Apache Hadoop is a proven platform for long-term storage and archiving of structured and unstructured data. Related ecosystem tools, such as Apache Flume and Apache Sqoop, allow users to easily ingest structured and semi-structured data without requiring the creation of custom code. Unstructured data, however, is a more challenging subset of data that typically lends itself to batch-ingestion methods. Although such methods are suitable for many use cases, with the advent of technologies like Apache Spark, Apache Kafka, and Apache Impala (Incubating), Hadoop is also increasingly a real-time platform.
In particular, compliance-related use cases centered on electronic forms of communication, such as archiving, supervision, and e-discovery, are extremely important in financial services and related industries where being “out of compliance” can result in hefty fines. For example, financial institutions are under regulatory pressure to archive all forms of e-communication (email, IM, social media, proprietary communication tools, and so on) for a set period of time. Once data has grown past its retention period, it can then be permanently removed; in the meantime, such data is subject to e-discovery requests and legal holds. Even outside of compliance use cases, most large organizations that are subject to litigation have some form of archive in place for purposes of e-discovery.
Traditional solutions in this area comprise various moving parts and can be quite costly and complex to implement, maintain, and upgrade. By using the Hadoop stack to take advantage of cost-efficient distributed computing, companies can expect significant cost savings and performance benefits.
In this post, as a simple example of this use case, I’ll describe how to set up an open source, real-time ingestion pipeline from the leading source of electronic communication, Microsoft Exchange.
Setting Up Apache James
Being the most common form of electronic communication, email is almost always the most important thing to archive. In this exercise, we will use Microsoft Exchange 2013 to send email via SMTP journaling to an Apache James server v18.104.22.168 located on an edge node in the Hadoop cluster. James is an open source SMTP server; it’s relatively easy to set up and use, and it’s perfect for accepting data in the form of a journal stream.