Introduction to Amazon Managed Blockchain and its use cases

In this recipe, we will learn about Amazon Managed Blockchain. We will also learn about the use cases of Amazon Managed Blockchain.

Recipe Objective - Introduction to Amazon Managed Blockchain and its use cases?

The Amazon Managed Blockchain is a widely used service and is defined as a fully managed service that makes it easy to join public networks or create and manage scalable private networks. With blockchain, multiple parties can execute transactions without the need for a trusted central authority. Building a scalable blockchain network today using existing technologies is difficult to set up and maintain. Each network member must manually provision hardware, install software, create and manage access control certificates, and configure networking components to create a blockchain network. Once the blockchain network is up and running, users must constantly monitor the infrastructure and adapt to changes, such as increased transaction requests or the addition of new members. With just a few clicks, users can join public networks or set up and manage scalable private networks with Amazon Managed Blockchain, a fully managed service. Amazon Managed Blockchain eliminates the time and effort required to set up a network or join one that is already in use, and it scales automatically to meet the needs of thousands of applications processing millions of transactions. Managed Blockchain makes it simple to manage and maintain your blockchain network once it's up and running. It keeps track of your certificates and makes it simple to add new members to the network.

Benefits of Amazon Managed Blockchain

  • With Amazon Managed Blockchain, users can quickly create blockchain networks that span multiple AWS accounts with Amazon Managed Blockchain, allowing a group of members to execute transactions and share data without the need for a central authority. Unlike self-hosting the blockchain infrastructure, Amazon Managed Blockchain eliminates the need for manual hardware provisioning, software configuration, and networking and security configuration. Network participants can vote to add or remove members using Managed Blockchain's voting API. Managed Blockchain allows a new member to launch and configure multiple blockchain peer nodes to process transaction requests and store a copy of the ledger once they are added. Managed Blockchain also keeps an eye on the network and replaces nodes that aren't performing well thus it's fully managed. Amazon As the number of applications on the network grows, Managed Blockchain can easily scale the blockchain network. When a network member needs more capacity for creating and validating transactions, they can use Managed Blockchain's APIs to quickly add a new peer node. Managed Blockchain offers a variety of instance types with varying combinations of CPU and memory, giving you the freedom to select the best resource mix for their workload. Additionally, Managed Blockchain uses AWS Key Management Service (KMS) technology to secure its network's certificates, removing the need for users to set up their secure key storage and thus it's scalable and secure. The "ordering service," a component of the Hyperledger Fabric framework that ensures transaction delivery across the blockchain network, is more reliable with Amazon Managed Blockchain. The default ordering service in Hyperledger Fabric does not save a complete transaction history, making it difficult to keep track of and recover transaction history when needed. The ordering service from Managed Blockchain is built on Amazon QLDB technology and has an immutable change log that accurately maintains the complete history of all transactions in the blockchain network, ensuring that users can save this information for the long term and thus provides reliability.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Managed Blockchain and the Use cases of Amazon Managed Blockchain.

Use cases of Amazon Managed Blockchain

    • Its trading and asset transfer

Importers, exporters, banks, shipping companies, and customs departments must all collaborate to conduct business. Financial and trading consortiums can easily create a blockchain network using Amazon Managed Blockchain, allowing all parties to transact and process trade-related paperwork electronically without the need for a central trusted authority. Transactions in a blockchain network built with Managed Blockchain can process instantly, unlike other processes that require trade-related paperwork to be sent back and forth between stakeholders, taking 5-10 days to complete.

    • It finds its use case in Retail

Retailers frequently seek to improve customer loyalty programmes by collaborating with other retailers, banks, and third parties to provide a broader range of customer rewards that can be redeemed across a broad network of partners. Using a central agency as an intermediary for reward transactions can slow things down, taking 5-7 days on average. A group of retailers can use Amazon Managed Blockchain to create a blockchain network that allows them to share and validate rewards information quickly and transparently, without the need for a central authority to process rewards transactions between retailers.

    • It finds its use case in Supply Chain

Small businesses frequently rely on distributed supply chain networks, in which no single entity is in charge of the end-to-end movement of goods. Jewellery stores, for example, must frequently track the provenance of gemstones to ensure their authenticity and value. Such businesses can quickly implement a blockchain across their supply chain network using Amazon Managed Blockchain, providing greater transparency and real-time recording and tracking of goods from one party to the next. Each supplier or distributor can join the blockchain network, create their own distributed ledger, and track all information related to the movement of goods independently, including timestamps, ports of entry, and volume of goods received.

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

Azure Data Factory and Databricks End-to-End Project
Azure Data Factory and Databricks End-to-End Project to implement analytics on trip transaction data using Azure Services such as Data Factory, ADLS Gen2, and Databricks, with a focus on data transformation and pipeline resiliency.

PySpark Project-Build a Data Pipeline using Hive and Cassandra
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations by integrating PySpark with Hive and Cassandra

Create A Data Pipeline based on Messaging Using PySpark Hive
In this PySpark project, you will simulate a complex real-world data pipeline based on messaging. This project is deployed using the following tech stack - NiFi, PySpark, Hive, HDFS, Kafka, Airflow, Tableau and AWS QuickSight.

GCP Data Ingestion with SQL using Google Cloud Dataflow
In this GCP Project, you will learn to build a data processing pipeline With Apache Beam, Dataflow & BigQuery on GCP using Yelp Dataset.

Learn to Build Regression Models with PySpark and Spark MLlib
In this PySpark Project, you will learn to implement regression machine learning models in SparkMLlib.

Python and MongoDB Project for Beginners with Source Code-Part 2
In this Python and MongoDB Project for Beginners, you will learn how to use Apache Sedona and perform advanced analysis on the Transportation dataset.

Orchestrate Redshift ETL using AWS Glue and Step Functions
ETL Orchestration on AWS - Use AWS Glue and Step Functions to fetch source data and glean faster analytical insights on Amazon Redshift Cluster

How to deal with slowly changing dimensions using snowflake?
Implement Slowly Changing Dimensions using Snowflake Method - Build Type 1 and Type 2 SCD in Snowflake using the Stream and Task Functionalities

Build a Streaming Pipeline with DBT, Snowflake and Kinesis
This dbt project focuses on building a streaming pipeline integrating dbt Cloud, Snowflake and Amazon Kinesis for real-time processing and analysis of Stock Market Data.

Build an ETL Pipeline with DBT, Snowflake and Airflow
Data Engineering Project to Build an ETL pipeline using technologies like dbt, Snowflake, and Airflow, ensuring seamless data extraction, transformation, and loading, with efficient monitoring through Slack and email notifications via SNS