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In September 2018, IBM announced a brand new product, IBM Db2 AI for z/OS. This synthetic intelligence engine displays facts entry patterns from executing SQL statements, makes use of desktop getting to know algorithms to choose choicest patterns and passes this assistance to the Db2 query optimizer to be used by way of subsequent statements.laptop researching on the IBM z Platform
In may also of 2018, IBM announced version 1.2 of its laptop discovering for z/OS (MLz) product. here's a hybrid zServer and cloud utility suite that ingests efficiency information, analyzes and builds fashions that signify the fitness popularity of a number of indicators, monitors them over time and offers precise-time scoring capabilities.
several points of this product offering are geared toward assisting a neighborhood of mannequin developers and executives. as an example:
This computing device discovering suite turned into initially aimed at zServer-based mostly analytics applications. one of the vital first glaring choices become zSystem performance monitoring and tuning. equipment management Facility (SMF) records which are automatically generated by using the working system supply the uncooked facts for equipment useful resource consumption reminiscent of principal processor utilization, I/O processing, reminiscence paging and the like. IBM MLz can collect and keep these records over time, and construct and educate fashions of gadget behavior, rating these behaviors, establish patterns not conveniently foreseen by means of humans, boost key efficiency warning signs (KPIs) and then feed the mannequin results back into the gadget to affect gadget configuration adjustments that may enrich performance.
The next step become to enforce this suite to analyze Db2 efficiency facts. One answer, called the IBM Db2 IT Operational Analytics (Db2 ITOA) solution template, applies the computer getting to know know-how to Db2 operational statistics to gain an understanding of Db2 subsystem health. it could actually dynamically construct baselines for key performance indicators, provide a dashboard of these KPIs and provides operational group of workers true-time insight into Db2 operations.
while normal Db2 subsystem performance is a crucial factor in general software fitness and efficiency, IBM estimates that the DBA guide team of workers spends 25% or more of its time, " ... fighting entry path problems which trigger performance degradation and service have an effect on.". (See Reference 1).AI involves Db2
accept as true with the plight of up to date DBAs in a Db2 environment. In present day IT world they ought to guide one or extra big records applications, cloud software and database capabilities, application installing and configuration, Db2 subsystem and software efficiency tuning, database definition and management, disaster recuperation planning, and extra. question tuning has been in existence seeing that the origins of the database, and DBAs are always tasked with this as well.
The heart of question course analysis in Db2 is the Optimizer. It accepts SQL statements from applications, verifies authority to entry the statistics, studies the locations of the objects to be accessed and develops an inventory of candidate information access paths. These access paths can include indexes, table scans, various table join strategies and others. within the statistics warehouse and big information environments there are usually extra choices obtainable. One of those is the existence of summary tables (on occasion called materialized query tables) that include pre-summarized or aggregated statistics, as a result permitting Db2 to evade re-aggregation processing. another choice is the starjoin access direction, ordinary in the facts warehouse, where the order of table joins is changed for performance explanations.
The Optimizer then experiences the candidate entry paths and chooses the entry path, "with the lowest can charge." charge in this context skill a weighted summation of useful resource utilization including CPU, I/O, reminiscence and different substances. ultimately, the Optimizer takes the lowest charge access path, stores it in memory (and, optionally, within the Db2 directory) and begins access path execution.
massive information and information warehouse operations now encompass application suites that permit the enterprise analyst to make use of a graphical interface to build and manipulate a miniature data model of the records they want to analyze. The packages then generate SQL statements based on the users’ requests.
The difficulty for the DBA
with a view to do decent analytics to your distinctive records stores you want a very good figuring out of the statistics requirements, an understanding of the analytical functions and algorithms available and a excessive-efficiency information infrastructure. lamentably, the number and location of statistics sources is increasing (each in dimension and in geography), records sizes are growing to be, and applications proceed to proliferate in number and complexity. How may still IT managers help this environment, certainly with probably the most experienced and mature workforce nearing retirement?
bear in mind additionally that a huge part of decreasing the entire charge of possession of those systems is to get Db2 applications to run quicker and extra efficiently. This usually translates into using fewer CPU cycles, doing fewer I/Os and transporting less information throughout the community. since it is frequently tricky to even establish which functions could improvement from efficiency tuning, one method is to automate the detection and correction of tuning concerns. here's the place desktop learning and artificial intelligence may also be used to exquisite impact.Db2 12 for z/OS and synthetic Intelligence
Db2 edition 12 on z/OS makes use of the machine learning facilities mentioned above to gather and store SQL query text and access route details, in addition to actual performance-related historic suggestions akin to CPU time used, elapsed instances and outcome set sizes. This providing, described as Db2 AI for z/OS, analyzes and retailers the statistics in desktop getting to know fashions, with the mannequin evaluation outcomes then being scored and made available to the Db2 Optimizer. The next time a scored SQL statement is encountered, the Optimizer can then use the mannequin scoring statistics as enter to its entry direction choice algorithm.
The effect may still be a discount in CPU consumption because the Optimizer makes use of mannequin scoring enter to opt for superior access paths. This then lowers CPU expenses and speeds software response instances. a significant knowledge is that the use of AI application doesn't require the DBA to have information science advantage or deep insights into question tuning methodologies. The Optimizer now chooses the top-quality access paths based not only on SQL question syntax and facts distribution data but on modelled and scored historic efficiency.
This will also be above all critical in case you shop statistics in dissimilar locations. for example, many analytical queries in opposition t massive facts require concurrent entry to definite statistics warehouse tables. These tables are often known as dimension tables, and they comprise the records aspects constantly used to handle subsetting and aggregation. for example, in a retail environment agree with a table known as StoreLocation that enumerates every shop and its vicinity code. Queries towards store sales information may additionally wish to combination or summarize earnings by using vicinity; therefore, the StoreLocation table will be used by means of some massive information queries. during this ambiance it's typical to take the dimension tables and duplicate them consistently to the big information application. in the IBM world this place is the IBM Db2 Analytics Accelerator (IDAA).
