Tag Archives: Mainframe

Generation Z and Mainframe Programming

When you think of mainframe programming, images of scruffy old men stuck in the 1960s might come to mind. Yet as Caroline McNutt, a young mainframe programmer at Ensono explained recently, this image does not reflect reality.

Ensono provides managed IT services for a variety of infrastructure, including mainframes. McNutt, who has worked with Ensono’s mainframe teams in the company’s Conway, Arkansas location since 2016, recently spoke to us about the state of the mainframe, the role of young women in computer science, and more.

Here’s what McNutt had to say.

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What’s your role at Ensono, and how long have you been in the position?

I am an associate mainframe systems programmer. I’ve been with Ensono for about two years.

I first worked with Ensono in summer 2016 for a two-month college internship. After I graduated, they hired me to work full-time.

What does your day-to-day mainframe programming work entail?

I’ve been going through some of the older, legacy processes and trying to automate them through SAS. I also work on mainframe monitoring.

I work with z/OS. Other people at the company work with mainframe VM systems, but I kind of like my green screen.

How much interest do you see in mainframes among other young people and women?

Among women, a lot! On my current team at Ensono, we’re about 50/50 males and females. And there are quite a few females across the company as a whole.

[For more on women in the technology industry, check out our recent blog post “Women in Tech: Recognizing Female Leadership in Technology.”]

As for young people, most people at the company are older than me. But I’m twenty-four, so that’s not necessarily saying much.

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What was your experience with becoming a woman programmer who focuses on mainframes like?

At Ensono, I have faced no challenges at all as a woman programmer.

In college, though, things were harder. Even female teachers looked down on [women majoring in computer science]. A professor told me I was only hired for mainframe programming because I was a quota filler. And as I progressed further into the computer science degree program, [women programmers] would drop off.

Learning about mainframes in college was hard, too, even as a computer science student. They don’t teach mainframes. I didn’t even know what a mainframe was at first. And I think that’s a problem.

Given the lack of coverage of mainframes at universities, what do you think the future looks like for mainframes?

I definitely don’t think the mainframe is going anywhere for the foreseeable future.

A lot of people talk about cloud coming in and replacing mainframes. But cloud performance just doesn’t match what we already have in place on the mainframe.

Plus, a lot of the time, mainframes have been around for so long that the effort it would take to convert a mainframe to another platform would be so costly and time-intensive that it’s not practical to do that.

I definitely feel like I have a stable career here working on mainframes.

Download our eBook, Data Encryption in the Mainframe World, for even more on mainframes!

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Common Security Challenges on the Mainframe and IBM i Platforms

Security on the mainframe and IBM i platform (and all of IT) is at the forefront of CIO/CTO minds today. Various compliance laws and regulations, most recently GDPR, has forced these leaders to focus on how safe and secure their data is – as well as the ability to have security team visibility into this data.

There are various areas to consider with respect to security. I’ll touch on each of them briefly here:

Controlling access to system and data

Are the people entrusted with access to data the only ones who are accessing this data? Regulations such as GDPR require that access to protected data be limited to those that need it and only for periods of time where that access is required. How are organizations ensuring that nobody else is able to get at this data? Can the organization prove that it is monitoring all access and has governance in place to determine who has access, when, and for how long?

Limiting activity granted to user profiles

The days where entire teams and groups of people are granted wide open access to all systems has come to an end. Having necessary security in place means that only those that need access to systems are granted such access and that few are granted this access on an ongoing basis.

Allowing access to those that don’t need it or allowing it for too long shows that the organization has inadequate controls and may not be protecting private data.

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Tracking database and system activity

Just controlling access to data and limiting user activity isn’t enough to prove the organization is doing everything possible to protect the infrastructure and the data. The organization must track all system and database activity and be prepared to react when there are users and activities present that aren’t expected or thought to be allowed or necessary.

Reporting on security violations

GDPR and other regulations/laws prescribe timeframes on what must be reported and when, but it’s best practice to have a plan in place to report on security violations and breaches that have been found. Many organizations have suffered lost revenue, fines, and lost reputation because of breaches that were either not caught or not reported in a timely manner.

Ensuring compliance with regulations

Not complying with security regulations is a quick path to being out of business. Large fines (to the organizations and to the leaders personally) can cause irrevocable damage to the business financially and to the reputation to the business. It’s also the right way to do business, especially today when so much private data exists and can be exploited in ways that can do harm to the individual.

