It has a flexible approach to data privacy: data isolation where it uses

“channels” or share private data on a need-to-know basis using private

data “collections”

It offers multi-language smart contract support with Go, J ava, and

J avaScript

It has support for EVM and Solidity

It is designed for continuous operations, including rolling upgrades and

asymmetric version support

It offers governance and versioning of smart contracts

It has a flexible endorsement model for achieving consensus across

required organizations

It offers queryable data (key-based queries and J SON queries)

The security, usability, robustness, performance, and feature set—

all qualities

that are of critical importance to enterprise users have been continuously

improved by a strong Hyperledger Fabric community which is comprised of

many world-class technology providers and individual contributors, all

collaborating to evolve blockchain technologies at a record pace. It supports

the foundation for innovation, quality, and quick delivery that only open source

can provide due to the diverse ecosystem. Blockchain is a foundational

technology, one that is poised to transform and optimize the way the world

transacts; it only makes sense for the technology to be open and for a variety of

contributors to be involved. There are several key contributors to Hyperledger

Fabric, and you can find them listed at: hyperledger.org/ resources/ vendor-

directory

What drives continuous, rapid innovation of Hyperledger Fabric is the

combination of industry, academic, and individual contributions.

There exist hundreds of networks in production today with blockchain

technology has moved forward. Many of the production blockchain solutions

in production today are built with Hyperledger Fabric. A list of tools and

solutions

built

with

Hyperledger

Fabric

can

be

found

at:

hyperledger.org/resources/blockchain- show case.

How data privacy is maintained? End-to-End transaction flow

In Hyperledger Fabric, the four aspects of maintaining data privacy are: