Getting started with Big Data

So, you’ve decided that you’re taking your organization down the route of Big Data, what components do you need? What are the available components that make Big Data work? Well. Let’s take a brief overview.

In terms of hardware, you’ll need lots of servers grouped into a very large cluster, with each server having its own internal disk drives. Ideally, you’d have Linux, but you might have Windows. And, of course, you could use Linux on System z if you have a mainframe.

You’re going to need a file system and that’s HDFS (Hadoop Distributed File System). Data in a Hadoop cluster gets broken down into smaller pieces that are called blocks, and these are distributed throughout the cluster. Any work on the data can then be performed on manageable pieces rather than on the whole mass of data.

Next you want a data store – and that’s HBase. HBase is an open source, non-relational, distributed database modelled after Google’s BigTable and is written in Java. It’s a column-oriented database management system (DBMS) that runs on top of HDFS. HBase applications are written in Java.

As a runtime, there’s MapReduce – a programming model for processing large data sets with a parallel, distributed algorithm on a cluster.

What about workload management, what options do you have for that? Your open source choices are ZooKeeper, Oozie, Jaql, Lucerne, HCatalog, Pig, and Hive. According to Apache, ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services. Similarly, according to Apache, Oozie is a workflow scheduler system to manage Hadoop jobs. Oozie Workflow jobs are Directed Acyclical Graphs (DAGs) of actions. Oozie Coordinator jobs are recurrent Oozie Workflow jobs triggered by time (frequency) and data availabilty. Oozie is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs out of the box (such as MapReduce, Streaming MapReduce, Pig, Hive, Sqoop, and Distcp) as well as system specific jobs (such as Java programs and shell scripts). Jaql is primarily a query language for JavaScript Object Notation (JSON). It allows both structured and non-traditional data to be processed. Lucerne is an information retrieval software library from Apache that was originally created in Java. HCatalog is a table and storage management service for data created using Hadoop. Pig, also from Apache is a platform for analysing large data sets. It consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The structure of Pig programs allows substantial parallelization, which enables them to handle very large data sets. Finally on the list is Hive, which is a data warehouse system for Hadoop that facilitates easy data summarization, ad hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems.

So what are your integration options? Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. There’s also Sqoop, which is a tool designed for efficiently transferring bulk data between Hadoop and structured datastores such as relational databases.

And finally, is there an open source advanced analytic engine? There is and it’s called R. R is a programming language and a software suite used for data analysis, statistical computing, and data visualization. It is highly extensible and has object-oriented features and strong graphical capabilities. It is well-suited for modelling and running advanced analytics.

That will pretty much get you started and on your way. You may feel that you’d like more integration products, some form of administration, or some kind of visualization and discovery product. But this is where you need to go to specific vendors. I’m expecting to be at liberty to talk more about how IBM is looking at this in future blogs.

Why is everyone talking about Hadoop?

Hadoop is an Apache project, which means it’s open source software, and it’s written in Java. What it does is support data-intensive distributed applications. It comes from work Google were doing and allows applications to use thousands of independent computers and petabytes of data.

Yahoo has been a big contributor to the project. The Yahoo Search Webmap is a Hadoop application that is used in every Yahoo search. Facebook claims to have the largest Hadoop cluster in the world. Other users include Amazon, eBay, LinkedIn, and Twitter. But now, there’s talk of IBM taking more than a passing interest.

According to IBM: “Apache Hadoop has two main subprojects:

• MapReduce – The framework that understands and assigns work to the nodes in a cluster.
• HDFS – A file system that spans all the nodes in a Hadoop cluster for data storage. It links together the file systems on many local nodes to make them into one big file system. HDFS assumes nodes will fail, so it achieves reliability by replicating data across multiple nodes.”

It goes on to say: “Hadoop changes the economics and the dynamics of large-scale computing. Its impact can be boiled down to four salient characteristics. Hadoop enables a computing solution that is:

• Scalable – New nodes can be added as needed, and added without needing to change data formats, how data is loaded, how jobs are written, or the applications on top.
• Cost effective – Hadoop brings massively parallel computing to commodity servers. The result is a sizeable decrease in the cost per terabyte of storage, which in turn makes it affordable to model all your data.
• Flexible – Hadoop is schema-less, and can absorb any type of data, structured or not, from any number of sources. Data from multiple sources can be joined and aggregated in arbitrary ways enabling deeper analyses than any one system can provide.
• Fault tolerant – When you lose a node, the system redirects work to another location of the data and continues processing without missing a beat.”

According to Alan Radding writing in IBM Systems Magazine (http://www.ibmsystemsmag.com/mainframe/trends/whatsnew/hadoop_mainframe/) IBM “is taking a federated approach to the big data challenge by blending traditional data management technologies with what it sees as complementary new technologies, like Hadoop, that address speed and flexibility, and are ideal for data exploration, discovery and unstructured analysis.”

Hadoop could run on any mainframe already running Java or Linux. Radding lists tools to make life easier like:

• SQOOP – imports data from relational databases into Hadoop.
• Hive – enables data to be queried using an SQL-like language called HiveQL.
• Apache Pig – a high-level platform for creating the MapReduce programs used with Hadoop.

There’s also ZooKeeper, which provides a centralized infrastructure and services that enable synchronization across a cluster.

Harry Battan, data serving manager for System z, suggests that 2,000 instances of Hadoop could run on Linux on the System z, which would make a fairly large Hadoop configuration.

Hadoop still needs to be certified for mainframe use, but sites with newer hybrid machines (z114 or z196) could have Hadoop today by putting it on their x86 blades, for which Hadoop is already certified, and it could then process data from DB2 on the mainframe. But you can see why customers might be looking to get it on their mainframes because it gives them a way to get more information out of the masses of data they already possess. And data analysis is often seen as the key to continuing business success for larger organizations.