Ensuring compliance requires a plan as well as the tools to capture the information necessary to monitor the infrastructure for breaches and potential breaches.

Protecting data via encryption, masking, scrambling, etc.

Putting encryption and other techniques into software solutions that look at, keep, and transport data is a big step towards protecting the data and show that the organization is serious about doing what’s necessary to protect their customers and their private data. CIO’s and other leaders should be examining all of the ways that data is transported, stored, viewed, and used and ensure that the data is thoroughly protected throughout its lifecycle.

Visibility of data

Finally, let’s address the visibility of all of this key security data. Organizations have invested a lot of money in various platforms that help make their business money, but all of them store critical system and security logs in various ways. Mainframes store data in logs, typically accessible with tools only used by mainframe system programmers. IBM i systems have data in journals that are, again, only accessible natively by IBM i experts.

But most organizations have centralized teams now who are looking at security data and how successful can those teams be if they are missing data from vital platforms like the mainframe and IBM i?

Many organizations have standardized on Splunk as a place to receive and view all kinds of data, security data included. That means that in order for the security people to be successful, they only need to know how to work with the data in Splunk, not all the various platforms and technologies that capture and provide the data.

Ironstream and our new Ironstream for IBM i take data from the mainframe and IBM i systems and forward that crucial data to Splunk and other SIEM consoles for viewing, alerting, and analysis. Having that vital data to hand means that organizations have complete visibility into these environments without the need for costly monitoring systems or for specialized, scarce, and costly expertise. Make sure to register for our upcoming webcast: Ironstream for IBM i – Enabling Splunk Insight into Key Security and Operational Metrics.

Download the Next-Gen Operational Intelligence checklist to discover what you need to start monitoring on your mainframe.

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To Achieve Scalability, Don’t Overlook Your Mainframe

Scalability is the be-all, end-all of the IT world today. And when it comes to achieving scalability, mainframes can often help you more than commodity servers. Here’s why.

Scalability refers to the ability of IT infrastructure or software to support workloads of increasing size. Being able to scale is essential for ensuring that the extent of your IT resources doesn’t stifle your business’s ability to handle more transactions or support more customers.

Today, when most people think about how to achieve scalability, the go-to solution is the public cloud. On a public cloud, you can purchase a virtually unlimited amount of resources, ensuring endless scalability.

Yet if you own a mainframe, you may have another scalability solution already at your fingertips. While mainframe infrastructure is probably not the first type of IT resource that comes to mind when most people think about scalability, the fact is that mainframes can in many ways scale even faster and more cost-effectively than the cloud (or, for that matter, on-premise commodity servers).

Here are several ways in which mainframes can scale better than the cloud — and reminders of why mainframes remain valuable even in the age of the cloud.

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Transactions Per Minute

Mainframes are beasts when it comes to processing transactions. While the exact number of transactions that a mainframe can handle each second will vary from one machine to the next, figures above one million are not unusual.

Again, that’s transactions per second; per day, you’re talking billions of transactions.

Can your cloud match this figure? Maybe, if you buy a huge number of virtual servers. But while the numbers will vary from case to case, there’s a pretty good chance that at the end of the day, your cloud computing budget will hit its ceiling before your mainframe runs out of transaction processing capacity.

Operating System Virtualization

One of the reasons people move workloads to the cloud is that they can create as many operating system instances as they want using virtual servers. Plus, they can use multiple types of operating systems at the same time.

That’s great. But what’s greater, at least for certain use cases, is that mainframes can also support effectively as many virtual operating system instances as you need.

What’s more, mainframes can host not only Linux instances, but also z/OS, all on the same hardware. Good luck finding a public cloud that lets you run z/OS.

What all of this means is that mainframes provide agility, which is the mother of scalability, so to speak.

Scale-Out Storage

Mainframe storage systems were easy to expand by adding new hardware long before software-defined file systems brought similar scale-out storage functionality to commodity servers. In this sense, mainframes have always been ahead of the scalability curve.

And while cloud-based storage is easy to expand by purchasing more storage space, it can also be expensive. Most public cloud providers charge not only a flat monthly or daily storage fee, but also impose extra fees for each time you access data or move it on the network.