Now suppose about SQL queries from each operational applications, facts warehouse clients and large information business analysts. From Db2's perspective, all these queries are equal, and are forwarded to the Optimizer. youngsters, in the case of operational queries and warehouse queries they may still undoubtedly be directed to entry the StoreLocation table in the warehouse. however, the query from the business analyst in opposition t massive data tables should doubtless access the replica of the table there. This results in a proliferations of expertise entry paths, and more work for the Optimizer. happily, Db2 AI for z/OS can supply the Optimizer the assistance it needs to make sensible access path decisions.the way it Works
The sequence of hobbies in Db2 AI for z/OS (See Reference 2) is often here:
There are additionally numerous person interfaces that provide the administrator visibility to the reputation of the accumulated SQL statement efficiency records and model scoring.abstract
IBM's computer studying for zOS (MLz) providing is getting used to first-rate impact in Db2 edition 12 to increase the efficiency of analytical queries in addition to operational queries and their linked purposes. This requires management consideration, as you need to verify that your business is prepared to consume these ML and AI conclusions. How will you measure the costs and merits of using desktop getting to know? Which IT help staff need to be tasked to reviewing the influence of mannequin scoring, and perhaps approving (or overriding) the results? How will you assessment and justify the assumptions that the software makes about access path selections?
In different words, how well were you aware your records, its distribution, its integrity and your existing and proposed entry paths? this may verify where the DBAs spend their time in helping analytics and operational software efficiency.
# # #
John Campbell, IBM Db2 uncommon EngineerFrom "IBM Db2 AI for z/OS: boost IBM Db2 software performance with laptop getting to know"https://www.worldofdb2.com/hobbies/ibm-db2-ai-for-z-os-enhance-ibm-db2-software-performance-with-ma
Db2 AI for z/OShttps://www.ibm.com/help/knowledgecenter/en/SSGKMA_1.1.0/src/ai/ai_home.html
See all articles by means of Lockwood Lyon
The movement will deliver resellers with various earnings, advertising and technical substances that IBM referred to will make it more straightforward to market and promote Netezza systems. IBM is also providing new financing options to channel partners who resell the Netezza home equipment, including zero-percent financing and flexible charge options for consumers.
while Netezza mostly offered its information warehouse home equipment direct to shoppers, IBM has had its eye on the channel for selling Netezza items given that it acquired the enterprise in November for $1.7 billion. at the Netezza user conference in June IBM executives unveiled a associate recruitment effort for Netezza and noted they expect the channel to account for 50 percent of Netezza income inside four years.
"business analytics is going mainstream and IBM's intention is to arm its companions with the correct capabilities and help to assist their purchasers take skills of this vogue," said Arvind Krishna, general manager of IBM suggestions administration, in a press release. "These &#ninety one;new] materials are geared to make it easy for their companions to immediately infuse Netezza into their enterprise model."
IBM has recognized company analytics as one of its strategic initiatives and has forecast that company analytics and optimization items and functions will generate $sixteen billion in annual revenue for the enterprise by using 2015.
Netezza's programs are according to IBM's BladeCenter servers.
Channel companions should be approved to resell IBM products that come beneath the software cost Plus (SVP) software. Authorization requirements include having at the least two employees who've passed a technical mastery exam and one who has passed a income mastery exam.
Resellers who qualify for the SVP application are eligible for co-advertising funds for lead era and other market planning information. IBM additionally presents partners a knowledge bootcamp where group of workers can educate on the way to install, control and preserve Netezza techniques. And SVP-member resellers can deliver sales prospects into IBM Innovation centers to check-drive Netezza items.
starting Oct. 1 the Netezza products additionally will come under IBM's application value Incentive program, which gives economic rewards for partners who identify and strengthen earnings opportunities, but do not necessarily tackle product fulfillment.
On the financing aspect companions can present zero-p.c financing through IBM global Financing to credit score-certified consumers for Netezza purchases. also accessible is 24- and 36-month financing with alternate options that permit clients in shape payments to anticipated money flows.
And companions can lease a Netezza device for 24 months to run within their personal facts centers for demonstration, building, checking out and working towards functions, IBM spoke of.
Charlotte, N.C.-primarily based solutions provider and IBM associate Fuzzy Logix, which components predictive analytics application and functions to consumers, "will use these elements from IBM to uncover international enterprise alternatives and bring bigger value capabilities to their valued clientele," stated COO Mike Upchurch, in an announcement.
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More and more business schools are offering business masters degrees, from generalist programs in management to specialized master’s offerings covering topics like accounting, data analytics, finance, and marketing.
With so many options out there, choosing the best master’s degree to suit your career ambitions can be a challenge.
Before applying, candidates should know the answers to the following questions: Who are the different master’s programs for? What jobs do they lead to? What do you study on the course? What do you need to know to get accepted?
To answer those questions, they spoke with students and faculty from five of the most common business master’s degree options at some of the world’s leading business schools.
Read on to find out how to choose the best master’s degree for you.
You can find out more about the master’s degree that interests you most by clicking on one of the links below.
Masters in Accounting
Masters in Analytics
Masters in Finance
Masters in Management
Masters in Marketing
Find out more about business master’s degrees via webinar
Masters in Accounting
Find out more about Masters in Accounting via webinar
Dr Wang Jiwei, MSc in Accounting director at Singapore Management University (SMU)
Who is a Master’s in Accounting for?
Our Master of Professional Accounting (MPA) is designed to create a pathway for non-accounting graduates working or seeking to work in a field that requires specialized knowledge in accounting.
We also have a Master of Science in Accounting (MSA) which specializes in data and analytics. It’s designed for students who already have a background in accounting or business related disciplines, but wish to further their career and skills set in this data-driven era.
What skills/qualifications do you need to apply?
The overall quality of the applicant determines admission to the program. This would include prior academic performance at university, work experience, referees reports, interview scores, and GMAT/SMU Admission Test is compulsory.
Applicants must have sufficient background in math. Applicants with post-undergraduate working experience will have an added advantage. Applicants to the MSA must also have a business or accounting background.
What will you learn on a Master’s in Accounting?
