IT certifications have always been playing a vital role in getting a job or required knowledge.

In an interview, if you have a certification, you have more advantages to get the job and I have experienced it personally.

There are lots of other channels as well to learn or to enhance the knowledge and skills these days but the thing which matters a lot is the certification, and no one can give a certified degree instead of an institute, and like the way things are evolving the demand of certification is getting increased as they need an expert for their work.

So having knowledge before going to ask for the job is much beneficial to you.

So today I am going to share the top 10 high-paying IT certifications in the world in 2022. So let’s begin.

Master in DevOps engineering (MDE) certification – This certification gives you entire information about DevOps and their related toolsets.

DevOps is just a process to be followed to achieve a high quality of software by continuous integration and continuous delivery, and their open-source tools help it to achieve the goal efficiently and effectively.

Basically, DevOps only direct the way but the major works are done by these toolsets and you can have the proper knowledge and skills by only getting trained in any institute and have the completion certification.

The demand for certification is getting higher to get a good job role in the IT sector.

DevOps is liable to do the planning, designing, coding, testing, deploying, and monitoring.

As DevOps has shown its capability the salary of candidates will be more in 2022 and will be continued.

Site reliability engineering (SRE) certification – SRE is also one of the important certifications. SRE is mainly focused on operations where the goal of SRE is to improve the reliability of software systems, through automation and continuous integration and delivery.

SRE has also open-source toolsets that cover during the certification. SRE has shown tremendous growth till now and getting used all over the world.

It’s expecting the demand of SRE would be consistent and will be on a high-paying salary list.

SRE is for those software engineers who want to work as an operation team.

DevSecOps certified professional certification – It had been forecasted to be achieved a growth of 33.7% during the period of 2017-2023. And even it has been seen the growth in the market.

So as per the result, it will dominate the market in 2022 as well.

The national average salary for a Devsecops Engineer is Rs 10,00,000 in India.

DevSecOps course is for security professionals who are willing to work in the security field like cyber security.

DevSecOp’s assumption is security is everyone’s priority and everyone should work by keeping security concerns in mind. DevSecOps also works in the collaboration with DevOps.

Docker Certified Associate (DCA) certification – Docker is a containerization tool that creates containers and allows to build, test and deploy applications.

This certification helps to learn how Docker is used to package and ship the app as well as how to create containers and so many things.

Docker has become the number 1 choice of all companies and its demand is high.

The average salary of Docker candidates in India is Rs 4,79, 074 to Rs 8,14,070, and in the USA $1,45000.

Being a Docker certified candidate is much important to get a job.

Certified Kubernetes Administrator (CKA) Certification – It has been seen CKA course is at the top to get the certification into. Kubernetes are much important to organize the containers. So Kubernetes certification is important as here you will learn so many things and most significantly how to integrate with Docker to work with.

Kubernetes has already shown its growth as it is in demand at all companies.

Kubernetes candidates can earn salaries up to 6 to 8 lakh in India and in USA between $92,500 and $147,500 per year as per a new report.

AIOps Certified Professional (AIOCP) certification – AIOps stands for artificial intelligence for operations drive automation to solve the issues by speed analyzing the root cause of the issue and taking care of the events with any human interruption.

AIOps is now trending to market and is achieving heights of success. So the growth of AIOps is getting really good and opening so many job roles in AI.

AIOps certification is very important to get into this job domain as certification can grow your chances more to pass the interview and to get the full knowledge.

Based on research the average salary of AIOps is 21 lakh per annum in India.

Master in artificial intelligence – The AI is future and there is no doubt the candidates who are trying to achieve mastery in AI studies have a great future.

Certification is playing a key role here to get your foot into the AI world.

Having good knowledge and the advantage to get the priority in an interview is not so bad. This is the advantage of certifications.

The average salary of AI in the USA is $164, 769 and in India Rs 9,01,800 per annum.

AI is the main driver of emerging technologies like big data, robotics, and IoT.

GitOps certification – GitOps is a set of practices to manage infrastructure and application configurations using Git.

Gitops uses Git as the main repository for managing all the information, documentation. It maintains infrastructure as code and keeps them too in Git.

Some developers believe Gitops is the future of DevOps that replace the Developer part with a single repository that grasps all the information needed by a developer. 

That’s why GitOps certification is important.

GitOps employee’s salary is also high according to experience. One candidate has 45 lakh per annum.

The job openings are also in good numbers to apply.

MlOps certification – Mlops is communication between data scientists and the operation or production team, it is deeply collaborative in nature, designed to eliminate waste automate as much as possible, and produce richer and consistent insights through machine learning.