With a mainframe, your storage costs are much more fixed. They’re tied pretty closely to storage acquisition costs, without much overhead on top of that.

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“Vertical” Scalability

In the public cloud or on commodity servers, accommodating a larger workload typically entails scaling “horizontally.” That means you add more servers to support more instances of the application that you want to scale up.

Mainframes could do the same thing using virtual operating systems. But mainframes also enable “vertical” scalability: Because of their immense processing and storage power, they can host a single application that accommodates a very large workload.

In many ways, vertical scalability is better than horizontal scalability. The more servers and application instances you add as you scale horizontally, the trickier it becomes to manage all of them and keep them secure. If you can instead scale vertically by supporting a huge workload with a single application instance, you only have one host and application to worry about.

This is like the difference between expanding your existing house to accommodate a growing family (that’s vertical scaling) and building an additional house, then spreading your family across your two houses (that’s horizontal scaling). Which approach seems easier to manage and more effective in the long run?

Download our eBook and discover how to address the top 5 mainframe security vulnerabilities.

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What Makes Mainframes Different? Mainframe vs. Server

What is a mainframe? One of the best ways to answer that question is to explain what makes mainframes different from other types of computers — especially servers you’d find in a data center.

Toward that end, let’s take a look at the key differences between commodity servers — that is, the relatively inexpensive, usually x86-based servers that you find in large numbers in today’s data centers — and mainframe computers.

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Mainframes Handle Much More Data

Z13 mainframes can handle about 2.5 billion transactions per day, according to IBM. That’s a staggering amount of throughput and data.

It’s hard to draw a direct comparison to commodity servers, because the number of transactions that they can support will vary depending on exactly what’s in the server you’re talking about — plus, the types of transactions may be very different, so you can’t compare apples to apples.

If you figure, however, that a typical database on a typical commodity server might be able to do 300 transactions per second, that works out to around 26 million per day — a large number, but far short of the billions of transactions that a mainframe can support.

Mainframes Run Unique Software (Sometimes)

A key distinguishing feature of mainframes is that they are typically powered by mainframe-specific applications written in languages like COBOL. They also run their own operating systems, like z/OS.

You can’t move mainframe-native workloads to commodity servers.

You can, however, take workloads that you’d find on a commodity server and move them to a mainframe. Most mainframes can run Linux as well a z/OS using virtualization.

Mainframes, therefore, give you the best of both worlds: Access to a unique set of applications that you can ‘t run elsewhere, plus the ability to handle the workloads of commodity servers.

Mainframes are Bigger

mainframes What Makes Mainframes Different? Mainframe vs. Server

Your mainframe is bigger — in a physical sense — than a typical commodity server.

That’s not because mainframes are huge. Today’s mainframes are about the size of a refrigerator. They don’t take up an entire room.

But you could fit about a dozen commodity servers in a server rack of the same size. Mainframes will probably always be a little bigger than commodity servers.

Mainframes Cost More

A single mainframe could cost you around $ 75,000 — far more than the two or three thousand dollars that you might pay for a good x86 server.

Of course, that does not mean that mainframes are more expensive in the long run. You’ll get a lot more computing power for your $ 75,000 mainframe than you will from a commodity server. When used properly, mainframes can deliver significant cost savings.

Mainframes Support Unique Use Cases

Mainframes stand apart because you find them used in situations that commodity servers just can’t handle.

Mainframes’ ability to support massive volumes of transactions, their high reliability and their support for diverse types of workloads make them essential to a range of industries.

These businesses may be using commodity servers as well as mainframes, but mainframes fill certain voids that other servers just can’t.

Download our 2018 State of the Mainframe report to learn about what this year has in store.

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New eBook! The Ultimate Guide to Mainframe Machine Data

Mainframe logs and data sources can provide a wealth of information about the operational health of your system while shedding light on potential security concerns. Syncsort’s latest eBook, The Ultimate Guide to Mainframe Machine Data, takes a look at the different data sources and how they can be used to best benefit your organization.

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Companies are looking to incorporate mainframe logs into their analytics processes to get a bigger and more complete picture of what’s happening in their IT environments. Mainframe machine data can be correlated and analyzed along with data from other systems to obtain Big Data insights that you can trust and confidently act on.

