Deepak Kaushik Saskatoon, SK, Canada Email: kaushik.deepak@outlook.comLinkedIn: linkedin.com/in/davekaushikTwitter: ThinkForDeepakWebsites: deepak-kaushik.com (Portfolio) c-sharpcorner.com/members/deepak-kaushik (Blog) About Mentor: 4X Microsoft Azure MVPAzure Architect and AdvisorInternational Speaker, Trainer & Industry ExpertTOGAF Certified Architect Multiple Microsoft technology certificationsVarious publications & blogs Education: Master of Science in Information Technology, India
SAS Certified Advanced Programmer with experience in data engineering, Data analysis, Business Intelligence
Data Science Enthusiast with experience in Retail, Insurance, Agriculture, Banking & Finance domain
Education:
Master of Engineering from, DAVV, India (Assessed by WES)
Pursuing Master of Science in Data Science with IIITB, India and LJMU, UK.
The objective of Mentorship –
I am starting my cloud journey with Industry experts like Deepak. The objective is to learn the fundamentals of cloud and apply the gained knowledge in Industry-specific projects & POCs.
Understand & build Architecture of Azure Data Factory based applications
Understand and apply various applications of Azure Data Factory in the industry
Learn & apply Data Visualization techniques using Power BI
Learn various concepts used in Azure Data engineering & Azure AI & ML certification preparation
Another architect, a friend of mine, requested me for assistance with Change Data Capture with Data Brick. I assisted him and realized it might be valuable to others as well, so I decided to share the architecture with the team.
The Delta change data feed represents row-level changes between versions of a Delta table. When enabled on a Delta table, the runtime records “change events” for all the data written into the table. This includes the row data along with metadata indicating whether the specified row was inserted, deleted, or updated.
Create a silver table that contains a list of addresses
We are going to use this table to simulate appends and update commands that are common for transactional workloads.
%sql
CREATE OR REPLACE TEMPORARY VIEW updates
as
select 11 primaryKey, "A updated address" as address, true as current, "2021-10-27" as effectiveDate, null as endDate
union
select 99 primaryKey, "A completely new address" as address, true as current, "2021-10-27" as effectiveDate, null as endDate;
SELECT * FROM updates;
primaryKey
address
current
effectiveDate
endDate
1
2
11
A updated address
true
2021-10-27
null
99
A completely new address
true
2021-10-27
null
Showing all 2 rows.
We want to merge the view into the silver table. Specifically if the address already exists we want to set the endDate of the old record to be the effectiveDate of the new address record and change the flag for the current column to false. We then want to append the new update address as a brand new row. For completely new addresses we want to insert this as a new row.
%sql
MERGE INTO cdf.silverTable as original USING (
select
updates.primaryKey as merge,
updates.*
FROM
updates
UNION ALL
SELECT
null as merge,
updates.*
FROM
updates
INNER JOIN cdf.silverTable original on updates.primaryKey = original.primaryKey
where
original.current = true
) mergedupdates on original.primaryKey = mergedUpdates.merge
WHEN MATCHED
and original.current = true THEN
UPDATE
set
current = false,
endDate = mergedupdates.effectiveDate
WHEN NOT MATCHED THEN
INSERT
*
num_affected_rows
num_updated_rows
num_deleted_rows
num_inserted_rows
1
3
1
0
2
Showing all 1 rows.
%sql
select * from cdf.silverTable
primaryKey
address
current
effectiveDate
endDate
1
2
3
4
5
11
A updated address
true
2021-10-27
null
11
A new customer address
false
2021-10-27
2021-10-27
12
A different address
true
2021-10-27
null
13
A another different address
true
2021-10-27
null
99
A completely new address
true
2021-10-27
null
Showing all 5 rows.