Mlops is the major function of machine learning engineering.

Mlops Goals –

• faster experimentation and model development
• faster deployment of the updated model into production
• Quality assurance.

The salary for an MLOps Engineer in India is approx Rs 11,40,000 per annum.

It has been predicted to be more job openings in 2022 and the certification is a must to get the job as certification can give you an advantage during an interview.

Its shows at least you have such knowledge pertaining to this and you are trained.

DataOps certification – As per Andy Palmer “DataOps is a data management method that emphasizes communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals”.

The aim of DataOps is to quickly deliver business value from data.

The DatOps engineer’s salary in India is Rs 7,78,290 and  $92,468 in the United States per annum.

It is predicted, to improve data quality and reduce time to insight, enterprises will increasingly embrace DataOps practices across the data life cycle in 2022.

The certification will play a vital role here to get the job as Dataops is new and also it has so many scenarios to cover so certification is a must.

And it has always been seen certifications always give an advantage during an interview. That means certification increase the status of your knowledge as well as your resume.

                      Training Place

I would like to tell you about one of the best places to get trained and certification in DevOps, DevSecOps, SRE, AIOps, MLOps, GitOps, AI, and Machine learning courses is DevOpsSchool. This Platform offers the best trainers who have good experience in DevOps and also they provide a friendly eco-environment where you can learn comfortably and free to ask anything regarding your course and they are always ready to help you out whenever you need, that’s why they provide pdf’s, video, etc. to help you.

They also provide real-time projects to increase your knowledge and to make you tackle the real face of the working environment. It will increase the value of yours as well as your resume. So do check this platform if you guys are looking for any kind of training in any particular course and tools.

The post Top 10 high paying IT certifications in the world in 2022 appeared first on Artificial Intelligence.

Earlier, it was common practice to segregate business functions from IT operations. But those practices are now a distant memory and for good reason. The Ops prospect has moved beyond the general “IT” to include DevOps, DataOps, AIOPs, MLOps, and more. Each of these ops practices are cross-functional across the organization, and each offers a unique advantage.

And each of the Ops areas arises from the same common mechanism – Applying agile methods and principles, originally created to guide software development, to the overlap of different flavors of software development, related technologies (data-driven applications, AI, and ML), and operations.

In this blog we are going to understand the overview of DataOps, AIOps and MLOps.

What is DataOps?

DataOps – DataOps is an automated, process-oriented methodology, used by analytic and data teams, to improve the quality and reduce the cycle time of data analytics. (Wikipedia)

DataOps is aimed directly at data operations teams, data engineers and software developers who build data-driven applications and the software-defined infrastructure that supports them. With massive data these days it’s really hard for teams to collect, clean, and analyze it to find insights that can help their businesses. This is where AIOps can save our lives, by helping DevOps and data operations teams choose what to automate, from development to production, this practices helps teams to evaluate and predict performance problems, do root cause and end to end analysis, find inconsistencies, and more.

What is MLOps?

MLOps – MLOps is a process for collaboration and communication between data scientists and operations professionals to help manage the production of ML lifecycle. It emphasize increasing automation and improve the quality of production ML while also focusing on business and regulatory requirements. (Wikipedia)

MLOps helps simplify the management, logistics, and deployment of machine learning models between operations teams and machine learning researchers. It is pretty similar to DataOps – the amalgamation of practices (machine learning in case of MLOps, data science in case of DatOps) and the operationalization of projects from that discipline.

What is AIOps?

AIOps – AIOps is an industry category for machine learning analytics technology that enhances IT operations analytics. Such operation tasks include automation, performance monitoring and event correlations among others. (Wikipedia)

AIOps abbreviation of Artificial Intelligence for IT Operations. It is a new methodology that enables machines to solve IT ops issues without the need for human intervention. It observes IT operations data intelligently in order to find the root causes and recommend solutions based on that observation quickly and it may be implemented without human interaction.

Hopefully, this short discussion on topics was interesting and will help you to understand DataOps – AIOps – MLOps. If you are looking for guidance on these concepts training and certification – you may connect with course advisors on-call/WhatsApp +91 700 483 5930 | contact@devopsschool.com

The post DataOps – AIOps – MLOps – Explained appeared first on Artificial Intelligence.

But, still, there are a lot of high-paying certification programs that professionals can opt for to compete in the competitive world.

If you are planning for a next move in USA, then completing these IT certification programs will boost your skills and abilities, and it will surely help you to stand out in the job market of united states.

