Azure Data Factory V2 – Incremental loading with configuration stored in a table – Complete solution, step by step.

Michał Pawlikowski

This article is obsolete. A lot has changed since 2018, both the documentation and ADF contain a lot of key information, so I recommend that you refer to the official sources like “Delta copy”, or “ADF templates” link: https://docs.microsoft.com/en-us/azure/data-factory/solution-template-delta-copy-with-control-table

This post explains things that are difficult to find even in English. That’s why I will break my rule and will not write it in my native language! Po wersję polską zapraszam do google translate :>

Introduction

Loading data using Azure Data Factory v2 is really simple. Just drop Copy activity to your pipeline, choose a source and sink table, configure some properties and that’s it – done with just a few clicks!

But what if you have dozens or hundreds of tables to copy? Are you gonna do it for every object?

Fortunately, you do not have to do this! All you need is dynamic parameters and a few simple tricks 🙂

Also, this will give you the option of creating incremental feeds, so that – at next run – it will transfer only newly added data.

Mappings

Before we start diving into details, let’s demystify some basic ADFv2 mapping principles.

  • Copy activity doesn’t need to have defined column mappings at all,
  • it can dynamically map them using its own mechanism which retrieves source and destination (sink) metadata,
  • if you use polybase, it will do it using column order (1st column from source to 1st column at destination etc.),
  • if you do not use polybase, it will map them using their names but watch out – it’s case sensitive matching!
  • So all you have to do is to just keep the same structure and data types on the destination tables (sink), as they are in a source database.

Bear in mind, that if your columns are different between source and destination, you will have to provide custom mappings. This tutorial doesn’t show how to do it, but it is possible to pass them using “Get metadata” activity to retrieve column specification from the source, then you have to parse it and pass as JSON structure into the mapping dynamic input. you can read about mappings in official documentation: https://docs.microsoft.com/en-us/azure/data-factory/copy-activity-schema-and-type-mapping

String interpolation – the key to success

My entire solution is based on one cool feature, that is called string interpolation. It is a part of built-in expression engine, that simply allows you to just inject any value from JSON object or an expression directly into string input, without any concatenate functions or operators. It’s fast and easy. Just wrap your expression between  @{ ... } . It will always return it as a string.

Below is a screen from official documentation, that clarifies how this feature works:

Read more about JSON expressions at https://docs.microsoft.com/en-us/azure/data-factory/control-flow-expression-language-functions#expressions

 

So what we are going to do? :>

Good question 😉

In my example, I will show you how to transfer data incrementally from Oracle and PostgreSQL tables into Azure SQL Database.

All of this using configuration stored in a table, which in short, keeps information about Copy activity settings needed to achieve our goal 🙂

Adding new definitions into config will also automatically enable transfer for them, without any need to modify Azure Data Factory pipelines.

So you can transfer as many tables as you want, in one pipeline, at once. Triggering with one click 🙂

 

Every process needs diagram :>

 

 

Basically, we will do:

  1. Get configuration from our config table inside Azure SQL Database using Lookup activity, then pass it to Filter activity to split configs for Oracle and PostgreSQL.
  2. In Foreach activity created for every type of database, we will create simple logic that retrieves maximum update date from every table.
  3.  Then we will prepare dynamically expressions for SOURCE and SINK properties in Copy activity. MAX UPDATEDATE, retrieved above, and previous WATERMARK DATE, retrieved from config, will set our boundaries in WHERE clause. Every detail like table name or table columns we will pass as a query using string interpolation, directly from JSON expression. Sink destination will be also parametrized.
  4. Now Azure Data Factory can execute queries evaluated dynamically from JSON expressions, it will run them in parallel just to speed up data transfer.
  5. Every successfully transferred portion of incremental data for a given table has to be marked as done. We can do this saving MAX UPDATEDATE in configuration, so that next incremental load will know what to take and what to skip. We will use here: Stored procedure activity.
This example simplifies the process as much as it is possible. Remember, in your solution you have to implement logic for every unsuccessful operation. You can achieve that using On Failure control flow with some activities (chosen depending on your needs) and timeout/retry options set individually for every activity in your pipeline.

 

About sources

I will use PostgreSQL 10 and Oracle 11 XE installed on my Ubuntu 18.04 inside VirtualBox machine.

In Oracle, tables and data were generated from EXMP/DEPT samples delivered with XE version.

