{"id":2369,"date":"2023-06-12T10:35:18","date_gmt":"2023-06-12T10:35:18","guid":{"rendered":"https:\/\/www.workfall.com\/learning\/blog\/?p=2369"},"modified":"2025-09-16T05:24:29","modified_gmt":"2025-09-16T05:24:29","slug":"unleashing-the-power-of-cdc-with-snowflake","status":"publish","type":"post","link":"https:\/\/learning.workfall.com\/learning\/blog\/unleashing-the-power-of-cdc-with-snowflake\/","title":{"rendered":"Unleashing the Power of CDC With Snowflake"},"content":{"rendered":"<span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span>\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/SzBebU5FlJegpqlmY-a6ANiiKhC_I5ymI4y5k9qeqRCkEi0DNQfinHc-nz0I37XEc-jhviGDieKoP7bIF_MqxMgnGuPQN76GcCB1TB36MNB6oiMH-NhLqxGEPk0sIfzP0WjoKix_JXAgN_IFe8lUh2s\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Picture a vibrant landscape of bustling tech hubs, where businesses thrive and decisions are made in the blink of an eye. In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Data, the lifeblood of organisations, holds the key to unlocking untapped potential and propelling businesses forward. Timely analytics, the secret sauce that empowers companies to gain a competitive edge, are in high demand. Imagine businesses across diverse industries &#8211; from finance to healthcare, manufacturing to retail &#8211; feverishly generating vast volumes of data and information. The question then arises: how can they keep pace with this deluge of data?<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Enter Change Data Capture (CDC), a transformative concept that seamlessly weaves its way into the very fabric of data engineering. Like a skilled conductor, CDC orchestrates a symphony of capturing and propagating data modifications effortlessly. It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. As the data ecosystem evolves, CDC becomes the compass that guides businesses in the right direction, equipping them to navigate through turbulent waters with ease.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Now, envision a world where data updates flow effortlessly, converging like tributaries into a mighty river. Organisations harness this stream of information, leveraging it to innovate, optimise, and thrive. The possibilities become boundless when data-driven insights are at your fingertips.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">So, embrace the power of Change Data Capture, and embark on a captivating journey where the magic of real-time data awaits. It&#8217;s time to harness the pulse of the digital era and seize the endless opportunities that lie ahead.<\/p>\n\n\n\n<p>In this blog, we will cover:<\/p>\n\n\n\n<ul><li>What Is CDC and Its Benefits?<ul><li>Where Is CDC Used and Who Uses It?<\/li><li>Types of CDC<\/li><\/ul><\/li><li>Hands-On<ul><li>Implement CDC in Snowflake Using Streams<\/li><\/ul><\/li><li>Conclusion<\/li><\/ul>\n\n\n\n<h2>What Is CDC and Its Benefits?<\/h2>\n\n\n\n<p class=\"has-text-align-justify\">Change Data Capture (CDC) is a powerful technique that revolutionises data engineering by capturing and applying incremental changes to databases or data sources. It bridges gaps in data ecosystems, ensuring consistency and synchronisation across systems. In the past, capturing data changes was tedious and inefficient, leading to delays and discrepancies. CDC, however, propels data engineering forward by leveraging transaction logs to identify and extract changes in near real-time.<\/p>\n\n\n\n<p>Implementing CDC offers numerous benefits. Some of them are discussed below:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/HxFpfAcT-9g2kMcRxRPt6DcnuoIvoUTwgJneIV9PSwXYymBM8f1hZP0tDOjawmK1skwr_8IKweMqkNUJ-zBp0_ex9iMtgwi9onKfGdXidRviv9rX8RR2naN-j-iz_5FTRL5iRpnq4tQ3aqNBdVaMhoY\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Firstly, it enables precise data synchronisation, improving integrity and minimising inconsistencies. Secondly, CDC empowers organisations with near real-time integration and analytics, enhancing decision-making and competitiveness. Moreover, it facilitates the implementation of microservices architectures and event-driven systems, automating reactions to data changes without manual intervention.