With the changing business landscape, the demand for skilled individuals has increased immensely and organizations are focusing more on candidates who have completed certificate courses in these specific fields.  The demand for below-mentioned skills is sky-high, and the supply of professionals is meager.

Master in DevOps Engineering (MDE):- DevOps positions are consistently ranked among the highest paying salaries in the IT industry. DevOps is here to stay. And with that, the need to obtain a DevOps certification is no longer trendy, it’s quickly becoming a non-negotiable. Having DevOps certification makes you ready to work in a team of cross-functional members, including QA, developers, operation engineers, and business analysts.

DevSecOps Certified Professional (DSOCP):- Security in IT’s is a significant issue in today’s digital era, and Cyber attacks are on the rise and the Cyber threats won’t go away overnight. With this harsh reality, it’s inconceivable that any organization today would neglect the security aspect of the DevOps methodology. With this in mind, all businesses are driven digitally and IT management have moved to prioritize security and compliance at all levels. As more organizations see the benefit of end-to-end security implementation, DevOps will either fade away or get absorbed into DevSecOps.

SRE Certified Professional (SRECP):- SRE certification proves that the professional have understanding of set principles and practices designed to help organizations reliably and economically scale services. A certified professional can make an organization’s infrastructures far more stable, predictable, and scalable – all essential elements of software engineering, development, and operations.

Docker & Kubernetes Certification:- The container management tool Docker and Kubernetes are used in DevOps process to manage software parts as isolated, self-sufficient containers that can be deployed and run in any environment. In today’s market, professionals with Docker and Kubernetes skills are highly sought.

Master in Microservices:- Designing your product or application architecture can be tough as much a business decision as a technological one. Microservices is a particular way of developing software, where applications are structured as a collection of autonomous services OR we can say its a way where a large complex application are broken down into individual small-apps that are responsible for one specific product function. This skill allows large companies to gain agility and new tech capabilities to meet the growing customer demands.

Master in Big Data:- Big data professionals helps organizations to work with their data efficiently and use that data to identify new opportunities. Different techniques and algorithms can be applied to predict from data. Multiple business strategies can be applied for future opportunities and success of the organization and that leads to smarter business strategies, more efficient operations, and higher profits. With huge opportunities and investment in the Big Data technologies, certified professionals carrying the skills of big data are in huge demand.

Master in Artificial Intelligence:- This is one of the best certifications you can own if you want to lead the AI-driven technological revolution. It is not just about replacing the human component of the industry. It’s also about making it easier to make decisions based on observable patterns, use logic and reasoning to form conclusions, and build pathways to boost efficiency and production. It is not an easy discipline, but this is the reason why salaries in the AI industry are much higher than average.

Master in Machine Learning:- Machine Learning is one of the fast-emerging technology with high demand in the industry. Whether it be medicine, cybersecurity, automobiles, etc. all these fields are exploring the capabilities of machine learning. It’s obvious that learning more about Machine Learning and becoming a Certified Machine Learning Professional is a great idea and may even be a very wise career move! Naturally, you will be a hot asset for potential employers if you possess domain knowledge and skills in this field.

Master in Data Science OR Analytics:- Data Science is the latest tech trend that has taken the industry by storm. Companies and organizations, irrespective of their trade, are adopting Data Science tools, technologies, and solutions to promote innovation, increase productivity, boost sales, and maximize customer satisfaction. Once you receive the certification, you can apply for promising roles like Data Science, Data Analyst, and many more DATA-driven roles.

Dynatrace

Quantum Computing

AIOps

DataOps

OpenShift/Tanzu/Rancher/Linkerd/Envoy/Traefik/istio/consul

These certifications program will require a few months of hard work and an investment of time and money but once you’ll successfully complete the course you will be ready to achieve your goals no matter what they are.

Wrapping Up

Hope you would found our list of In-Demand IT Certifications For A Great Career in USA useful. Whether you’re a new working professional or an experienced professional, you won’t have trouble following these courses. At DevOpsSchool.com All of these certification courses are delivered by best-in-class trainers and mentors who will guide you every step of the way. Before selecting any certifications program, one only needs to be clear about their goal, know which path to take, and preserve in order to make the best of that opportunity.

Talk to our certification advisor if you need more information and guidance. (contact@devopsschool.com or +91 700 483 5930 (Call/WhatsApp))

The post The 13 In-Demand IT Certifications For A Great Career in USA appeared first on Artificial Intelligence.

The emergence of technologies such as AI and machine learning, along with sophisticated analytics, offers opportunities for smart manufacturers to transform their businesses radically — to create new product and service offerings while maximising the efficiency of supply chains and processes.

