When worlds collide

We know that mainframes are rock solid workhorses that ensure the banks and insurance companies and airlines and pretty much every other large organization get their work done correctly and swiftly. And we know that access to mainframes has been extended outside the world of green screens to anyone on a browser with proper authorization. And we also know that there’s little distinction between the world of cloud computing and distributed mainframe computing. But the latest big thing is Big Data – and that seems like a different world.

Big Data is used to refer to huge amounts (exabytes) of data, often unstructured, that can originate from a variety of sources – such as cameras, weather satellites, credit card machines, barcode readers, the Internet of Things, anything! This Big Data usually sits on Linux or Windows boxes and some of the early developers were Google, Amazon, and Facebook. The data is stored in HBase, a non-relational, distributed database, written in Java. And the file system is what’s called a Hadoop Distributed File System (HDFS). At runtime, a process maps the data and reduces it – that’s called MapReduce.

So how do these two worlds come together? For a start a lot of the things you need for Big Data are Open Source and come from the Apache Foundation. IBM is a member of the foundation and has a number of products that extend Big Data’s functionality. IBM provides InfoSphere BigInsights, Data Stage, Streams, and Guardium. There’s Big SQL with Big Insights V2.1, and the spreadsheet-like Big Sheets.

If you want to run Big Data – Hadoop – on your mainframe, you’ll need to do it in a Linux partition (Linux on System z). But IBM isn’t the only mainframe software vendor that’s getting in on the act. We’ve recently heard from BMC, Syncsort, Compuware, and Informatica about their products.

BMC has extended its Control-M automated mainframe job scheduler with Control-M for Hadoop. The product enables the creation and management of Hadoop workflows in an automated environment and is aimed at Hadoop application developers and enterprise IT administrators who are using Hadoop as part of their production workload.

Syncsort has Hadoop Connectivity, which prevents Hadoop becoming another silo within an enterprise. The product makes it easy to get data in and out of Hadoop. The product provides: native connectivity to all major data sources and targets; native mainframe connectivity and support for EBCDIC/ASCII, VSAM, Packed decimal, Comp-3, and more; heterogeneous database access on Hadoop; direct I/O access for faster data transfers; and high-performance compression.

Compuware has extended its Application Performance Management (APM) software with Compuware APM for Big Data. This, they claim, allows organizations to tame Big Data applications to eliminate inefficiencies and rapidly identify and resolve problems. Using PurePath Technology, it provides visibility into Hadoop and NoSQL applications. Organizations, they say, use Compuware APM for Big Data to reduce costs, analyse issues, and ensure optimal efficiency from their Big Data investments.

Informatica PowerExchange for Hadoop provides native high-performance connectivity to the Hadoop Distributed File System (HDFS). It enables organizations to take advantage of Hadoop’s storage and processing power using their existing IT infrastructure and resources. PowerExchange for Hadoop can bring any and all enterprise data into Hadoop for data integration and processing. Fully integrated with Informatica PowerCenter, it moves data into and out of Hadoop in batch or real time using universal connectivity to all data, including mainframe, databases, and applications, both on-premises and in the cloud. Informatica PowerCenter Big Data Edition is, they claim, highly scalable, high-performance enterprise data integration software that works with both Hadoop and traditional data management infrastructures.

Clearly, these two different worlds have more than collided – we are beginning to see the integration of these previously quite separate worlds with software from a number of vendors helping users with the integration process. And as users, we get the best of both worlds!

IBM’s approach to Big Data

IBM has taken lots of the open source Big Data technologies – like Hadoop, MapReduce, HBase – and added its own technology – like Big Sheets, DB2, DataStage – to create something hugely more powerful.

IBM’s InfoSphere BigInsights builds on open source Hadoop capabilities for enterprise class deployments. The enterprise-level capabilities can be grouped together as: visualization and exploration, development tools, advanced engines, connectors, workload optimization, and administration and security.

IBM claims the business benefits are: quicker time-to-value because of IBM’s technology and support, reduced operational risk, enhanced business knowledge with a flexible analytical platform, and it leverages and complements existing software.

In terms of administration and security, the Web console can start and stop services, run and monitor jobs (applications), explore and modify the file system, and built-in apps make it easy to do common tasks.

The connectors link to databases like DB2, Netezza, Oracle, Teradata. And there’s integration with: InfoSphere Data Stage (data collection and integration), InfoSphere Streams (real-time streams processing), InfoSphere Guardium (security and monitoring), Cognos Business Intelligence (Business Intelligence capabilities), and IBM Platform Computing (cluster/grid infrastructure and management), and more. Big SQL is coming with BigInsights V2.1. This will provide SQL access to data stored in BigInsights through JDBC/ODBC and use rich standard SQL to leverage Map/Reduce parallelism or achieve low-latency.

Advanced engines include an advanced text analytics engine that can automatically identify and understand key information in text. Text Analytics is really useful because most of the world’s data is in unstructured or semi-structured text; social media is full of discussions about products and services; internal information in organizations is locked in blobs, description fields, and sometimes even discarded. It’s been suggested that over 80% of stored information is unstructured – such as e-medical records, hospital reports, case files, police records, emergency calls, tech notes, call logs, online media, insurance claims, Twitter, Facebook, blogs, and forums.

In terms of development tools, there is an Eclipse-based development environment for building and deploying applications. There are developer tools and a set of analytic extractors for fast adoption that reduce coding and debugging time by up to 30% (IBM claims). There are also plug-ins for text analytics, MapReduce programming, Jaql development, Hive query, etc.

Visualization and exploration has Big Sheets, providing Web-based analysis and visualization for users with a familiar spreadsheet-like interface that can define and manage long-running data collection jobs.

Meanwhile, Microsoft has identified Hadoop users as a useful market to get into. Speaking recently at the Hadoop summit, Quentin Clark, corporate VP of data platforms said: “We believe Hadoop is the cornerstone of a sea change coming to all businesses”.

Microsoft is integrating Hadoop with its products and services. And, Clark says that Microsoft intends to stick to the principles of open source by contributing to the Hadoop project, rather than simply using it and adding its own stuff. Hortonworks recently announced management packs for Microsoft System Center Operations Manager and Microsoft System Center Virtual Machine Manager – both products for administering the Hortonworks Data Platform (HDP) distribution.

Apparently Microsoft is positioning itself as a big data player with a powerful set of Business Intelligence (BI) tools. Data Explorer for Excel 2013 is a self-service BI add-in allowing users to import data from a variety of sources, including Hadoop. SQL Server 2012 Parallel Data Warehouse (PDW) is a massively parallel processing data warehousing appliance designed for Hadoop integration. Microsoft is also trying to bring Hadoop into the cloud using Windows Azure.

Businesses can’t ignore Hadoop, and the fact that major software vendors are getting behind it means it’s not going to be some flash-in-the-pan idea. Certainly, I can imagine major organizations looking to get a huge business advantage by embracing the technology now – to be ahead of their competitors. Smaller organizations will probably take a few years before they see a business case for it. By then the IBM products (and Microsoft’s) will be very mature and eminently suitable.

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.