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">With CDC, data engineering takes a leap forward, allowing organisations to stay ahead of the curve, respond swiftly to market dynamics, and unleash the full potential of their data resources.<\/p>\n\n\n\n<h3>Where Is CDC Used and Who Uses It?<\/h3>\n\n\n\n<p class=\"has-text-align-justify\">CDC is widely used in data engineering across industries and organisations. It finds applications in database replication, where it captures changes from the source database and updates target databases, ensuring data consistency without manual updates.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">CDC also plays a crucial role in data integration and ETL processes. It captures incremental changes from transactional databases or other sources, efficiently loading them into data warehouses or data lakes. This keeps analytical systems up to date for accurate reporting and analysis.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">In real-time data streaming and event-driven architectures, CDC captures data changes to trigger actions or workflows. It enables automation in business processes, event-driven applications, and microservices, reacting to data changes in near real-time.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Data engineers, administrators, analysts, and various industries like finance, healthcare, e-commerce, and telecommunications rely on CDC. It facilitates data synchronisation, replication, real-time analytics, and event-driven processing, empowering data-driven decision-making and operational efficiency.<\/p>\n\n\n\n<h3>Types of CDC<\/h3>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/vG1Z52m6mKrc0Qw_5d0j3pwnmLRyYGmE4IrKp3i1ze9YyUPAo9OIdsC0aqG7-dgjdkZ_FA3Kl96T2YT3YFluFgLKKMJ_nS2CSIFmT_raIQBQHyF-q2Z1HXYvr0FEJXPS0aAjVXsN9NcjSxlkaXAj6wk\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<ul><li><strong>Audit Columns:<\/strong> This method involves using designated columns within tables to track incremental changes. These additional columns store metadata like timestamps, user IDs, and change types, ensuring granular change tracking and auditability. It is commonly employed for compliance monitoring, data lineage, and maintaining historical records.<br><\/li><li><strong>Log-Based:<\/strong> Log-based CDC captures and processes incremental changes by monitoring the transaction log of a database. The transaction log records all modifications made to the database, and log-based CDC reads and interprets the log to extract individual changes. It offers an efficient approach for data replication, real-time data warehousing, and event-driven architectures without placing additional strain on the database.<br><\/li><li><strong>Trigger-Based:<\/strong> Trigger-based CDC utilises database triggers associated with specific tables to capture and track changes. These triggers automatically execute predefined actions when events such as inserts, updates, or deletes occur on the tables. The triggered actions capture the changed data, which can be processed and propagated to downstream systems for analysis, reporting, or integration with other applications.<br><\/li><li><strong>Table Deltas:<\/strong> Table delta-based CDC compares two versions of a table to track incremental changes. It involves taking periodic snapshots of the table and comparing them to identify added, modified, or deleted records. By analysing the differences between snapshots, table delta-based CDC enables the extraction and propagation of changes to downstream systems for further processing or analysis.