Contemporary computing models — such as Cloud and, increasingly, Edge computing — release huge amounts of sensor- and device-related data, to help with decision-making.

To succeed, however, companies need to be able to leverage this vast trove of data. In far too many cases, much of the data that is produced at the Edge is discarded, rather than transferred to a core environment for long-term storage.

For now, at least, the sector is lagging many other industries when it comes to capturing this opportunity.

That is a key finding in a report written by Seagate based on IDC’s surveys, called ReThink Data: Put More of your Business Data to Work from Edge to Cloud. That study, which surveyed 1500 global enterprises, delved into the performance of several sectors including manufacturing and revealed that the vast majority of data available to organisations simply goes unused.

• Visit Seagate ReThink Data and download the report today

Manufacturing, despite having one of the highest enterprise data centre footprints and high levels of instrument connectivity, is actually recording one of the lowest levels of data growth compared to other sectors. In part, this is because it is difficult to expand the capacity of all that on-premises infrastructure — especially when compared to scalable Cloud infrastructure more common in other sectors.

The irony is that the methods, strategies, and processes that typically make manufacturers successful seem to stop on the shop floor. For an industry steeped in the heritage of automation and integration, evidence suggests that these lessons are lost when it comes to managing data.

The authors of ReThink Data found that somewhat counterintuitively, manufacturing had the lowest level of task automation in data management and the lowest rate for full integration (single platform) of data management functions. 

Among the key findings of the report:

• Manufacturing lags in both multi-Cloud and hybrid Cloud adoptions;
• Along with the telco and CDN/media industries, it indicates below-average satisfaction with its data-management approach;
• Along with telco, respondents in manufacturing indicate low satisfaction with data-management tools;
• Manufacturing’s greatest data-management challenge is storage management. 

The disconnect between assets and data management is not only inhibiting manufacturers from maximising the return from their current plant and equipment, it is also stifling innovation and opening up opportunities for more nimble and aggressive competitors to grab lucrative niches in the market.

So Why Does This Disconnect Exist?

A study in 2018 by industry analyst IDC, looking into the integration of Information technology and operational technology, said that many assets on the factory floor remain disconnected.

IDC says that this speaks to a strategic weakness around core enterprise architecture and infrastructure, with the researchers explaining that much current legacy infrastructure simply won’t be able to cope with the inevitable growth of connected assets entering the plant. 

The temptation for many companies will be to try to implement ad hoc processes to connect and manage assets, leaving them unable to rely on the underlying infrastructure for comprehensive management.

IDC also found that, even for companies that want to solve these issues, there is another problem: their aging workforces were trained for a different era of manufacturing. Too many lack the hard and soft IT skills that are prerequisites for the shop floor of tomorrow.

The Importance Of DataOps

As plant and equipment become more digitised, generating vastly more data, the successful manufacturers will be those who can connect the data creators (which can include people and machines) with the data consumers (such as C-Suite and general executives). 

It’s a discipline in IT referred to as DataOps.

Seagate argues that DataOps should be part of every data-management strategy, which should also include data orchestration from endpoints to the core, data architecture, and data security. 

The mission of DataOps is to provide managers with a holistic view of data and to enable users to access and derive the most value from data whether it is in motion or at rest. 

This is another area, however, where many manufacturers are lagging. The ReThink Data report reveals that less than a third of manufacturers indicated that they have fully or even partially implemented a DataOps capacity.

The development and deployment of this skillset need to accelerate if manufacturers are to remain at the crest of the competitive curve. DataOps capabilities are essential to leveraging emerging technologies such as AI and machine learning, and to correlate data from core, Cloud, and Edge data sources. 

The work of DataOps teams allows data science and data analytics professionals to use technologies like AI to transform data into the information needed by decision-makers.

And as the ReThink Data Report explains, “Being able to correlate data from disparate sources is a capability not easily available through other means. Because it is difficult, those organisations able to master it can expect to have an edge over the competition.”

More Than Technology

The authors of ReThink Data also make clear that DataOps is an important part of implementing the necessary cultural change required to implement new ways of working — by facilitating the sharing of data and breaking down organisational silos.

To succeed, however, leaders still need to drive a strategy that implements global standards, global data architecture, and global data management and delivers access to the same analytical tools by global teams.

Seagate says that rolling back reporting functions to IT can provide global tools, capabilities, and solutions that every group can leverage. 

“The various groups within the enterprise should get out of siloed management of their own data, and allow the IT-instituted tools to do that globally. In doing so, the teams will be freed to make decisions based on insights from reliable, global, accessible pools of data.”

