Bitquery

bitquery

Creating a web app for historical data analysis of blockchain data using React and Tailwind CSS is an excellent way to build a user-friendly and visually appealing application. In this tutorial, you will use API to analyze block data. Bitquery's API provides access to various blockchain data, making it a powerful tool for analysis. Bitquery's Ethereum You will focus on Ethereum blocks and extract valuable information from the provided data. How to get Bitquery API Go to to see blockchain data from different chains. https://explorer.bitquery.io/ Click on any blockchain of your choice(for this tutorial, you will click Gorlie Ethereum Testnet) To get the API, click the " " button on the right. Get API Click the green button to query the blockchain. You get a response data to analyze. Prerequisites Before you start, make sure you have the following prerequisites: You need an from Bitquery to access their services. You can sign up for an account and obtain an API key from their website. Bitquery API Key: API key You can use a tool like cURL or a programming language like javascript to request HTTP to the Bitquery API. () API Client: Ensure you have Node.js and npm (Node Package Manager) installed on your computer. If you haven't already, you can install them from the official website: . Nodejs: Node.js 💡 This tutorial focuses on the first Ethereum block in the data. Step 1: Create a New React App Start by creating a new React app using Create React App. Open your terminal and run the following commands: npx create-react-app blockchain-analysis-app
cd blockchain-analysis-app Step 2: Install Dependencies In your project directory, install the necessary dependencies. You'll need for making API requests, for data visualization and for styling: Axios Chart.js Tailwind CSS npm install axios chart.js tailwindcss Step 3: Create Components In the Folder, create the following components: src : This component will fetch historical blockchain data from Bitquery's API. DataFetcher.js : This component will render the data using Chart.js for visualization. Chart.js : This component will generate reports based on the data. Report.js Step 4: Implement DataFetcher Component In , you can use Axios to fetch historical blockchain data from Bitquery's API. Add this code to the DataFetcher.js DataFetcher.js import React, { useEffect, useState } from "react";
import axios from "axios";

const DataFetcher = ({ onDataFetched }) => {
  const [historicalData, setHistoricalData] = useState([]);

  useEffect(() => {
    // Define your API endpoint
    const API_ENDPOINT = "https://graphql.bitquery.io/";
    // Define your API key
    const API_KEY = "YOUR_BITQUERY_API_KEY"; // Replace with your Bitquery API key

    // Define the query variables
    const variables = {
      network: "ethereum",
      limit: 10,
      offset: 0,
      from: "2023-10-01T00:00:00Z",
      till: "2023-10-30T23:59:59Z",
    };

    // Define the GraphQL query
    const query = `
      query ($network: EthereumNetwork!, $limit: Int!, $offset: Int!, $from: ISO8601DateTime, $till: ISO8601DateTime) {
        ethereum(network: $network) {
          blocks(
            options: {desc: "height", limit: $limit, offset: $offset}
            time: {since: $from, till: $till}
          ) {
            timestamp {
              time(format: "%Y-%m-%d %H:%M:%S")
            }
            height
            transactionCount
            address: miner {
              address
              annotation
            }
            reward
            reward_usd: reward(in: USD)
            rewardCurrency {
              symbol
            }
          }
        }
      }
    `;

    // Make an API request to fetch historical blockchain data from Bitquery
    axios
      .post(
        API_ENDPOINT,
        {
          query: query,
          variables: variables,
        },
        {
          headers: {
            "Content-Type": "application/json",
            "X-API-KEY": API_KEY,
          },
        }
      )
      .then((response) => {
        const data = response.data.data.ethereum.blocks;
        setHistoricalData(data);
        onDataFetched(data);
      })
      .catch((error) => {
        console.error("Error fetching data:", error);
      });
  }, []);

  return (
    <div>{/* Display a loading indicator or data summary if needed */}</div>
  );
};

export default DataFetcher; 💡 Replace with your actual Bitquery API key. 'YOUR_BITQUERY_API_KEY' Step 5: Implement Chart Component In , you can use Chart.js to create visualizations from the historical data. Chart.js Add this code to the Chart.js import React, { useEffect, useRef } from "react";
import Chart from "chart.js/auto";

const ChartComponent = ({ data }) => {
  const chartRef = useRef(null);
  const chartInstance = useRef(null);

  useEffect(() => {
    if (!data || data.length === 0 || !Array.isArray(data)) {
      return;
    }

    const ctx = chartRef.current.getContext("2d");

    // Destroy the previous Chart instance if it exists
    if (chartInstance.current) {
      chartInstance.current.destroy();
    }

    chartInstance.current = new Chart(ctx, {
      type: "line",
      data: {
        labels: data.map((item) => item.height),
        datasets: [
          {
            label: "Transaction Count",
            data: data.map((item) => item.transactionCount),
            borderColor: "rgb(75, 192, 192)",
            borderWidth: 1,
          },
        ],
      },
      options: {
        scales: {
          x: {
            type: "linear",
          },
        },
      },
    });
  }, [data]);

  return (
    <div className="w-full md:w-3/4 mx-auto">
      <canvas ref={chartRef}></canvas>
    </div>
  );
};

export default ChartComponent; This component will render a line chart based on the historical data provided. Step 6: Implement Report Component In , you can generate reports based on historical blockchain data. Report.js Add this code to the Report.js import React from "react";

const Report = ({ data }) => {
  // Implement logic to generate reports from the data
  return <div></div>;
};

export default Report; Step 7: Styling with Tailwind CSS Tailwind CSS allows you to style your React components easily. You can customize the appearance of your components by adding Tailwind CSS classes directly to your JSX elements. For example, in the code provided, Tailwind CSS classes are used for styling. Step 8: Use Components in App.js In , you can use the created components to display the data, visualizations, and reports. Add this code to the src/App.js App.js import React, { useState } from 'react';
import './App.css';
import DataFetcher from './DataFetcher';
import ChartComponent from './Chart';
import Report from './Report';

function App() {
  const [historicalData, setHistoricalData] = useState([]);

  const onDataFetched = (data) => {
    setHistoricalData(data);
  };

  return (
    <div className="bg-gray-100 p-4">
      <div className="max-w-3xl mx-auto">
        <header className="bg-white p-4 shadow-md rounded-lg mb-4">
          <h1 className="text-2xl font-semibold mb-2">Blockchain Historical Data Analysis</h1>
          <DataFetcher onDataFetched={onDataFetched} />
          <ChartComponent data={historicalData} />
          <Report data={historicalData} />
        </header>
      </div>
    </div>
  );
}

export default App; Step 9: Start the Development Server Start your React app with the following command: npm run dev Your app will be available in your web browser at . It will fetch historical blockchain data, display it in a chart, and generate a simple report, all styled using Tailwind CSS. http://localhost:3000 Step 10: Analyzing the Response After making the API request, you'll receive a response from Bitquery. JSON Here's a breakdown of the important fields in the response: : The timestamp when the block was mined. timestamp : The block's height or number. height : The number of transactions in the block. transactionCount : The address of the miner. address : The block reward in (Gas Token Holder). reward GTH : The block reward in . reward_usd USD : The symbol of the currency used for the reward (in this case, ). rewardCurrency GTH Conclusion In this tutorial, you have seen how to use Bitquery's API to access and analyze Ethereum block data. By leveraging the power of Bitquery, you can gain insights into the Ethereum network's activity and make informed decisions based on blockchain data. You can extend this analysis to multiple blocks, timeframes, or any other criteria you need for your project.

