Mapping Data Flows Training April 2021

Mapping Data Flows
Code-free data transformation at scale with Azure Data Factory and
Synapse Analytics

What are mapping data flows?
 Code-free data transformation at scale
 Serverless, scaled-out, ADF/Synapse-
managed Apache Spark™ engine
 Resilient flows handle structured and
unstructured data
 Operationalized as a data pipeline activity

Code-free data transformation at scale
 Intuitive UX lets you focus on building transformation logic
 Data cleansing
 Data validation
 Data aggregation
 No requirement of knowing Spark, cluster management, Scala, Python, etc
vs

INGEST
Modern Data Warehouse (MDW)
PREPARE TRANSFORM,
PREDICT
& ENRICH
SERVE
STORE
VISUALIZE
On-premises data
Cloud data
SaaS data
Data Pipeline Orchestration & Monitoring

Fact loading into a data warehouse

Metadata Validation Rules
https://www.youtube.com/watch?v=E_UD3R-VpYE

Data De-Duplication and Distinct Rows

Data Profiling
https://techcommunity.microsoft.com/t5/azure-data-
factory/how-to-save-your-data-profiler-summary-stats-in-adf-data-flows/ba-p/1243251

Building transformation logic
 Transformations: A ‘step’ in the data flow
 Engine intelligently groups them at runtime
 19 currently available
 Core logic of data flow
 Add/Remove/Alter Columns
 Join or lookup data from datasets
 Change number or order of rows
 Aggregate data
 Hierarchal to relational

Source transformation
 Define the data read by your
data flow
 Import projection vs generic
 Schema drift
 Connector specific properties and
optimizations
 Min: 1, Max: ∞
 Define in-line or use dataset

Source: In-line vs dataset
 Define all source properties within a data flow or use a separate
entity to store them
 Dataset:
 Reusable in other ADF activities such as Copy
 Not based in Spark -> some settings overridden
 In-line
 Useful when using flexible schemas, one-off source instances or parameterized sources
 Do not need “dummy” dataset object
 Based in Spark, properties native to data flow
 Most connectors only in available in one

Supported connectors
 File-based data stores (ADLS Gen1/Gen2, Azure Blob Storage)
 Parquet, JSON, DelimitedText, Excel, Avro, XML
 In-line only: Common Data Model, Delta Lake
 SQL tables
 Azure SQL Database
 Azure Synapse Analytics (formerly SQL DW)
 Cosmos DB
 Coming soon: Snowflake
 If not supported, ingest to staging area via Copy activity
 90+ connectors supported natively

Duplicating data streams
 Duplicate data stream from any
stage of your data flow
 Select ‘New branch’
 Operate on same data with
different transformation
requirements
 Self-joins
 Writing to different sinks
 Aggregating in one branch

Joining two data streams together
 Use Join transformation to append columns from incoming stream to
any stream in your data flow
 Join types: full outer, inner, left outer, right outer, cross
 SQL Join equivalent
 Match on computed columns or use non-equality conditions
 Broadcast small data streams to cache data and improve
performance

Lookup transformation
 Similar to left outer join, but with more functionality
 All incoming rows are passed through regardless of match
 Matching conditions same as a join
 Multi or single row lookup
 Match on all, first, last, or any row that meets join conditions
 isMatch() function can be used in downstream transformations to
verify output

Exists transformation
 Check for existence of a value in another stream
 SQL Exists equivalent
 See if any row matches in a subquery, just like SQL
 Filter based on join matching conditions
 Choose Exist or Not Exist for your filter conditions
 Can specify a custom expressoin

Union transformation
 Combine rows from multiple
streams
 Add as many streams as
needed
 Combine data based upon
column name or ordinal
column position
 Use cases:
 Similar data from different connection
that undergo same transformations
 Writing multiple data streams into the
same sink

Conditional split
 Split data into separate streams
based upon conditions
 Use data flow expression language to
evaluate boolean
 Use cases:
 Sinking subset of data to different
locations
 Perform different calculations on data
depending on a set of values

Derived column
 Transform data at row and column level using expression language
 Generate new or modify existing columns
 Build expressions using the expression builder
 Handle structured or unstructured data
 Use column patterns to match on rules and regular expressions
 Can be used to transform multiple columns in bulk
 Most heavily used transformation