The post Manufacturers Need To Maximise The Competitive Opportunity Of Data appeared first on Artificial Intelligence.

Today organizations are carrying out more and more data projects that promise great opportunities to drive agility and competence. But they are facing a growing pressure to extract meaningful insights from data. Most of them realize the potential of data science to deliver business value, even some are already investing heavily in data science programs. There is no wonder that the landscape of data is growing rapidly and processing and analyzing that data requires a vital approach. This is where data scientists step in performing data visualization, data mining, and information management.

As most companies view a significant return from data science investments, most data science implementations are high-cost IT projects. Meanwhile, they often not generate value for businesses. Therefore, experts are now talking about DataOps, a new and independent approach to delivering data science value at scale. DataOps arises from the need to productionalize a rapidly increasing number of analytics projects and then to manage their lifecycles.

With the introduction of DataOps, data scientists and data engineers can work together and can bring a level of collaboration and communication to generate actionable insight for a business.

Significantly, DataOps is driven by data lifecycles and insights. It basically applies the DevOps process to data pipelines, using automation and Agile methodology to cut the time spent fixing issues in pipelines as well as get data science models into production quicker. Despite this, both are carrying distinct features and capabilities. While DevOps is the collaborative process between two technical teams, DataOps simplifies collaboration between data analysts, engineers, and data scientists, among others within an organization who use data. This essentially makes DataOps a much more multifaceted process than DevOps.

DataOps for Data Science Success in an Enterprise

Translating structured or unstructured data into business and operational insights, and subsequently incorporating them into a data monetization value chain is a very complex task. Even data analysis by companies doesn’t produce much value for them. According to Gartner, 80 percent of analytics is likely to not deliver business outcomes through 2020, and only 20 percent of data insights will deliver business outcomes through 2022.

In this regard, DataOps emerges as an agile way of developing, deploying and operating data-intensive applications, helping in fostering a data factory mindset. This is also orchestrating, monitoring and managing the data pipeline in an automated way for everyone handling data.

For a majority of organizations, DataOps currently is slowly becoming a crucial practice to endure in an evolving digital world, where coping with real-time business intelligence is necessary to gain a competitive edge over peers. Instability of data, rapidly evolving technology landscape, and increasing demand of the Agile business ecosystem are few reasons surging the need of DataOps.

IBM DataOps, for instance, enables agile data collaboration to accelerate speed and scale of operations and analytics throughout the data lifecycle. This also assists in creating a business-ready analytics foundation by offering market-leading technology that works together with AI-powered automation, infused governance, data protection, and a robust knowledge catalog to operationalize relentless, high-quality data across the business.

Comprehensively, applying DataOps practices in all data activities, from data management and integration to data engineering and data security, enterprises can simplify the process of Data Science across an organizational level.

The post WHY DOES DATAOPS FOR DATA SCIENCE PROJECTS MATTER? appeared first on Artificial Intelligence.

Today as the competition is at surge among tech organizations, agile principles and priorities are employed for greater productivity. Most of them could be leveraged for data science (DS) projects. Moreover, data scientists do not know how to schedule the project because it is impossible to determine a specific timeline for the type of “research” and exploratory work. Most data science projects require trial and error by going down different paths and trying different techniques. They do not have an element of certainty in the output, so Agile is most suitable to be adopted to direct the workflow.

On the other hand, DataOps in itself is an agile methodology for developing and deploying data-intensive applications, including data science and machine learning. A DataOps workflow supports cross-functional collaboration and fast time to value. With an emphasis on both people and process, as well as the empowering platform technologies that underlie it, a DataOps process allows each collaborating group to increase productivity by focusing on their core competencies while enabling an agile, iterative workflow.

Moreover, applying agile methodologies to analytics and machine learning lifecycle is a significant opportunity, but it requires redefining some terms and concepts. For example:

• Instead of an agile product owner, an agile data science team may be led by an analytics owner who is responsible for driving business outcomes from the insights delivered
• Data science teams sometimes complete new user stories with improvements to dashboards and other tools, but more broadly, they deliver actionable insights, improved data quality, Dataops automation, enhanced data governance, and other deliverables. The analytics owner and team should capture the underlying requirements for all these deliverables in the backlog
• Agile data science teams should be multidisciplinary and may include Dataops engineers, data modelers, database developers, data governance specialists, data scientists, citizen data scientists, data stewards, statisticians, and machine learning experts. The team makeup depends on the scope of work and the complexity of data and analytics required

Agility is going to be adopted by more data science and DataOps project teams soon. Many data scientists have reported that agility makes them more productive. This is not because the data scientists have become more skillful, but because agility can help them optimize their projects. Instead of spending time on models that are unlikely to reveal any productive results, it is better to spend that time for other result-driven purposes.