Analyzing Ethereum Block Data with Bitquery's API

This is not a history lesson on Bitcoin, & I’m not going to preach about how incredible it is (which it is). This is meant to serve you as a simple resource to get all of the data/information that relates to Bitcoin so that you can create your own unique toolkit. Like a domino effect from hell, institutions that got faked out by COVID & the FED are beginning to topple. It has been unfortunate for many innocent people that got unrightfully caught in the crossfire, my heart goes out to you. However, as one door closed, another opened. This economic shitshow has accelerated the adoption of crypto. Moving forward, digital assets will only grow in popularity as they prove to be more trustworthy than any government; especially BTC. If you still don’t agree with this statement, GTFO. If you don’t want to be the government’s bit*h anymore, WELCOME! Being the foundation upon which the greater cryptocurrency industry has been built, BTC has become the defacto choice for individuals & institutions that are serious about building a diversified, evergreen portfolio. The most difficult task of all when getting into the cryptoverse is organization. Back in the day, navigating the interwebs for Bitcoin was a daunting task; tons of single purpose sites that were tucked away in the ultra-niche crevices of private chatrooms & cypherpunk threads were unusable by many. After 10 years of innovation, one would think its much better now…however, the smorgasbord of multi-purpose tools that are not so neatly randomized by the highest bidder under googles algorithms causes just as much organizational confusion. Instead of just telling you to just go scavenger the internet for different tools/resources, I’ve tried to organize them for you below. The vast majority of these tools are free; very few are behind paywalls. While I don’t appreciate paywalls, I must admit that any paid service included below are honestly awesome. The Original Bitcoin Whitepaper https://bitcoin.org/bitcoin.pdf Market Data: Prices, money flows, trading pairs, exchanges & all the other good stuff — LiveCoinWatch https://www.livecoinwatch.com/price/Bitcoin-BTC — CoinMarketCap https://coinmarketcap.com/currencies/bitcoin/ — Messari https://messari.io/asset/bitcoin — CryptoCompare https://www.cryptocompare.com/coins/btc/overview/ — CoinCodex https://coincodex.com/crypto/bitcoin/ — Conlib https://coinlib.io/coin/BTC/Bitcoin — Blockchain https://www.blockchain.com/explorer/assets/btc — TheBlock https://www.theblock.co/data/on-chain-metrics/bitcoin — CoinDance https://coin.dance/stats — Bitcoinity.org https://data.bitcoinity.org/markets/volume/30d?c=e&t=b — LunarCrush https://lunarcrush.com/coins/btc/bitcoin Tokensight— https://tokeninsight.com/en/coins/2365/overview On-Chain Explorers: Raw blockchain data: blockheights, blocktimes, mempools, & more. — Blockchair https://blockchair.com/bitcoin — Blockstream https://blockstream.info/ — Bitaps https://bitaps.com/ — Bitquery https://explorer.bitquery.io/bitcoin — Blockcypher https://live.blockcypher.com/btc/ — BitcoinExplorer https://bitcoinexplorer.org/ — Sochain https://sochain.com/BTC — Blockpath https://blockpath.com/ — OXT https://oxt.me/ Cool Extras: Data, stats, & insights that you won’t find anywhere else. — Fear & Greed Index https://alternative.me/crypto/fear-and-greed-index/ — Bitcoin Death Tracker https://99bitcoins.com/bitcoin-obituaries/ BitcoinAbuse — https://www.bitcoinabuse.com/ Bitnodes — https://bitnodes.io/ Educational: I have only used a few of these, but have recommended the others to many of my friends that have used & loved them. — Bitcoin Intro https://bitcoin-intro.com/ — Nakamoto Institute https://nakamotoinstitute.org/literature/ — Crypto Startup School https://a16zcrypto.com/crypto-startup-school/ — Cryptocurrency Engineering & Design (MIT) https://ocw.mit.edu/courses/mas-s62-cryptocurrency-engineering-and-design-spring-2018/video_galleries/lecture-videos/ — Blockchain & Money (MIT) https://ocw.mit.edu/courses/15-s12-blockchain-and-money-fall-2018/video_galleries/video-lectures/ — Bitcoin for Everyone https://learn.saylor.org/course/view.php?id=468 — LearnMeABitcoin https://www.youtube.com/@learnmeabitcoin/videos?view=0&sort=dd&shelf_id=0 Books: This is a very small selection of books that I have read (par 1). There are not all just about Bitcoin; these are readings that help structure a mental framework for understanding the bigger picture & implications of it. — The Bitcoin Standard https://saifedean.com/books/the-bitcoin-standard — The Fiat Standard https://saifedean.com/books/the-fiat-standard — Antifragile https://www.amazon.com/Antifragile — The Black Swan https://www.amazon.com/Black-Swan — Digital gold https://www.amazon.com/Digital-Gold — Book of Satoshi https://www.bookofsatoshi.com/ — The Blocksize War https://www.amazon.com/Blocksize-War — Mastering Bitcoin https://github.com/bitcoinbook/bitcoinbook Podcasts: Listen to some of the best quality conversations & discussions about Bitcoin. What Bitcoin Did — https://www.whatbitcoindid.com/ The Bitcoin Standard — https://saifedean.com/podcast Bitcoin Audible — https://bitcoinaudible.com/ Orange Pill — https://orangepill.buzzsprout.com/ Bitcoin Infinity — https://bitcoininfinitypodcast.com/ Blue Collar Bitcoin — https://bluecollarbitcoinpodcast.podbean.com/ Bitcoin Matrix — https://bitcoinmatrix.libsyn.com/ Why Bitcoin — https://whybitcoinpodcast.com/ Cashback & Micro Rewards: A little free fun along the way never hurt anyone (ref links below) — Fold App https://foldapp.com/ — Lolli https://www.lolli.com/ — Satsback https://satsback.com/ — Bitcoinget https://www.bitcoinget.com/ — FreeBitcoin Faucet https://freebitco.in/site/bitcoin-faucet/ These are just a few of the resources that I am personally familiar with & use/have used on a regular basis. There are many reputable people that provide more incredible resources in the space such as & . Jameson Lopp Andreas Antonopoulos I always suggest rather than getting lost in the organization of a million links, just start somewhere. Before you know it, you’ll get further than you think. A little Poem on BTC to close this out: Hard times come & go. Great men suffer & they grow. Fear not failure dear friend, for it too shall pass. Until that time comes, more Bitcoin you must amass See all you sexy motherfuckers on the Moon 🥂 Also published here