Through the MPA course, students will learn about the three pillars of accounting; the business fundamentals, accounting core and the professional services which would include the following modules: management accounting, taxation, audit, corporate financial management and financial accounting.
The MSA equips students with the essential skills to harness data for critical decision making. They learn to make use of tools such as R, Python, Tableau, and SQL for data analysis, data visualization and data management. They also can learn machine learning techniques, and understand how they can be applied to the accounting and finance work.
What jobs can you qualify for after a Master’s in Accounting?
An accounting master’s degree is versatile. Every company needs somebody who is well versed in finance and accounting. Hence, the career of an accountant is not limited to the accounting, banking and finance industry. Good accountants are in demand in every sector.
In the recent years, the role of an accountant has evolved. Many organizations are eliminating manual accounting work, because computers and robots can automatically generate accounting entries.
But at the same time, they also see a growing demand in other job functions, mainly in the areas of data processing, management analysis and financial analysis. In the current market, there is a demand for domain experts who understand finance, accounting and data technology and how the systems work.
Top tip for applying to SMU?
What will give their applicants an edge is whether they have the drive to excel, the gumption to overcome challenges and the passion to make a meaningful impact through their work.
Master’s in Accounting student mp;A
Ryan Guo Yu Sheng, SMU MSA student (part-time), manager of Global Operating Certificates at Visa in Singapore
Why pursue a Master’s in Accounting?
I have always been a tech-savvy accountancy graduate, and most of the process and data technology applied in my work was self-taught and unstructured. After completing a specialist diploma in Business Analytics in 2017, I was looking to further my knowledge and build on the basics learned in that course.
An understanding of data analysis and usage of automation and robotics have become essential to the modern finance function and this is also part of my effort in future-proofing my own career. There is an increasing focus on the application of technology to finance processes and simply relying on their IT colleagues to fill us in on technical developments is a thing of the past.
What advice do you have for prospective Master's in Accounting students?
A candidate should be technically savvy. Dealing with data requires a highly analytical and structured skillset, along with a sense of creativity for data transformation and programming. Having a conceptual understanding of accounting is important.
Masters in Analytics
Find out more about Masters in Analytics via webinar
Crystal Grant, director of admissions (pictured above), and Kalyan Talluri, MSc Business Analytics program director at Imperial College Business School (pictured below)
Who is a Master’s in Analytics for?
There's a huge range of Master's in Analytics programs available, which reflects the growing demand from candidates and employers for graduates who are able to derive business insights and competitive advantage from big data.
Some will be aimed at new or recent graduates looking to launch their career, whilst others will be suited to professionals hoping to up-skill to progress or change jobs.
What skills/qualifications do you need to apply?
Programs vary in the balance of business versus technical experience required, but if you have a passion for data analytics, quantitative skills and commercial awareness, you should be able to find a Master's in Analytics that supports your goals.
Successful candidates to their MSc Business Analytics bring a wide variety of academic and work experience. They look for strong quantitative, communication, problem-solving and team-working skills. A passion for coding is also important, as the curriculum is very practical and you should be ready to learn and use programming languages.
What will you learn on a Master’s in Analytics?
Masters in Analytics aim to help graduates and young professionals understand the content, relevance, and importance of big data problems facing businesses. Their program takes a practical approach including a variety of machine learning, statistical, and operations research techniques.
Students can expect to learn solid technical skills in programming, including R, Python and SQL, data structures, and algorithms. They’ll learn skills in optimization, econometrics and statistics, networks, and machine learning. Industry-specific application is core to the program, and students learn to apply their mastery of tools to real business problems.
What jobs can you qualify for after a Master’s in Analytics?
The most popular destination for their graduates is consulting, however others go on to work in technology, FMCG, e-commerce, finance, healthcare, and media. Graduate roles vary depending on candidates’ previous experience, but include positions like business analyst, data scientist and, consultant. Employers include Accenture, Amazon, BCG, eBay, EY, Microsoft, and Revolut.
Top tip for applying to Imperial?
Don’t just list your experience of coding and programming languages. Highlight your problem solving skills by showing how you’ve applied your technical expertise on interesting projects. Demonstrating big-picture thinking, commercial awareness, and the ability to articulate complex information in a compelling and accessible way will help your application stand out.
Master’s in Analytics student mp;A
Pinelopi Chamalelli, MSc Business Analytics student at Imperial, joining the analytics graduate scheme of a major consulting firm in London after graduation
Why pursue a Master’s in Business Analytics?
Analytics is the future; every business no matter its size collects more and more data, and desires to utilize them for a more efficient decision making. So, the opportunities they have after this program are endless not only in terms of quantity but also in terms of variety, since analytics can be applied in every single industry.
What advice do you have for prospective Master's in Analytics students?
Coding skills or generally the ability to learn a new programming language or a new software package quickly is crucial. Three main programming languages they use are Python, R, and SQL, since these are widely used in the industry.
Good knowledge of linear algebra and statistics also help a lot. A good business understanding is also crucial for a successful data scientist.
Masters in Finance
Find out more about Masters in Finance via webinar
Diane Jordan, associate director of admissions, MIT Sloan
Who is a Master’s in Finance for?
Our Master of Finance is for individuals interested in learning the fundamentals of modern finance that emphasizes a foundation in how markets work. Students of the program come from a variety of bachelor’s degree disciplines including business, economics, finance, and significant representation from the STEM areas.
What skills/qualifications do you need to apply?
We seek smart, motivated applicants who are passionate about finance. Recent graduates, early career professionals in finance, engineers, mathematicians, physicists, computer programmers, and other high-tech professionals are encouraged to apply.
We look for: Success in academic, extracurricular, and professional endeavors; Ability to collaborate to accomplish a common goal; Ability to inspire others to achieve success; Excellent communication skills; Willingness to seek alternative solutions to existing challenges; Motivation to pursue goals.
What will you learn on a Master’s in Finance?
You’ll begin with rigorous courses in finance theory, financial mathematics, and financial accounting. You’ll be required to pass the Programming Literacy Test in either R or Python programming languages.
