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Azure Synapse Analytics Updates: Ignite 2022 Highlights
Azure Synapse Analytics continues to evolve as Microsoft’s unified analytics platform. Ignite 2022 brought several updates that improve performance, integration, and the migration experience from legacy systems.
Serverless SQL Pool Enhancements
Query Performance Improvements
-- New query hints for better performance
SELECT
c.CustomerName,
SUM(o.OrderTotal) as TotalSpent,
COUNT(DISTINCT o.OrderId) as OrderCount
FROM
OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/sales/orders/*.parquet',
FORMAT = 'PARQUET'
) AS o
JOIN
OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/customers/customers.parquet',
FORMAT = 'PARQUET'
) AS c ON o.CustomerId = c.CustomerId
WHERE
o.OrderDate >= '2022-01-01'
GROUP BY
c.CustomerName
OPTION (
MAXDOP 8, -- Parallel execution hint
LABEL = 'CustomerSales' -- Query labeling for monitoring
);Delta Lake Integration
-- Query Delta Lake tables directly
SELECT *
FROM OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/delta/products/',
FORMAT = 'DELTA'
) AS products
WHERE Category = 'Electronics';
-- Time travel queries
SELECT *
FROM OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/delta/products/',
FORMAT = 'DELTA'
) AS products
OPTION (DELTA_TIMESTAMP = '2022-10-01T00:00:00Z');
-- Query specific version
SELECT *
FROM OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/delta/products/',
FORMAT = 'DELTA'
) AS products
OPTION (DELTA_VERSION = 42);Dedicated SQL Pool Updates
Workload Isolation Improvements
-- Create workload group with resource limits
CREATE WORKLOAD GROUP ReportingGroup
WITH (
MIN_PERCENTAGE_RESOURCE = 20,
CAP_PERCENTAGE_RESOURCE = 50,
REQUEST_MIN_RESOURCE_GRANT_PERCENT = 5,
REQUEST_MAX_RESOURCE_GRANT_PERCENT = 25,
IMPORTANCE = NORMAL,
QUERY_EXECUTION_TIMEOUT_SEC = 3600
);
-- Create classifier to route queries
CREATE WORKLOAD CLASSIFIER ReportingClassifier
WITH (
WORKLOAD_GROUP = 'ReportingGroup',
MEMBERNAME = 'ReportingUser',
WLM_LABEL = 'Reporting'
);
-- Monitor workload groups
SELECT
wg.name as WorkloadGroup,
wg.min_percentage_resource,
wg.cap_percentage_resource,
rs.active_request_count,
rs.queued_request_count
FROM sys.dm_workload_management_workload_groups_stats rs
JOIN sys.workload_management_workload_groups wg
ON rs.group_id = wg.group_id;Result Set Caching
-- Enable result set caching
ALTER DATABASE YourDatabase
SET RESULT_SET_CACHING ON;
-- Check cache hit rates
SELECT
request_id,
command,
result_cache_hit,
total_elapsed_time
FROM sys.dm_pdw_exec_requests
WHERE result_cache_hit = 1
ORDER BY start_time DESC;Spark Pool Improvements
Optimized Apache Spark 3.3
# New Spark 3.3 features in Synapse
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
spark = SparkSession.builder \
.config("spark.sql.adaptive.enabled", "true") \
.config("spark.sql.adaptive.coalescePartitions.enabled", "true") \
.getOrCreate()
# Read Delta Lake with optimizations
df = spark.read \
.format("delta") \
.option("versionAsOf", 10) \
.load("abfss://data@datalake.dfs.core.windows.net/products")
# Intelligent partitioning
df_processed = df \
.repartition(col("category")) \
.sortWithinPartitions("product_id") \
.write \
.format("delta") \
.partitionBy("category") \
.option("optimizeWrite", "true") \
.option("autoCompact", "true") \
.save("abfss://data@datalake.dfs.core.windows.net/products_optimized")Synapse ML Integration
from synapse.ml.cognitive import *
from synapse.ml.core.spark import FluentAPI
# Use Azure OpenAI from Synapse
openai_completion = OpenAICompletion() \
.setSubscriptionKey(cognitive_key) \
.setDeploymentName("text-davinci-003") \
.setMaxTokens(200) \
.setPromptCol("prompt") \
.setOutputCol("completion")
# Apply to DataFrame
df_with_prompts = spark.createDataFrame([
("Summarize this customer feedback: Great product, fast shipping",),
("Summarize this customer feedback: Poor quality, will not buy again",)
], ["prompt"])
results = openai_completion.transform(df_with_prompts)
results.show()Data Integration Improvements
Mapping Data Flows
{
"name": "IncrementalLoadFlow",
"properties": {