All of the Tools You Need to Get Involved With Bitcoin: Get Ahead of the Game

Information is the foundation of good decision-making. Cryptocurrency innovates at the speed of light. New protocols are constantly coming to market, promising to solve some of mankind's greatest financial troubles. While their intentions might be good, the result is a cluttered, noisy environment that promotes FOMO & makes it nearly impossible to make sound decisions. In order to succeed in the wild, volatile, & often opaque world of cryptocurrency it is necessary to quiet the noise & hone in on real signals. The best way to do that is by building out your own Crypto ToolKit. Quick Notes on building a Crypto ToolKit: -) Do not use every single tool (unless you’re a crypto savant) -) Review each resource/tool individually (5–10 min per resource) -) Select only the ones that appeal most to your specific preferences As you might already be aware, the crypto-verse is highly fragmented in the sense that there are many different chains & protocols. Given that not all chains & protocols are interoperable (yet), all of the tools listed provided below are will be as general & multi-purpose as possible. Without further ado, Let's dive in: 📊 Market Data 📊 The starting point of almost every person’s crypto journey, Market Data providers are listing sites that aggregate all open financial digital asset data. All of the fundamental economic elements, Price, Volume, Circulating supply, Orderbooks, market dominance, historic data, & everything else. 1) Token Terminal - https://tokenterminal.com/ 2) Messari - https://messari.io/ 3) Crypto Slate - https://cryptoslate.com/coins/ 4) CoinGlass - https://www.coinglass.com/ Coin Market Cap -** 5) https://coinmarketcap.com/ - 6) Coin Gecko https://www.coingecko.com/ 7) Coin Metrics - https://coinmetrics.io/ 8) Coinlib - https://coinlib.io/ 9) Crypto Compare - https://www.cryptocompare.com/ 10) CoinCap - https://coincap.io/ - 11) CoinRanking https://coinranking.com/ 12) Coin360 — https://coin360.com/ — 13) Bitgur https://bitgur.com/ — 14) Nomics https://nomics.com/ - 15) CryptoWatch https://cryptowat.ch/ 16) BraveNewCoin — https://bravenewcoin.com/ 17) LiveCoinWatch — https://www.livecoinwatch.com/ 18) MultiCoinCharts - https://www.multicoincharts.com/home/ 19) Nansen - https://www.nansen.ai/ 20) CoinPaprika - https://coinpaprika.com/ 21) CoinAlpha - https://coinalpha.app/ 22) CoinScope - https://www.coinscope.co/ 23) DropsTabs - https://dropstab.com/ 24) CryptoView - https://www.cryptoview.com/ 25) CoinCarp - https://www.coincarp.com/ 26) Mytokencap - https://www.mytokencap.com/ 27) CoinCodex — https://coincodex.com/ — 28) CoinLobster https://coinlobster.com/ 💱 DEFI Dashboard & Aggregators💱 Decentralized finance is an incredibly broad term that includes a wide range of applications that vary based on functionality & intended purpose. While the majority of current DEFI activity happens across 4 blockchains (Ethereum, Binance Smart Chain, Polygon & Solana) there are so many up & coming alternative platforms such as Optimism & Arbitrum that have been garnering attention that for the sake of brevity I will only list the few products that I am personally familiar with. 1) Dapp Radar — https://dappradar.com/defi 2) DEFI Llama — https://defillama.com/ 3) DEFI Prime — https://defiprime.com/ 4) DEFI Pulse — https://www.defipulse.com/ 5) YieldWatch - https://www.yieldwatch.net/ 6) ZapperFI - https://zapper.xyz/ 7) Zerion - https://zerion.io/ 8) InstaDapp - https://instadapp.io/ 9) Frontier - https://www.frontier.xyz/ 10) Apebored - https://apeboard.finance/ 11) Debank - https://debank.com/ - 12) APYvision https://app.apy.vision/ - 13) Plasma Finance https://plasma.finance/ - 14) Farm Army https://farm.army/ - 15) DEFISaver https://defisaver.com/ - 16) Gnosis Safe https://gnosis-safe.io/ - 17) DefiYield https://defiyield.app/ 18) Coin98 - https://coin98.com/ 19) AstroTools - https://astrotools.io/ 20) MultiFarm - https://www.multifarm.fi/ 21) OctoFi - https://octo.fi/ 22) Parallel - https://parallel.fi/ 23) Exponential - https://exponential.fi/ 24) WalletDefi - https://walletdefi.app/ 25) DEFImarketCap - https://defimarketcap.io/ 26) QuantifyCrypto - https://quantifycrypto.com/ 📰 News 📰 Crypto is becoming all the more politicized as time goes on. An absurd amount of crypto/DEFI news sites pop up constantly & push their own agendas. The best way to source news (in any industry) is by having a few reliable individual outlets & cross-referencing information across them all. 1) CoinDesk - https://www.coindesk.com/ 2) Coin Telegraph - https://cointelegraph.com/ 3) Bitcoin Magazine - https://bitcoinmagazine.com/ 4) The Cryptonomist - https://en.cryptonomist.ch/ 5) Be In Crypto - https://beincrypto.com/ 6) CoinGape - https://coingape.com/ 7) CryptoSlate - https://cryptoslate.com/ 8) Crypto Briefing - https://cryptobriefing.com/ 9) CryptoDaily - https://cryptodaily.co.uk/ 10) Crypto Potato - https://cryptopotato.com/ 11) BraveNewCoin - https://bravenewcoin.com/ 12) Coin Journal - https://coinjournal.net/ 13) Coin Speaker - https://www.coinspeaker.com/ 14) CoinCu - https://news.coincu.com/ 15) Bitcoinist - https://bitcoinist.com/ 16) CryptoPanic - https://cryptopanic.com/ 17) Crypto News - https://cryptonews.com/ 18) U Today - https://u.today/ 19) CoinGraph - https://www.coingraph.news/ - 20) Decrypt https://decrypt.co/ - 21) Blockworks https://blockworks.co/ - 22) CryptoCapitalNews https://cryptocapitalnews.com/ — 23) Protos https://protos.com/ 👛 Wallets 👛 Wallets are the foundation for getting involved & staying in crypto. Wallets are exactly what they sound like, places to securely store/view your crypto asset holdings. Wallets typically come in 2 flavors, Chain-specific & Chain-agnostic. Chain-specific wallets are tied to a single blockchain platform (i.e ETH/BNB). These will only be able to store their native chain tokens, but allow the users to interact with on-chain assets/applications. Whereas chain-agnostic wallets might allow their users to store crypto assets from any blockchain, but that is the limit of their capabilities as they are usually single-purpose only (storage) 1) MetaMask - https://metamask.io/ 2) MEW - https://www.myetherwallet.com/ 3) Dynamic - https://www.dynamic.xyz/ 4) XDEFI - https://www.xdefi.io/ 5) Enkryptic - https://www.enkrypt.com/ 6) Argent - https://www.argent.xyz/ 7) Dopamine - https://dopamineapp.com/ 8) TrustWallet - https://trustwallet.com/ 9) Eidoo - https://eidoo.app/ 10) Dapppocket - https://www.dapppocket.io/ 11) Atomic Wallet - https://atomicwallet.io/ 12) Bread Wallet - https://brd.com/ 13) Guarda - https://guarda.com/ 14) Exodus - https://www.exodus.com/ 15) ZenGo - https://zengo.com/ 16) Coinomi - https://www.coinomi.com/en/ 17) Luno - https://www.luno.com/en-US 18) Ambire - https://www.ambire.com/ 19) Jaxx - https://jaxx.io/ 20) Edge - https://edge.app/ 21) Freewallet - https://freewallet.org/ 22) Coinbase Wallet - https://www.coinbase.com/wallet 23) Coin Wallet - https://coin.space/ 24) Ballet Wallet - https://www.ballet.com/coins/ 25) Unstoppable - https://unstoppable.money/ 26) Brave Wallet - https://brave.com/wallet/ 27) BitPay - https://bitpay.com/wallet/ 28) Crypto.com - https://crypto.com/us/defi-wallet 🔎 Explorers 🔍 Explorers are software tools that allow users to inspect the activity that has happened/is happening on-chain. Explorers are typically built-to-blockchain, meaning every chain needs its own explorer. Given the girth of data these tools access, explorers tend to be multi-purpose tools (i.e. data providers/ analytics dashboards/compliance). 