Increase the quantity of an item ( UPDATE cart SET. ![]() Remove an item from cart ( DELETE FROM cart.Add an item to cart ( INSERT INTO cart.Search for the items ( SELECT \* FROM items.These SQL operations either generate or update the data in your application’s backend database, which is also referred to as an operational data store (ODS): Let’s consider an example of a shopping app, where basic user actions within the app trigger SQL operations. For further insights into the fundamental concepts of Airflow and the reasons for its adoption, refer to the Astronomer documentation. Starting with only a basic knowledge of Python and the core components of Airflow, you can achieve a well-balanced combination of a flexible, scalable, extensible, and stable environment that supports a wide range of use-cases. For the purpose of this blog, we will take a simple example of a data pipeline in Airflow that updates the data from an ODS to DWH using CDC.Īpache Airflow plays a significant role in this data journey by enabling you to author, schedule, and monitor your data pipelines at scale. It is widely used to sync changes in real-time or near-real-time from databases like PostgreSQL and MongoDB to non-DWH data stores such as ElasticSearch, data lakes, cloud storage, using a wide variety of tools like Kafka, FiveTran, etc. The following diagram represents one such example in a classic data pipeline:Įven though the concept of CDC originated in the context of data warehousing and applying changes in batch mode, today its role has evolved in the data stack. They haven’t yet decided on the types of reports they want to generate, but they intend to perform analysis on the raw data, which should be updated in real-time.Īs you can observe, a common pattern emerges: data is generated within a source system, and for various business use cases, it needs to be captured and propagated to other data stores downstream, either in batch-mode or streaming mode. A startup has setup a data lake in cloud and wants to bring in the operational data from its main database.An access controller for an office space logs users in and out, and the goal is to generate an audit trail of all events for security review.New products are added to the inventory multiple times a day, and these additions need to be visible to app users in real-time from the UI.Consumers interact with a shopping app for groceries, and the marketing team aims to understand the shopping patterns by product and region on a weekly basis in order to effectively to cross-sell new products.An Overview: CDC in your Data Stackīefore beginning with what is CDC, let’s first understand why do we need CDC and where does it fit in your data stack by looking at some use-cases: ![]() This blog is the first part of two-blog series where we discuss what is CDC, why we need CDC, and how to handle CDC in Airflow with ease. The frequency of this sync - inter-day, intra-day, or real-time - is one of the primary factors that dictate the terms of your CDC process. ![]() ![]() This sync between ODS and DWH is crucial to ensure that your key strategic decisions are taken based on the latest data. CDC process is a critical component of your data pipelines that keep your data warehouse (DWH) in sync with your operational data store (ODS). Change Data Capture (CDC) is a term popularized by Bill Inmon, the father of Data Warehousing, to refer to the process of identifying and tracking record-level changes to your operational data.
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