Last week, I was catching up with a friend of mine and we started chatting about his most recent project. As we were chatting, he made an offhand comment about how some of the business guys on the team love to refer to what they are working on as a “big data” play, even though it really wasn’t. This stuck with me, since because of the vague definitions around “big data”, it’s easy to shoe horn problems into a “big data” play. Because of this, I think its worth taking a step back and discussing what big data really is and what tools are available to work with it.
At the end of the day, data is data. It doesn’t really matter if its stored in a CSV text file, a MySQL database, or a NoSQL datastore like Cassandra or MongoDB. Typically though, web applications tend to use a relational database like MySQL or Postgres to persist data. Relational databases store data in a series of tables which are in turn arranged as a series of rows and columns. As an abstraction, think of a series of Excel worksheets which can have links between the rows of each sheet.
For most applications, this works out fine, the database ends up managing say a few thousand customer accounts, each with a few hundred thousand objects associated with them and the total dataset fits conveniently into the server’s RAM. Since the dataset is relatively small, things like retrieving information, updating records, and running ad-hoc analytics queries are all easy to implement and relatively fast. But what happens if your dataset doesn’t fit into memory of even the beefiest of servers? Therein lies the “big data” problem.
Certain applications generate an enormous amount of data on a daily basis. For example, look at Mixpanel, tracking discreet user interactions is going to produce hundreds of thousands of datapoints every day even with just a few clients. With this volume of data, typical relational databases quickly start performing sluggishly and eventually stop being effective entirely. Even simple queries like counting the “# of clicks by user” start to take hours to run, effectively becoming intractable. Although specialized relational databases like Vertica and Oracle 11g do exist to help solve this problem, they’re expensive and proprietery.
One of the first companies to publicly discuss their big data strategies was Google in Bigtable: A Distributed Storage System for Structured Data which described their BigTable datastorage system. Although a proprietary solution, the research paper was used as the basis for Apache Hadoop, an open source framework for running MapReduce style jobs over large datasets.
At this point, Hadoop has distinguished itself as the most popular open source big data solution with a rich ecosystem of tools and several companies providing professional services and support including Cloudera and Hortonworks. What Hadoop provides is a low level framework for allowing computation jobs to be distributed across several servers within a cluster. This allows tools to split up very large datasets into smaller chunks, distribute computational tasks across the cluster, and finally assemble the result. So with the Hadoop framework in place, you still need specific tools built to leverage the distributed framework.
There are several tools that effectively leverage Hadoop but here are some of my favorites for quickly building out a cluster:
– Apache Whirr – Automates deploying, bootstrapping, and configuring a Hadoop cluster. Whirr will save you hours of time because instead of manually starting 4 EC2s and configuring them all you can kickstart a cluster with a single command.
– Apache HBase – A column store database that is similar to Google’s original BigTable system. Great for storing billions of records across a Hadoop HDFS file system.
– Apache Hive – A datawharehousing solution that allows you to run “SQL like” queries using Hadoop. It also has native support for pulling data out of MySQL, making it a convenient addition to a stack includes MySQL.
Apart from these, there are dozens of other Hadoop powered tools but its impossible to recommend a single silver bullet without knowing the details of your “big data” problem.
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