Welcome back to our series on building a successful data ecosystem framework and comprehensive data strategy! If you are just joining us, be sure to check out the first post on data acquisition best practices. This week, the Blueprint team, continues with a discussion on near-real-time (NRT) and reverse ETL to enable teams to utilize data in the context of their roles.
Near-real-time (NRT) processing and reverse ETL are essential components of a modern data ecosystem. The need to consume data in real-time and take immediate action based on that data has made the established practice of continuously refreshing operational reports to receive the most up-to-date business conditions inefficient and far too slow to take advantage of any shifts in business. It’s for this reason that Blueprint utilizes the latest tools and techniques in reverse ETL and event management to ensure timely and relevant ways for teams to interact with their data.
Improved event response with timely insights
NRT processing relies on event-driven architecture, where an event is a change or notification that occurs in a system, like a database update or a new message in a queue. The event triggers a response in the system—such as a data transformation or an alert notification. On the Databricks, organizations can use the event-driven architecture to process multiple feeds about their order system and supply chain data in near-real-time.
Here’s a real-world example: An order isn’t going to be manufactured on time, so the data science routine within the data pipeline immediately flags the customer record in the data estate or a staging table for the reverse ELT processor. Reverse ETL enables the organization to take insights derived from data processing and push them back to business applications such as a CRM or a supply chain control-tower system.
In the above scenario, the reverse ETL component of the NRT system allows the data to be pushed to the CRM, flagging the customer record for the sales rep to take immediate action. With traditional BI reports or operational reports, these timely and actionable insights aren’t possible. With NRT and reverse ETL, data is delivered to users in the context in which they work for faster time-to-action.
Configurable data processing pipelines at scale
The Databricks platform provides several tools for building NRT processing and reverse ETL pipelines. The platform’s data engineering and data science features allow organizations to build highly customized data processing pipelines that can be used to process events as they occur. Additionally, Databricks provides a highly scalable and efficient way to process large amounts of data in near-real-time, enabling organizations to quickly respond to events as they occur. The platform also provides a highly secure environment for processing sensitive data, ensuring that data privacy and security are always maintained.
One of the benefits of using Databricks for NRT processing and reverse ETL is the ability to use the platform’s built-in machine learning capabilities to detect anomalies or unusual patterns in the data. For example, the platform can be trained to recognize unusual spikes in customer orders, which can then trigger an immediate alert to the appropriate sales rep or supply chain manager.
A modern data ecosystem
NRT processing and reverse ETL are essential components of a modern data ecosystem, enabling organizations to consume data in real-time and take immediate action based on insights derived from the data. With Databricks, organizations can build highly customized data processing pipelines that can be used to process events as they occur, while also leveraging the platform’s machine learning capabilities to detect anomalies and unusual patterns in the data. This allows organizations to quickly respond to events as they occur, ensuring that business operations run smoothly and efficiently.