<\/li><\/ul>\n\n\n\n<h2>Hands-On<\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/9Vx_c_arYFQC_Y9eJ5b29S2UiRfTkkVwVwJPB9KoEncS1FiGwJwFB6_aZYI6lupbhDpL1bPjRbHoxsi7KGsULsNX0wJWAo9Mf9G0LPptlGl6zy2bfPOesjx8AhqEnToMJ2zD0InWNZCA-yUAiCoyJxg\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">In order to implement a CDC workflow we would be following the following steps:<br><br><strong>Step 1: Create an OLTP transactional table<\/strong><br><br>Start by creating a transactional database table where you can insert rows. This table will serve as the source of data, and any changes made here will be reflected in your data warehouse.<br><br>Query2:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/pzqNUtswO-_ZPkcFFVOd0MFUmnuxJInfp9ou9C14UnP5A2OhxN_3MFAO6YMzAOxb1u2oU3sfOFTzTePNflI0sC6l1IS6N3yjr-L7wEXo7nrQpRzpu3iVzB27GiDBH3mImuH9JJj4TfQyQaXOEzUtlvE\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p>Here, we have created a simple employee table and we are going to add a row:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/Jn2vO35ki45iqO050mCuAaXLqssXlGN2FOd0z39zoRmpIZak_dnkplkTflHYQIAAM_o6pAzzUdMz_9O2rtqmlmwKh9VBJB1EakQ0vgcuKsrrDXXh9Y3pyksokGjKxMq3ChIZW5z9CUmxroPYfhRmqSg\" alt=\"\"\/><\/figure>\n\n\n\n<p><br>The inserted data is as follows:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/Z8pVcG8Usw4jaWusQEyKfZ_Rx7GX4p7DbNUAI7vRBhQGmhgll2OgOP8sw_Gx8h2KJBDB0_-g31jUZbi-n8ZIHsAYhlUnox0t8v2kzhPXKZFBNwH8RVTA_nBccTAdJy3uQ6UwGx-ZSGHvZcKpbAic2gQ\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><strong>Step 2: Python script to load the data from our transactional database to Snowflake:<\/strong><strong><br><\/strong><strong><br><\/strong>Now, we will write a Python script to load this table data from our transactional database to Snowflake. But first, we need to do certain things on Snowflake to get started.<\/p>\n\n\n\n<ul><li>Snowflake signup: Click on this link if you are new to Snowflake, the signup process is fairly simple. <a href=\"https:\/\/signup.snowflake.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/signup.snowflake.com\/<\/a><br><\/li><li>After signing up, create a database by clicking data -&gt; +Database -&gt; create a database<\/li><\/ul>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/j-uBY-HBvW0v4vAlmkxva-SWKW4FtRj1G_TDdXOnL_TH1vYlbvNzIq9gZYqcSXUvNOLQqv-SDPpZvC-y0cVu60_8GE2Y02eSjcHp6GfFnhq0BWtiGmTOQeIJpjjV1GvNOj7NAAh9aHH3IspfR4lj-Rw\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">After creating the database, go to worksheets and select the database and schema you have created and write the create table query which would create a target table for data migration. Also, grant necessary privileges to this table.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/c467o-_qH0KgLMhAqyHesndCrlOyR8pycol8WdCKiccTk-D4l8Igk8K9C4LWTBsEuR8BH5Fx87iWwMuacpc_uEKQ1RaPM0ZINDJ9fHAKhIfdRMorBRui9N1iAsmrys1BOQ4-8V8hFKlSMyESPLNHqqI\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><br>That&#8217;s it, now let&#8217;s switch to Python and write a script that would populate this snowflake table. We would be using Jupyter Notebook for Python.<\/p>\n\n\n\n<p><strong>1. Install the following libraries: psycopg2 and sqlalchemy<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/pvfVYpLzsoCG-_Rdb0SKk4G4fsKWXX54lOIzkatwMj2TUkomu6U7yi75U4HdwyWFTaEF-NL_2YtnEPrNNyI8HIohAxmruyVmSn3VvJoENAvMdZUxXHWTpehhieUr1TnYFaiJ-__1nzGu4CMgws6aYEM\" alt=\"\"\/><\/figure>\n\n\n\n<p><strong>2. Create the database engine and database connection using the following commands:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/Ij_aBIC5cezZCWr7N8a5Tzzrpcb_s4DAiRwxu66S4I3-LhrLhnyZ8jy66Uf_0MF_j-NzT1CMPxemwRkUVsQYtLD3rV-lfWJLFvOoYagv9buQnwwqdyvJ9By4JZoE3S8qZDdLqC_gw-VwM9cHt_oUNL0\" alt=\"\"\/><\/figure>\n\n\n\n<p><strong>3. Now, read the table