Being “agile” (flexible) means you need to adopt a dynamic approach in planning and be adaptable to the changing needs of the new situation when it arises. An agile environment appeals to quick action, fail quickly, evaluate and learn, then try again using a different approach or an improved method. It works great in dynamic environments where there is a potential for changing or evolving requirements.

The post SIGNIFICANCE OF AGILITY FOR DATA SCIENCE AND DATAOPS appeared first on Artificial Intelligence.

Data is not only “big,” it’s also unruly. It populates every pocket of the enterprise. Every information system, every cloud, is dripping with it. And not unlike Jed Clampett’s “bubbling crude (oil that is),” it takes a lot of machinery, a lot of refining, to make it useful.

It took the 2010s to build out the infrastructure of big data, the constellation of platforms and applications that store, tag, govern, manage and deliver it. In that regard, I see the past decade as a test lab with the focus on developing, implementing and integrating a large swath of heterogenous solutions to streamline turning data into real business intelligence. 

That period of testing has paid off. In 2020, we’ve not only turned the page on a new decade, we’ve turned the corner on making data the true currency of value creation. And, the good news for every enterprise — whether large or small, brimming with IT staff or manned (or womanned) by a hearty few — is that turning data into insights at speed and scale is available now for everyone. (And it doesn’t cost a lot or take years to implement.)

The rise of DataOps

Two complementary developments have delivered this transformation. One is the evolution of an Agile mindset, a framework for approaching, implementing, and demanding more from data management solutions, called DataOps.

The other development is a series of technological breakthroughs, perhaps not obvious amid the sheer volume of data management solutions in the market, that make end-to-end data management not only possible, but safer, faster and more useful than ever before. 

First up is DataOps, which Gartner calls “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.” 

I can say from firsthand experience that the rise of DataOps, modeled after the success of DevOps (an Agile engineering framework for enterprise IT that streamlined application development, deployment and continuous improvement), is the result of reorienting data management around value creation. It’s a get-to-value-first, get-to-value-fast philosophy that enables the enterprise to either fail or succeed quickly and rapidly build on what works.

A lot is riding on this shift. In Getting DataOps Right, O’Reilly’s authors summarized: 

“The necessity of DataOps has emerged as individuals in large traditional enterprises realize that they should be using all the data generated in their company as a strategic asset to make better decisions every day.” They concluded, “Just like the internet companies needed DevOps to provide a high-quality, consistent framework for feature development, enterprises need a high-quality, consistent framework for rapid data engineering and analytic development.”

For the purposes of this article, suffice it to say that a DataOps mentality, one that emphasizes cross-functional collaboration in data management, learning by doing, rapid deployment and building on what works, is beginning to sweep the enterprise, and early adopters have strong results to show for it.

While the rise of DataOps may prove to be the tip of the spear in 2020’s data management, the heft behind it, which is making it so effective, is a new generation of great technology.

DataOps technology drivers

Without the best tools, great teams can only go so far. We now have at our disposal a new generation of platforms and applications that 1) make all the data management solutions amassed by the enterprise over the last decade work better together and 2) offer a quick-to- implement, low-cost alternative for smaller enterprises looking to play and win the data management game at scale.

Here are three key technology drivers enabling DataOps excellence and the pursuit and attainments of rapid time-to-value:

Extensible platforms. Enterprise data can live anywhere — on premises, in the cloud and, as is often the case, among multiple clouds. For many enterprises, this data sprawl across siloed systems has seemed insurmountable. However, it’s not. Extensible platforms, which can easily pull data from myriad sources and align them in a metadata catalog, solve sprawl without requiring the building of a data lake. A win for agility, internet-native technology and for all enterprise users.

Augmented data catalog. Just as an extensible platform enables companies to leverage data regardless of where it resides, next-generation metadata catalogs, where data is easily accessed, tagged, annotated, enriched and shared, allows companies to orchestrate their current data management systems and turbocharge their performance. As Gartner urges companies to evolve from “storage-centric” to “streaming-centric” data management solutions to speed time to value, metadata catalogs, which greatly reduce administrative costs through machine learning, hold the key. Consider this the new cockpit for end-to-end data visibility and management. 

