By leveraging cutting-edge automation tools and cloud-based infrastructure, DevOps as a Service providers enable businesses to reduce time-to-market, improve software quality, and achieve greater agility in their software development efforts.

In today’s highly competitive business environment, organizations are under increasing pressure to deliver high-quality software products faster and more efficiently than ever before. To meet these demands, many organizations are turning to DevOps as a Service solutions to streamline their software development processes and enhance collaboration between development and operations teams.

What is DevOps as a Service?

DevOps as a Service is a cloud-based model that offers a range of services to support the development, deployment, and maintenance of software applications. DevOps as a Service providers typically offer a range of tools, technologies, and best practices to streamline the software delivery pipeline, reduce development cycles, and improve collaboration between teams.

Is DevOps a SaaS?

DevOps as a Service is often confused with Software as a Service (SaaS) since both models rely on the cloud infrastructure. However, there are some key differences between the two. SaaS is a software delivery model where the provider hosts the software application and makes it available to users over the internet. On the other hand, DevOps as a Service is a delivery model for DevOps services that provide a range of tools and services to support the development, testing, and deployment of software applications.

Understanding DevOps as a Service models

DevOps as a Service models are designed to provide organizations with the tools and services they need to implement DevOps best practices and streamline their software delivery pipeline. There are several models available, each with its own unique features and benefits.

• Self-service DevOps as a Service: This model allows organizations to build and manage their own DevOps pipeline using pre-configured tools and templates.
• Managed DevOps as a Service: This model is ideal for organizations that require more hands-on support from their provider. In this model, the provider takes care of the entire DevOps pipeline, from planning to deployment.
• Hybrid DevOps as a Service: This model is a combination of self-service and managed DevOps as a Service. It allows organizations to have greater control over their DevOps pipeline while still receiving support from the provider when needed.

The importance of DevOps as a Service

Working with a DevOps as a Service provider may bring a lot of benefits to an organization.

Efficient timing

DevOps is important for your company because it allows you to produce software faster because of improved procedures, automation, and release planning, among other things. If you have a shorter time to market, you have a better chance of beating your competitors.

Fostering innovation

One of the DevOps benefits is faster innovation. Because of speedier product delivery to the market, you can innovate faster than your competition. The DevOps culture allows the team to openly contribute ground-breaking ideas and communicate their thoughts in real time.

Better development opportunities

DevOps eliminates the need for software engineers to spend time on things that are perfectly automated. The quantity of manual labor is kept to a bare minimum. Parallel workflows, acceleration tools, scalable infrastructure, continuous integration servers, and much more all help to ensure efficient development and deployment.

Reliable processes

Through the implementation of DevOps and continuous testing, the development, deployment, and related processes can become more dependable and less susceptible to errors. The team can readily identify any discrepancies or issues within the program, resulting in faster development cycles. Swift resolution of problems is facilitated through effective communication and knowledge sharing. Additionally, the ability to roll back deployments at any point further simplifies the process.

Customer experience

DevOps offers compelling benefits that contribute to more successful software development outcomes. By adopting a customer-centric approach and continually soliciting feedback, teams can more effectively deliver products that meet the needs and expectations of their customers. The shorter time to market associated with DevOps enables businesses to respond rapidly to market changes and emerging opportunities. Continuous improvement fosters a culture of innovation and facilitates the ongoing refinement of processes and practices. As a result, DevOps is widely recognized as a critical factor in achieving more fulfilling and impactful software development results.

Embrace SDDC and DevOps to accelerate digital transformation

The future of DevOps

The future of DevOps as a Service is bright, with many exciting developments and innovations on the horizon. As businesses continue to embrace digital transformation, these solutions are expected to play an increasingly important role in enabling organizations to accelerate their software development processes, improve collaboration between development and operations teams, and achieve greater agility and flexibility in their software development efforts.

One of the key trends shaping the future of DevOps as a Service is the growing adoption of artificial intelligence and machine learning technologies. With AI and ML tools, companies can automate routine tasks, identify patterns in data, and generate predictive insights to help organizations optimize their software development processes and improve overall software quality.

Another important trend is the increasing focus on cloud-native and serverless architectures as more organizations move their software development efforts to the cloud, DevOps as a Service providers are expected to offer more cloud-native tools and services to help businesses take advantage of the scalability and flexibility of cloud-based infrastructure.

