With the rapid growth of the Industrial Internet of Things (IIoT) devices, managing extensive volume of IIoT data becomes a significant challenge. While the conventional cloud storage approaches with centralised data centres suffer from high latency for large-scale IIoT data storage due to the increased communications and latency overheads, distributed storage frameworks such as blockchains have become promising solutions. In this paper, we design and analyse a dual-blockchain framework for secure and scalable distributed data management in large-scale IIoT networks. The proposed framework, named MapChain-D , consists of a data chain that is mapped to an index chain to provide efficient data storage and lookup. MapChain-D is designed for practical IIoT applications with storage, latency, and communications constraints. Detailed data exchange protocols are presented for the data insertion and retrieval operations in MapChain-D . Based on these, theoretical analyses are provided on the space, time, and communications complexities of MapChain-D compared with conventional single-chain frameworks with local and distributed data storage. We implement our MapChain-D prototype using open-source LoRaWAN communications with multiple RPi and Arduino devices, Kademlia-based distributed hash table (DHT), and Ethereum-based blockchain with proof-of-authority (PoA) consensus. Experimental results from our prototype show that MapChain-D is more suitable to be deployed on resource-constrained IIoT devices. We also highlight the scalability and flexibility of MapChain-D with different numbers of edge nodes in the system.