1) Ethscan - https://etherscan.io/ 2) Bitquery - https://explorer.bitquery.io/ 3) Blockchair - https://blockchair.com/ 4) Tokenview - https://tokenview.io/ 5) Blockcypher - https://www.blockcypher.com/ 6) GetBlock - https://getblock.io/explorers/ 7) ViewBlock - https://viewblock.io/ - 8) BreadCrumbs https://www.breadcrumbs.app/ 9) Beaconchain - https://beaconcha.in/ 10) Blockstream - https://blockstream.info/ 11) Ethplorer - https://ethplorer.io/ 12) OXT - https://oxt.me/ 13) Xscan - https://xscan.io/ 14) Sochain - https://sochain.com/ 15) OkLink - https://www.oklink.com/en 16) Pingpub - https://ping.pub/ ⛓ On-Chain Data ⛓ On-chain data tools are closely related to explorers in the sense that they allow users to inspect the activity that has happened / is happening on-chain. The main difference between the On-chain Data tools & Explorers is the UI & granularity of data they provide.*The main difference between On-Chain data providers & traditional market data is the absence of information that is external to any blockchain. 1) Glassnode - https://glassnode.com/ 2) Dune Analytics - https://dune.com/home 3) IntoTheBlock - https://www.intotheblock.com/ - 4) Mempool https://mempool.space/ 5) Artemis - https://www.artemis.xyz/ - 6) Parsiq https://www.parsiq.net/ 🌉 Bridges 🌉 Within the context of cryptocurrency, a bridge is a software tool or protocol that allows otherwise non-compatible blockchains a method to transfer digital assets between one another. 1) MultiChain - https://multichain.org/ 2) CrossChain Bridge - https://www.crosschainbridge.org/ 3) Celer C Bridge - https://cbridge.celer.network/1/56/USDC 4) Synapse - https://synapseprotocol.com/ 5) Connext - https://bridge.connext.network/USDC-from-ethereum-to-binance 6) Relay - https://www.relaychain.com/ 7) Symbiosis - https://symbiosis.finance/ 8) Umbria - https://umbria.network/ 9) EverBridge - https://www.everrise.com/everbridge/ 10) Bungee - https://www.bungee.exchange/ 11) Portal Bridge - https://www.portalbridge.com/#/transfer 12) Nomad - https://www.nomad.xyz/ 13) Hop - https://hop.exchange/ 14) PolyBridge - https://bridge.poly.network/ 15) SolarBeam - https://app.solarbeam.io/bridge 16) Orbitchain - https://orbitchain.io/about 💹 DEXes 💹 Decentralized Exchanges are the new financial trading paradigm. Generally free from traditional regulatory scrutiny, DEXs are places to buy, sell, swap & provide liquidity for crypto assets. 1) UniSwap - https://uniswap.org/ 2) 1inch - https://app.1inch.io/ 3) DexTools - https://www.dextools.io/app/en 4) DexGuru - https://dex.guru/ 5) GeckoTerminal - https://www.geckoterminal.com/ 6) PancakeSwap - https://pancakeswap.finance/ 7) TraderJoe - https://traderjoexyz.com/avalanche 8) Arken - https://swap.arken.finance/ 9) Flooz - https://flooz.trade/trade 10) Balancer - https://balancer.fi/ 11) Bancor - https://home.bancor.network/ 12) Airswap - https://www.airswap.io/#/ - 13) Sushiswap https://www.sushi.com/ 14) Paraswap - https://www.paraswap.io/ 15) Jupiter - https://jup.ag/ 16) ThorChain - https://thorchain.org/ 17) WooFi - https://fi.woo.org/ 18) DYDX - https://trade.dydx.exchange/ - 19) Slingshot https://app.slingshot.finance/swap/ETH 20) Apex Exchange - https://www.apex.exchange/ 21) Osmosis - https://app.osmosis.zone/ 22) Curve - https://curve.fi/#/ethereum/swap 23) Tokenlon - https://tokenlon.im/instant 24) Swapz — https://www.swapz.app/ 📅 Calendars & Event Schedules 📅 Keeping up with the Kardashians is much easier than keeping up with your favorite crypto protocol; the question then becomes HOW do you plan to manage the onslaught on events? While there is no easy answer, using a Crypto Calander will definitely help: 1) CoinMarketCal - https://coinmarketcal.com/en/ 2) CoinDar - https://coindar.org/ 3) CoinsCalendar - https://coinscalendar.com/ 4) CryptoTotem - https://cryptototem.com/ico-calendar/ 5) CoinCarp events - https://www.coincarp.com/events/ 6) VestLabs - https://vestlab.io/calendar 🪂 Airdrops 🪂 The most popular tool for distribution in the entire industry, airdrops are incentives that are offered/provided at the earlier stages of a project’s development to help bootstrap it’s alpha community members. 1) Earnifi - https://earni.fi/ 2) airdropsio - https://airdrops.io/ 3) AirDropAlert - https://airdropalert.com/ 4) AirdropsLive - https://airdrops.live/ 5) AirdropBob - https://www.airdropbob.com/ 6) AirDropAddict - https://airdropaddict.com/ 7) CoinAirDrops - https://coinairdrops.com/ 8) ICOdrops - https://icodrops.com/ 💸 Grants 💸 Grants are a necessary component to helping startups & innovators get their ideas off the ground. In the world of crypto & blockchain grants are usually offered & deployed on a chain/case-specific basis. In order to be properly up to date on grant opportunities it is best to be actively involved in individual projects and/or blockchains. 1) Blockchain Grants - https://www.blockchaingrants.org/ 2) GitCoin - https://gitcoin.co/grants/ 3) Consensys - https://consensys.net/grants/ — 4) Concoridum https://concordium.com/grants/ 5) Web3 grants - https://web3grants.net/ 6) Web3Foundation - https://web3.foundation/grants/ 7) Ramp Rate - https://ramprate.com/blockchain-grant-opportunities/ - 8) Unigrants https://www.unigrants.org/ 9) Aave grants - https://aavegrants.org/ 10) Near grants - https://near.org/ecosystem/get-funding/ 11) Eth Foundation - https://esp.ethereum.foundation/ 12) Zcash grants - https://grants.zfnd.org/ 13) Alchemy Dev grants - https://www.alchemy.com/developer-grant-program — 14) dydx grants https://www.dydxgrants.com/ 15) Solana grants — https://solana.org/grants 16) Gate grants - https://www.gate.io/grants 17) Cryptoneur - https://www.cryptoneur.xyz/en/web3-grants 🛡 Audits 🛡 Audits are used to assess the quality/security of a protocol. When considering a protocol to use, it is nearly impossible to understand all of the associated risk factors, even for professionals. It is crucial that every serious project employs some form of audit for every individual piece of software. 1) Certik - https://www.certik.com/ 2) Consensys Dilligence - https://consensys.net/diligence/ 3) Hacken - https://hacken.io/services/smart-contract-security-audit/ 4) Open Zepplin - https://www.openzeppelin.com/security-audits 5) Trail of Bits - https://www.trailofbits.com/ 6) Slowmist - https://www.slowmist.com/ 7) Solidified - https://solidified.io/ 8) Chain Security - https://chainsecurity.com/smart-contract-audit-reports/ 9) PeckShield - https://peckshield.com/#home 10) Halborn - https://halborn.com/ 11) Quantstamp - https://quantstamp.com/ 12) Solidity Finance - https://solidity.finance/ - 13) Immune Bytes https://www.immunebytes.com/ 14) NumenCyber - https://www.numencyber.com/ 🌊😎 Cool & Honorable Mentions 😎🌊 There are always exotic instruments & services being built to help make sense of the wild world of crypto; some of these are difficult to classify but still deserve attention for serving their own unique & useful purpose(s). 1) Fiat Leak - https://fiatleak.com/ 2) Everest - https://everest.link/ 3) Revoke.cash - https://revoke.cash/ 4) CryptoQuant - https://cryptoquant.com/asset/btc/summary - 5) CryptoFees https://cryptofees.info/ - 6) Hashrate Index https://data.hashrateindex.com/asic-index-data — 7) LunarCrush https://lunarcrush.com/home — 8) Santiment https://santiment.net/ 9) Gas Fee Calculator - https://www.cryptoneur.xyz/en/gas-fees-calculator This list is by no means exhaustive, there are thousands of other tools/resources out there. Odds are, you do not even need 1/10 of those on this list; so my recommendation is don’t waste time wishing for better tools — learn how to masterfully use a few key ones provided above. If you went through the entire list, you might have noticed that there are no CEFI (centralized financial) tools on this list. This was done on purpose, given the madness that transpired with centralized exchanges/service providers throughout 2022. If you would like me to add any specific resource that might be beneficial to others, just drop a comment below or send me a DM on Twitter. Otherwise; I hope you find these tools helpful in building your knowledge & financial freedom. Thank you so much for reading! 🥂🚀 See you in the next Bull Market 🚀🥂 Also published here.

