Michael G. Solomon - Blockchain Data Analytics For Dummies

4 Part 2: Fetching Blockchain Chain Chapter 6: Parsing Blockchain Data and Building the Analysis Dataset Comparing On-Chain and External Analysis Options Integrating External Data Identifying Features Building an Analysis Dataset Chapter 7: Building Basic Blockchain Analysis Models Identifying Related Data Making Predictions of Future Outcomes Analyzing Time-Series Data Chapter 8: Leveraging Advanced Blockchain Analysis Models Identifying Participation Incentive Mechanisms Managing Deployment and Maintenance Costs Collaborating to Create Better Models

5 Part 3: Analyzing and Visualizing Blockchain Analysis Data Chapter 9: Identifying Clustered and Related Data Analyzing Data Clustering Using Popular Models Implementing Blockchain Data Clustering Algorithms in Python Discovering Association Rules in Data Determining When to Use Clustering and Association Rules Chapter 10: Classifying Blockchain Data Analyzing Data Classification Using Popular Models Implementing Blockchain Classification Algorithms in Python Determining When Classification Fits Your Analytics Needs Chapter 11: Predicting the Future with Regression Analyzing Predictions and Relationships Using Popular Models Implementing Regression Algorithms in Python Determining When Regression Fits Your Analytics Needs Chapter 12: Analyzing Blockchain Data over Time Analyzing Time Series Data Using Popular Models Implementing Time Series Algorithms in Python Determining When Time Series Fits Your Analytics Needs

6 Part 4: Implementing Blockchain Analysis Models Chapter 13: Writing Models from Scratch Interacting with Blockchains Connecting to a Blockchain Examining Blockchain Client Languages and Approaches Chapter 14: Calling on Existing Frameworks Benefitting from Standardization Focusing on Analytics, Not Utilities Leveraging the Efforts of Others Chapter 15: Using Third-Party Toolsets and Frameworks Surveying Toolsets and Frameworks Comparing Toolsets and Frameworks Chapter 16: Putting It All Together Assessing Your Analytics Needs Choosing the Best Fit Managing the Blockchain Project

7 Part 5: The Part of Tens Chapter 17: Ten Tools for Developing Blockchain Analytics Models Developing Analytics Models with Anaconda Writing Code in Visual Studio Code Prototyping Analytics Models with Jupyter Developing Models in the R Language with RStudio Interacting with Blockchain Data with web3.py Extract Blockchain Data to a Database Accessing Ethereum Networks at Scale with Infura Analyzing Very Large Datasets in Python with Vaex Examining Blockchain Data Preserving Privacy in Blockchain Analytics with MADANA Chapter 18: Ten Tips for Visualizing Data Checking the Landscape around You Leveraging the Community Making Friends with Network Visualizations Recognizing Subjectivity Using Scale, Text, and the Information You Need Considering Frequent Updates for Volatile Blockchain Data Getting Ready for Big Data Protecting Privacy Telling Your Story Challenging Yourself! Chapter 19: Ten Uses for Blockchain Analytics Accessing Public Financial Transaction Data Connecting with the Internet of Things (IoT) Ensuring Data and Document Authenticity Controlling Secure Document Integrity Tracking Supply Chain Items Empowering Predictive Analytics Analyzing Real-Time Data Supercharging Business Strategy Managing Data Sharing Standardizing Collaboration Forms