In PostgreSQL – from dvd rental sample database: http://www.postgresqltutorial.com/postgresql-sample-database/

 

I simply chose three largest tables from each database. You can find them in a configuration shown below this section.

 

Every database is accessible from my Self-hosted Integration Runtime. I will show an example how to add the server to Linked Services, but skip configuring Integration Runtime. You can read about creating self-hosted IR here: https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime.

 

About configuration

In my Azure SQL Database I have created a simple configuration table:

Id is just an identity value, SRC_name is a type of source server (ORA or PG).

SRC and DST tab columns maps source and destination objects. Cols defines selected columns, Watermark Column and Value stores incremental metadata.

And finally Enabled just enables particular configuration (table data import).

As Andy rightly noted in the comment below this post, it is possible to use “Cols” also to implement SQL logic, like functions, aliases etc. The value from this column is rewritten directly to the query (more precisely – concatenated between SELECT and FROM clause). So you can use it according to your needs.

 

This is how it looks with initial configuration:

Create script:

Transact-SQL
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
SET ANSI_NULLS ON
GO
 
SET QUOTED_IDENTIFIER ON
GO
 
CREATE TABLE [load].[cfg](
[id] [SMALLINT] IDENTITY(1,1) NOT NULL,
[SRC_name] [NVARCHAR](128) NOT NULL,
[SRC_tab] [NVARCHAR](128) NOT NULL,
[DST_tab] [NVARCHAR](128) NOT NULL,
[Cols] [NVARCHAR](MAX) NOT NULL,
[WatermarkColumn] [NVARCHAR](128) NOT NULL,
[WatermarkValue] [DATETIME] NOT NULL,
[Enabled] [BIT] NOT NULL,
CONSTRAINT [PK_load] PRIMARY KEY CLUSTERED
(
[id] ASC
)WITH (STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF) ON [PRIMARY]
) ON [PRIMARY] TEXTIMAGE_ON [PRIMARY]
GO
 
ALTER TABLE [load].[cfg] ADD  CONSTRAINT [DF__cfg__WatermarkVa__4F7CD00D]  DEFAULT ('1900-01-01') FOR [WatermarkValue]
GO

 

EDIT 19.10.2018

Microsoft announced, that now you can parametrize also linked connections!

https://azure.microsoft.com/en-us/blog/parameterize-connections-to-your-data-stores-in-azure-data-factory/

Let’s get started (finally :P)

Preparations!

Go to your Azure Data Factory portal @ https://adf.azure.com/

Select Author button with pencil icon:

 

Creating server connections (Linked Services)

We can’t do anything without defining Linked Services, which are just connections to your servers (on-prem and cloud).

  1. Go to   and click 
  2. Find your database type, select and click 
  3. Give all needed data, like server ip/host, port, SID (Oracle need this), login and password.
  4. You can  if everything is ok. Click Finish to save your connection definition.
Some types of servers, such as PostgreSQL or MySQL, require separate .NET drivers. Check your server type here in Microsoft Docs and search for Prerequisites to match your scenario.

I have created three connections. Here are their names and server types:

 

Creating datasets

Creating linked services is just telling ADF what are connection settings (like connection strings).

Datasets, on the other hand, points directly to database objects.

BUT they can be parametrized, so you can just create ONE dataset and use it passing different parameters to get data from multiple tables within same source database 🙂

Source datasets

Source datasets don’t need any parameters. We will later use built-in query parametrization to pass object names.

  1. Go to   and click + and choose 
  2. Choose your datataset type, for example 
  3. Rename it just as you like. We will use name: “ORA”
  4. Set proper Linked service option, just like this for oracle database: 
  5. And that’s it! No need to set anything else. Just repeat these steps for every source database, that you have.

In my example, I’ve created two source datasets, ORA and PG

As you can see, we need to create also the third dataset. It will work as a source too, BUT also as a parametrizable sink (destination). So creating it is little different than others.

Sink dataset

Sinking data needs one more extra parameter, which will store destination table name.

  1. Create dataset just like in the previous example, choose your destination type. In my case, it will be Azure SQL Database.
  2. Go to , declare one String parameter called “TableName”. Set the value to anything you like. It’s just dummy value, ADF just doesn’t like empty parameters, so we have to set a default value.
  3. Now, go to , set Table as dynamic content. This will be tricky :). Just click “Select…”, don’t choose any value, just click somewhere in empty space. The magic option “Add dynamic content” now appears! You have to click it or hit alt+p. 
  4. “Add Dynamic Content” windows is now visible. Type: “@dataset().TableName” or just click “TableName” in “Parameters” section below “Functions”.
  5. The table name is now parameterized. And looks like this: 

 

Parametrizable PIPELINE with dynamic data loading.