You’ll work with analytical tools—financial modeling, portfolio and pricing theory, statistics and data analytics, and computational methods—to help tackle multi-faceted challenges that arise in finance, from capital budgeting and cash flow analysis, to dynamic asset allocation and big-data-based investment strategies, and more.
You’ll take part in project-based courses solving real-world problems with MIT's partner corporations and culminating in presentations to corporate decision makers. You’ll choose electives from financial technology, to quantitative methods, to economics, to specialized disciplines such as healthcare finance, fixed income, mergers and acquisitions and asset management.
What jobs can you qualify for after a Master’s in Finance?
Jobs at asset managers, consulting firms, investment and corporate banks, brokerage firms, financial data providers, ratings firms, hedge funds, venture capitalists, insurance companies, public institutions, fintech, and more—from Fortune 500 companies to leading-edge boutiques.
Companies who have hired Master of Finance graduates from MIT for each of the last three years include Bain & Company, BCG, BlackRock, Deloitte, Morgan Stanley, McKinsey, and Citi.
Top tip for applying to MIT Sloan?
Get to know us and the admissions process through online chats and events to help you decide if MIT is a good fit.
Master’s in Finance student mp;A
David Smadja, student at MIT Sloan, has an offer for an associate position at a major investment bank in New York
Why pursue a Master’s in Finance?
Prior to my master’s, I graduated from a master of applied mathematics. Though I had a good understanding of the quantitative side of financial markets, I felt I was missing some important financial knowledge. To me, masters in accounting, for example, are not capable to cover everything you need to know to succeed in the field of finance like a master’s in finance does.
What advice do you have for prospective Master's in Finance students?
The role of the master’s is to teach you what’s necessary to break through the industry but I reckon that some preliminary skills are beneficial. If you’re interested in financial markets, some coding and analytics skills (statistics, optimization, AI a plus) are a minimum to have. If you’re more interested by the corporate side, a good understanding of financial statements analysis and accountability rules is crucial.
Masters in Management
Find out more about Masters in Management via webinar
Eric Simard, associate director, MSc admissions at Ivey Business School, University of Western Ontario, Canada
Who is a Master’s in Management for?
It’s for ambitious, recent graduates who want a specialized Master’s degree, that will hone their skill-set, develop their leadership abilities and accelerate their career success. Students are admitted from a variety of disciplines, backgrounds, cultures.
This creates a unique experience in classrooms which is meant to help students understand, reflect on, and challenge their tendencies, preferences and biases.
At Ivey, students apply to a specific field of study which will allow them to focus their learning in one of three areas; Business Analytics, Digital Management, or International Business.
What skills/qualifications do you need to apply?
We evaluate applicants on a number of dimensions including; Relationship management; Leadership orientation; Openness to learning; Adaptability; Program readiness; Academic performance in their final two years of their undergraduate degree; Motivation for joining the program; Short and long-term career objectives; Pre-program experience; and International exposure/experience.
What will you learn on a Master’s in Management?
Master’s in Management programs are designed to help students bridge the gap between a solid theoretical foundation received in undergraduate studies and the practical and soft skills needed to make a difference in the early stages of their career.
Through Case-Method approach to education, Ivey MSc students are put in the position of the decision-maker and are asked to analyze data, develop alternatives and then make and defend their recommendations. They are focused on creating the next group of business leaders who are able to walk into a new role and take action.
What jobs can you qualify for after a Master’s in Management?
Graduates take positions in a variety of industries including; Technology, Management Consulting, Financial Institutions, Telecommunications, Consumer Packaged Goods, and more.
The majority of their graduates take starting positions in leadership rotational programs or in consulting firms focused in Risk, Technology or Human Capital. Many have titles such as data science associate, area manager or data analyst, and they work in a variety of organizations such as PwC, Scotiabank, Amazon, Walmart, Deloitte, Proctor & Gamble, Accenture, Sun Life Financial, BMO, Bell, EY, and Morgan Stanley.
Top tip for applying to Ivey?
Candidates should connect with their admission and recruitment team before submitting their application. They should spend time learning about what their programs have to offer by attending webinars, talking to current students, and reaching out to their MSc alumni. And they should consider and reflect on how the Ivey MSc in Management can support their short and long-term career goals.
Master’s in Management student mp;A
Deanna Hamilton, student at Ivey Business School, starting a consulting career after graduation
Why pursue a Master’s in Management?
I wanted to pursue a career in business and a Master’s in Management seemed like the perfect way for me to develop my knowledge as I did not study business during my undergrad. I also aspire to work internationally one day. Ivey’s Global Lab opportunity offered an eight-week, hands-on, international in-company experience which allowed me to immerse myself in an unfamiliar environment and work cross-culturally.
What advice do you have for prospective Master's in Management students?
Aside from the obvious skills of being a hard worker and passionate about the program, I think it is crucial for students to be open to learning and willing to adapt. It is essential for you to be willing to step out of your comfort zone to really take advantage of every opportunity on the program. Interpersonal and communication skills are also necessary for most jobs and, coming into the program, many of my classmates already had exceptional communication skills.
Masters in Marketing
Find out more about Masters in Marketing via webinar
Ning Li, academic program director of the MS in Marketing at Johns Hopkins, Carey School of Business
Who is a Master’s in Marketing for?
For students looking to continue in their marketing studies or students looking to pivot into marketing from another field. Those who are looking to become marketing experts will receive training in business foundations in addition to numerous high-level marketing electives.
What skills/qualifications do you need to apply?
Candidates will need undergraduate level communication and analytical skills.
What will you learn on a Master’s in Marketing?
Students will learn behavioral and quantitative methods and best marketing practices to address the most critical issues facing today’s marketer, including the impact of data and social media on marketing strategy, consumer behavior, social responsibility, competitive economics, business-to-business marketing, and more.
What jobs can you qualify for after a Master’s in Marketing?
Students can pursue careers in Marketing and Business Analytics, Marketing and Sales, General Management, Consulting, and more. Some employers who have hired their recent graduates include Amazon, China Telecom Americas, IBM, Johns Hopkins Medicine, P&G, and Tencent.
