What is Snowflake Change Data Capture (CDC)? | Simplified
In today’s world, data in an operational system changes quite frequently. Traditional Data Warehouses, on the other hand, do not contain up-to-date data. Up-to-date data is stored in operational systems before being put into the Data Warehouse at a set frequency. Snowflake is a modern Data Warehouse that has become an industry-leading Cloud-Based SaaS (Software-as-a-service) Data Platform. Snowflake Change Data Capture (CDC) makes it easy to detect new changes/data in a table for efficient processing.
Table of Contents
In today’s data-driven economy, organizations are relying on evidence-based decision-making more than ever before. Today, almost all companies collect data to better understand their business and customers. The valuable insights extracted from the company data help in making smarter and informed business decisions that help drive business growth and profitability. Hence, it becomes increasingly important for companies to manage their data in a better and more efficient way, and this is where Change Data Capture comes in.
This article is all about Snowflake Change Data Capture and how it keeps you updated with the current data changes. But before getting started with Snowflake Change Data Capture, let’s discuss Snowflake and its salient features in brief.
Table of Contents
- What is Snowflake?
- What is Change Data Capture (CDC)?
- What is Snowflake Streams?
- How to Setup Snowflake Change Data Capture with Streams?
What is Snowflake?
Snowflake is a modern Cloud Data Warehousing solution provided as a SaaS offering. It is built on Amazon Web Service, Microsoft Azure, or Google Cloud infrastructure that provides an unbounded platform for storing and retrieving data. Snowflake Data Warehouse makes use of a proprietary SQL Database Engine with a Cloud-specific architecture.
Snowflake requires no hardware or software to install, configure, or manage, making it suitable for enterprises that do not want to devote resources specifically to in-house server setup, maintenance, or support. Snowflake’s security and sharing features make it easy for businesses to securely exchange and share data in real-time using any ETL solution. Snowflake’s architecture allows for Big Data flexibility. When compared to other Data Warehouses in the market, Snowflake is noted for its scalability and relative ease of use.
Key Features of Snowflake
Below are the reasons responsible for the immense popularity of Snowflake.
- Caching Paradigm: Snowflake employs a Caching Paradigm to quickly deliver results from the cache. It leverages Persistent Query results to avoid re-generation of the report.
- Standard and Extended SQL Support: Snowflake supports most of the DDL and DML commands of SQL for querying data. In addition to that, it also supports advanced DML transactions, stored procedures, lateral views, etc.
- Scalability: It is a well-known fact that Snowflake’s architecture separates its “Compute” and “Storage” units, thereby allowing them to scale differently. As a result, customers can pay only for the services used.
- Security: Snowflake supports a variety of improved mechanisms for authentication. This includes Two-Factor Authentication and SSO via Federated Authentication.
- Semi-Structured Data Support: Snowflake’s architecture supports the VARIANT schema on the Read data type that enables the storage of Structured and Semi-Structured data in the same area.
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What is Change Data Capture (CDC)?
Change Data Capture (CDC) is an ideal solution to capture the near real-time movement of data in Databases. CDC refers to the collection of software design patterns used to detect and track data changes in a Database. It triggers the event connected with data, causing a specific action to be executed for any Change Data Capture. All companies require access to real-time Data Streams to perform efficient Data Analytics and CDC provides near-real-time movement of data by processing data as soon as new database events occur.
Events are captured and streamed in real-time using CDC and it helps to achieve reliable, low-latency, and scalable data replication in high-velocity data environments. It eliminates the process of bulk data loading by implementing incremental data loading. This way, Data Warehouses or Databases remain active in order to execute specific actions as soon as a Change Data Capture event occurs. Furthermore, companies can send fresh data updates to BI (Business Intelligence) tools and team members in near real-time with CDC, keeping them up to date.
What is Snowflake Streams?
In today’s data-driven economy, data in your systems change quite frequently, and it would be a complex task to load the data fully into Snowflake every time. It will cost you both time and money. This is where Snowflake Change Data Capture (CDC) comes in. You can implement CDC in Snowflake effectively by just using a few commands, and this is made possible with the concept of Streams in Snowflake.
A Snowflake Stream object basically tracks all DML changes made to rows in a source table and stores the metadata of each change. This metadata between two transactional points of time in a table is used later in order to retrieve the changed data.
Snowflake Streams capture an initial snapshot of all the rows present in the source table as the current version of the table with respect to an initial point in time. Streams then enable Change Data Capture every time you insert, update, or delete data in your source table. The Streams will have additional columns whenever any DML change is committed. So, by capturing the CDC Events you can easily merge just the changes from source to target using the
How to Setup Snowflake Change Data Capture with Streams?
To get started with Snowflake Change Data Capture, log in to Snowflake Web-Based UI or SnowSQL. Then, follow the below-mentioned steps.