data which you have just created using pandas&#8217;s read_sql_query function<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/tZmkMJEhK-9p2sTtz254VOkKk1VXUjZNkevxQrhfgffghCRC6yeSecLwaapSb8dytJ_C0SI2D9DbvQ8fdnw_ujqz9kzK5_wmWiS6DAuaJQ1pFH-kxhagksfS-x4E5W6zt4Bxl9R_WEepjUzay2C22GA\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><strong>4. Now we have to connect Python with Snowflake so in the next shell, run:<br><\/strong> <code>!pip install snowflake-connector-python==2.7.9<\/code><\/p>\n\n\n\n<p class=\"has-text-align-justify\"><strong>5. Create a connection between Snowflake and Python by following these steps and replace the placeholders with your credentials:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/xQzv1MpQxLTVGLoT5dWRNwFahD-AtnvJtviW1r7s8sZuQJglqZmZQEeavBKxTyxHN4x6m3s24qFIF9klPE_czNy94nSD0zbpE9DsjwO9sHxi_UDLJgSb6l3Gw3eVjb9nsZVTPvmLrtc4ITtH2nh4Jos\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><strong>6. Next step is to create a cursor object and switch the role to sys admin to make databases, schemas, etc. Execute the following commands:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/73AJjZMg7QwLet6yF8Xfl7d-EFYgxKDy8cVSuS-o1QUqGzWu5omvmxmgVb-WHQatB8pNsqjlCstiHt-3E-LhDfgw24XPa1eJiM8Iw3BzZkzAThIMbhUk8ZMfIwjcVNfCn7_gmIGDKgxCGGPK2pJvIs8\" alt=\"\"\/><\/figure>\n\n\n\n<p><strong>7. Execute the use command to use the database, warehouse, and schema.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/0ArLF9T3OH3rVfEXMhIudpvnbPL5NOYNr5O9Tix2wmjsTGrDHyciNkct_DqjdOOKgeVBZKtPqGsN7JBSxaZNVkGUmO84059rdc_7SlgyL235HhtQi6MSyQJabWG6G8XdR9doheHSpUq-WG2KKWj3Nec\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><br><strong>8. Now, the data is ready to be inserted, we will use the write_pandas library to load this data frame onto Snowflake:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/IKbehWmaZByNBgZQUmYq8w3Edg9T0vE-QwngnZd__tZZxtRPkxlm0Z6ECXXPmvr8pxMtQ7PR0tUQSCEB851P7nCzwHgJsZXBZIlaRd54f1mcZSFjObQjPELHYSxOr4p3NxufnTQiNCOJ3GhCq0pV7s8\" alt=\"\"\/><\/figure>\n\n\n\n<p><strong>9. The success is true so let&#8217;s now verify this change in our snowflake table.<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh6.googleusercontent.com\/mzfAfxUn7oYCUHjjIrDvBmCKRaYlN_eTwuZMJDFV9OXGWpM8mMgnOtfiK5mDkOpXBqurzBOV-t_HexV36JOjKkx4vfTWf10zxHdnnkilGrtI-25DVs_C7G00taLIv_nN5n1x64wRjhqbiiAiiNLxu_E\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">and we have now migrated the data from our transactional database to the Snowflake data warehouse. Now we can easily create data streams and create a CDC mechanism in Snowflake.<\/p>\n\n\n\n<h3>Implement CDC in Snowflake Using Streams<\/h3>\n\n\n\n<p class=\"has-text-align-justify\"><strong>Snowflake Stream<\/strong> enables real-time data processing and continuous data integration, allowing for seamless and efficient data flow across various systems.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Snowflake streams act as a conduit for capturing and propagating changes made to tables, ensuring data consistency and enabling near-instantaneous updates. With their ability to efficiently handle high volumes of data and provide reliable, low-latency streaming capabilities, snowflake streams have become an indispensable tool for modern data engineering workflows. Their versatility and reliability make them a valuable asset in building robust and scalable data pipelines, facilitating timely and accurate data analysis and decision-making processes.<\/p>\n\n\n\n<p class=\"has-text-align-justify\"><strong>Table stream <\/strong>is an object that records the DML changes made to a source object (eg: tables). It returns additional columns like:<\/p>\n\n\n\n<ul><li>Metadata$Action: Insert or Delete (the operation that happened)<\/li><li>Metadata$Update: True or False (if the data was updated or not)<\/li><li>Metadata$RowId: to track changes on the row over time.