Self-service. Drawing on the advances of extensible platforms and augmented metadata catalogs, today’s data management systems provide a breakthrough capability once only dreamed of by most enterprises — true self-service data provisioning. In the past, analysts might have to wait weeks or months to have IT find, pull, and perform jujitsu on required data sets to empower better decision making. Now, analysts, data scientists and business intelligence users can “shop” for the data they need at data marts, sparing valuable IT resources. As an added bonus, that data will be pre-commissioned, quality-checked, tokenized and enriched by DataOps collaborative efforts through the platform. Today, through self-service, the Amazonification of data is upon us, and everyone is invited.

How to succeed with DataOps

The best part of these developments is that they are happening now. I am not describing a “future state.” Peer companies are taking advantage of these tools today as part of successful DataOps initiatives that are delivering business value as we speak, more quickly and at less cost than ever before.

One such success, recognizable to any business with loyalty rewards programs, is occurring at a regional financial services company. Leveraging an extensible data management platform, the firm integrated data that had been siloed across five lines of business and third-party sources to create a 360-degree view or “golden record” of its customers. It then made that data available to users through a metadata catalog, providing self-service access to its data scientists that resulted in eight hours per day of saved data engineering work and the ultimate prize of increased revenues through personalized, golden-record-driven sales.

Oh, and the firm started seeing great results in less than six months.

Democratizing data gains with DataOps

Data, measured in petabytes across the enterprise, the cloud and third-party sources, has the potential to be one of every company’s most valuable resources. The Agile approach of DataOps, turbocharged by a powerful new generation of data management platforms and tools, is flipping the script on who gets to benefit most from data. 

Big, small or anywhere in between, enterprises today have at their disposal the methodology and technology needed to tap data at scale, turn on their data pipelines and deliver their people and businesses a game-changing intelligence advantage.

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The one thing that is common between development projects and data projects is that they both hold a lot of promise. But, at the time of rolling out production, the latter is delivered late and once that is done, they tend to underperform. One of the main reasons for their potential underperformance is that there is a lack of collaboration between departments, and at the same time, a cultural imbalance as well. To counter these, DataOps brings automation and cultural shift to an organization’s data project, which is similar to what DevOps offers the software world.

DataOps is more like a mindset than a job title. It encourages collaboration, automation, and constant innovation related to data inside a data-driven environment. Just as software that is developed outside its live environment can deviate from the expected results, data projects can do the same and often have to be reworked entirely to work in a production environment. And even after deploying them, they have to be closely monitored in case they shift away from the fixed historical data. This involves heavy involvement from both data scientists and infrastructure engineers, so DataOps becomes even more necessary.

With the increasing need for DataOps, let us take a look at what benefits it offers, and the roadblocks it faces:

Benefits Of DataOps

Data scientists spend most of their time looking for data. Then they have to label it, clean it and perform other tasks. The time taken for these increases if the business also has a significant amount of backlog legacy data to maintain. With the consensus among data scientists that the amount of data doubles every 12 months, the need for DataOps will increase and here is why:

Building Best Practices: Similar to most xOps, DataOps tooling plays a vital role in building best practices throughout a function. Using automation and agile methodologies, the DataOps creates best practices that enable organizations to deliver value to a range of stakeholders through continuous production.

Automation: Data within an organization moves through a particular process. The data entered in one form and exits in another. Before the data is deployed, data scientists must build data pipelines, test them and change them. By adopting the DataOps standards and best practices, one can ideally have a constant stream of data flowing through the pipeline. This unlocks one of the most significant advantages of DataOps, the potential to obtain real-time insights from data. Obtaining real-time insights from data shortens the time it takes to turn raw data into valuable business information.

Machine Learning: When machine learning modelling meets DataOps mindset, a continuous workflow is maintained through feedback loops and internal communication. Here, one can improve the quality of data through version control, continuous development and continuous integration. Machine learning offers improved insights and unlimited potential for extracting value from DataOps.

Shifting The Culture: DataOps involves changes in the work process of an organization. It helps in building a new ecosystem where there is uninterrupted communication between departments. The various types of workers, such as data engineers, operators, analysts, operators’ marketing team etc collaborate in real-time to achieve a common corporate goal.

Obstacles To DataOps

As helpful as DataOps is for data scientists, it has its own sets of roadblocks:-

Unrealistic Expectations: Having unrealistic expectations with pipelines can get complicated. Data scientists should have an keen operationalization understanding to set up working and efficient pipelines.

No Visibility: It is often the case that more data means more insights, and that leads to more areas for growth. But, if the one dealing with this massive amount of data has no idea where this data is, the history of its usage and how it is stored, then it creates a huge problem. One needs to know everything about their data and put necessary systems in place for its governance.