Ok, our connections are defined. Now it’s time to copy data :>

 

Creating pipeline

  1. Go to you ADF and click PLUS symbol near search box on the left and choose “Pipeline“: 
  2. Reanme it. I will use “LOAD DELTA“.
  3. Go to Parameters, create new String parameter called ConfigTable. Set value to our configuration table name: load.cfg . This will simply parametrize you configuration source. So that in the future it would be possible to load a completely different set of sources by changing only one parameter :>
  4. In case you missed it, SAVE your work by clicking “Save All” if you’re using GIT or “Publish All” if not ;]

 

Creating Lookup – GET CFG

First, we have to get configuration. We will use Lookup activity to retrieve it from the database.

Bear in mind, that lookup activity has some limits. Currently, the maximum number of rows, that can be returned by Lookup activity is 5000, and up to 2MB in size. Also max duration for Lookup activity before timeout is one hour. Go to documentation for latest info and updates.
    1. Drag and drop  into your pipline
    2. Rename it. This is important, we will use this name later in our solution. I will use value “GET CFG“.
    3. In “Settings” choose 
    4. Now, don’t bother TableName set to dummy :> Just in “Use Query” set to “Query“, click “Add dynamic content” and type:
      JavaScript
      1
      2
      SELECT * from @{pipeline().parameters.ConfigTable}
      IF @@ROWCOUNT = 0 THROW 50000,'No rows in configuration table!',1
    5. Unmark “First row only“, we need all rows, not just first. All should look like this:

 

Creating Filters – ORA CFG & PG CFG

Now we have to split configs for oracle and PostgreSQL. We will use Filter activity on rows retrieved in “GET CFG” lookup.

  1. Drag and drop twice.
  2. Rename the first block to “ORA CFG“, second to “PG CFG“.
  3. Now go to “ORA CFG“, then “Settings“.
  4. In Items, click Add dynamic content and type:  @activity('GET CFG').output.value . As you probably guess, this will point directly to GET CFG output rows 🙂
  5. In Condition, click Add dynamic content and  type: @equals(item().SRC_name,'ORA') . We have to match rows for oracle settings. So we know, that there is a column in config table called “SRC_name“. We can use it to filter out all rows, except that with value ‘ORA’ 🙂 .
  6. Do the same with lookup activity “PG CFG“. Of course, change the value for a condition.

It should look like this:

Creating ForEach – FOR EACH ORA & FOR EACH PG

Now it’s time to iterate over each row filtered in separate containers (ORA CFG and PG CFG).

  1. Drag and drop two  blocks, rename them as “FOR EACH ORA” and “FOR EACH PG“. Connect each to proper filter acitivity. Just like in this example:  
  2. Click “FOR EACH ORA“, go to “Settings“, in Items clik Add dynamic content and type:  @activity('ORA CFG').output.value . We are telling ForEach, that it has to iterate over results returned in “ORA CFG”. They are stored in JSON array.
  3. Do this also in FOR EACH PG. Type:  @activity('PG CFG').output.value
  4. Now, you can edit Activities and add only “WAIT”  activity to debug your pipeline. I will skip this part. Just remember to delete WAIT block at the end of your tests.

 

Inside ForEach – GET MAX ORA -> COPY ORA -> UPDATE WATERMARK ORA

Place these blocks into FOR EACH ORA. Justo go there, click “Activities” and then 

Every row, that ForEach activity is iterating over, is accessible using @item() .

And every column in that row, can be reached just by using  @item().ColumnName .

Remember, that you can surround every expression in brackets @{ }  to use it as a string interpolation. Then you can concatenate it with other strings and expressions just like that:  Value of the parameter WatermarkColumn is: @{item().WatermarkColumn}

 

GET MAX ORA

  1. Go to “GET MAX ORA“, then Settings
  2. Choose your source dataset “ORA“, Use Query: “Query” and click Add dynamic content
  3. Type  SELECT MAX(@{item().WatermarkColumn}) as maxd FROM @{item().SRC_tab} . This will get a maximum date in your watermark column. We will use it as RIGHT BOUNDRY for delta slice.
  4. Check if  First row only is turned on.