This eBook focuses on:

  • SMF Data
  • Syslog Data
  • UNIX System (USS) Files
  • Log4j Data
  • And more!

Download the eBook now and explore how mainframe data can provide valuable insight.

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What You Need to Know About Tracing Transactions From Mobile to Mainframe

Having visibility of key, up-to-date metrics from the elements within a system is incredibly valuable, but difficult to achieve without the right tooling. Syncsort’s new eBook, “What You Need to Know About Tracing Transactions From Mobile to Mainframe,” takes a look at which tools best prepare you in the case of an emergency.

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The situation in which a system goes down can be detrimental to an organization and the damage can vary greatly by how quick the downtime is resolved. With transaction tracing, the issue can be narrowed down quickly and a fix can be efficiently put into place.

Download the eBook now and find out more!

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Mainframe Meets Machine Learning and Artificial Intelligence at SHARE Sacramento

Winter SHARE 2018 was held in Sacramento, California the week of March 12th. Throughout the years the scope and tone of the SHARE conference has changed based upon the technologies everyone is discussing. In the past, it was focused on the mainframe and how to better manage your environment. This year, SHARE was focused on emerging technologies that can be used to manage and monitor your environment. The most talked about next-generation tech being Machine Learning and Artificial Intelligence. As I listened to the sessions, I it became clear that Machine Learning is where many of the attendees were particularly focused.

Mainframe Meets Machine Learning and Artificial Intelligence at SHARE Sacramento banner2 Mainframe Meets Machine Learning and Artificial Intelligence at SHARE Sacramento

A Common Thread between SHARE and Computer Measurement Group

This is the fourth SHARE conference I have presented at with different organizations. For years, I have presented at a number of different conferences, both national and regional. Each conference has its own flavor and target audience. Many of the individuals that go to SHARE would not go to the Computer Measurement Group (CMG) event and vice versa. That said, this year, I came to present a topic that would be of interest at either event… the Changing Landscape of Capacity Management for the Mainframe. The basis of the topic is how Mainframe capacity managers need to ensure they are looking at the process from a business standpoint rather than just as a technical application.

Capacity Management’s Role in Machine Learning

So where does Capacity Management fit into the white hot new trend of Machine Learning? By definition, Machine Learning is “a field of computer science that gives computer systems the ability to “learn” with data, without being explicitly programmed.” Therefore, the key is to provide enough meaningful data for Machine Learning capabilities to automate into analytics. Capacity Management delivers a vast amount of information about IT resources and their utilization, including enabling machine learning programs to perform analytics in the background for the reporting of “Time to Live” until a resource is exhausted. The key to the performance of this analysis is the setting of thresholds, whether those thresholds are static or self-learned.

Self-Learning thresholds is where Capacity Managers want to get to within their environments. One of the discussions that was held at SHARE was focused on whether, with machine learning, we are eliminating the human factor. My answer to that statement is that we can never eliminate the human factor. There are dynamics within the environment that a machine cannot account for in its processing and learning. As we say many times, humans can do things we never have planned for with the software. There will continue to be analysis that must be performed on the reports to ensure a variable is not missed. Syncsort, with software products like Athene and Ironstream with Splunk is using this type of technology to enable this type of machine learning. Over the next twelve months I think this area will be one with large scale growth. The key to making this work is to ensure the quality of the data that is gathered, and having it correlated with the technical and business data. The business and application view is what organizations are looking to display on dashboards for all levels within the enterprise.

One of the benefits of going to a conference is seeing what the vendors are presenting in the exhibition hall along with meeting up with friends and former colleagues from the past. At each conference, you have the stalwarts including Syncsort, BMC, ASG, IntelliMagic, Velocity and IBM. Every year there are some new companies that show up at the shows. Throughout my long career, I have either worked with many people in the exhibition hall or know them from the industry. The nice feature about the technical aspect in the relationships is that we talk about the how our jobs are changing within the industry and the new technologies affecting us.

Conferences such as SHARE continue to provide a great venue for all attendees to share thoughts and insights about what’s changing and what technologies can help address the challenges and opportunities that come with that change.

Download our free eBook, “Mainframe Meets Machine Learning“, to learn about the most difficult challenges and issues facing mainframes today, and how the benefits of machine learning could help alleviate some of these issues.