200+ Essential Crypto & DEFI Tools for 2023

Why We Need Blockchain Explorers Blockchain explorers are more than just transaction history UIs. They hold huge potential to replace app dashboards and provide great tooling for developers. Blockchain explorers are a fundamental part of the cryptocurrency world. They provide UIs for reading data from the blockchain and allow us to see important metrics, view who sent or received funds, and even search through blocks of the blockchain in order to investigate transactions, smart contracts, and assets in more detail. But these common uses of explorers are just the tip of the iceberg when it comes to what block explorers can do. More advanced explorers can be used by developers as interfaces for their tooling, or even as full dashboards for their apps. In this series of articles, we will take a look at some of the most popular blockchain explorers and what they can do, give an overview of more specific explorers with innovative and useful features, and look into why blockchain explorers are so much more than just transaction histories. " " A block explorer is essentially a search engine for the blockchain.  Unlike Google, the data you enter is always true. It's like being able to look at all the accounts and transactions in your bank at any time. What Blockchain Explorers Show You Blocks with all transactions and data Wallet address transaction histories Mempool data and transaction statuses Largest transactions, double-spend transactions A list of miners or stakers that mined/staked specific blocks Voting results in DAOs Transaction fees Blockchain explorers connect to blockchain nodes through APIs to show recent transactions on the given blockchain. In addition to the data listed above, they can also help you audit wallet addresses and change them if necessary, which helps to avoid wasting input value on transaction fees. They are often used by developers to help debug their contracts, allowing them to track the progress of a contract or to view the transaction data for a contract that has already been deployed. Many of them can shed light on the inner workings of a particular cryptocurrency, providing crucial insights for trading decisions and allowing you to track your finances. As the number of blockchains has increased, so has the number of blockchain explorers. In the next instalment of this series, we’ll take a look at some of the more popular block explorers, what features they provide, and how they compare to each other. Top Features of Three of the Most Advanced Blockchain Explorers The No. 1 Explorer and Its Recent Innovations is a free Ethereum blockchain explorer that lets you search and view transactions, blocks, and smart contracts. It's designed for developers and can be used without connecting to a wallet. It allows you to quickly browse recent transactions and view the Ethereum network’s status. The popular explorer also offers creation and based on specific transactions and supports to keep track of smart contracts and create . Etherscan data feed alerts personal accounts watchlists Users can look up transactions using a transaction ID and/or wallet address. Etherscan displays transaction details, including the transaction hash (TxHash) and the amount transferred. Developers can also view the history of their wallets and approve smart contract transactions with Etherscan's and features. Users can review the status of their ETH balances and other tokens. Read Contract Write Contract A highly popular feature of Etherscan is its . This allows you to estimate transaction fees for various types of transactions and can help you track DeFi protocol founders’ wallets, as they use the same randomized letters and numbers as Ethereum addresses. If you're a developer, you can also use the to find out which currency is being used for Ethereum transactions. gas tracker Gwei/ETH conversion calculator Etherscan is constantly innovating and improving its explorer to provide even more value for Ethereum users. Some recent innovations include the ability to input and a . With , users can now input information about their transactions, making it easier than ever to track and manage transactions. The is a great tool for those who use decentralized exchanges, allowing them to see the most up-to-date information on prices and volume. data messages DEX tracker data messages DEX tracker Even though the explorer is completely free, it offers advanced features like developer APIs and a database of Ethereum-related websites, projects, and exchanges. If you're not a developer but want to learn more about Ethereum, Etherscan is an invaluable resource for gaining insight into the blockchain. Advanced Etherscan techniques: | Read & write contract Verify and publish smart contract Blockchair and its browser extension take blockchain explorer UX to the next level is a company that specializes in developing products that make blockchain data accessible to individuals, development teams, and research organizations. Their REST API is used by thousands of crypto companies, analysts, academics, and students to fetch data and power their projects. Some of the projects that Blockchair has powered include a Bitcoin cash flow analysis tool, a Bitcoin options pricing calculator, and a block explorer for the Ethereum network. Blockchair Blockchair has taken the explorer to another level by incorporating different blockchains into one browser. With over 20,000 installs of its , Blockchair supports 19 blockchains, including Bitcoin, Ethereum, Ripple, Bitcoin Cash, Litecoin, and 14 others. This makes it easy to track transactions and activity across all supported chains. In addition, Blockchair provides tools for developers to build applications on top of its platform. browser extension Blockchair is a powerful and versatile tool that can be used for a variety of purposes. Their suite of tools is available as a browser extension and provides features suitable for both developers and novice users alike. The feature allows you to view all the nodes on the network and see how they are interconnected. node explorer The feature lets you see how transactions are made so that you can better understand the process. transaction recipes The gathers all the latest news about blockchain so that you can stay up-to-date with recent developments. news aggregator The lets you compare different blockchains to see which one is right for you. blockchain comparison tool The feature allows you to broadcast your transactions to the network so that they can be confirmed and added to the blockchain. transaction broadcasting The lets you keep track of when new releases and halvings occur so that you can be prepared. release and halving monitoring tool Finally, the lets you search for addresses on the Ethereum network. Ethereum Name Service search tool Kaleido Block Explorer as a part of a robust blockchain infrastructure as a service solution is a digital transformation platform that provides solutions for enterprises. Their starter plan is free to use and can be found on the . Kaleido’s Blockchain Business Cloud AWS marketplace Developers can use Kaleido to analyze smart contracts and gain a general understanding of how the blockchain operates. It provides access to contract data for a broad range of use cases. Customized Explorer Solutions - Access data through the ledger API and search for patterns in the data. - This binary promotion process is used to create a verified binary file before deploying the contract to Ethereum. This new approach promises to bring a standard practice to enterprise applications and middleware that will last for decades tome. Smart Contract Compiler Source Code Management Framework - built-in GitHub support with search functionality for both public and private repositories. You can pull from branches, tags, or even specific commits. Each compile will show the exact version of the source code that was used in the compilation. Digging Around The Block: Blockchain Analysis Tools You Don't Know Visible transactions, hidden details Privacy coins such as or Secret represent a major exception to the transparency of public blockchain ledgers. With their explorers, can see that a transaction has taken place, but who sent what to whom is completely hidden. Both coins have built their concept and reputation on protecting the privacy of users, but have also been criticized for their potential for facilitating criminal activity. Monero The People’s Wireless Network The Geo Explorer is a web-based tool that shows the coverage of people-powered networks around the world. It allows users to search for specific locations and find the node nearest to them. Helium A connected 3D universe The blockchain explorer is a beautiful and interactive 3D universe view of IBC blockchains that makes it easy to explore and visualize the relationships between various blockchain projects. It is also helpful in understanding inter-chain communications between different blockchains. Mintscan Baker Street of validators play an important role in the Tezos protocol by validating all transactions and adding them to the blockchain. If a baker behaves dishonestly, the protocol has a built-in mechanism that can cause them to lose their security deposit. With the Bakers blockchain explorer, you can view the different validators (bakers), their commission rate, capacity, and share, among other useful stats. Bakers Guess Gas There are a few different gas fee estimators that can be helpful for predicting the gas fee of the next block in a blockchain. gas fee estimator is one such option. With gas fee tracker, you can stay up to date on gas prices or find and compare the latest TX fees in DeFi. A third option is to use a gas price oracle like Gas Now. Blocknative's Crypto.com's Pictogram tags for Transactions In the , you can easily identify the type of any given transaction type upon first glance thanks to pictogram tags. These visual representations allow you to instantly distinguish the nature of a transaction: normal transactions, pending transactions, vote transactions, or others like token unlock transactions. This makes it easy to understand what's happening on the blockchain and makes it easier to track down specific transactions. Lisk observer What is parliament up to? The is a great tool for browsing through all Cosmos-based DAO votes, allowing you to quickly view all proposals and monitor the status of each one. It also gives you access to the deposit period, voting period, whether or not the proposal passed, who proposed the idea in the first place, and the percentage of people who voted yes/no/abstain/veto. Mintscan by Cosmostation Yields, Oracles, Airdrops, Forks is a great resource for anyone interested in the world of decentralized finance (DeFi). The site provides a variety of interesting perspectives on different protocols and aspects of DeFi, from airdrops and oracles to forks and yields. Perhaps most importantly, DefiLlama offers up-to-date information on the current yields of different protocols and how they have changed over time. DefiLlama Contract verification (by Avalanche) source code verification provides transparency for users interacting with smart contracts. When you upload source code, SnowTrace matches the compiled code with that on the blockchain. Just like physical contracts, a "smart contract" should provide end-users with more information on what they are "digitally signing" and allow them to audit the code to independently verify that it actually does what it is supposed to do. Snowtrace Google that is a search engine created by that allows users to search for on-chain data from more than 30 blockchains. The interface is similar to Google, allowing users to input their search query and see all related information. Bloxy makes it easy to find information about blockchain transactions, addresses, and smart contracts. Bloxy Bitquery What about Layer 2? Layer 2 explorers are tools that help you navigate the different layer 2 systems built on top of Ethereum. They include , , , , and others.. Each of these explorers has different features and capabilities, but they all allow you to view and interact with layer 2 data in one place. Arbiscan ZKscan Optimism Loopring L2fees D(une)IY is a data platform that enables users to query data from datasets and create visualisations. It is widely used by crypto-asset investors and analysts to help them research individual projects, sectors, and blockchain ecosystems. Dune provides a user-friendly interface that makes it easy to create customised reports and visualisations. Additional features include the ability to export data, share reports, and collaborate with other users. Dune True Decentralization is an open-source Decentralized Blockchain Explorer (DBE) that enables developers to contribute to the explorer project itself. It allows users to connect any blockchain platform, the main advantage being that it is self-hosted and decentralized. This means that users can run their own blockchain explorer on a PC and always have secure access to it without being tracked. LibreScan Tracking criminal activities with advanced blockchain analytical tools One particularly interesting practical application for explorers is using blockchain data to track transactions on any smart contract address, such as Bitcoin or Ethereum, to see whether any transaction has been sent or received. Since these tools also provide public evidence regarding the ownership of a wallet, they can also help law enforcement find criminals who use cryptocurrencies for criminal activity. This information can be used to find out who is behind an act of fraud, and what their motives are. It is also possible to track criminals by following their transaction history. In addition to explorers, blockchain analytical tools can help law enforcement track criminal activity with more powerful features. is a tool that can help track down criminals who use cryptocurrencies for illegal purposes by providing public evidence of wallet ownership. is another tool that can help law enforcement by using artificial intelligence to analyze blockchain data and identify patterns that may be associated with criminal activity. Chainlysis Maltego For more on the topic, check out this . It outlines the issue nicely and provides useful tips on how to read different blockchains with practical use cases like NFTs. video introduction The problem with public blockchain explorers Public data means you’re being tracked There are two types of blockchain explorers: public and private. Public blockchains are permissionless, meaning anyone can view their ledgers of transactions. Private blockchains are permissioned, meaning only those with access can view their ledgers. When using public data, you are being tracked, and everything is dependent on third-party infrastructure. If the infrastructure is down, you can't do anything about it. In the worst-case scenario, etherscan.io can collect and use data from blockchain explorer users to predict upcoming market events. And we’re not even getting into how much data they could potentially collect when you are logged into these pages. Issues with public block explorers: You have or what information is collected. no control over what is scanned at any given moment. The service could be unavailable There can be some (Etherscan, for example, doesn't process requests that return more than 10,000 transactions). data restrictions The service is . centralized You are . always being tracked How to solve them One solution to these issues is to create a script that you can run locally, on your system. This script will monitor all traffic data for the specified address and display all activity. However, private solutions often run on a single node, so the potential for overloading the system would create a significant risk of it not functioning. For more on locally running scripts as private blockchain explorers, check out these articles: , A smart contract monitoring tool How to run your own Bitcoin Explorer Conclusion With the increasing popularity of blockchain technology, it is becoming more and more important to have tools that help explore and understand this complex ecosystem. Growing differences between protocols and apps makes data implementation and interpretation difficult, but blockchain explorers and similar tools can help overcome these difficulties. By providing a user-friendly interface for viewing transaction data, they make it easy to track the activity on networks and spot potential issues. For developers, they can be used to quickly test smart contracts and debug transactions, and can even be used to replace app dashboards and provide greater transparency for users. We believe that blockchain explorers have great potential to improve the way we interact with cryptocurrencies and provide valuable insights into their movement. This article was first published here.

Blockchain Explorers: What Are They Good For? (The Tools You Didn't Know You Need To Know About)