<\/li><\/ul>\n\n\n\n<p><strong>Creating a table stream<\/strong><\/p>\n\n\n\n<p>Run the following SQL command to create a stream:<\/p>\n\n\n\n<p>create or replace stream <em>employee_stream<\/em> on table <em>WORKFALL_DB.PUBLIC.EMPLOYEE;<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/NjCWYTpb3nQwDpxanvwKPdxOr6eeXLrO6SXzshL83liTSS7OzXLPCJLKSXTug-6wF-yTvgUwK1b0b5nMEfZvaVM3uswk8tX9mGYCIf93yVu9-mS6fxjpbfLZJ0dsGzK8j9z29Qm3Bx-tcZB4cyGCmD0\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">We can see a stream has been created and additional audit columns have been added automatically. Let&#8217;s insert a new row and see the changes in the stream table.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/53tSDm6Qw7FXC6v_yQ9-N7VK7xt0uti7NgpFz5J9gUb1yvAhMf6vKiLNN-ARkuhur8pb_MhsPGcowVnk7Rt6mvzKv6xEIoFRflDw_ndG1xQG1nvVZced0jtxdrWqf8cEx-w1lAjFER32mIfg5K07bfg\" alt=\"\"\/><\/figure>\n\n\n\n<p>After running the Python script :<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/PZLVrHWMRSMgNRe3eIgyYFkObm13Gj-D8szEYDf1GvIdjrJbXD31QJ9SfZ7SH4BCvDlwkc4jeLUGT7LAKVzD3vSEltNjgk36Qexcg9Y0homrybxfKJ2If7eyHcQp3hYTmEVdInmGUnMOeS9WSUFsEbI\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">As we can see, the new record is inserted as an insert into the stream table. We can now use this information to write just new rows and not existing rows each time.<\/p>\n\n\n\n<p>Let&#8217;s create a final table and in that, we would merge the new rows.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/G0k6LPpEKVUweSM2evXbP_r-iYaufojUyM-Wclwt8v8pOCQwuO5s1cEfXRTmsl0AnRKyiLA6gOJwNokF3V-sNyfMMzuMLyOTOWJKjfKXB2__1BQcWVdpw__5BUIHUfqJZR7325T42EPyhAVy78egp1k\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Now execute this merge query and it will insert\/update\/delete records based on the operations performed on the source table.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh4.googleusercontent.com\/AU-6CcnKklCyKxAobN4PBBcm8o6j6_D569o5qmKlE0AL-e4tqSq3oY-0kKELhQ4EMnYljLPwtxfu9Tp6xdZN8fEprdkMOVoM5mbVV6bd4ZM3vmgWWRvKXBrvDj6kHpQR2jfCCn-G3QX7hk6EvveRy9I\" alt=\"\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\">Now let us insert a new record and update the name of the entry with id 2.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Source:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh3.googleusercontent.com\/HaRKjKmy_7SJzubgNO_5tgkw9UnBy222ZGnfwpiD3yytU_gVEkeJFkFQBeRJ6daEB9ib2NxC6fkoSxfR7ikIfYTH9C_EK36Fi-RzYyYvbzJIs8TVPbSKDbRFsj-XLfc0cWzAc-V3nHJnx4DrZEPKF4k\" alt=\"\"\/><\/figure>\n\n\n\n<p>Records in the final table after updating and inserting new rows:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img src=\"https:\/\/lh5.googleusercontent.com\/3oTu0pBl91cRcEMIv_DeFT_bgF8CFMwKYjlgnF9B30JE3CBy4F-Zkgt-vydier7rrQt6qxIXzXh0FLXm4nRejFa6tlc-l6g3Vntoci8laID0RAdtO7AWDGJUshSnneLOy2TtaBEcXgDe_kPJTFQOF6M\" alt=\"Unleashing the Power of CDC With Snowflake\"\/><\/figure>\n\n\n\n<p class=\"has-text-align-justify\"><br>We can also create data workflows in the form of data pipelines using apache airflow to know more, you can check our blog at <a href=\"https:\/\/www.workfall.com\/learning\/blog\/how-to-easily-build-etl-pipeline-using-python-and-airflow\/\">Easily Build ETL Pipeline Using Python And Airflow<\/a><\/p>\n\n\n\n<h2>Conclusion<\/h2>\n\n\n\n<p class=\"has-text-align-justify\">In this blog, we demonstrated implementing a CDC workflow. In the realm of data engineering, CDC has transformed how organisations handle data modifications. By automating the capture and propagation of changes, CDC ensures consistent data and real-time integration. It&#8217;s a guiding light that keeps organisations synchronised in the ever-changing data landscape.