Step 1: Run the following command to create a
create or replace database CDC_STREAM; Use CDC_STREAM;
Step 2: You’ll need a source and a destination table to operate upon. Run the following command to create a source table named “
create or replace table employees_source ( id int, first_name varchar(200), last_name varchar(200) )
Step 3: Run the following command to create a destination table named “
create or replace table employees_destination ( id int, first_name varchar(200), last_name varchar(200) );
Step 4: Run the following command to create a stream on top of the source table. This stream will track any data changes made in the
create or replace stream employee_stream on table employees_source;
Note: As long as there is no data management command to consume it, any changes to the source table with respect to data would be considered as
INSERT and not
For the purpose of this demonstration, let’s add a couple of records to the
employees_source table. Run the following command to do so.
insert into employees_source values (1,’Ron’,’Higgins’); insert into employees_source values (2,’Joseph’,’Geller’); insert into employees_source values (3,’Charlie’,’Bricks’); insert into employees_source values (4,’Mathew’,’Smith’); insert into employees_source values (5,’Daisy’,’Whelsh’); insert into employees_source values (6,’Scott’,’Hall’); insert into employees_source values (7,’Jose’,’Martinez’); insert into employees_source values (8,’Bryan’,’Son’); insert into employees_source values (9,’Nisha’,’Grant’); insert into employees_source values (10,’Hamilton’,’Gell’); insert into employees_source values (11,’Larry’,’Armstrong’); insert into employees_source values (12,’Emilia’,’Rodrigez’); insert into employees_source values (13,’Kate’,’Becket’);
Step 5: Let’s view the change log in the stream before proceeding.
select * from employee_stream;
As you can see, here are the records and metadata fields. There are 3 additional META columns (
METADATA$ACTION) introduced and they make it very easy to detect if a row has been updated, deleted, or inserted. And, since it’s an initial load everything is inserted.
Now, there are no records in the destination table as they’re still in the stream.
Step 6: Let’s move the records to the destination using the
Use CDC_STREAM; MERGE into employees_destination as T using (select * from employee_stream) AS S ON T.id = s.id when matched AND S.metadata$action = ‘INSERT’ AND S.metadata$isupdate THEN update set T.first_name = S.first_name, T.last_name = S.last_name When matched And S.metadata$action = ‘DELETE’ THEN DELETE when not matched And S.metadata$action = ‘INSERT’ THEN INSERT (id, first_name, last_name) VALUES (S.id, S.first_name, S.last_name);
Now, the data has moved to the destination and there will be nothing in the stream. After the
MERGE command has consumed, the stream object will become empty.
The cycle continues, and the stream will continue to record all the changes that happen in the
Step 7: Let’s view the destination table now. Run the following command to do so.
select * from employees_destination;
As you can see, the destination table is updated with the records.
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Step 8: Now, it’s time to demonstrate the Snowflake Change Data Capture. For that purpose, go ahead and make a change in the source table. Let’s update a record and observe the stream.
update employees_source set last_name="Harper" where id=1;
This basically sets the
last_name as “Harper” for
- While observing the stream, you’ll find 2 records,
DELETE. This is because the original
last_name(i.e Higgins) was deleted for
id=1and the new
last_name(i.e Harper) was updated.
- Use the
MERGEcommand to update the destination table as shown below.
Use CDC_STREAM; MERGE into employees_destination as T using (select * from employee_stream Where Not (metadata$action = ‘DELETE’ AND metadata$isupdate = TRUE)) AS S ON T.id = s.id when matched AND S.metadata$action = ‘INSERT’ AND S.metadata$isupdate THEN update set T.first_name = S.first_name, T.last_name = S.last_name When matched And S.metadata$action = ‘DELETE’ THEN DELETE when not matched And S.metadata$action = ‘INSERT’ THEN INSERT (id, first_name, last_name) VALUES (S.id, S.first_name, S.last_name);
Step 9: Run the following command to take a look at the destination table.
select * from employees_destination;
Here, you will be clearly able to observe the changes made to the source table updated in the destination table. That’s it, that’s how easy Snowflake Change Data Capture is.
For a better understanding, you can try out Snowflake Change Data Capture by deleting or updating other records in the source table. And, don’t forget to use the
MERGE command for the changes to reflect in the destination table.
Snowflake Change Data Capture has totally replaced the old ways of implementing CDC. Although Snowflake is already an industry-leading Cloud-Based Data Platform known for its speed and flexible warehousing options, Snowflake CDCs make it much worth it to use.
Snowflake Change Data Capture proves to be very helpful in cases where millions of records get transacted on a daily basis but you only want to update the modified ones. Doing a full load will eat up your resources and time, so just leverage the Snowflake Change Data Capture and use the
MERGE command to update the destination.
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Share your experience of working around Snowflake Change Data Capture (CDC) in the comments section below.