Lack of Monitoring: DataOps relies on effective monitoring with attainable goals. For a pipeline, addressing the root cause of a problem and standardising success measurements can make or break it. The AI-powered data pipeline is helping with the load, but DataOps requires an integrated approach from business stakeholders to implement it.

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A dataops team will help you get the most out of your data. Here’s how people, processes, technology, and culture bring it all together Have you noticed that most organizations are trying to do a lot more with their data?

Businesses are investing heavily in data science programs, self-service business intelligence tools, artificial intelligence programs, and organizational efforts to promote data-driven decision making. Some are developing customer facing applications by embedding data visualizations into web and mobile products or collecting new forms of data from sensors (Internet of Things), wearables, and third-party APIs. Still others are harnessing intelligence from unstructured data sources such as documents, images, videos, and spoken language.

Much of the work around data and analytics is on delivering value from it. This includes dashboards, reports, and other data visualizations used in decision making; models that data scientists create to predict outcomes; or applications that incorporate data, analytics, and models.

What has sometimes been undervalued is all the underlying data operations work, or dataops, that it takes before the data is ready for people to analyze and format into applications to present to end users.

Dataops includes all the work to source, process, cleanse, store, and manage data. We’ve used complicated jargon to represent different capabilities such as data integration, data wrangling, ETL (extract, transform and load), data prep, data quality, master data management, data masking, and test data management.

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More than 75 percent of organisations are into Big Data processing and putting those insights into business use. But, data scientists might be in short supply.

Big Data is big. Huge, in fact. It is the hottest segment in information technology and according to IDC, Big Data revenues are estimated to cross $187 billion in 2019. It is said that the amount of data in the world doubles every two years, and by 2020, the digital universe will reach 44 zettabytes, or 4 trillion gigabytes in data.

The biggest revenue opportunities are in manufacturing and banking sectors. But, almost all major organisations are heavily invested in Big Data now, and it is being touted as the “definitive source of competitive advantage” across industries.

Here are five interesting trends from the worldwide Big Data market, as observed by Qubole, a big data-as-a-service firm, in its 2018 Big Data Activation Report.

• Big Data processing is widespread

About 76 percent companies “actively leverage at least three big data open source engines” and put those findings into “active use”. The most popular engines are Apache Hadoop/ Hive, Apache Spark, and Presto, and these are used for data preparation, machine learning, and reporting and analysis workloads. “Data activation strategies are becoming more nuanced in matching the best tool for the individual job,” says Qubole Co-founder and CEO, Ashish Thusoo.

• Huge volumes of commands being run

Over 58 million commands were processed by users in the three main engines (Apache Hadoop/Hive, Apache Spark, and Presto) in 2017. In one year, total usage across the three major engines has grown by 162 percent. Presto and Apache Spark are the fastest growing engines. Presto, particularly, has surged, “experiencing a 420 percent growth in compute hours and 365 percent expansion in total number of commands run.” Customers in aggregate are running 24X more commands per hour in Presto than Apache Spark and 6X more commands than Apache Hadoop/Hive.

• New tools gaining adoption

In addition to the top-three engines, nearly 30 percent of organisations have used new tools like Apache Airflow for “orchestrating sophisticated data preparation pipelines and operationalising machine learning using Python code”. It allows monitoring of jobs, handling of failures, and so on. Other tools like XGBoost(predictive machine learning tool), Pandas (Python-based data science tool used for statistical analysis) and MLLib (Apache Spark’s ML library) are also gaining in acceptance.

• Increased productivity and automation in focus

While usage and implementation grows, data-driven organisations are focused on optimising the number of users running commands in each engine, such that costs reduce and the process is nearly automated. For small-scale implementations, there are 16 users per engine; for medium implementations, the ratio is 48 to 1; and for large-scale implementations, it rises to 188 to 1.

Qubole states,

“With self-serve data access, analytics and data teams are able to spend more time on higher-value tasks, such as uncovering previously hidden insights, identifying new revenue streams, improving the user experience, or modernising their processes, with minimal intervention from the DataOps or DevOps teams.”

• Data scientists in short supply

In the US, the trend of hiring data scientists has grown over 650 percent since 2012. There are approximately 35,000 people in the US who have data science skills. Despite that, there are over 190,000 unfilled data-related jobs in the US alone, and hundreds of organisations are on a hiring spree. There is a “huge skill gap” in Big Data, and it is one of the reasons why organisations look to automate their engines as much as possible.

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