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">CDC allows data engineers to capture and use incremental changes, ensuring accurate data views. It&#8217;s essential for maintaining data integrity and enabling timely analytics, aiding informed decision-making. CDC is embraced by various industries, from finance to healthcare, e-commerce to telecommunications. It ensures granular data synchronisation, minimizing missed updates and inconsistencies.<\/p>\n\n\n\n<p class=\"has-text-align-justify\">Beyond data integrity, CDC empowers organisations with real-time integration and analytics. It enables agile decision-making in dynamic markets, where up-to-date insights are crucial for staying competitive. CDC also shapes the future of data engineering. It facilitates microservices architectures and event-driven systems, automating processes and eliminating manual interventions. As the volume and velocity of data grow, embracing CDC becomes increasingly critical for organisations striving to succeed in the data-driven era. We will come up with more such use cases in our upcoming blogs.<\/p>\n\n\n\n<p><strong>Meanwhile\u2026<\/strong><\/p>\n\n\n\n<p class=\"has-text-align-justify\">If you are an aspiring Snowflake enthusiast and want to explore more about the above topics, here are a few of our blogs for your reference:<\/p>\n\n\n\n<ul><li><a href=\"https:\/\/www.workfall.com\/learning\/blog\/how-to-connect-snowflake-with-python-and-execute-queries\/\">Connect Snowflake with Python and execute queries<\/a><\/li><li><a href=\"https:\/\/www.workfall.com\/learning\/blog\/how-to-easily-build-etl-pipeline-using-python-and-airflow\/\">Easily build ETL Pipeline using Python and Airflow<\/a><\/li><\/ul>\n\n\n\n<p>Stay tuned to get all the updates about our upcoming blogs on the cloud and the latest technologies.<\/p>\n\n\n\n<p class=\"has-text-align-justify\"><strong>Keep Exploring -> Keep Learning -> Keep Mastering\u00a0<\/strong>At <a href=\"https:\/\/www.workfall.com\/\">Workfall<\/a>, we strive to provide the best tech and pay opportunities to kickass coders around the world. If you\u2019re looking to work with global clients, build cutting-edge products and make big bucks doing so, give it a shot at <a href=\"https:\/\/www.workfall.com\/partner\/\">workfall.com\/partner<\/a> today!<\/p>\n\n\n<style type=\"text\/css\"><\/style><section id='' \n                class='helpie-faq accordions faq-toggle open-first groupSettings-493__enabled' \n                data-collection='' \n                data-pagination='0' \n                data-search='0' \n                data-pagination-enabled='0'\n                role='region'\n                aria-label='FAQ Section'\n                aria-live='polite'><h3 class=\"collection-title\">Frequently Asked Questions:<\/h3><article class=\"accordion \"><div class='helpie-faq-row'><div class='helpie-faq-col helpie-faq-col-12' ><ul><li class=\"accordion__item \"><div class=\"accordion__header \" \r\n                id=\"accordion-header-post-2738\"\r\n                role=\"button\"\r\n                aria-expanded=\"false\"\r\n                aria-controls=\"accordion-content-post-2738\"\r\n                data-id=\"post-2738\" \r\n                data-item=\"hfaq-post-2738\" \r\n                style=\"background:transparent;\" \r\n                data-tags=\"\"\r\n                tabindex=\"0\"><div class=\"accordion__title\">Q: What exactly is Change Data Capture (CDC)?<\/div><\/div><div id=\"accordion-content-post-2738\" \r\n                class=\"accordion__body\" \r\n                role=\"region\"\r\n                aria-labelledby=\"accordion-header-post-2738\"\r\n                style=\"background:transparent;\"><p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><span style=\"font-weight: 400\"><strong>\u00a0A:<\/strong> CDC is a method to detect and capture changes (inserts, updates, deletes) made in a source database and propagate them to another system, often in near-real time.