Top tip for applying to Carey?
Be Honest. No applicant is perfect and if there is a part of your application you are concerned about, be honest. Be open to why this program appeals specifically to you. This is important because honestly addressing concerns is important in marketing roles.
Are you interested in a business master’s degree?
Register now for the Master’s Week webinar series held by the Graduate Management Admission Council (GMAC).
Running from 8-to-12 April, different business schools will talk about a different master’s program each day.
Monday: Accounting; Tuesday: Business Analytics; Wednesday: Finance; Thursday: Management; Friday: Marketing
We take Uber, browse Pinterest, send tweets, and update Facebook. They hear every day about instant millionaires and the growing number of billionaires that built the latest gadgets in their high-tech world. But they ignore the fact that over 70% of all business transactions are processed on mainframes. Their visual and audio world is presented with tools hastily slapped together by young hotshots yet the reality is that the chairs they sit on, the paychecks they cash, the health care they utilize are available through data managed by mainframes. That’s right, over 80% of manufacturing, banking, and healthcare industries are on mainframes.
We hear so much hype about monetization of new markets that they fail to acknowledge the importance of mainframe code. Well, there’s this little issue… most mainframe developers are baby boomers who are approaching or past retirement. The harsher side to this reality is that the aren’t just retiring, they are expiring. (Short prayer for Jim Stanicki and John Stalher who were two exemplary mainframe developer friends of mine that died in the last few years.)I Smell Rewrite
Shouldn’t those old mainframe applications just be rewritten? It ain’t that easy. Yeah, I know, you’ve heard about rewrites for years. But the reason why most of those Visual Basic, dBase III, and PHP apps (that’s right, I’m saying they weren’t mainframe apps) were rewritten every 5 years is because they weren't written that well to begin with. Meanwhile, the mainframe apps have been running well for decades. The Return On Investment (ROI) for rewrites of mainframe applications just hasn’t been there. Case in point: In the mid ‘80s I wrote a traffic system for Hanover Brands Inc. that is still in use today.
But then there’s this retiring and expiring thing. Why not just bite the bullet and do the rewrite?
Rewrites are never easy and, for huge applications, they are often failures. Just a few weeks ago, I did a rewrite of a little, itty, bitty, PHP application to Ruby and Rails. Now, I’m pretty good with Ruby and OK with PHP but, even though it was just over a thousand lines, I still missed stuff. Mainframe Cobol and RPG applications are a wee bit more complex. It is common for an RPG program to be ten, and Cobol to be twenty, thousand lines long. Multiply that by hundreds and hundreds of programs and you have an application that has a mega-million lines. Worse that that, many of those programs were written before modular programming techniques became available. Typically, all variables in one of these behemoths are global. I remember, dozen years or so ago, I had a jest-quest in articles and seminars of a Diogenes-like search for a local variable in mainframe code. Diogenes never found an honest man and I had problems finding local variables in circa-70s code.
RPG specifically can be crazy hard to read and understand. For years RPG could only use six character variable names. Actually, its was worse than that. RPG had a bug where if you used the same column name in two different tables, they would share the same memory space. So RPGers used two to four of those precious six character column names to identify the system and table they were associated with. The bug was fixed decades ago and today the variable and database column name length supports at least thirty two characters. But six character variable names are still prevalent in RPG programs.
Somewhere around 1992 I gave a presentation on Cobol modularizing techniques to the team of Circuit City coders of which I was a member. After the presentation, one of Circuit City’s best Cobol programmers said she didn't see the benefit of modularizing Cobol.
Understand that I can get in a fist fight with other Ruby developers over a discussion on whether a method should be no more than 6 or 9 lines long. In Ruby development, wanton use of global variables might get you fired. So I kind of chuckle when I recall working on these 10-20,000 line programs. Maintaining Cobol and RPG monstrosities is often more voodoo than mastery. You try something that you have a gut feel will work and you light incense, sprinkle holy water, and pray to a variety of gods that your change will work.
The prevalent development practices of Cobol and RPG propagates use of outdated syntax. They are talking ugly code. Often thousands of lines of code in one program have been commented out. Many sections are completely unused. Looking at some of this code is like walking into a hoarder's house -- it's full of useless junk. To push the analogy perhaps too far -- the more junk that piles up the more chance of rot and, yeah, stench (and they talk of code smells.) The thing is: processes and materials you use everyday flow through this old rotting code the maintainers of which are retiring and expiring.Agility
Let me be honest… The reason I migrated my career away from the mainframe development workspace is velocity -- or the lack thereof. Development practices and toolsets for RPG and Cobol have languished. Test driven development, source control, modern editors, refactoring, agility… for years I proselytized such concepts in articles and seminars and not only was I, for the most part, ignored, but I couldn’t find projects to work on that followed such practices.
The essential word in my last paragraph was agility. Because mainframe application development practices lack agility they are slow to adapt to market demands. Often, the tarnishing of these old apps and the impending retirement of support staff cause new hot-shot C-level executives to suggest the procurement of costly ERP systems or complete rewrites. And we’ve all heard horror stories about such projects.Modern Machines
Understand that the mainframes are not antiquated. They are not the System 360 and AS/400 of yesteryear but the IBM z/OS and IBM i 64-bit operating systems with reliability and scalability that Linux and Windows can’t approach. They also have a lower total cost of ownership for complex data centers. You scale mainframes horizontally, rather than vertically. Those mainframes can run the latest software as well. Case in point: you can run thousands of Docker images on one mainframe. DB2 for i is arguably the best database on the planet. As to hot technology, A few years ago I was on a team the moved Ruby and Rails to the the native IBMi operating system. Banking applications run on mainframes primarily for security reasons. And it was a banker that funded the Rails port to the IBM i platform. There are huge advantages to mainframes: ranging from huge horizontal growth to the ultimate in security and very close to 100% reliability. It may be true that IBM is selling less mainframes but they are doing quite well upgrading and expanding the existing machines as they have astronomical horizontal scalability.
What is aging is not the machines but mainframe applications and application programmers.So, They Are Screwed?