Select transformation
 Metadata and column maintenance
 SQL Select statement
 Alias or renames data stream and columns
 Prune unwanted or duplicate columns
 Common after joins and lookups
 Rule-based mapping for flexible schemas, bulk mapping
 Map hierarchal columns to flat structure

Surrogate key transformation
 Generate incrementing key to use as a non-business key in your data
 To seed the starting value of your surrogate key, use derived column
and a lookup from an existing table
 Examples are in documentation
 Useful for generating keys for star schema dimension tables

Aggregate transformation
 Aggregate data into groups using aggregate function
 Like SQL GROUP BY clause in a Select statement
 Aggregate functions include sum(), max(), avg(), first(), collect()
 Choose columns to group by
 One row for each unique group by column value
 Only columns used in transformation are in output data stream
 Use self-join to append to existing data
 Supports pattern matching

Pivot and unpivot transformations
 Pivot row values into new columns and vice-versa
 Both are aggregate transformations that require aggregate functions
 If pivot key values not specified, all columns become drifted
 Use map drifted quick action to add to schema quickly

Window transformation
 Aggregate data across
“windows” of data partitions
 Used to compare a row of data against
others in its ‘group’
 Group determined by group by
columns, sorting conditions
and range bounds
 Used for ranking rows in a
group and getting lead/lag
 Sorting causes reshuffling of
data
 “Expensive” operation

Filter transformation
 Filter rows based upon an
expression
 Like SQL WHERE clause
 Expressions return true or false

Alter row transformation
 Mark rows as Insert, Update, Delete, or Upsert
 Like SQL MERGE statement
 Insert by default
 Define policies to update your database
 Works with SQL DB, Synapse, Cosmos DB, and Delta Lake
 Specify allowed update methods in each sink

Flatten transformation
 Unroll array values into individual rows
 One row per value
 Used to convert hierarchies to flat structures
 Opposite of collect() aggregate function

Rank transformation
 Rank data across an entire dataset
 Same as Rank() function in Window transformation,but
scales better for a ranking an entire dataset
 For ranking of data partitions, use Window rank()
 For ranking entire dataset, use Rank transformation

Parse transformation
 Parses string data from columns that are formatted
text
 Currently supported formats: JSON, text delimited
 Ex: Turn plain text JSON strings from a source file in
a single column into formatted JSON output

Sink transformation
 Define the properties for landing your data in your destination
target data store
 Define using dataset or in-line
 Can map columns similar to select transformation
 Import schema definition from destination
 Set actions on destinations
 Truncate table or clear folder, SQL pre/post actions, database update methods
 Choose how the written data is partitioned
 Use current partitioning is almost always fastest
 Note: Writing to single file can be very slow with large amounts
of data

Mapping data flow expression language

Visual expression builder
List of columns
being modified
All available
functions, fields,
parameters …
Build expressions
here with full
auto-complete
and syntax
checking
View results of your
expression in the data
preview pane with
live, interactive
results

Expression language
 Expressions are built using the data flow expression language
 Expressions can reference:
 Built-in expression functions
 Defined input schema columns
 Data flow parameters
 Literals
 Certain transformations have unique functions
 Count(), sum() in Aggregate, denseRank() in Window, etc
 Evaluates to spark data types
 100s of transformation, analytical, array, metadata, string, and
mathematical functions available

Debug mode
 Quickly verify logic during development on small interactive cluster
 4 core, 60-minute time to live
 Enables the following:
 Get data preview snapshot at each transformation
 Preview output of expression in expression builder
 Run debug pipeline with no spin up
 Import Spark projection of source schema
 Rule of thumb: If developing Data Flows, turn on right away
 Initial 3-5-minute start up time

Parameterizing data flows
 Both dataset properties and data-flow expressions can be
parameterized
 Passed in via data flow activity
 Can use data flow or pipeline expression language
 Expressions can reference $parameterName
 Can be literal values or column references

Referencing data flow parameters

Schema drift
 In real-world data integration solutions, source/target data stores
change shape
 Source data fields can change names
 Number of columns can change over time
 Traditional ETL processes break when schemas drift
 Mapping data flow has built-in handling for flexible schemas
 Patterns, rule-based mappings, byName(s) function, etc
 Source: Read additional columns on top of what is defined in the source schema
 Sink: Write additional columns on top of what is defined in the sink schema