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New eBook! 50 Years of Mainframe Innovations: Observations From a Long-Time Mainframer

Over the past five decades, the focus for the mainframe has been on bigger, faster and cheaper processing power. This is no surprise, considering the technology advancements that have occurred. In Syncsort’s new eBook, “50 Years of Mainframe Innovations: Observations From a Long-Time Mainframer“, Ed Hallock talks about how amazing the past 50 years has been for the mainframe, with unbelievable advances in technology and business practices.

50 Years of Mainframe Innovations Observations From a Long Time Mainframer banner New eBook! 50 Years of Mainframe Innovations: Observations From a Long Time Mainframer

Not only have mainframes evolved, but organizations have evolved as well. That said, organizations struggle today with supporting growth, controlling IT budgets and costs, and ensuring the security of their IT infrastructure against potential threats and attacks.

Download the eBook today to learn more about what was observed.

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Maximizing Mainframe Visibility

Do you have a mainframe visibility problem? If so, you probably also have mainframe security and performance problems. Here’s how to improve visibility for your mainframe.

In IT, visibility means an understanding of what is happening in your systems and infrastructure.

Visibility is important because it provides the foundation for making informed decisions about security, performance optimization and the expansion of your infrastructure. If you lack visibility, you’re shooting in the dark when it comes to managing your hardware and software.

Ideally, visibility will be continuous, meaning it is ongoing and there are no gaps in your ability to work with your systems.

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Overcoming Mainframe Visibility Hurdles

The tricky thing about visibility is that there is no one-stop solution for achieving it. An effective visibility strategy requires a mix of tools and processes. And it must be tailored to your organization, of course.

Visibility is also challenging to achieve because having visibility into one part of your infrastructure does not necessarily mean the rest of it is visible, too. You may do a great job of maintaining visibility into your storage systems but have much less visibility into front-end software, for example. Or you may have maximum visibility into commodity servers but none for your mainframes.

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How do you address these challenges, particularly when you are working with a mainframe? The following tools and strategies can help you to overcome common mainframe visibility challenges:

  • Real-time system monitoring

Monitoring tools alert you to performance and availability issues as they occur, so that you have a decent shot of addressing them before they impact operations. Mainframe monitoring tools like Syncsort Ironstream Dataset Analyzer can be integrated with Splunk to provide easy visualization and interpretation of monitoring information.

  • Security monitoring

General-purpose mainframe monitoring tools may provide some security insights, but to maximize security visibility for the mainframe you should use a dedicated security monitoring solution. Syncsort’s ZEN Suite provides mainframe security monitoring.

  • Network monitoring

Optimizing mainframe performance requires you to keep tabs on your network, too. Mainframe network monitoring is another feature of Syncsort’s ZEN Suite.

  • Log analysis

Logs can enable both real-time and historical visibility into your systems. They can help you detect problems as they occur, as well as investigate issues after the fact. Because mainframe log data is so vast, analyzing mainframe logs manually is usually not feasible. Instead, log aggregation and analytics tools provide the insight you need. For guidance on leveraging mainframe logs, check out Syncsort’s whitepaper on the topic.

  • Organizational visibility

In addition to monitoring and analyzing your mainframe itself, full visibility requires taking into account the organizational context of the mainframe. Understand who is responsible for maintaining the mainframe, who is on-call when something goes wrong and who has access to it. Having this information on hand ensures that you can troubleshoot issues as quickly as they arise.

Again, your mainframe visibility strategy should be tailored to your infrastructure and organization. In general, however, the tips above should help you achieve the continuous visibility that you need to keep your mainframe lean, mean and running well.

For more on the mainframe, download our 2018 State of the State of the Mainframe report and learn what every business needs to know about Big Iron in a Big Data world

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Webcast: Key Mainframe Trends for 2018

The results from Syncsort’s annual State of the Mainframe survey are in! Professionals from Financial Services, Health Care, IT, Government and more have weighed in on which key mainframe trends are most important to them.

Our latest webcast, “Key Mainframe Trends for 2018” takes an in-depth look at the survey results and speaks about five mainframe trends to watch for in 2018.

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In addition to those trends, you’ll also learn the importance of:

  • Investmenting in the mainframe
  • Modernizing mainframe environments
  • Meeting security and compliance requirements
  • Using mainframe data from Big Data analytics

Check out the on-demand webcast now and see what this year’s results have brought in.

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