<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><\/p>\n<\/div><\/li><li class=\"accordion__item \"><div class=\"accordion__header \" \r\n                id=\"accordion-header-post-2739\"\r\n                role=\"button\"\r\n                aria-expanded=\"false\"\r\n                aria-controls=\"accordion-content-post-2739\"\r\n                data-id=\"post-2739\" \r\n                data-item=\"hfaq-post-2739\" \r\n                style=\"background:transparent;\" \r\n                data-tags=\"\"\r\n                tabindex=\"0\"><div class=\"accordion__title\">Q: Why use CDC with Snowflake? What advantages does Snowflake streams provide?<\/div><\/div><div id=\"accordion-content-post-2739\" \r\n                class=\"accordion__body\" \r\n                role=\"region\"\r\n                aria-labelledby=\"accordion-header-post-2739\"\r\n                style=\"background:transparent;\"><p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><span style=\"font-weight: 400\"><strong>\u00a0A:<\/strong> Using CDC with Snowflake enables analytics or downstream systems to stay up to date with minimal lag. Snowflake streams specifically let you capture table changes (with metadata like which rows changed, the type of change, etc.) and then allow efficient querying or merging of just the changed data rather than full table scans.<\/span><\/p>\n<\/div><\/li><li class=\"accordion__item \"><div class=\"accordion__header \" \r\n                id=\"accordion-header-post-2740\"\r\n                role=\"button\"\r\n                aria-expanded=\"false\"\r\n                aria-controls=\"accordion-content-post-2740\"\r\n                data-id=\"post-2740\" \r\n                data-item=\"hfaq-post-2740\" \r\n                style=\"background:transparent;\" \r\n                data-tags=\"\"\r\n                tabindex=\"0\"><div class=\"accordion__title\">Q: How do I set up a basic CDC workflow in Snowflake?<\/div><\/div><div id=\"accordion-content-post-2740\" \r\n                class=\"accordion__body\" \r\n                role=\"region\"\r\n                aria-labelledby=\"accordion-header-post-2740\"\r\n                style=\"background:transparent;\"><p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><\/p>\n<ol>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">\u00a0A: The blog outlines:<\/span><\/li>\n<\/ol>\n<p><span style=\"font-weight: 400\"><span style=\"font-size: 1rem\">Create a source (OLTP) table<\/span><\/span><\/p>\n<p><span style=\"font-weight: 400\">Use Python (and libraries like <\/span><span style=\"font-weight: 400\">snowflake-connector-python<\/span><span style=\"font-weight: 400\">, <\/span><span style=\"font-weight: 400\">sqlalchemy<\/span><span style=\"font-weight: 400\">, <\/span><span style=\"font-weight: 400\">pandas<\/span><span style=\"font-weight: 400\">) to load data into Snowflake<\/span><\/p>\n<p><span style=\"font-weight: 400\">Create a Snowflake Stream object on that table to capture changes (captures metadata such as <\/span><span style=\"font-weight: 400\">METADATA$ACTION<\/span><span style=\"font-weight: 400\">, <\/span><span style=\"font-weight: 400\">METADATA$ROWID<\/span><span style=\"font-weight: 400\">, etc.)<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><span style=\"font-weight: 400\">Use a SQL <\/span><span style=\"font-weight: 400\">MERGE<\/span><span style=\"font-weight: 400\"> into a final target table to apply inserts\/updates\/deletes based on captured changes.<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><\/p>\n<\/div><\/li><li class=\"accordion__item \"><div class=\"accordion__header \" \r\n                id=\"accordion-header-post-2741\"\r\n                role=\"button\"\r\n                aria-expanded=\"false\"\r\n                aria-controls=\"accordion-content-post-2741\"\r\n                data-id=\"post-2741\" \r\n                data-item=\"hfaq-post-2741\" \r\n                style=\"background:transparent;\" \r\n                data-tags=\"\"\r\n                tabindex=\"0\"><div class=\"accordion__title\">Q: How does this help reduce cost \/ computation compared to non-CDC approaches?