Absolutely, just like they were when they hit year 2000 and everything crashed. It didn’t. They were just fine. They were fine because management finally started taking the two-digit to four-digit year thing seriously. If management begins to take the mainframer skills loss thing seriously, they will be fine.
Let me summarize my solution to the retiring and expiring mainframe coders thing before going into more detail:
Modernizing the database is step one. A wealth of data kept in the world’s best database and operating systems is hidden behind applications that are twenty to forty years old. Many of the mainframe databases were created before today's well known database normalization and optimization techniques were created. At little as ten years ago I put a web front end on a table that was clearly housed, at one point, in a card deck. There are tools and available techniques that allow you to mock the old database schema’s structure so legacy programs may continue to run with little or no modification. From experience I know that refactoring the database is not that difficult to do.Refactor Application Code
I made a big stink earlier about the state of mainframe code. Know that mainframe code can be refactored to the point that development can be much more agile. Understand that I am not talking rewrite. Refactoring is the process of restructuring existing code without changing its behavior. The C-level executives should not have fear of the downtime that would be probable with a massive rewrite.
The first step to refactoring is to put code under source control. I highly recommend git. Perhaps the mainframe code is already managed by a source control package but my experience with mainframe source control packages is that they propagate sluggish development and dissuade common refactoring strategies. As soon as the code is in git, remove the commented out code -- it’s source control’s job to retain old versions of code.
The second step to refactoring is updating the development environments. Many mainframers still use green-screen editors even though powerful IDEs have been available for at least two decades. These modern IDE are bundled with refactoring tools.
The third step is to set up a unit testing strategy. Unit tests typically have very specific and detailed tests for program behavior. They don’t have time for that. What I recommend is to follow the Approvals Testing strategy developed by Llewellyn Falco (http://llewellynfalco.blogspot.com/). The basic concept of Approvals Testing is that you take snapshots of state before and after execution of a routine. That snapshot could be anything from a database query result, to a PDF, to a CSV. Be creative. With the snapshot stored, you modify the routine and use the before and after images to verify that the refactoring did not change behavior. You may end up using a testing infrastructure that uses Java or Ruby or Python to invoke the mainframe routines but this layer will not be complex.
With a unit testing strategy constructed, the refactoring should begin with making variable names readable and understandable. Then start to whack away at reducing the use of global variables as you move into modularization. Duplicate code is rampant in mainframe code so use tools to find that duplicate code and then create common modules for them.Empower Existing Developers
The golden key to a refactoring strategy is your best mainframe developer(s). And now I’m going to get a bit nasty: Mainframe developers have been treated by their employers like bus drivers. Management feels it is the programmer’s job to move data from point to point and, when something breaks, they are expected to twiddle with the engine until they can get back on the bus and begin moving data again. From my personal experience, they are often underappreciated and underpaid. Many of these mainframers have no degree and deal with C-level executives with Ivy-league MBAs that seem to lack the ability to consider the ROI for more than this next quarter.
These mainframers need to be empowered. The will need retraining on agile development practices and they also need to become trainers themselves. They will need assistance in the refactoring and the later creation of modular APIs. And then there’s the issue of retiring and expiring so it will be part of the empowered mainframer’s job to manage the training of new developers.
If one of your existing mainframers doesn’t stand-out as a project lead, go out and get one. The problem is that many of the folks out there that have more than enough ability to help have moved on. They’ve moved on to management, or training or, like me, they’ve moved on to other programming languages and platforms. The problematic thing with these people is that, decades ago, they grew tired of the revolving door of C-level executives who turned down their recommendations for modernization. Many of these ex-mainframers may simply tell you to buy an ERP, give Oracle a call, or do a complete rewrite (which they know from experience will probably be a failure.) So be prepared to wheel and deal with these guys.
Once you know what you are looking for you might not have to look too far. This paragraph may be cut because my editor is an ex-mainframer and an Agile coach. He probably will cut this paragraph because he left his mainframe bus driver job years ago. Go find a person like him to manage your mainframe modernization project. [EDITOR: No - I left it in because I agree]Coerce Smart People Into Becoming Mainframers
Colleges aren't teaching Cobol and RPG. There are blog posts and articles on Skill Loss that suggest colleges need to add mainframe course and otherwise attract millennials to careers in mainframes.
I don’t think millennials are the solution. I would not suggest a career in mainframe development to any youngster. There are far less jobs available and the slow development velocity makes for a less satisfying career. My suggestion is 1) retrain existing non-IT staff and 2) coerce folks in their 30s and 40s into a mainframe career. Both of those suggestions sound crazy but I question the capabilities of a young IT graduate that is willing to begin their career on a mainframe.
The technical knowledge required to be a mainframe developer is more focused than the polyglot, multi-platform, programmer of today’s vogue developer. What companies need is not someone with NodeJS and functional programming skills but business acumen, a desire to learn, and, quite simply, smarts. There are plenty of smart people with non-IT mid-career blues that would be willing to have the opportunity to potentially double their salary after a year of on the job retraining. One area to tap would be retired military.Convert Code into APIs
The final step in a mainframe modernization process is to turn your refactored code into APIs. The will them be reusable software components. Often the parameter list of mainframe code will be so complex that it might seem that creating an API for it would be impossible. For those you create one or more wrapper programs (which can be written with Cobol or RPG or your new language of choice.) One technique I liked to use was to create SQL Stored Procedures that wrapped the legacy modules. With SQL Stored Procedures available, anybody with an SQL interface (JDBC-, ODBC-driver or whatever) can use those routines.
With legacy code available via an API (SOAP, REST, or otherwise) and unit tests for everything, development can be fully agile. New code can be written in whatever language you choose. And, yeah, you can hire millennials.
The more sensitive and complex programs that show up high on your churn reports can then be slated for conversion to a new language.Mainframe Agility
Mainframe applications, while being crazy reliable for all but half-a-century, are brittle with little, if any, automated tests, and the mainframe workforce either does not know about refactoring strategies or hasn’t had the C-Level management backing to start it. In short the code is not adaptable to changing business requirements. Existing mainframe programmers are retiring at an alarming rate and the workforce is not being replenished.
