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Azure Synapse Analytics Updates: Ignite 2022 Highlights
I wrote “Azure Synapse Analytics Updates: Ignite 2022 Highlights” to share practical, production-minded guidance on this topic.
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.