<\/div><\/div><div id=\"accordion-content-post-2741\" \r\n                class=\"accordion__body\" \r\n                role=\"region\"\r\n                aria-labelledby=\"accordion-header-post-2741\"\r\n                style=\"background:transparent;\"><p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><span style=\"font-weight: 400\">\u00a0A: Since only incremental changes are processed (via streams + merges), you avoid re-processing the whole table on every run. That reduces compute and data transfer. Also, less storage\/io overhead for frequent full loads. (Implicitly discussed in the blog through the stream + merge pattern.)<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><\/p>\n<\/div><\/li><li class=\"accordion__item \"><div class=\"accordion__header \" \r\n                id=\"accordion-header-post-2742\"\r\n                role=\"button\"\r\n                aria-expanded=\"false\"\r\n                aria-controls=\"accordion-content-post-2742\"\r\n                data-id=\"post-2742\" \r\n                data-item=\"hfaq-post-2742\" \r\n                style=\"background:transparent;\" \r\n                data-tags=\"\"\r\n                tabindex=\"0\"><div class=\"accordion__title\">Q: What metadata does the Snowflake stream provide, and how is it useful?<\/div><\/div><div id=\"accordion-content-post-2742\" \r\n                class=\"accordion__body\" \r\n                role=\"region\"\r\n                aria-labelledby=\"accordion-header-post-2742\"\r\n                style=\"background:transparent;\"><p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><\/p>\n<ol>\n<li style=\"font-weight: 400\"><span style=\"font-weight: 400\">Streams provide things like:<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><\/li>\n<\/ol>\n<ul>\n<li><span style=\"font-weight: 400\">METADATA$ACTION<\/span><span style=\"font-weight: 400\"> (whether a change was an insert, delete, etc.)<\/span><span style=\"font-weight: 400\">\n<p><\/span><\/li>\n<li><span style=\"font-weight: 400\">METADATA$ROWID<\/span><span style=\"font-weight: 400\"> (to identify rows across changes)<\/span><span style=\"font-weight: 400\">\n<p><\/span><\/li>\n<li><span style=\"font-weight: 400\">METADATA$ISUPDATE<\/span><span style=\"font-weight: 400\"> flag or similar to check if the changed row is an update vs just a change in value.<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><span style=\"font-weight: 400\"> This metadata helps in merging efficiently into downstream tables and applying logic depending on type of change.<\/span><span style=\"font-weight: 400\"><br \/>\n<\/span><\/li>\n<\/ul>\n<\/div><\/li><\/ul><\/div><\/div><\/article><\/section>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading Time: <\/span> <span class=\"rt-time\">9<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span> Picture a vibrant landscape of bustling tech hubs, where businesses thrive and decisions are made in the blink of an eye. In this dynamic realm of data engineering, a monumental challenge takes centre stage: efficiently managing the ever-changing tides of real-time data. Data, the lifeblood of organisations, holds the key to unlocking untapped potential and [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2370,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"spay_email":""},"categories":[288],"tags":[432,433,336,6],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Unleashing the Power of CDC With Snowflake - The Workfall Blog<\/title>\n<meta name=\"description\" content=\"Unlock the true power of CDC with Snowflake! 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