Moving off the mainframe is far from the most optimal solution. Complete rewrites or converting to an ERP is costly and fraught with peril. The mainframe boxes themselves are not aging. In fact they outcompete Microsoft and Linux on features like performance, scalability, security, and reliability. It’s not the machines but applications and programmers that are aging.
Modernize that database, refactor that code, become agile, shore up programming staff with internal training. And finally, where it shows benefit, start to move your newly modularized code into a new language.
The thing is: these solutions are dependent on C-level executive buy in. For them to do that they need to look past quarterly results and think about what the next two to five years will bring as they lose their undervalued mainframe developers.About the Author
Don Denoncourt is a developer for simplethread.com. He has been coding since before Windows and Linux, much less the Internet. In the early nineties, Don moved from RPG and Cobol to C and C++. He adopted Java before it was real: 1996. After coding his way through the proliferation of Java frameworks (including Struts, Spring, and EJB) Don pined for the Convention-over-Configuration framework of Ruby and Rails. Don did Groovy and Grails before finally moving to Rails in 2011. Don enjoys writing and has published a couple of books and hundreds of technical articles. Don has been working from home since last century. When Don is not working, he loves spending time with his 3 grandchildren. To keep his mind young, Don reads and listens to novels in Italian. And, to keep his body young, Don is an avid off-road and street unicyclist.
Data and big data analytics are fast becoming the lifeblood of any successful business. Getting the technology right can be challenging, but building the right team with the right skills to undertake big data initiatives can be even harder.
Not surprisingly, that challenge is reflected in the rising demand for big data skills and certifications. According to research by IT research firm Foote Partners, both noncertified advanced data analytics skills and certified big data skills have gained value in recent years: with 74 advanced data analytics related skills and certifications rising in average value by 6 percent in 2015, followed by 116 advanced data analytics related skills and certifications increasing 4.8 percent overall in market value in 2016. Additionally, Foote Partners research found 123 related certified and noncertified big data skills seeing a 0.3 percent gain in value in the first quarter of 2017.
Organizations are on the hunt for data scientists and analysts with expertise in the techniques required to analyze big data. They also need big data systems architects to translate requirements into systems, data engineers to build data pipelines, developers who know their way around Hadoop clusters and other technologies, and systems administrators and managers to tie everything together.
[ Find out the hottest data and analytics trends today. | Get ahead with the top certs for big data, project management, agile, and the cloud. | Get weekly career tips by signing up for their CIO Leader newsletter. ]
These skills are in high demand and are relatively rare. Individuals with the right mix of experience and skills can demand high salaries. The right certifications can help.
"Advanced data analytics capabilities are just too critical for staying competitive," David Foote, co-founder, chief analyst and chief research officer of Foote Partners, said in a statement released with the research. "They've expanded in popularity from a few industries to nearly every industry and market. And there is the Internet of Things, the next critical focus for data and analytics services. IDC is predicting a 30 percent CAGR over the next five years, while McKinsey is expecting IoT to have a $4 trillion to $11 trillion global economic impact by 2025 as businesses look to IoT technologies to provide more insight."
While the market value of noncertified advanced analytics skills has actually increased faster as a percentage of base salary than the value of certified big data skills, according to Foote Research, Foote believes pay premiums for both noncertified and certified skills will steadily rise over the next 12 to 24 months.
If you're looking for a way to get an edge — whether you're job hunting, angling for a promotion or just want tangible, third-party proof of your skills — big data certification is a great option. Certifications measure your knowledge and skills against industry- and vendor-specific benchmarks to prove to employers that you have the right skillset. The number of big data certs is expanding rapidly.
Below is their guide to the most sought after big data certifications to help you decide which cert is right for you.
If you would like to submit a big data certification to this directory, please email us.Analytics: Optimizing Big Data Certificate
The Analytics: Optimizing Big Data Certificate is an undergraduate-level program intended for business, marketing and operations managers, data analyst and professionals, financial industry professionals, and small business owners. The program brings together statistics, analysis, and written and oral communications skills. It introduces students to the tools needed to analyze large datasets, covering topics including importing data into an analytics software package, exploratory graphical and data analysis, building analytics models, finding the best model to explore correlation among variables and more.
Organization: University of Delaware
Price: $2,895 course fee
How to prepare: A basic background in statistics and some prior college coursework is recommended.Certificate in Engineering Excellence Big Data Analytics and Optimization (CPEE)
Offered in Hyderabad and Bengaluru, India, the Certificate in Engineering Excellence Big Data Analytics and Optimization is an intensive 18-week program that consists of 10 courses (lectures and labs) for students of all aspects of analytics, including working with big data using Hadoop. It focuses on R and Hadoop skills, as well as statistical modeling, data analytics, machine learning, text mining and optimization. Students are evaluated on a real-world capstone project and a series of quizzes.
Organization: International School of Engineering (INSOFE)
Price: ₹3000 (INR) application fee and a program fee of ₹3,25,000 + 15 percent service tax.
How to prepare: INSOFE admits students based on performance on its entrance exam and prior academic background and work experience.Certification of Professional Achievement in Data Sciences
The Certification of Professional Achievement in Data Sciences is a non-degree program intended to develop facility with foundational data science skills. The program consists of four courses: Algorithms for Data Science (CS/IEOR), Probability & Statistics (STATS), Machine Learning for Data Science (CS), and Exploratory Data Analysis and Visualization (STATS).
Organization: Columbia University
Price: $1,858 per credit (a minimum of 12 credits, including the four courses, are required to complete the program). In addition, there is an $85 non-refundable application fee for the on-campus program and $150 for the online program. The online program also includes an additional non-refundable technology fee of $395 per course.
How to prepare: An undergraduate degree and prior quantitative and introductory to computer programming coursework are required.Certified Analytics Professional
The Certified Analytics Professional (CAP) credential is a general analytics certification that certifies end-to-end understanding of the analytics process, from framing business and analytic problems to acquiring data, methodology, model building, deployment and model lifecycle management. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.