Polkadot has been a legendary project from inception, bringing interoperability among various chains. Since October 2017, after successfully closing three rounds of public fundraising that spanned over three years, it was able to raise a sum of $250 million USD. As the king of interoperability with an overall valuation of 1.5 billion US dollars, Polkadot officially launched its mainnet in September 2020. Since then, the Polkadot ecosystem has continued to expand and flourish. Currently, with a market cap of over $40 billion, Polkadot ranks as the seventh biggest crypto on the list of cryptocurrency market value. And bolstered by solid fundamentals and various infrastructure projects in the pipeline, the Polkadot ecosystem keeps thriving. Prominent among such projects is the fast-rising decentralized infrastructure platform, Apron Network. Solid Foundation - Polkadot Just as he was in Ethereum, "Gavin Wood", the co-founder of Ethereum, brought a strong personal imprint to Polkadot. In the early days of Ethereum, Gavin wrote the Beige Book and almost single-handedly wrote the technical architecture of Ethereum - a legend that made Gavin naturally attract a loyal following. In terms of technical architecture, Gavin surprised many developers with its one-click launch of blockchain in 15 minutes. With the Substrate framework, anyone can write blockchain logic in any language (Rust, C/C++, C#, Go, etc.) - multi-language support lays the groundwork for a vast audience of Polkadot developers (unlike Ethereum, which is confined to Solidity language) to develop their applications. Polkadot has made an outstanding contribution to educating developers, including several introductory development courses and frequent online and offline developer conferences. On this basis, Polkadot's Web3 Foundation has been very supportive of other projects, and has completed 6 batches of funding for 129 Polkadot Eco projects between 2019 and 2020 to the tune of more than $6 million USD. The percentage change in developers building on each protocol over the past year As a result, the explosion of the Polkadot ecosystem was a natural progression, with several great projects gaining prominence on the "super carrier" -- of which Apron Network is emerging as a new star. The Polkadot Ecosystem Explosion Expectedly, once a good blockchain infrastructure is in place, all kinds of infrastructures will emerge. Consequently, the explosion of the Polkadot ecosystem was a natural progression, with several great projects leveraging the "super carrier" to gain prominence, of which Apron Network is emerging as a new shining star. Currently, Polkadot is home to an increasing population of excellent projects including the cross-chain solution "ChainX", Darwinia Network - the Substrate-based based heterogeneous cross-chain bridge protocol, "CrustNetwork" - decentralized storage service for web3 web3 ecosystem, "Acala Network" - DeFi hub for Polkadot ecosystem as well as the recently hot "Phala Network", and "OptionRoom" - oracle and prediction protocol. These crops of top-tier projects with the mission to solve different problems constitute the entire Polkadot ecosystem. However, we found that there was a critical lack of middleware in this infrastructure, which is where Apron Network was built. According to its website, Apron Network is a decentralized platform that provides decentralized infrastructure services for DApp developers, DApp users, and DApp operators. It builds a cross-chain platform on Polkadot based on the Substrate framework and is committed to providing more complete infrastructure services for the entire blockchain ecosystem. In terms of specific products, Apron Network provides integrated APIs for infrastructure providers, mid-tier services for DApps, DeFi developers, and various infrastructure services such as node services, on-chain data indexing, and pricing for the Kusama, Polkadot, and Heco ecosystems. In the future, Apron Network will also provide services similar to traditional infrastructure platforms such as AWS and Alibaba Cloud, albeit, in a decentralized manner. Apron, a middleware platform based on Polkadot At present, due to the lack of standardization of the underlying blockchain data storage, it is very difficult to query and read the data, which brings friction for the project side or the DApp to read the blockchain data. So the API and other services provided by the middleware have become an indispensable link; this is also the reason why middleware is popular this year. Quite a few other ecosystem projects are trying or already have such solutions, some centralized, some decentralized, focusing on on-chain data queries or connecting on-chain data off the chain. Some of the representative projects include: a platform that provides developers access to Ethereum and IPF node data through API services by building their own Ethereum and IPF nodes. ‌- Infura: It operates in a manner similar to Infura, but more blockchain-like, and is believed to currently offer 45 API on-chain services. - NowNodes: It develops a public blockchain that provides the Prophet, provides the Prophet data to the service provider, and provides the cross-blockchain aggregation data for the DApp. - API3: API3 data feeds, dAPIs, aggregate data from first-party oracles, operated by some of the world’s premier API providers. By aggregating blockchain data, BitQuery provides users with a data API on the blockchain, as well as simple data analysis capabilities and the GraphQl API. - BitQuery: An on-chain service indexing protocol which supports Ethereum and IPFS, through a specific SubGraph, retrieving and building indexing information in smart contracts, providing users with GraphQl API to obtain data information corresponding to dApps. - The Graph: However, these existing projects focus on a specific domain and do not provide a general technical solution. Specifically, Infura and NowNodes use a centralized approach, which is inherently vulnerable to the risk of a single point of failure. API3 provides off-chain data aggregation services to data sources via oracles. Both BitQuery and The Graph use The GraphQl API, which focuses on aggregation and indexing of on-chain data. The above infrastructure services, which have three key components: service discovery, service calling, and service billing, are well established in a centralized IT infrastructure, but have the disadvantage of being controlled by the operator. In November 2020, the Infura API, an ethereum node service, crashed several services that relied on Infura, including Metamask, Coinbase, Cryptokitties and the lending platform, Compound, as well as a host of other dApps, dealing a big blow to the "decentralized" world. It is also a reminder that the current Web 3.0 world is incomplete and that we still have a long way to go. Fortunately, we now have Apron Network, a decentralized, secure blockchain API service. Apron focuses on enabling developers to provide and use any infrastructure services thus reaching out to both the real world and the Web 3.0 world. Apron Network in the spotlight As an essential infrastructure with clear vision and positioning, Apron Network's value was discovered by the public and welcomed by the market at the very beginning of its inception. Apron Network, a project supported by the Web3 Foundation, received early grants from the Web3 Foundation. In January 2021, Apron Network successfully completed a seed round of funding from Vega Ventures, PAKA, NGC Ventures, Digital Renaissance Foundation, DFG, and CandaQ, among others. In February 2021, Apron Network completed a private funding round with investments from CMS Holdings, Spark Digital Capital, Monday Capital, Republic, AU21 Capital, BlockArk, Symbol CapitalVentures, R8 Capital, OneMax Capital, Substrate Capital, and others. NGC Ventures, DFG, and Vega Ventures continue to invest. With the support of well-known organizations in the industry, Apron has strong support for its continued development and market expansion. In terms of community building, Apron Network has almost a combined 50,000 followers on both official Twitter and Medium channels, and more than 40,000 followers on Telegram and Discord combined, which are indicators of solid fundamentals. According to the new weekly report, the Apron community has grown to more than 100,000 people worldwide. At the same time, Apron has been engaged in a flurry of campaigns to increase awareness about the project. As an infrastructure, Apron is actively integrating or partnering with many of the industry's leading ecosystem partners, such as Phala Network, Bifrost, Darwinia, OneBlock, Rebase, Bandot, Trustbase, and others, to provide them with better node services to help blockchain developers develop and integrate features flexibly and efficiently to keep their projects safe and stable. As a result, Apron Network has grown at a steady pace and so far wields the right combination of funding, technology, and community to deliver a blockbuster project to the middleware market. Apron Network Looking Beyond the Horizon Built on Polkadot, Apron will support multiple public chains such as Ethereum, Bitcoin, Polkadot, Tron, BSC, Filecoin, and Layer 2, thanks to its cross-chain capability. As a result, Apron will extend beyond Polkadot ecosystem to serve the entire blockchain industry. While The Graph only recently announced future expansion to other Layer 1 blockchains, Apron Network has already charted plans for cross-chain functionality on its roadmap which it has begun to pursue and achieve. Polkadot's cross-chain nature also makes it easier for Apron to support multiple chains, so in this respect Apron is much more forward-looking and forward-thinking than The Graph. About Apron Network Apron Network, is a decentralized, secure, token-incentived infrastructure service that developers can use easily and securely. It is a middleware that you can leverage to safely access the Web 3.0 world. 🛎 Learn more about Apron Network here: | | | | | | Telegram Twitter Medium ANN Channel Whitepaper Website GitHub

Introducing Apron Network: A Gateway to Decentralized Infrastructure Services

Introduction: Google Bigquery is renowned for its ability to process massive datasets quickly and efficiently. However, it has its limitations, especially when handling large JOIN operations. These operations can result in performance degradation, increased costs, and even query failures. In this article, we explore these challenges and present a strategic approach to optimize JOINs in BigQuery. Challenge: Handling Large JOIN Operations in Bigquery Bigquery handles JOIN operations by distributing the workload across multiple nodes. However, issues like data skew, inefficient use of resources, and the complexity of joining large tables can lead to significant performance issues. This problem becomes more frequent as data volumes grow, causing longer query execution times and higher costs. Let’s consider joining large transactions table with a customers table: SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROMcdl.dim.customers AS c JOINcdl.fact.transacations AS t ON c.customer_id = t.customer_id WHERE t.transaction_date > '2024-01-01'; This query may take several minutes to execute, especially if the transactions table contains billions of records. Solution: Optimizing JOINs With Partitioning and Pre-Filtering Below are a few strategies that can be leveraged to overcome the limitations of large JOINs. Partitioning and Clustering: Partitioning the transactions table by transaction_date and clustering it by customer_id ensures that Bigquery processes only the relevant partitions, reducing the amount of data scanned during the JOIN. CREATE OR REPLACE TABLE cdl.fact.transactions_partitioned_clusteredPARTITION BY DATE(transaction_date) CLUSTER BY customer_id AS SELECT * FROM cdl.fact.transactions; Pre-filtering Data: Filtering the transactions table before performing the JOIN reduces the data processed, leading to faster execution and lower costs. WITH filtered_transactions AS ( SELECT * FROMcdl.fact.transactions_partitioned_clusteredWHERE transaction_date > '2024-01-01' ) SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROMcdl.dim.customers AS c JOIN filtered_transactions AS t ON c.customer_id = t.customer_id; Materialized views for Frequent JOINs: Creating a materialized view to store the result of frequent JOINs can significantly speed up future queries by avoiding repeated calculations. CREATE MATERIALIZED VIEW cdl.fact.customer_transaction_summary AS SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROMcdl.dim.customers AS c JOINcdl.fact.transactions_partitioned_clustered AS t ON c.customer_id = t.customer_id WHERE t.transaction_date > '2024-01-01'; Experimental Results: Performance Comparison To measure the effectiveness of these optimizations, we ran a series of query executions comparing the performance of the original unoptimized query with the optimized versions. The below table has the results of each approach Query Type