It should look like this:

 

COPY ORA

Now the most important part :> Copy activity with a lot of parametrized things… So pay attention, it’s not so hard to understand but every detail matters.

Source

  1. In source settings, choose Source Dataset to ORA, in Use query select Query.
  2. Below Query input, click Add dynamic content and paste this:
    JavaScript
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    SELECT
    @{item().Cols} FROM @{item().SRC_tab}
     
    WHERE
     
    @{item().WatermarkColumn} >
    TO_DATE('@{item().WatermarkValue}', 'YYYY-MM-DD"T"HH24:MI:SS"Z"')
    AND
    @{item().WatermarkColumn} <=
    TO_DATE('@{activity('GET MAX ORA').output.firstRow.MAXD}', 'YYYY-MM-DD"T"HH24:MI:SS"Z"')

Now, this needs some explanation 🙂

 

 

  • ORA CFG output has all columns and their values from our config.
  • We will use SRC_tab as table name, Cols as columns for SELECT query, WatermatkColumn as LastChange DateTime column name and WatermarkValue for LEFT BOUNDRY (greater than, >).
  • GET MAX ORA output stores date of a last updated row in the source table. So this is why we are using it as a RIGHT BOUNDRY (less than or equal, <=)
  • And the tricky thing, ORACLE doesn’t support implicit conversion from the string with ISO 8601 date. So we need to extract it properly with TO_DATE function.

So the source is a query from ORA dataset:

 

Sink

Sink is our destination. Here we will set parametrized table name and truncate query.

  1. Select 
  2. Parametrize TableName as dynamic content with value:  @{item().DST_tab}
  3. Also, do the same with Pre-copy script and put there:  TRUNCATE TABLE @{item().DST_tab}
As De jan properly noticed in comments, you are not obligated to use any Pre-copy script here. You can leave this box empty or run other commands like partitions switching.

It should look like this:

 

Mappings and Settings

All other things should just be set to defaults. You don’t have to parametrize mappings if you just copy data from and to tables that have the same structure.

Of course, you can dynamically create them if you want, but it is a good practice to transfer data 1:1 – both structure and values from source to staging.

 

UPDATE WATERMARK ORA

Now we have to confirm, that load has finished and then update previous watermark value with the new one.

We will use a stored procedure. The code is simple:

Transact-SQL
1
2
3
4
5
6
7
8
9
10
11
12
CREATE PROC [load].[usp_UpdateWatermark]
    @id SMALLINT,
    @NewWatermark DATETIME
AS
SET NOCOUNT ON;
 
UPDATE load.cfg
SET WatermarkValue = @NewWatermark
WHERE id = @id;
 
 
GO

Create it on your Azure SQL database. Then use it in ADF:

  1. Drop  into project, connect constraint from COPY ORA into it. Rename as “UPDATE WATERMARK ORA” and view properties.
  2. In SQL Account set 
  3. Now go to “Stored Procedure”, select our procedure name and click “Import parameter”.
  4. Now w have to pass values for procedure parametrs. And we will also parametrize them. Id should be  @{item().id}  and NewWatermatk has to be:  @{activity('GET MAX ORA').output.firstRow.MAXD} .

 

And basically, that’s all! This logic should copy rows from all Oracle tables defined in the configuration.

We can now test it. This can be done with “Debug” or just by triggering pipeline run.

If everything is working fine, we can just copy/paste all content from “FOR EACH ORA” into “FOR EACH PG“.

Just remember to properly rename all activities to reflect new source/destination names (PG). Also, all parameters and SELECT queries have to be redefined. Luckily PostgreSQL support ISO dates out of the box.

Source code

Here are all components in JSON. You can use them to copy/paste logic directly inside ADF V2 code editor or save as files in GIT repository.

Below is source code for pipeline only. All other things can be downloaded in zip file in “Download all” at the bottom of this article.