Price: $495 if you are an INFORMS member, or $695 if you're not. Team pricing is available for organizations.
How to prepare: A list of study courses and a series of webinars are available through registration.Cloudera Certified Associate (CCA) Data Analyst
A SQL developer who earns the CCA Data Analyst certification demonstrates core analyst skills to load, transform and model Hadoop data to define relationships and extract meaningful results from the raw output. It requires passing the CCA Data Analyst Exam (CCA159), a remote-proctored set of eight to 12 performance-based, hands-on tasks on a CDH 5 cluster. Candidates have 120 minutes to implement a technical solution for each task. They must analyze the problem and arrive at an optimal approach in the time allowed.
How to prepare: Cloudera recommends candidates take the Cloudera Data Analyst Training course, which has the same objectives as the exam.Cloudera Certified Associate (CCA) Spark and Hadoop Developer
The CCA Spark and Hadoop Developer credential certifies a professional has proven their core skills to ingest, transform and process data using Apache Spark and core Cloudera enterprise tools. It requires passing the remote-proctored CCA Spark and Hadoop Developer Exam (CCA175), which consists of eight to 12 performance-based, hands-on tasks on a Cloudera Enterprise cluster. Each question requires the candidate to solve a particular scenario. Some cases may require a tool such as Impala or Hive, others may require coding. Candidates have 120 minutes to complete the exam.
How to prepare: There are no prerequisites required, but Cloudera says the exam follows the same objectives as the Cloudera Developer Training for Spark and Hadoop course, making it excellent preparation for the exam.Cloudera Certified Professional (CCP): Data Engineer
The CCP: Data Engineer credential certifies the ability to perform core competencies required to ingest, transform, store and analyze data in Cloudera's CDH environment. It requires passing the remote-proctored CCP: Data Engineer Exam (DE575), a hands-on, practical exam in which each user is given five to eight customer problems each with a unique, large data set, a CDH cluster and four hours. For each problem, the candidate must implement a technical solution with a high degree of precision that meets all the requirements.
How to prepare: Cloudera suggests professionals seeking this certification have hands-on experience in the field and take the Cloudera Developer Training for Spark and Hadoop course.EMC Proven Professional Data Scientist Associate (EMCDSA)
The EMCDSA certification demonstrates an individual's ability to participate and contribute as a data science team member on big data projects. It includes deploying the data analytics lifecycle, reframing a business challenge as an analytics challenge, applying analytic techniques and tools to analyze big data and create statistical models, selecting the appropriate data visualizations and more.
Organization: Dell EMC Education Services
Price: $600 for video-ILT streaming; $5,000 for instructor-led
How to prepare: EMC offers a training course, available as either a video or an instructor-led course.IBM Certified Data Architect – Big Data
Designed for data architects, the IBM Certified Data Architect – Big Data certification requires passing a test that consists of five sections containing a total of 55 multiple-choice questions. It demonstrates a data architect can work closely with customers and solutions architects to translate customers' business requirements into a big data solution.
Organization: IBM Professional Certification Program
How to prepare: IBM recommends a series of seven multi-day courses on SPSS Modeler to InfoSphere BigInsights to prepare for the test.IBM Certified Data Engineer – Big Data
The IBM Certified Data Engineer – Big Data certification is intended for big data engineers, who work directly with data architects and hands-on developers to convert an architect's big data vision into reality. Data engineers understand how to apply technologies to solve big data problems and have the ability to build large-scale data processing systems for the enterprise. They develop, maintain, test and evaluate big data solutions within organizations, providing architects with input on needed hardware and software. This certification requires passing a test that consists of five sections containing a total of 53 multiple-choice questions.
Organization: IBM Professional Certification Program
How to prepare: IBM recommends a series of nine multi-day courses to prepare for the test.Mining Massive Data Sets Graduate Certificate
Designed for software engineers, statisticians, predictive modelers, market researchers, analytics professionals, and data miners, the Mining Massive Data Sets Graduate Certificate requires four courses and demonstrates mastery of efficient, powerful techniques and algorithms for extracting information from large datasets like the Web, social network graphs and large document repositories. The certificate usually takes one to two years to complete.
Organization: Stanford Center for Professional Development
Price: $18,000 tuition
How to prepare: A Bachelor's degree with an undergraduate GPA of 3.0 or better is required. Applicants should have knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.MongoDB Certified DBA Associate
The MongoDB Certified DBA Associate credential is intended to demonstrate that operations professionals understand the concepts and mechanics required to administrate MongoDB. It requires a 90 minute, multiple choice exam.
Organization: MongoDB University
How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M102: MongoDB for DBAs; M202: MongoDB Advanced Deployment Operations). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.MongoDB Certified Developer Associate
The MongoDB Certified Developer Associate credential is intended for software engineers who want to demonstrate a solid understanding of the fundamentals of designing and building applications using MongoDB. It requires a 90 minute, multiple choice exam.
Organization: MongoDB University
How to prepare: There are no prerequisites, but MongoDB suggests candidates complete an in-person training or one of its online courses (M101J: MongoDB for Java Developers; M101JS: MongoDB for Node.js Developers; M101N: MongoDB for .NET Developers; M101P: MongoDB for Developers). MongoDB also provides the MongoDB Certification Exam Study Guide, available to those who have registered for a certification exam.SAS Certified Big Data Professional
The SAS Certified Big Data Professional certification program is for individuals seeking to build on their basic programming knowledge by learning how to gather and analyze big data in SAS. The program focuses on SAS programming skills; accessing, transforming and manipulating data; improving data quality for reporting and analytics; fundamentals of statistics and analytics; working with Hadoop, Hive, Pig and SAS; and exploring and visualizing data. The program includes two certification exams, both of which the participants must pass.
Organization: SAS Academy for Data Science
Price: $9,000 for classroom (Cary, NC), $4,725 for blended learning (combination of 24/7 online access and instructor-led training)
How to prepare: At least six months of programming experience in SAS or another programming language is required to enroll.Related articles Join the newsletter!
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