"type": "MappingDataFlow",
"sources": [
{
"name": "SourceData",
"dataset": "SourceDataset",
"partitionOption": "dynamicRange",
"partitionLowerBound": 1,
"partitionUpperBound": 1000000,
"partitionColumn": "id"
}
],
"transformations": [
{
"name": "FilterNew",
"type": "Filter",
"expression": "ModifiedDate > $lastRunTime"
},
{
"name": "DeriveColumns",
"type": "DerivedColumn",
"columns": [
{
"name": "ProcessedAt",
"expression": "currentTimestamp()"
},
{
"name": "HashKey",
"expression": "sha2(256, concat(ProductId, ProductName))"
}
]
},
{
"name": "LookupExisting",
"type": "Lookup",
"dataset": "TargetDataset",
"lookupColumn": "HashKey",
"multiple": false
},
{
"name": "SplitNewUpdates",
"type": "ConditionalSplit",
"conditions": [
{
"name": "NewRecords",
"expression": "isNull(LookupExisting.id)"
},
{
"name": "UpdatedRecords",
"expression": "!isNull(LookupExisting.id)"
}
]
}
],
"sinks": [
{
"name": "InsertNew",
"dataset": "TargetDataset",
"input": "NewRecords",
"insertMethod": "insert"
},
{
"name": "UpsertUpdates",
"dataset": "TargetDataset",
"input": "UpdatedRecords",
"updateMethod": "upsert",
"keyColumns": ["id"]
}
]
}
}Synapse Link for Cosmos DB
-- Query Cosmos DB analytical store
SELECT
c.customerId,
c.customerName,
c.orders,
c._ts as lastUpdated
FROM OPENROWSET(
PROVIDER = 'CosmosDB',
CONNECTION = 'Account=cosmosaccount;Database=ecommerce',
OBJECT = 'customers',
SERVER_CREDENTIAL = 'CosmosDBCredential'
) AS c
WHERE c._ts > DATEDIFF(second, '1970-01-01', DATEADD(day, -1, GETUTCDATE()));
-- Join with data lake
SELECT
c.customerName,
p.productName,
SUM(o.quantity * o.unitPrice) as totalSpent
FROM OPENROWSET(
PROVIDER = 'CosmosDB',
CONNECTION = 'Account=cosmosaccount;Database=ecommerce',
OBJECT = 'orders',
SERVER_CREDENTIAL = 'CosmosDBCredential'
) AS o
JOIN OPENROWSET(
PROVIDER = 'CosmosDB',
CONNECTION = 'Account=cosmosaccount;Database=ecommerce',
OBJECT = 'customers',
SERVER_CREDENTIAL = 'CosmosDBCredential'
) AS c ON o.customerId = c.customerId
JOIN OPENROWSET(
BULK 'https://datalake.dfs.core.windows.net/products/*.parquet',
FORMAT = 'PARQUET'
) AS p ON o.productId = p.productId
GROUP BY c.customerName, p.productName;Security Enhancements
Data Masking
-- Column-level security with masking
CREATE TABLE Customers (
CustomerId INT NOT NULL,
CustomerName NVARCHAR(100) NOT NULL,
Email NVARCHAR(256) MASKED WITH (FUNCTION = 'email()') NOT NULL,
PhoneNumber NVARCHAR(20) MASKED WITH (FUNCTION = 'partial(0, "XXX-XXX-", 4)'),
SSN CHAR(11) MASKED WITH (FUNCTION = 'default()'),
CreditLimit DECIMAL(18,2) MASKED WITH (FUNCTION = 'random(1000, 5000)')
);
-- Grant unmask permission
GRANT UNMASK TO FinanceAnalyst;Row-Level Security
-- Create security policy
CREATE FUNCTION dbo.fn_securitypredicate(@Region AS NVARCHAR(50))
RETURNS TABLE
WITH SCHEMABINDING
AS
RETURN SELECT 1 AS fn_securitypredicate_result
WHERE @Region = USER_NAME()
OR USER_NAME() = 'Admin';
CREATE SECURITY POLICY RegionFilter
ADD FILTER PREDICATE dbo.fn_securitypredicate(Region)
ON dbo.SalesData
WITH (STATE = ON);Monitoring and Governance
# Monitor Synapse usage with Python
from azure.identity import DefaultAzureCredential
from azure.monitor.query import LogsQueryClient
from datetime import timedelta
credential = DefaultAzureCredential()
client = LogsQueryClient(credential)
query = """
SynapseSqlPoolExecRequests
| where TimeGenerated > ago(24h)
| summarize
TotalQueries = count(),
AvgDuration = avg(TotalElapsedTimeMs),
P95Duration = percentile(TotalElapsedTimeMs, 95),
FailedQueries = countif(Status == 'Failed')
by bin(TimeGenerated, 1h)
| order by TimeGenerated desc
"""
response = client.query_workspace(
workspace_id="your-workspace-id",
query=query,
timespan=timedelta(days=1)
)
for row in response.tables[0].rows:
print(f"{row[0]}: {row[1]} queries, avg {row[2]}ms, {row[4]} failed")Conclusion
Azure Synapse Analytics continues to mature as a comprehensive analytics platform. The improvements in serverless SQL, Spark integration, and data lake connectivity make it easier to build modern data architectures. The migration tools and hybrid capabilities provide a clear path for organizations moving from legacy systems.