Pipeline

JavaScript
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
{
"name": "LOAD DELTA",
"properties": {
"activities": [
{
"name": "GET CFG",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "SqlSource",
"sqlReaderQuery": {
"value": "SELECT * from @{pipeline().parameters.ConfigTable}\nIF @@ROWCOUNT = 0 THROW 50000,'ojej...',1",
"type": "Expression"
}
},
"dataset": {
"referenceName": "SQL",
"type": "DatasetReference",
"parameters": {
"TableName": "dummy"
}
},
"firstRowOnly": false
}
},
{
"name": "FOR EACH ORA",
"type": "ForEach",
"dependsOn": [
{
"activity": "ORA CFG",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "@activity('ORA CFG').output.value",
"type": "Expression"
},
"isSequential": false,
"activities": [
{
"name": "COPY ORA",
"type": "Copy",
"dependsOn": [
{
"activity": "GET MAX ORA",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"userProperties": [
{
"name": "Destination",
"value": "@{item().DST_tab}"
}
],
"typeProperties": {
"source": {
"type": "OracleSource",
"oracleReaderQuery": {
"value": "SELECT \n @{item().Cols} FROM @{item().SRC_tab} \n\nWHERE \n\n@{item().WatermarkColumn} > \nTO_DATE('@{item().WatermarkValue}', 'YYYY-MM-DD\"T\"HH24:MI:SS\"Z\"')\nAND\n@{item().WatermarkColumn} <=\nTO_DATE('@{activity('GET MAX ORA').output.firstRow.MAXD}', 'YYYY-MM-DD\"T\"HH24:MI:SS\"Z\"')",
"type": "Expression"
}
},
"sink": {
"type": "SqlSink",
"writeBatchSize": 10000,
"preCopyScript": {
"value": "TRUNCATE TABLE @{item().DST_tab}",
"type": "Expression"
}
},
"enableStaging": false,
"cloudDataMovementUnits": 0
},
"inputs": [
{
"referenceName": "ORA",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "SQL",
"type": "DatasetReference",
"parameters": {
"TableName": {
"value": "@{item().DST_tab}",
"type": "Expression"
}
}
}
]
},
{
"name": "GET MAX ORA",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "OracleSource",
"oracleReaderQuery": {
"value": "SELECT MAX(@{item().WatermarkColumn}) as maxd FROM @{item().SRC_tab} ",
"type": "Expression"
}
},
"dataset": {
"referenceName": "ORA",
"type": "DatasetReference"
}
}
},
{
"name": "UPDATE WATERMARK ORA",
"type": "SqlServerStoredProcedure",
"dependsOn": [
{
"activity": "COPY ORA",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"storedProcedureName": "[load].[usp_UpdateWatermark]",
"storedProcedureParameters": {
"id": {
"value": {
"value": "@{item().id}",
"type": "Expression"
},
"type": "Int16"
},
"NewWatermark": {
"value": {
"value": "@{activity('GET MAX ORA').output.firstRow.MAXD}",
"type": "Expression"
},
"type": "DateTime"
}
}
},
"linkedServiceName": {
"referenceName": "AzureSQL",
"type": "LinkedServiceReference"
}
}
]
}
},
{
"name": "ORA CFG",
"type": "Filter",
"dependsOn": [
{
"activity": "GET CFG",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "@activity('GET CFG').output.value",
"type": "Expression"
},
"condition": {
"value": "@equals(item().SRC_name,'ORA')",
"type": "Expression"
}
}
},
{
"name": "PG CFG",
"type": "Filter",
"dependsOn": [
{
"activity": "GET CFG",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "@activity('GET CFG').output.value",
"type": "Expression"
},
"condition": {
"value": "@equals(item().SRC_name,'PG')",
"type": "Expression"
}
}
},
{
"name": "FOR EACH PG",
"type": "ForEach",
"dependsOn": [
{
"activity": "PG CFG",
"dependencyConditions": [
"Succeeded"
]
}
],
"typeProperties": {
"items": {
"value": "@activity('PG CFG').output.value",
"type": "Expression"
},
"isSequential": false,
"activities": [
{
"name": "Copy PG",
"type": "Copy",
"dependsOn": [
{
"activity": "GET MAX PG",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"userProperties": [
{
"name": "Destination",
"value": "@{item().DST_tab}"
}
],
"typeProperties": {
"source": {
"type": "RelationalSource",
"query": {
"value": "SELECT @{item().Cols} FROM @{item().SRC_tab} \n\nWHERE \n\n@{item().WatermarkColumn} > \n'@{item().WatermarkValue}'\nAND\n@{item().WatermarkColumn} <=\n'@{activity('GET MAX PG').output.firstRow.MAXD}'",
"type": "Expression"
}
},
"sink": {
"type": "SqlSink",
"writeBatchSize": 10000,
"preCopyScript": {
"value": "TRUNCATE TABLE @{item().DST_tab}",
"type": "Expression"
}
},
"enableStaging": false,
"cloudDataMovementUnits": 0
},
"inputs": [
{
"referenceName": "PG",
"type": "DatasetReference"
}
],
"outputs": [
{
"referenceName": "SQL",
"type": "DatasetReference",
"parameters": {
"TableName": {
"value": "@{item().DST_tab}",
"type": "Expression"
}
}
}
]
},
{
"name": "GET MAX PG",
"type": "Lookup",
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"source": {
"type": "RelationalSource",
"query": {
"value": "SELECT MAX(@{item().WatermarkColumn}) as maxd FROM @{item().SRC_tab} ",
"type": "Expression"
}
},
"dataset": {
"referenceName": "PG",
"type": "DatasetReference"
}
}
},
{
"name": "UPDATE WATERMARK PG",
"type": "SqlServerStoredProcedure",
"dependsOn": [
{
"activity": "Copy PG",
"dependencyConditions": [
"Succeeded"
]
}
],
"policy": {
"timeout": "7.00:00:00",
"retry": 0,
"retryIntervalInSeconds": 30,
"secureOutput": false
},
"typeProperties": {
"storedProcedureName": "[load].[usp_UpdateWatermark]",
"storedProcedureParameters": {
"id": {
"value": {
"value": "@{item().id}",
"type": "Expression"
},
"type": "Int16"
},
"NewWatermark": {
"value": {
"value": "@{activity('GET MAX PG').output.firstRow.MAXD}",
"type": "Expression"
},
"type": "DateTime"
}
}
},
"linkedServiceName": {
"referenceName": "AzureSQL",
"type": "LinkedServiceReference"
}
}
]
}
}
],
"parameters": {
"ConfigTable": {
"type": "String",
"defaultValue": "load.cfg"
}
}
}
}
 