%sql
describe history cdf.silverTable
version
timestamp
userId
userName
operation
operationParameters
job
notebook
1
2
3
4
3
2022-03-26T00:16:51.000+0000
428915142038362
guanjie.shen@databricks.com
MERGE
{"predicate": "(original.`primaryKey` = mergedupdates.`merge`)", "matchedPredicates": "[{\"predicate\":\"(original.`current` = true)\",\"actionType\":\"update\"}]", "notMatchedPredicates": "[{\"actionType\":\"insert\"}]"}
null
{"notebookId": "4233158071160993"}
2
2022-03-26T00:16:43.000+0000
428915142038362
guanjie.shen@databricks.com
WRITE
{"mode": "Append", "partitionBy": "[]"}
null
{"notebookId": "4233158071160993"}
1
2022-03-26T00:16:39.000+0000
428915142038362
guanjie.shen@databricks.com
SET TBLPROPERTIES
{"properties": "{\"delta.enableChangeDataFeed\":\"true\"}"}
null
{"notebookId": "4233158071160993"}
0
2022-03-26T00:16:36.000+0000
428915142038362
guanjie.shen@databricks.com
CREATE TABLE
{"isManaged": "true", "description": null, "partitionBy": "[]", "properties": "{}"}
null
{"notebookId": "4233158071160993"}
Showing all 4 rows.
%sql
select * from table_changes('cdf.silverTable',2,3) order by _commit_timestamp desc
primaryKey
address
current
effectiveDate
endDate
_change_type
_commit_version
_commit_timestamp
1
2
3
4
5
6
7
11
A updated address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
11
A new customer address
true
2021-10-27
null
update_preimage
3
2022-03-26T00:16:51.000+0000
11
A new customer address
false
2021-10-27
2021-10-27
update_postimage
3
2022-03-26T00:16:51.000+0000
99
A completely new address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
11
A new customer address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
13
A another different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
12
A different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
Showing all 7 rows.
%python
changes_df = spark.read.format("delta").option("readChangeData", True).option("startingVersion", 2).option("endingversion", 3).table('cdf.silverTable')
display(changes_df)
primaryKey
address
current
effectiveDate
endDate
_change_type
_commit_version
_commit_timestamp
1
2
3
4
5
6
7
11
A updated address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
11
A new customer address
true
2021-10-27
null
update_preimage
3
2022-03-26T00:16:51.000+0000
11
A new customer address
false
2021-10-27
2021-10-27
update_postimage
3
2022-03-26T00:16:51.000+0000
99
A completely new address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
11
A new customer address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
13
A another different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
12
A different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
Showing all 7 rows.
Generate Gold table and propagate changes
In some cases we may not want to show each data at the transaction level, and want present to users a high level aggregate. In this case we can use CDF to make sure that the changes are propaged effieciently without having to merge large amounts of data
%sql DROP TABLE IF EXISTS cdf.goldTable;
CREATE TABLE cdf.goldTable(
primaryKey int,
address string
) USING DELTA;
OK
%sql
-- Collect only the latest version for address
CREATE OR REPLACE TEMPORARY VIEW silverTable_latest_version as
SELECT *
FROM
(SELECT *, rank() over (partition by primaryKey order by _commit_version desc) as rank
FROM table_changes('silverTable',2,3)
WHERE _change_type ='insert')
WHERE rank=1;
SELECT * FROM silverTable_latest_version
primaryKey
address
current
effectiveDate
endDate
_change_type
_commit_version
_commit_timestamp
rank
1
2
3
4
11
A updated address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
1
12
A different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
1
13
A another different address
true
2021-10-27
null
insert
2
2022-03-26T00:16:43.000+0000
1
99
A completely new address
true
2021-10-27
null
insert
3
2022-03-26T00:16:51.000+0000
1
Showing all 4 rows.
%sql
-- Merge the changes to gold
MERGE INTO cdf.goldTable t USING silverTable_latest_version s ON s.primaryKey = t.primaryKey
WHEN MATCHED THEN UPDATE SET address = s.address
WHEN NOT MATCHED THEN INSERT (primarykey, address) VALUES (s.primarykey, s.address)
num_affected_rows
num_updated_rows
num_deleted_rows
num_inserted_rows
1
4
0
0
4
Showing all 1 rows.
%sql
SELECT * FROM cdf.goldTable
primaryKey
address
1
2
3
4
11
A updated address
12
A different address
13
A another different address
99
A completely new address
Showing all 4 rows.
Example that Combines Snapshots with Change Data Feed
Create an intial dataset and save this as a Delta table.
This will be source table we'll use to propogate changes downstream.
