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  1. Stackups
  2. Application & Data
  3. Databases
  4. Blockchain
  5. BigchainDB vs IPFS

BigchainDB vs IPFS

OverviewComparisonAlternatives

Overview

IPFS
IPFS
Stacks209
Followers181
Votes0
BigchainDB
BigchainDB
Stacks27
Followers71
Votes0
GitHub Stars4.0K
Forks769

BigchainDB vs IPFS : What are the differences?

Introduction

In this article, we will explore the key differences between BigchainDB and IPFS, two popular technologies used in decentralized data storage and management. Both BigchainDB and IPFS offer innovative approaches to address the challenges of traditional centralized databases and file systems.

  1. Data Structure and Use Case: BigchainDB is a blockchain database that enables the storage and management of structured data, making it suitable for scenarios that require querying and indexing. On the other hand, IPFS (InterPlanetary File System) is a distributed file system that focuses on storing and sharing files across a decentralized network. IPFS can handle unstructured data and is well-suited for scenarios where content addressing is crucial.

  2. Consensus Mechanism: BigchainDB employs a consensus algorithm called "Byzantine Fault Tolerant (BFT) consensus," which is similar to traditional blockchain systems. BFT consensus ensures data integrity through a decentralized network of nodes, making it suitable for use cases that demand high reliability and security. IPFS, however, does not use a specific consensus mechanism. It relies on content addressing and a distributed hash table (DHT) protocol to ensure content availability and persistence.

  3. Data Duplication and Deduplication: In BigchainDB, each transaction creates a new block which contains the entire dataset. This approach results in data redundancy, but it enables efficient querying and indexing. On the other hand, IPFS uses a content-addressable system, where identical files are automatically deduplicated. Instead of duplicating the same file multiple times, IPFS creates a single content identifier for each unique file, reducing storage redundancy.

  4. Data Accessibility and Decentralization: BigchainDB operates as a permissioned blockchain, where access to data is controlled by predefined rules and permissions. It allows for different levels of access based on user roles and can integrate with existing authentication systems. IPFS, on the other hand, is a permissionless network that promotes open and unrestricted access to data. Anyone can read or write data to IPFS without requiring permissions or authentication, providing greater decentralization but potentially raising concerns about data privacy and security.

  5. Blockchain Integration: BigchainDB is designed to integrate with blockchain networks like Ethereum. It can write metadata and references to transactions on a blockchain, enabling additional functionalities such as smart contracts and interoperability with other blockchain applications. IPFS, on the other hand, can be used as a decentralized storage layer for blockchain platforms, allowing blockchain applications to benefit from decentralized file storage and content addressing.

  6. Scalability and Performance: BigchainDB focuses on providing high throughput and low latency compared to traditional blockchain systems. By using a database model instead of a raw ledger, BigchainDB achieves higher transaction speeds and can handle a large volume of data. IPFS, on the other hand, prioritizes data availability and resilience. While it can handle large file sizes and distribute data efficiently, the performance and latency of IPFS may vary based on network conditions and the availability of data nodes.

In summary, BigchainDB offers structured data storage with BFT consensus, permissioned control, and blockchain integration, suitable for scenarios that require querying and indexing. IPFS, on the other hand, focuses on decentralized file storage with content addressing, permissionless access, and seamless integration with blockchain platforms. Both technologies provide unique solutions to decentralized data management based on their distinct goals and design choices.

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Detailed Comparison

IPFS
IPFS
BigchainDB
BigchainDB

It is a protocol and network designed to create a content-addressable, peer-to-peer method of storing and sharing hypermedia in a distributed file system.

It is designed to merge the best of two worlds: the “traditional” distributed database world and the “traditional” blockchain world. With high throughput, low latency, powerful query functionality, decentralized control, immutable data storage and built-in asset support.

IPFS is a peer-to-peer distributed file system that seeks to connect all computing devices with the same system of files. In some ways, IPFS is similar to the World Wide Web, but IPFS could be seen as a single BitTorrent swarm, exchanging objects within one Git repository. In other words, IPFS provides a high-throughput, content-addressed block storage model, with content-addressed hyperlinks.[11] This forms a generalized Merkle directed acyclic graph (DAG). IPFS combines a distributed hash table, an incentivized block exchange, and a self-certifying namespace. IPFS has no single point of failure, and nodes do not need to trust each other not to tamper with data in transit.
Decentralization; Immutability; Native Support of Multiassets; Byzantine Fault Tolerant (BFT); Low Latency; Traditional Stack
Statistics
GitHub Stars
-
GitHub Stars
4.0K
GitHub Forks
-
GitHub Forks
769
Stacks
209
Stacks
27
Followers
181
Followers
71
Votes
0
Votes
0
Integrations
No integrations available
Golang
Golang
Python
Python
C++
C++
Blockchain
Blockchain
Wagyu
Wagyu

What are some alternatives to IPFS , BigchainDB?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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