Big Data 2.0

We were only just beginning to get our heads around Hadoop and Big Data in general when we find everyone is starting to talk about Big Data 2.0 – and it’s bigger, faster, and cleverer!

Hadoop, as I’m sure you know, is an open source project, and it’s available from companies like IBM, Hortonworks, Cloudera, and MapR. It provides a storage and retrieval method (HDFS – Hadoop Distributed File System) that can knock the socks off older, more expensive storage options on databases using SAN or NAS. It also means that more data can be stored. And that means not just human-keyed data, but data from the information of things (point of sales machines, sensors, cameras, etc) as well as social media. It’s an OCD sufferer’s dream come true. No need to delete (throw away) anything. But with all the data, it becomes important to find some way to ‘mine’ it – to derive information from the data that can be commercially useful. And that’s what’s happening, deeper and richer sets of results are being derived from the data that are beneficial to organizations.

With Version 2 of Hadoop, everything is faster. Data is processed at amazing speeds in-memory. The analysis is taking place at speed on terabytes of data. It also allows decisions to be made at speeds unavailable to humans. Research shows that algorithms with as many six variables out-perform human experts in most situations. This was tested on experts predicting the price of wine in future years and stock marketeers. So now, Big Data 2.0 means better decisions can be made at incredible speed.

It’s also possible for machines to learn using these techniques – such as the Google classic of having software that can identify the presence of a cat in video footage and no-one being quite sure how it is doing it.

For mainframe sites, Hadoop isn’t just some distant dream. You don’t need a room full of Linux servers to make it work – in fact that’s the clue to the solution. Much of this works very nicely on Linux on System z (or zLinux as many people still think of it). And once the data is on a mainframe, it becomes very easy to copy parts of it to a z/OS partition for more work to be done on the data. Cognos BI runs on the zLinux partition, so the first level of information extraction can be performed using that Business Intelligence tool. Software vendors are coming to market with products that run on the mainframe. BMC has extended its Control-M automated mainframe job scheduler with Control-M for Hadoop. Syncsort has Hadoop Connectivity. Compuware has extended its Application Performance Management (APM) software with Compuware APM for Big Data. And Informatica PowerExchange for Hadoop provides connectivity to the Hadoop Distributed File System (HDFS).

So what’s it like on the ground and away from the PowerPoint slides? At the moment, my experience is that really big companies – Google, Amazon, Facebook, and similar are pushing the envelope with Big Data. But it seems that many large organizations aren’t strongly embracing the new technology. Do banks, insurance companies, and airlines – the main users of mainframes – see a need for Big Data? Seemingly not – or not yet. Perhaps they are waiting for money to be spent and mistakes to be made before they adopt best practice and reap the benefits. Perhaps they are waiting for Big Data V3?

Big Data is definitely here to stay and those companies that could benefit from its adoption will gain a huge commercial advantage when they do.

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!

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.

Big SQL

Suppose you wanted to access ‘big data’ stored in HDFS or HBase. What would you do? Well, for many people, the first step is to find out what we’re talking about. So, let’s start with big data – it’s data that’s so large and complex that it’s difficult to process using standard and familiar database management tools or applications.

According to Wikipedia, there are issues around data capture, curation, storage, search, sharing, analysis, and visualization. You’re probably thinking: why not go back to using smaller and manageable data? It seems that people want access to larger and larger amounts of data because additional information can be gained from it – allowing people to “spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions”.

Now that’s clear, what are HDFS and HBase? HDFS stands for Hadoop Distributed File System. It’s a distributed, scalable, and portable file system written in Java for the Hadoop framework. HDFS stores large files across multiple machines, and replicates the data across multiple hosts. HBase is an open source, non-relational, distributed database and is also written in Java. It was developed as part of Apache Software Foundation’s Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System), providing a fault-tolerant way of storing large quantities of data.

Each node in a Hadoop instance typically has a single namenode; a cluster of datanodes form the HDFS cluster. So what’s needed is some way to access that cluster. At the moment, the choices are basically Hive, Impala, and Big SQL.

Again, a search on Wikipedia informs me that “Hive supports analysis of large datasets stored in Hadoop-compatible file systems such as Amazon S3 filesystem. It provides an SQL-like language called HiveQL while maintaining full support for map/reduce. To accelerate queries, it provides indexes, including bitmap indexes. By default, Hive stores metadata in an embedded Apache Derby database, and other client/server databases like MySQL can optionally be used. Currently, there are three file formats supported in Hive, which are TEXTFILE, SEQUENCEFILE, and RCFILE”

The Cloudera Impala project allows users to query data, whether stored in HDFS or HBase – including SELECT, JOIN, and aggregate functions – in real time. Furthermore, it uses the same metadata, SQL syntax (Hive SQL), ODBC driver, and user interface (Hue Beeswax) as Apache Hive. To avoid latency, Impala circumvents MapReduce to directly access the data through a specialized distributed query engine.

When you look up information about these things, names like Apache, Cloudera, Amazon, Facebook, Google crop up, but not IBM. You might think that’s a bit strange. Wouldn’t IBM be the organization you’d expect to have experience of big data? I mean just think of those massive IMS databases. So, why haven’t I mentioned IBM? The answer is because I haven’t got to Big SQL yet.

IBM claims that Big SQL provides robust SQL support for the Hadoop ecosystem:

•  it has a scalable architecture;
• it supports SQL and data types available in SQL '92, plus it has some additional capabilities;
• it supports JDBC and ODBC client drivers;
• it has efficient handling of ‘point queries’;
• there are a wide variety of data sources and file formats for HDFS and HBase that it supports;
• And, although it isn’t open source, it does interoperate well with the open source ecosystem within Hadoop.

The really interesting thing about this is that all the information is available in one place – Big Data University (http://bigdatauniversity.com). I’m looking forward to taking the course. Big data isn’t going away any time soon.

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.