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Five Cool Things About Connecting Downstream Systems to Real-Time Data

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Five Cool Things About Connecting Downstream Systems to Real-Time Data

Read on to explore messaging services, aggregated events, processing and persistence, data ingestion, and inventory tracking.

· Database Zone ·
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Compliant Database DevOps and the role of DevSecOps DevOps is becoming the new normal in application development, and DevSecOps is now entering the picture. By balancing the desire to release code faster with the need for the same code to be secure, it addresses increasing demands for data privacy. But what about the database? How can databases be included in both DevOps and DevSecOps? What additional measures should be considered to achieve truly compliant database DevOps? This whitepaper provides a valuable insight. Get the whitepaper

Real-time inventory systems provide a detailed review of the architecture that online retailers need to move to the cloud. Real-time inventory is designed to provide quick updates as soon as the inventory level changes. It helps update stock levels as soon as a retailer sells an item. The increased adoption of online and offline retail strategies as well as multi-channel scenarios have created the need to develop technical solutions to address challenges associated with data streaming.

Real-time inventory improves customer experience by enabling retailers to spend plenty of time helping customers find their desired products. Furthermore, real-time inventory helps increase operational efficiency by making it easier for retailers to know the exact amount of stock available. Actionable insights into inventory levels provide critical information that retailers need in order to devise ways to bring their products to the market. However, retailers need to deploy the right infrastructure for their real-time inventory systems to work. It could mean leveraging a cloud-based architecture and managed software systems to improve data processing and warehousing and reduce administrative intricacy. Below are some of the benefits of connecting downstream systems with real-time data.

Messaging Services

Messaging ensures that apps and services communicate in real time when it comes to the deployment of cloud infrastructure. For example, Google Cloud is a real-time messaging service that enables services and apps to send information through delivery schemes. As such, you can use Google Cloud to support several message delivery semantics that requires real-time communication. Google Cloud enables the messaging component to relay the information from the inventory system to downstream components.

Aggregated Events

Real-time inventory systems let retailers load incoming inventory data into a managed database software. Furthermore, it separates computing and storage functions to enable retailers to scale each service independently. This means that retailers can execute queries in a matter of seconds. Moreover, you can split the output into multiple tables to make it easier to view the incoming streams. You can organize the tables in numerous dimensions or aggregation levels such as weekly, hourly, or daily.

Processing and Persistence

The core service of a real-time inventory database is to stream processes. Stream processing receives inventory data streams, examines each incoming stream, and applies outbound and in-flight rules such as persistence. The rules could include roll-ups or time-based aggregations centered on each store or product. However, these types of workloads can be handled using cloud data flow. It can distribute stream processing across multiple databases and rebalance the workload. Data streaming platforms, such as Apache Kafka, help you pull the data that is streaming from the cloud to make it easier to view the incoming streams. You can use the cloud dataflow SDK to apply a series of operations within each data window.

Data Ingestion

The core function of a real-time inventory system is to receive data streams from each store and pass them to the downstream components. However, this service has to meet several functional standards. It has to provide low-latency response time and scale up or down as the data continues to flow. Furthermore, the service must authorize and authenticate devices accessing critical information to ensure only recognized devices can send data. Google App Engine meets all these requirements and can be an appropriate solution. It lets you deploy scalable web apps without the need to configure each server. In fact, it can scale up or down in real time based on the intensity of data streams. It is well suited to the needs of a real-time inventory system as it comes with security scanning, built-in load balancing, and versioning features.

Inventory Tracking

Real-time inventory systems use a tracking system that combines tagged merchandise and a gateway responsible for data streaming. Tracking systems use tag readers such as RFID tags because they're lightweight to attach to a product. You can position tag readers throughout the warehouse to make it easier to scan RFID tags. You can deploy more gateways depending on the size of your retail store. RFID tags collect data from each active scan and reader's passive before streaming it to centralized infrastructure.

Compliant Database DevOps and the role of DevSecOps DevOps is becoming the new normal in application development, and DevSecOps is now entering the picture. By balancing the desire to release code faster with the need for the same code to be secure, it addresses increasing demands for data privacy. But what about the database? How can databases be included in both DevOps and DevSecOps? What additional measures should be considered to achieve truly compliant database DevOps? This whitepaper provides a valuable insight. Get the whitepaper

Topics:
database ,stream computing ,real-time data ,data streams

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