8 Index

9 About the Author

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11 Connect with Dummies

12 End User License Agreement

1 Chapter 2 TABLE 2-1 Differences in Blockchain Types TABLE 2-1 Differences in Blockchain Types Feature Public Private Hybrid Permission Permissionless Permissioned (limited to organization members) Permissioned (limited to consortium members) Consensus PoW, PoS, and so on Authorized participants Varies; can use any method Performance Slow (due to consensus) Fast (relatively) Generally fast Identity Virtually anonymous Validated identity Validated identity TABLE 2-2 Business Requirements and Blockchain Features TABLE 2-2 Business Requirements and Blockchain Features Business Requirement Blockchain Feature Controlling and recording transactions Blockchain excels in transferring value in untrusted environments. Reducing or eliminating excessive cost By eliminating middlemen, blockchain can reduce overall, and incremental, transaction-processing costs. Pursuing efficiency Blockchain increases direct interaction among transaction participants and automates many steps in transactions, decreasing settlement time and reducing inefficient waiting time. Preserving artifacts for analysis Because existing blockchain data can't be changed, a historical transaction record is guaranteed. Protecting availability through redundancy Each node of the blockchain network maintains a copy of the blockchain, so the failure of any node has no effect on the overall network’s capability to access blockchain data. Exposing data without compromising privacy Blockchain technology promotes data transparency. Because all nodes maintain copies of the ledger, the data is available to any authorized user.

2 Chapter 5TABLE 5-1 Blockchain Analytics ToolsTABLE 5-2 Ethereum Blockchain Access Libraries

3 Chapter 6TABLE 6-1 Forms of Data Stored in a BlockchainTABLE 6-2 Sources of External Data

4 Chapter 7TABLE 7-1 Loan Default Data

5 Chapter 13TABLE 13-1 Pros and Cons of Popular Blockchain Client Languages

6 Chapter 15TABLE 15-1 Comparing Data Analytics Frameworks

1 Chapter 1FIGURE 1-1: Customer entities presented as a table.FIGURE 1-2: Linear regression model using hours practiced and audition scores d...

2 Chapter 3FIGURE 3-1: Viewing block header information in Etherscan.FIGURE 3-2: Listing transactions in a block in Etherscan.FIGURE 3-3: Examining a transaction in Etherscan.FIGURE 3-4: Exploring additional transaction details in Etherscan.FIGURE 3-5: Ethereum block header.FIGURE 3-6: Contents of an Ethereum transaction.FIGURE 3-7: Original format of input data.FIGURE 3-8: Decoded data for the cancelOrder()function.FIGURE 3-9: Ethereum events in Etherscan.

3 Chapter 4FIGURE 4-1: The Go Ethereum (Geth) Download web page.FIGURE 4-2: Installation Options window.FIGURE 4-3: Geth light node startup command.FIGURE 4-4: Geth runtime messages.FIGURE 4-5: The Ganache Download web page.FIGURE 4-6: Support Ganache Analytics window.FIGURE 4-7: Ganache Accounts window.FIGURE 4-8: Ganache Settings window’s Server tab.FIGURE 4-9: Truffle installation requirements.FIGURE 4-10: Error message in PowerShell when NodeJS isn’t installed.FIGURE 4-11: The NodeJS Download page.FIGURE 4-12: The NodeJS version message.FIGURE 4-13: Installing Truffle.FIGURE 4-14: Initializing a new Truffle project.FIGURE 4-15: The Microsoft Visual Studio Code download web page.FIGURE 4-16: Visual Studio Code install options window.FIGURE 4-17: The Visual Studio Code IDE desktop.FIGURE 4-18: The Visual Studio Code IDE with the Solidity extension.

4 Chapter 5FIGURE 5-1: Python version command.FIGURE 5-2: The Python Download web page.FIGURE 5-3: Python Setup window.FIGURE 5-4: The Anaconda Distribution download web page.FIGURE 5-5: The Anaconda Navigator desktop.FIGURE 5-6: The conda install pipcommand.FIGURE 5-7: The pip install web3command.FIGURE 5-8: Commands to create a new project directory.FIGURE 5-9: Visual Studio Code IDE with Python extension.FIGURE 5-10: The Remix web page.FIGURE 5-11: The Remix Solidity compiler page.FIGURE 5-12: Copying the SupplyChain.sol smart contract ABI in Remix.FIGURE 5-13: Copied ABI value in the SupplyChain.abifile.FIGURE 5-14: VS Code showSupplyChain.py.FIGURE 5-15: Connecting Remix to your Ganache blockchain.FIGURE 5-16: Copying a deployed contract's address.FIGURE 5-17: VS Code showSupplyChain.py(with contract address).FIGURE 5-18: VS Code after running showSupplyChain.pyfor the first time.FIGURE 5-19: Completed showSupplyChain.pyPython script.