Column pattern matching
 Match by name, type, stream, position

Operationalizing and monitoring data flows

Data flow activity
 Run as activity in pipeline
 Integrated with existing ADF control flow, scheduling, orchestration, montoring, C/ICD
 Choose which integration runtime (IR) to run on
 # of cores, compute type, cluster time to live
 Assign parameters

Data flow integration runtime
 Integrated with existing Azure IR
 Choose compute type, # of cores, time to live
 Time to live: time a cluster is alive after last execution concludes
 Minimal start up time for sequential data flows
 Parameterize compute type, # of cores if using Auto Resolve

Data flow security considerations
 All data stays inside VMs that run the Databricks cluster which are
spun up JIT for each job
• Azure Databricks attaches storage to the VMs for logging and spill-over from in-memory data frames
during job operation. These storage accounts are fully encrypted and within the Microsoft tenant.
• Each cluster is single-tenant and specific to your data and job. This cluster is not shared with any
other tenant
• Data flow processes are completely ephemeral. Once a job is completed,
all associated resources are destroyed
• Both cluster and storage account are deleted
• Data transfers in data flows are protected using certificates
• Active telemetry is logged and maintained for 45 days for troubleshooting
by the Azure Data Factory team

Data flow best practices and optimizations

Best practices – Lifecycle
1. Test your transformation logic using debug mode and data
preview
 Limited source size or use sample files
2. Test end-to-end pipeline logic using pipeline debug
 Verify data is read/written correctly
 Used as smoke test before merging your changes
3. Publish and trigger your pipelines within a Dev Factory
 Test performance and cluster size
4. Promote pipelines to higher environments such as UAT and PROD
using CI/CD
 Increase size and scope of data as you get to higher environments

Best practices – Debug (Data Preview)
 Data Preview
 Data preview is inside the data flow designer transformation properties
 Uses row limits and sampling techniques to preview data from a small size of data
 Allows you to build and validate units of logic with samples of data in real time
 You have control over the size of the data limits under Debug Settings
 If you wish to test with larger datasets, set a larger compute size in the Azure IR when
switching on “Debug Mode”
 Data Preview is only a snapshot of data in memory from Spark data frames. This feature does
not write any data, so the sink drivers are not utilized and not tested in this mode.

Best practices – Debug (Pipeline Debug)
 Pipeline Debug
 Click debug button to test your data flow inside of a pipeline
 Default debug limits the execution runtime so you will want to limit data sizes
 Sampling can be applied here as well by using the “Enable Sampling” option in each Source
 Use the debug button option of “use activity IR” when you wish to use a job execution
compute environment
 This option is good for debugging with larger datasets. It will not have the same execution timeout limit as the
default debug setting

Optimizing data flows
 Transformation order generally does not matter
 Data flows have a Spark optimizer that reorders logic to perform as best as it can
 Repartitioning and reshuffling data negates optimizer
 Each transformation has ‘Optimize’ tab to control partitioning
strategies
 Generally do not need to alter
 Altering cluster size and type has performance impact
 Four components
1. Cluster startup time
2. Reading from sources
3. Transformation time
4. Writing to sinks

Identifying bottlenecks
1. Cluster startup time
2. Sink processing time
3. Source read time
4. Transformation stage time
1. Sequential executions can
lower the cluster startup time
by setting a TTL in Azure IR
2. Total time to process the
stream from source to sink.
There is also a post-processing
time when you click on the Sink
that will show you how much
time Spark had to spend with
partition and job clean-up.
Write to single file and slow
database connections will
increase this time
3. Shows you how long it took to
read data from source.
Optimize with different source
partition strategies
4. This will show you bottlenecks
in your transformation logic.
With larger general purpose
and mem optimized IRs, most
of these operations occur in
memory in data frames and are
usually the fastest operations
in your data flow

Best practices - Sources
 When reading from file-based sources, data flow automatically
partitions the data based on size
 ~128 MB per partition, evenly distributed
 Use current partitioning will be fastest for file-based and Synapse using PolyBase
 Enable staging for Synapse
 For Azure SQL DB, use Source partitioning on column with high
cardinality
 Improves performance, but can saturate your source database
 Reading can be limited by the I/O of your source

