The hadoopcryptoledger library has been enhanced with a datasource for Apache Flink. This means you can use the Big Data processing framework Apache Flink to analyze the Bitcoin Blockchain.
It also includes an example that counts the total number of transactions in the Bitcoin blockchain. Of course given the power of Apache Flink you can think about more complex analysis applications, such as:
- Graph analysis on the Bitcoin transaction graph, e.g. to identify clusters or connected components to find out close interactions between Bitcoin addresses
- Trace money flows through the Bitcoin network
- Predict power of mining pools, difficulty of block processing, impact of changes on the Bitcoin protocol or rules
- Join it with other data to make predictions on prices, criminal activity and economics
In the future, we want to work on the following things :
- Support for other cryptoledgers, e.g. Ethereum
- Provide examples for analyzing other currencies based on the Bitcoin Blockchain, such as Litecoin and Namecoin
- A Flume data source to stream Bitcoin Blockchain data directly into your cluster
- Support selected blockchains provided via the Hyperledger Framework
This is an update of the second big data lab for the cloud. Similar to previous versions, this document described how you can create a Big Data Lab in the cloud on Amazon EMR.
Besides some major upgrades to the newest Amazon Hadoop AMI (3.6.0) Spark (1.3.0) and R, it includes now also the possibility to use Python in the browser. There, you have the same functionality as in R. This means you can use Hadoop M/R, Spark and SparkSQL in Python from the browser. Similar to R, Python has gained attention by data scientists.
You can find the newest version here.
In future blog posts, I will write how you can use some of the open datasets on the Internet in the Big Data lab.
Recently, I published on github.com several example Java projects for using various NoSQL technologies:
- cassandra-tutorial : Apache Cassandra tutorial (Column-oriented database)
- mongodb-tutorial : Mongo DB tutorial (Document database)
- neo4j-tutorial : Neo4J (Graph Database)
- redis-tutorial : Redis (Key/Value Store)
- solr-tutorial : Apache SolrCloud (Search technology)
Other example Java projects aim at standardized big data processing platforms:
- MapReduce: A simple hadoop map reduce job to count the number of tweets in a text file on HDFS
- SparkStreaming: A simple spark streaming job to count the number of tweets send by a simple network server
- tweet-server : A simple server for sending tweets to any client connecting to port 1234 on localhost. It is a shellscript using netcat.
You can use them in lectures or courses for teaching these topics. The projects use gradle, so you can make sure that the students always use the right libraries and thus avoiding conflicts with dependent libraries. Each project is a basic skeleton for using one software. Students can easily extend them and you can avoid a long bootstrapping phase until everybody has created the right project with right libraries for your environment. Unit testing can easily be added.