Original JOIN

Optimized with Partitioning and Clustering

Optimized with Pre-Filtering

Materialized View (Initial)

Materialized View (Subsequent)



Execution Time

3 mins 15 sec

1 min 20 sec

45 sec

2min 30sec

10sec



Data Scanned

1.2 TB

450 GB

300 GB

1.0TB

10MB



Cost

$6.00

$2.25

$1.50

$5.00

$0.01 Result Analysis Original JOIN: The unoptimized query was slow and costly due to the large volume of data processed. Partitioning and Clustering: Implementing partitioning and clustering reduced the data scanned by over 60%, resulting in a 58% reduction in execution time and cost. Pre-Filtering: By filtering before the JOIN, we further reduced the execution time to 45 sec and cut the data scanned to 300GB, reducing costs by 75%. Materialized View: The initial creation of the materialized view took time and scanned a significant amount of data, but subsequent queries were very fast and extremely cost-effective. Conclusion: The performance gains from these optimizations are substantial. By intelligently partitioning and clustering data, pre-filtering datasets, and leveraging materialized view, you can overcome BigQuery’s limitation with large JOIN operations. These strategies not only improve query performance but also significantly reduce costs, making your data processing more efficient. For Data Engineers dealing with large-scale datasets, these optimizations are crucial. They ensure that BigQuery can handle even the most complex queries swiftly and economically, allowing you to maximize the value of your cloud investment. Final Thoughts: As data volumes continue to grow, the ability to optimize and manage large JOIN operations in BigQuery will become increasingly important. By applying the techniques discussed in this article, you can stay ahead of performance bottlenecks and increased costs, ensuring that your data pipelines are both powerful and efficient. Introduction: Introduction: Google Bigquery is renowned for its ability to process massive datasets quickly and efficiently. However, it has its limitations, especially when handling large JOIN operations. These operations can result in performance degradation, increased costs, and even query failures. In this article, we explore these challenges and present a strategic approach to optimize JOINs in BigQuery. Challenge: Handling Large JOIN Operations in Bigquery Challenge: Handling Large JOIN Operations in Bigquery Bigquery handles JOIN operations by distributing the workload across multiple nodes. However, issues like data skew, inefficient use of resources, and the complexity of joining large tables can lead to significant performance issues. This problem becomes more frequent as data volumes grow, causing longer query execution times and higher costs. Let’s consider joining large transactions table with a customers table: SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROM cdl.dim.customers AS c JOIN cdl.fact.transacations AS t ON c.customer_id = t.customer_id WHERE t.transaction_date > '2024-01-01'; cdl.dim.customers cdl.fact.transacations This query may take several minutes to execute, especially if the transactions table contains billions of records. Solution: Optimizing JOINs With Partitioning and Pre-Filtering Solution: Optimizing JOINs With Partitioning and Pre-Filtering Below are a few strategies that can be leveraged to overcome the limitations of large JOINs. Partitioning and Clustering: Partitioning and Clustering: Partitioning the transactions table by transaction_date and clustering it by customer_id ensures that Bigquery processes only the relevant partitions, reducing the amount of data scanned during the JOIN. Partitioning the transactions table by transaction_date and clustering it by customer_id ensures that Bigquery processes only the relevant partitions, reducing the amount of data scanned during the JOIN. CREATE OR REPLACE TABLE cdl.fact.transactions_partitioned_clustered PARTITION BY DATE(transaction_date) CLUSTER BY customer_id AS SELECT * FROM cdl.fact.transactions ; cdl.fact.transactions_partitioned_clustered cdl.fact.transactions Pre-filtering Data: Pre-filtering Data: Filtering the transactions table before performing the JOIN reduces the data processed, leading to faster execution and lower costs. Filtering the transactions table before performing the JOIN reduces the data processed, leading to faster execution and lower costs. WITH filtered_transactions AS ( SELECT * FROM cdl.fact.transactions_partitioned_clustered WHERE transaction_date > '2024-01-01' ) SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROM cdl.dim.customers AS c JOIN filtered_transactions AS t ON c.customer_id = t.customer_id; cdl.fact.transactions_partitioned_clustered cdl.dim.customers Materialized views for Frequent JOINs: Materialized views for Frequent JOINs: Creating a materialized view to store the result of frequent JOINs can significantly speed up future queries by avoiding repeated calculations. Creating a materialized view to store the result of frequent JOINs can significantly speed up future queries by avoiding repeated calculations. CREATE MATERIALIZED VIEW cdl.fact.customer_transaction_summary AS SELECT c.customer_id, c.customer_name, t.transaction_id, t.transaction_amount FROM cdl.dim.customers AS c JOIN cdl.fact.transactions_partitioned_clustered AS t ON c.customer_id = t.customer_id WHERE t.transaction_date > '2024-01-01'; cdl.fact.customer_transaction_summary cdl.dim.customers cdl.fact.transactions_partitioned_clustered Experimental Results: Performance Comparison Experimental Results: Performance Comparison To measure the effectiveness of these optimizations, we ran a series of query executions comparing the performance of the original unoptimized query with the optimized versions. The below table has the results of each approach Query Type

Original JOIN

Optimized with Partitioning and Clustering

Optimized with Pre-Filtering

Materialized View (Initial)

Materialized View (Subsequent)



Execution Time

3 mins 15 sec

1 min 20 sec

45 sec

2min 30sec

10sec



Data Scanned

1.2 TB

450 GB

300 GB

1.0TB

10MB



Cost

$6.00

$2.25

$1.50

$5.00

$0.01 Query Type

Original JOIN

Optimized with Partitioning and Clustering

Optimized with Pre-Filtering

Materialized View (Initial)

Materialized View (Subsequent) Execution Time

3 mins 15 sec

1 min 20 sec

45 sec

2min 30sec

10sec Data Scanned

1.2 TB

450 GB

300 GB

1.0TB

10MB Cost

$6.00

$2.25

$1.50

$5.00

$0.01 Query Type Original JOIN Optimized with Partitioning and Clustering Optimized with Pre-Filtering Materialized View (Initial) Materialized View (Subsequent) Query Type Query Type Original JOIN Original JOIN Optimized with Partitioning and Clustering Optimized with Partitioning and Clustering Optimized with Pre-Filtering Optimized with Pre-Filtering Materialized View (Initial) Materialized View (Initial) Materialized View (Subsequent) Materialized View (Subsequent) Execution Time 3 mins 15 sec 1 min 20 sec 45 sec 2min 30sec 10sec Execution Time Execution Time 3 mins 15 sec 3 mins 15 sec 1 min 20 sec 1 min 20 sec 45 sec 45 sec 2min 30sec 2min 30sec 10sec 10sec Data Scanned 1.2 TB 450 GB 300 GB 1.0TB 10MB Data Scanned Data Scanned 1.2 TB 1.2 TB 450 GB 450 GB 300 GB 300 GB 1.0TB 1.0TB 10MB 10MB Cost $6.00 $2.25 $1.50 $5.00 $0.01 Cost Cost $6.00 $6.00 $2.25 $2.25 $1.50 $1.50 $5.00 $5.00 $0.01 $0.01 Result Analysis Result Analysis Original JOIN: The unoptimized query was slow and costly due to the large volume of data processed. Original JOIN: The unoptimized query was slow and costly due to the large volume of data processed. Original JOIN: Partitioning and Clustering: Implementing partitioning and clustering reduced the data scanned by over 60%, resulting in a 58% reduction in execution time and cost. Partitioning and Clustering: Implementing partitioning and clustering reduced the data scanned by over 60%, resulting in a 58% reduction in execution time and cost. Partitioning and Clustering: Pre-Filtering: By filtering before the JOIN, we further reduced the execution time to 45 sec and cut the data scanned to 300GB, reducing costs by 75%. Pre-Filtering: By filtering before the JOIN, we further reduced the execution time to 45 sec and cut the data scanned to 300GB, reducing costs by 75%. Pre-Filtering: Materialized View: The initial creation of the materialized view took time and scanned a significant amount of data, but subsequent queries were very fast and extremely cost-effective. Materialized View: The initial creation of the materialized view took time and scanned a significant amount of data, but subsequent queries were very fast and extremely cost-effective. Materialized View: Conclusion: Conclusion: The performance gains from these optimizations are substantial. By intelligently partitioning and clustering data, pre-filtering datasets, and leveraging materialized view, you can overcome BigQuery’s limitation with large JOIN operations. These strategies not only improve query performance but also significantly reduce costs, making your data processing more efficient. For Data Engineers dealing with large-scale datasets, these optimizations are crucial. They ensure that BigQuery can handle even the most complex queries swiftly and economically, allowing you to maximize the value of your cloud investment. Final Thoughts: Final Thoughts: As data volumes continue to grow, the ability to optimize and manage large JOIN operations in BigQuery will become increasingly important. By applying the techniques discussed in this article, you can stay ahead of performance bottlenecks and increased costs, ensuring that your data pipelines are both powerful and efficient.