Optimizing transformations
 Each transformation has its own optimize tab
 Generally better to not alter -> reshuffling is a relatively slow process
 Reshuffling can be useful if data is very skewed
 One node has a disproportionate amount of data
 For Joins, Exists and Lookups:
 If you have a lot, memory optimized greatly increases performance
 Can ‘Broadcast’ if the data on one side is small
 Rule of thumb: Less than 50k rows
 Increasing integration runtime can speed up transformations
 Transformations that require reshuffling like Sort negatively impact
performance

Best practices - Sinks
 SQL:
 Disable indexes on target with pre/post SQL scripts
 Increase SQL capacity during pipeline execution
 Enable staging when using Synapse
 File-based sinks:
 Use current partitioning allows Spark to create output
 Output to single file is a very slow operation
 Combines data into single partition
 Often unnecessary by whoever is consuming data
 Can set naming patterns or use data in column
 Any reshuffling of data is slow
 Cosmos DB
 Set throughput and batch size to meet performance requirements

Azure Integration Runtime
 Data Flows use JIT compute to minimize running expensive clusters
when they are mostly idle
 Generally more economical, but each cluster takes ~4 minutes to spin up
 IR specifies what cluster type and core-count to use
 Memory optimized is best, compute optimized doesn’t generally work for production workloads
 When running Sequential jobs utilize Time to Live to reuse cluster
between executions
 Keeps cluster alive for TTL minutes after execution for new job to use
 Maximum one job per cluster
 Rule of thumb: start small and scale up

Data flow script (DFS)
 DFS defines the logical intent of your data transformations
 Script is bundled and marshalled to Spark cluster as a job for
execution
 DFS can be auto-generated and used for programmatic creation of
data flows
 Access script behind UI via “Script” button
 Click “Copy as Single Line” to save version of script that is ready for
JSON
 https://docs.microsoft.com/en-us/azure/data-factory/data-flow-
script

Source projection (1)
Source properties
Unpivot transformation (3)
Aggregate Transformation (2)
Sort (4)
Sink (5)
1 2 3 4 5
• Syntax: input_name transform_type(properties)
~> stream_name

1 2
3
4
5
6
Source projection (1)
Source properties
Distinct Aggregate (3)
Row Count Agg (5)
Select transformation mappings (2)
Select properties (2)
Row Count Agg (4)
Sink transformation (6)
• ~> name_of_transform
• New branch does not require any script
element

ETL Tool Migration Overview
 Migrating from an existing large enterprise ETL installation to ADF and data flows requires
adherence to a formal methodology that incorporates classic SDLC, change management,
project management, and a deep understanding of your current data estate and ETL
requirements.
 Successful migration projects require project plans, executive sponsorship, budget, and a
dedicated team to focus on rebuilding the ETL in ADF.
 For existing on-prem ETL estates, it is very important to learn basics of Cloud, Azure, and ADF
generally before taking this Data Flows training.

Training
• On-prem to Cloud, Azure general training, ADF general training, Data Flows training
• A general understanding of the different between legacy client/server on-prem ETL
architectures and cloud-based Big Data processing is required
• ADF and Data Flows execute on Spark, so learn the fundamentals of the different between
row-by-row processing on a local server and batch/distributed computing on Spark in the
Cloud

Execution
• Start with the top 10 mission-critical ETL mappings and list out the primary logical goals and
steps achieved in each
• Use sample data and debug each scenario as new pipelines and data flows in ADF
• UAT each of those 10 mappings in ADF using sample data
• Lay out end-to-end project plan for remaining mapping migrations
• Plan the remainder of the project into quarterly calendar milestones
• Except each phase to take around 3 months
• Majority of large existing ETL infrastructure modernization migrations take 12-18 months to
complete

ETL System Integrator Partners
 Bitwise Global
 https://www.bitwiseglobal.com/webinars/automated-etl-conversion-to-adf-for-accelerated-data-warehouse-
migration-on-azure/
 Next Pathway
 https://blog.nextpathway.com/next-pathway-adds-ground-breaking-capability-to-translate-
informatica-and-datastage-etl-pipelines-to-the-cloud-in-latest-version-of-shift

Additional
resources
Documentation
List of tutorial videos
Expression language reference
Performance guide
ADF twitter
ADF tech community blog

Mapping Data Flows Training April 2021

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