results

Once you have test results for your data warehouse, it's important to interpret that data. A common approach you can take is to compare the runs in terms of price/performance. Simply put, price/performance removes the differences in price per DWU or service hardware and provides a single comparable number for each performance test.

Here's an example:

Test	Test duration	DWU	$/hr for DWU	Cost of test
Test 1	10 min	1000	�80.5/hr	�13.416
Test 2	30 min	500	�40.25/hr	�20.125

This example makes it easy to see that Test 1 at DWU1000 is more cost effective at �13.416 per test run compared with �20.125 per test run.

In summary, once you complete all the POC tests, you're ready to evaluate the results. Begin by evaluating whether the POC goals have been met and the desired outputs collected. Make note of where additional testing is warranted and additional questions that were raised.

Next steps