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In the dynamic landscape of data analytics, choosing an analytics platform can significantly impact your business's bottom line. In this educational article, we're on a quest to explore three heavyweight analytics contenders: Snowflake, BigQuery, and ClickHouse. We will delve into their cost models and reveal valuable strategies to help you master the art of cost-efficient analytics – it's a journey exploring insights that can transform your data game! Understanding Analytic DBMS Cost Models To make cost-effective decisions in data analytics, it's crucial to comprehend the cost models behind analytical database management systems (DBMS). A common starting point is to examine the cost structure of cloud-based businesses. So, first, let's talk about cloud-based analytic databases and how they handle costs when running in the cloud. Picture this: It’s before 2013, and setting up a data warehouse could take up to several months of dealing with multiple vendors, hardware bottlenecks, and configurations. Then came , allowing you to start your data warehouse in under 20 minutes with just a credit card—quite the leap forward. This paved the way for other cloud-based data warehouses like , , and cloud services that operate Snowflake. Amazon Redshift in 2013 BigQuery Snowflake Most developers learn about the importance of cost-efficiency as soon as they start getting their cloud bills. For example, you could run a long-running query on BigQuery that ends up costing you hundreds, often thousands of dollars. Take it from the following tweet/X post; this is not an isolated occurrence. https://twitter.com/FredKSchott/status/1631449132358598656?embedable=true We recently ran a webinar discussing this topic in further detail where Robert Hodges (CEO at Altinity Inc.) goes into the in-depth cost analysis of each option and shows you an alternative DIY solution at the end. Feel free to watch the on-demand once you’re done reading this article! recording of that webinar So, let's dig into how these databases work and, more importantly, how they price their services. We'll start with Snowflake, but before we get into its architecture, we will talk business. Snowflake, being a publicly traded company, gives us a chance to poke around its financials. In , they raked in a whopping $2 billion in total revenue. What's intriguing is the cost of delivering their service, roughly around $717 million. This cost, also known as the cost of goods sold, is what it takes Snowflake to run the show. a recent report Understanding the cost of running Snowflake's cloud services is like a hidden treasure hunt. If you crunch the numbers, it's roughly about one-third, maybe a tad more, of their total revenue. So, in plain English, if all that cost went straight into covering their cloud expenses and nothing else, they'd be marking things up by roughly three times when they charge you. But of course, it’s not that simple! The actual costs of running a powerhouse like Snowflake go beyond just running virtual machines and stashing data in Amazon S3. Now, if we do the math again, that markup on their costs? It's more like 5x. That is a nifty benchmark to carry in your back pocket. If something's got a markup greater than 5x, well, it's living large in the world of Snowflake's pricing. On the flip side, if it's less than 5x, you're looking at a more budget-friendly option. It's like having a secret decoder ring for deciphering the cost puzzle. Unpacking the Cost Models of Snowflake, BigQuery, and ClickHouse To master cost-efficient analytics, let's dissect the cost models of Snowflake, BigQuery, and ClickHouse. Snowflake Virtual Data Warehouse Model Now, let's break down Snowflake's costs. They use a "virtual data warehouse" model, where your data resides in S3 object storage. When you run SQL queries, you create virtual data warehouses powered by credits, essentially hosted processing units pulling data from storage. The pricing for these virtual machines ranges from about $2-4 /hour as listed in their pricing. Keep in mind: it's not the thriftiest choice. This would wrap up our Snowflake cost analysis if not for this intriguing twist: a recent bug in Snowflake unveiled that the credits for virtual data warehouses often translate to c5d2x large instances, costing about 38 cents per hour. Remarkably, Snowflake doesn't significantly mark up object storage, pricing it at $23-40 per terabyte per month, similar to Amazon's S3 costs. Instead, the real markup occurs in computing, which can be 5 to 10 times more expensive than storage. BigQuery Serverless Query Model Let's take a closer look at BigQuery, another heavyweight in cloud database analytics. BigQuery offers a distinct pricing model known as "serverless" or "on-demand." In this setup, you store your data on a unique distributed storage system, not the typical object storage used by most cloud services. However, the pricing is quite competitive, similar to object storage rates, at least for uncompressed data. Prices start at $0.016-0.023 per GB per month for storage—now that's more budget-friendly. Here's the catch: when you run a query, BigQuery dynamically allocates compute resources as needed. It charges you $6.25 for every terabyte of data scanned during your query processing. This pricing structure means that even seemingly innocent queries can rack up costs if they involve scanning a large amount of data from distributed storage. Now, comparing BigQuery costs to standard cloud resources isn't straightforward. Your actual expenses depend on various factors, like how often you run queries and your data storage methods. For instance, if you run queries sporadically, BigQuery might be cost-effective. But if you're running queries 24/7, it could get expensive. So, it's crucial to understand your workload and assess your real costs carefully. AWS RedShift "Buy the Box" Model The previous models we’ve discussed typically rely on object storage, or something quite similar, for data handling. However, there's another cloud database operating approach, one initially introduced by a decade ago. Let’s call it the "Buy-the-Box" model. Redshift Here's the deal: you rent a ), such as a dc28x large, which comes with attached SSD block storage. For instance, in Amazon us-west-2, this VM would cost you around 4.80 cents per hour. Now, let's break down the actual costs. This VM likely corresponds to an i38x large instance, an older Amazon instance type, which comes with local SSD storage. This type of instance costs approximately $2.50 per hour, offering nearly the same amount of RAM. virtual machine (VM In terms of markup, Redshift is 92% more expensive while providing 66% less storage than if you were to set up a similar VM yourself. Interestingly, Redshift's markup is relatively lower compared to some other services, like Snowflake, which tends to charge more for computing resources. ClickHouse Database Architecture To improve on the previous models for cost efficiency, let us first introduce you to ClickHouse.  It is a free, renowned, open-source, real-time analytics database. ClickHouse’s architecture revolves around interconnected servers capable of seamless data replication, especially with replicated tables. This system employs efficient columnar storage, where data resides in compressed arrays, not only reducing storage costs but also boosting query performance. Initially, ClickHouse was confined to block storage, but it has evolved to support S3-compatible object storage, making it versatile and open to integration with any S3 API-equipped object storage solution. To manage replication consensus efficiently, ClickHouse relies on either ClickHouseKeeper or ZooKeeper. You can read more about Clickhouse . here The Modernized "Buy-the-Box" Approach Now, let's envision this as a cloud service paradigm, which we term "Modernized Buy-the-Box." On the left side of the image below is the traditional Redshift architecture, while on the right, we embrace innovation. We replace the old i3 instances with faster Intel-based m6is, providing a significant speed boost. The game-changer is the use of EBS (Elastic Block Storage) gp3 storage, granting control over bandwidth and throughput. This, paired with efficient VMs akin to Redshift, results in an approximate cost of 2.64 cents per hour. The real magic happens with the separation of storage and computing. This flexible approach lets you easily adjust CPU and VM types while keeping the same storage, allowing cost scaling up or down. Our experience at Altinity.Cloud managing ClickHouse applications reflects this efficiency. In the graph below, you'll see the all-in on-demand cost for when running ClickHouse. For instance, opting for m6i 12x large might cost six dollars per hour, slightly more than Redshift. However, ClickHouse is so fast that smaller instance sizes get better performance than Redshift, which means lower cost without compromising performance. This showcases the tremendous benefits of separating storage and computing. Altinity.Cloud To summarize, we've explored three cloud-hosted analytic database models and their cost implications. In our comparison, we've created a wallet-sized table to make it clear how these models stack up against each other. The "Buy-the-Box" approach offers cost-effective computing but pricier storage due to block storage usage. Snowflake and BigQuery, on the other hand, offer economical storage but can be costly in different ways. Snowflake tends to be relatively expensive in terms of computing, while BigQuery's on-demand query model can become pricey when handling extensive data scans. Each model has its strengths and weaknesses, making it essential to align them with your specific analytics needs. "Buy-the-Box" suits customer-facing analytics with unpredictable workloads, while the virtual data warehouse model and BigQuery excel in specific scenarios but require careful cost management to avoid surprises. This overview helps you navigate the landscape effectively. In Conclusion When it comes to analytics, cost-efficiency is paramount. Understanding the cost models of popular platforms like Snowflake, BigQuery, and ClickHouse is essential for making informed decisions. By evaluating each platform's strengths and weaknesses and considering their cost structures, organizations can craft cost-efficient analytic solutions tailored to their specific needs. Leveraging open-source solutions and educational resources can further optimize cost, ensuring organizations achieve their analytics goals while being mindful of their budgets. This article was derived from an webinar. Altinity.com

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