Build with Bacalhau: Analysing Ethereum Data with Bacalhau скачать в хорошем качестве

Build with Bacalhau: Analysing Ethereum Data with Bacalhau

In this video we walk through how to analyse Ethereum data on Bacalhau. This example aims to show how easy and efficient it is to analyse large datasets with Bacalhau. We'll do this by: Running a local Analysis of a subset of ethereum data Using Bacalhau to analyse this same data subset Using Bacalhau for batch processing the entire ethereum dataset captured and plotting the transaction volumes against block datetimes. Full documentation for this example can be found here: 👉 https://docs.bacalhau.org/examples/da... 00:00 Intro to Bacalhau 02:50 Analysing Ethereum Data with Bacalhau 06:00 Local Analysis of a subset of Ethereum Data 09:50 Using Bacalhau to analyse the same subset of Ethereum Data 16:30 Massive Scale Analysis of the full Ethereum Dataset with Bacalhau ============================================================ About Bacalhau: Currently Filecoin is known for being a decentralised storage and retrieval network, providing over 17 EiB of distributed data storage capacity to the world through thousands of Storage providers globally. And we can think of this distributed storage network as being the layer zero of the Filecoin Architecture Network because .. Storage is really only the start! Bacalhau is aiming to revolutionise the way we compute and analyse over and with the data on both the Filecoin network and elsewhere by providing a platform for public, transparent, and optionally verifiable computation. It enables users to run arbitrary Docker containers and WebAssembly (wasm) images as tasks against data stored in the InterPlanetary File System (IPFS) It operates as a peer-to-peer network of nodes where each node has both a requestor and compute component What will you build?? Links: 🌐 See more examples on the docs: https://docs.bacalhau.org 🦄​ Follow our twitter @BachalauProject 👨‍👩‍👦‍👦 Join the chat: #bacalhau on the Filecoin Slack Channel (visit filecoin.io to join)