%sql DROP TABLE IF EXISTS cdf.example_source;
countries = [("USA", 10000, 20000), ("India", 1000, 1500), ("UK", 7000, 10000), ("Canada", 500, 700) ]
columns = ["Country","NumVaccinated","AvailableDoses"]
spark.createDataFrame(data=countries, schema = columns).write \
.format("delta") \
.mode("overwrite") \
.option("userMetadata", "Snapshot Example 1") \
.saveAsTable("cdf.example_source") \
streaming_silverTable_df = spark.read.format("delta").table("cdf.example_source")
streaming_silverTable_df.show()
%sql
SET spark.databricks.delta.commitInfo.userMetadata =;
ALTER TABLE cdf.example_source SET TBLPROPERTIES (delta.enableChangeDataFeed = true)
%sql
SET spark.databricks.delta.commitInfo.userMetadata =;
UPDATE cdf.example_source SET NumVaccinated = 1000, AvailableDoses = 200 WHERE COUNTRY = 'Canada';
UPDATE cdf.example_source SET NumVaccinated = 2000, AvailableDoses = 500 WHERE COUNTRY = 'India';
SELECT * FROM cdf.example_source
%sql
describe history cdf.example_source
Let's do a few more operations...
%sql
DELETE FROM cdf.example_source where Country = 'UK';
SELECT * FROM cdf.example_source;
%sql
SET spark.databricks.delta.commitInfo.userMetadata =;
INSERT into cdf.example_source
SELECT "France" Country, 7500 as NumVacinated, 5000 as AvailableDoses;
UPDATE cdf.example_source SET NumVaccinated = 1200, AvailableDoses = 0 WHERE COUNTRY = 'CANADA';
SELECT * FROM cdf.example_source
%sql
SET spark.databricks.delta.commitInfo.userMetadata =Snapshot Example 2;
INSERT into cdf.example_source
SELECT "Mexico" Country, 2000 as NumVacinated, 1000 as AvailableDoses;
SELECT * FROM cdf.example_source
Let's set up what the workflow might look like for a consumer.
This will first retrieve a point in time snapshot of the source table, then starts subscribing to incremental updates using Spark Structured Streaming and CDF.
Cleanup
%sql
DROP TABLE IF EXISTS cdf.example_source;
DROP TABLE IF EXISTS cdf.example_sink;
Despite the fact that it was not planned, I was invited to participate in the Canadian Global Power Platform Bootcamp 2022, and I thoroughly enjoyed sharing my Azure and Azure migration experience.
You won’t want to miss this demo-focused session with industry experts in Azure and AI. Let’s get together and learn how Azure and AI Model
About this event
Let’s take a look at a demo to better understand Azure’s well-architected framework. We will walk through the five pillars of the Azure Well-Architected Framework and conclude with a live demo to demonstrate the functionality.
In this session we will see a live demo to deploy, test and host an AI model trained in Azure machine learning or, any other platform. We will also show options to configure a ML cluster with ACI and AKS. We will also talk about data drift option in AML to monitor production inferencing.
This is free, event-driven by user groups and communities around the world, for anyone who wants to learn more about Microsoft’s Power Platform. In this boot camp, we will deep-dive into Microsoft’s Power Platform stack with hands-on sessions and labs, delivered to you by the experts and community leaders.
You won’t want to miss this demo-focused session with industry experts in Azure and AI. Let’s get together and learn how Azure and AI Model
About this event
Let’s take a look at a demo to better understand Azure’s well-architected framework. We will walk through the five pillars of the Azure Well-Architected Framework and conclude with a live demo to demonstrate the functionality.
In this session we will see a live demo to deploy, test and host an AI model trained in Azure machine learning or, any other platform. We will also show options to configure a ML cluster with ACI and AKS. We will also talk about data drift option in AML to monitor production inferencing.
Let’s take a look at a demo to better understand Azure’s well-architected framework. We will walk through the five pillars of the Azure Well-Architected Framework and conclude with a live demo to demonstrate the functionality.
Session Details:
Azure Data Lake Gen 2
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Run efficiently and reliably at any scale.
Provide insights through analytics dashboards, operational reports, or advanced analytics with PowerBI
Deepak is a Microsoft Azure MVP. He is Co-Founder of “Azure IoT Coast 2 Coast” focusing on Microsoft Azure & IoT technologies. He is passionate about technology and comes from a development background.
You could find his Sessions/ Recordings at https://channel9.msdn.com – Channel9, C# Corner, Blog and Deepak Kaushik -Microsoft MVP YouTube Channel.
Deepak Kaushik -> Cloud Blooded Beast 