Download all

IncrementalCopy_ADFv2.zip

 

130 thoughts on “Azure Data Factory V2 – Incremental loading with configuration stored in a table – Complete solution, step by step.”

  1. Hi Michal..good afternoon. I am working of change tracking mechanism for my project. We are on Hybrid Model with Azure. We are modernizing all our legacy applications. I already completed working on initial data loads from On-Prem multiple sources to new system (SQLMI on Azure). Now I have to implement change tracking mechanism …(When ever there are Updates and Inserts to the On-Prem sources…I should run the change tracking mechanism process on ADF to do the delta loads). I did a POC on ADF for a single source and single target…my ADF pipeline loads new inserts but is not doing Updated columns. Please help. I need to develop a complex process with multiple data sources and multiple destination now.

    Reply

    1. Hi Lalitha,

      Are there any watermark columns available in your On-prem tables ? If yes , then you can use Merge query to take care of your Update and Insert.

      Obviously , you need to create a Dynamic Merge Store Procedure. So , do a delta copy from Source to a Staging layer and from Staging Layer do a Merge

      Hope this helps.

      Regards,
      Ashish

      Reply

  3. Simply to the point for the implementation. I did it and it is working as expected.

    Great Article Michal !!!

    Reply

  4. Yes Michal. It was really.

    I am having a project requirement where I need to source the data from different source systems like On-Premise SQL Server, SalesForce, SAP System, SharePoint, SFTP Drive. I was thinking about how I can implement it in ADF 2 with some simplification (The control table approach)

    The approach here mentioned was really helpful and I am thinking to implement the same for my project requirements (For the Structured source systems like SQL Server, Salesforce & SAP System).

    Reply

    1. Cool, glad to hear that 🙂 They (Microsoft) should use it as general template 😀 Anyway, you can always create the template yourself and use it multiple times in your pipelines.
      Good luck in your projects!

      Reply

  7. impressive. I am new to ADF. and looking for delta and truncate reload pipelines. this one is great.
    without using blob storage.

    my case is from one source MSSQL(on-prem) to Azure DataWarehouse. will this ADF pipeline work as well? Please let me know.

    Reply

    1. Hi eduardo,
      Sure, it should work 🙂
      As a matter of fact, the example above was originally created on Azure Data Warehouse in the project I was involved in. We had to load data from different sources (mysql, oracle, mssql) and different tables.

      Reply

  8. Hi Michael,

    I am following along your ADF implementation. I only have one source for the time being and is MSSQL
    server on-prem to AZURE DW. QQ, I am in the part of COPY ORA (in my CASE COPY on-prem).
    How do I convert this query to just MSSQL server. Instead of to_date (which i oracle specific) in my case
    in MSSQL server and is datetime and display like this .(2019-09-18 12:18:24.617). Any help highly appreciated.

    SELECT
    @{item().Cols} FROM @{item().SRC_tab}

    WHERE

    @{item().WatermarkColumn} >
    TO_DATE(‘@{item().WatermarkValue}’, ‘YYYY-MM-DD”T”HH24:MI:SS”Z”‘)
    AND
    @{item().WatermarkColumn} <=
    TO_DATE('@{activity('GET MAX ONPREM').output.firstRow.MAXD}', 'YYYY-MM-DD"T"HH24:MI:SS"Z"')

    Reply

  9. Michal,

    when i debug it, it tells me that COPY ORA (in my case ONPREM) TABLE IS REQUIRED FOR COPY
    ACTIVITY. Below is the query for my source. TAB.

    SELECT
    @{item().Cols} FROM @{item().SRC_tab}

    WHERE

    @{item().WatermarkColumn} >
    CONVERT(‘@{item().WatermarkValue}’, ‘YYYY-MM-DD”T”HH24:MI:SS”Z”‘)
    AND
    @{item().WatermarkColumn} <=
    CONVERT('@{activity('GET MAX ONPREM').output.firstRow.MAXD}', 'YYYY-MM-DD"T"HH24:MI:SS"Z"')

    and SINK TAB is set to
    @{item().DST_tab}

    Any Help highly appreciated.

    Reply

    1. Hi Daniel,
      TO_DATE was just the way to convert string to ISO 8601 date in ORACLE. The lack of support native iso dates, makes it harder to just convert it. But they allow us to manually set the string, so the ugly YYYY-MM-DD"T"HH24:MI:SS"Z" was the only way to make it possible 🙂

      And in SQL Server things are different. There is a native support for ISO 8601, also with timezone which is ZULU in our case (the letter ‘Z’ at the end) and the ZULU means it is in GMT timezone which means UTC(UCT) standard 🙂
      Well, to make it easier to understand, let’s quote Wikipedia:

      Zulu (short for “Zulu time”) is used in the military and in navigation generally as a term for Universal Coordinated Time (UCT), sometimes called Universal Time Coordinated ( UTC ) or Coordinated Universal Time (but abbreviated UTC), and formerly called Greenwich Mean Time.

      Anyway, to properly convert string value from ADF into a proper DATETIME or SMALLDATETIME (the only supported types here) you must reference the documentation: CAST and CONVERT (Transact-SQL)

      Look at the notation of CONVERT function and date convert number 127 – ISO8601 with time zone Z. You can easily test the conversion on SQL Server:


      DECLARE @string VARCHAR(100) = '2019-10-21T10:01:22Z';

      SELECT CONVERT(DATETIME, @string, 127);

      The result will be: 2019-10-21 10:01:22.000

      Now, use this convertion in your example, without using explicit format string like in Oracle’s TO_DATE and with a proper style number 127, something like this:


      SELECT
      @{item().Cols} FROM @{item().SRC_tab}

      WHERE

      @{item().WatermarkColumn} >
      CONVERT(DATETIME, '@{item().WatermarkValue}', 127)
      AND
      @{item().WatermarkColumn} <= CONVERT(DATETIME,'@{activity('GET MAX ONPREM').output.firstRow.MAXD}', 127)

      I do not have this possibility to test it now, but it should work. Please, check it and tell me what was the result 🙂
      Regards,
      m.

      Reply

  10. Michal,

    Great explanation. it does compiled. However, when I run debug, I still get TABLE IS REQUIRED FOR COPY ACTIVITY.

    this is the sink activity.

    “sink”: {
    “type”: “SqlDWSink”,
    “allowPolyBase”: false,
    “preCopyScript”: “TRUNCATE TABLE @{item().DST_tab}”,
    “disableMetricsCollection”: false
    },
    “enableStaging”: false
    },
    “inputs”: [
    {
    “referenceName”: “ONPREM”,
    “type”: “DatasetReference”
    }
    ],
    “outputs”: [
    {
    “referenceName”: “AzureSqlDW”,
    “type”: “DatasetReference”,
    “parameters”: {
    “TableName”: {
    “value”: “@{item().DST_tab}”,
    “type”: “Expression”

    Reply

Leave a Reply