The role of automation is also key

The role of automation in DevOps transformation will remain critical, and the introduction of artificial intelligence for IT operations (AIOps) is set to significantly enhance these efforts. AIOps integrates various essential features, including machine learning, performance baselines, anomaly detection, automated root cause analysis (RCA), and predictive insights, to simplify and expedite regular operational processes. By leveraging these advanced technologies, businesses can revolutionize how their IT operations teams monitor alerts and resolve issues. As a result, AIOps is expected to assume an increasingly pivotal role in the future of DevOps.

How much does DevOps as a Service cost?

The cost of DevOps as a Service can vary depending on a range of factors, including the scope of the project, the complexity of the technology stack, the level of customization required, and the level of support needed. Some providers may offer standardized pricing models based on specific service packages, while others may provide more tailored pricing based on the unique needs of each client. As such, it is difficult to provide a specific cost estimate without a thorough understanding of the project requirements and other relevant details.

Best DevOps as a Service companies

DevOps as a Service providers typically offer a range of services, including:

• Continuous Integration/Continuous Deployment (CI/CD) pipelines
• Automated testing and quality assurance
• Infrastructure automation and management
• Monitoring and alerting
• Collaboration and communication tools

A helping hand: Enterprise SEO tools are crucial for data analytics

There are several reputable DevOps as a Service companies that can help businesses streamline their software development processes and enhance collaboration between teams. Some of the best-known providers in this space include:

• AWS DevOps: Amazon Web Services offers a comprehensive suite of DevOps tools and services that enable businesses to automate and accelerate their software development lifecycles.
• Microsoft Azure DevOps: Azure DevOps is a powerful platform that integrates with a wide range of tools and services to enable end-to-end DevOps capabilities.
• Google Cloud DevOps: Google Cloud DevOps provides businesses with a suite of tools and services for streamlined and automated software development, testing, and deployment.
• Atlassian: Atlassian offers a range of DevOps solutions, including Jira, Bitbucket, and Confluence, that can help businesses improve collaboration, tracking, and automation across the development process.
• GitLab: GitLab provides a single platform for managing the entire software development lifecycle, from planning and coding to testing and deployment.

Each of these companies has a proven track record of delivering innovative and effective DevOps solutions that can help businesses of all sizes achieve their software development goals.

Final words

In conclusion, DevOps as a Service offers tremendous potential for organizations seeking to optimize their software development processes and stay ahead of the competition. By partnering with a reputable provider, businesses can leverage the latest automation tools and cloud-based infrastructure to accelerate their software development efforts, reduce costs, and improve overall software quality. With the continued growth and innovation in the DevOps as a Service market, organizations that embrace this transformative approach to software development are well-positioned to thrive in today’s rapidly evolving digital landscape.

What does it mean that data is normalized? In a nutshell, data normalization is the act of organizing data in a database. This includes establishing tables and relationships between them according to rules intended to protect the data and make the database more adaptable by removing redundancy and inconsistent dependency.

What is database normalization?

The objective of data normalization is to develop clean data. It entails data to appear similar across all records and fields. It increases the cohesion of entry types, allowing for cleansing, lead generation, segmentation, and higher-quality information.

What does it mean that data is normalized?

The first step in data normalization is identifying and removing duplicate data by logically connecting redundancies. When a piece of data is dependent on another, the two should be kept within the same data set.

Normalization significantly enhances the usefulness of a data set by eliminating irregularities and organizing unstructured data into a structured form. Data may be more readily visualized, insights may be obtained more efficiently, and information can be updated quickly due to data normalization.

The chance of mistakes and duplicates increasing data disorder is eliminated when combined redundancies. Furthermore, a properly structured database takes less space, resolving many disk space issues while improving performance considerably.

Benefits of database normalization

Data normalization is an essential element of data management, improving data cleaning, lead routing, segmentation, and other data quality procedures:

One of the most important advantages of normalizing your data is minimizing duplicates in your database. If you don’t use a deduplication tool that does it automatically, normalizing your data before matching and merging identical records will make it simpler to discover duplicate pairs.

Data must be amended in the same manner everywhere if updated in numerous locations. Implementing a customer address update is easier if that data is only stored in the Customers’ table and nowhere else in the database.

With data volumes growing, organizations must be more cost-effective in their data management. It’s feasible to lower the total expense of data storage and maintenance by standardizing data and eliminating redundancies, resulting in greater profits.

Translating complicated statistics into a straightforward list
allows you to act on otherwise impossible or complex data

A further advantage of normalizing your data is that it will aid in the segmentation and scoring of leads, especially with job titles. Job titles differ significantly among businesses and industries, making it impossible to relate a specific job title to anything useful for segmentation or lead scoring. So, standardizing this value can be beneficial, and numerous methods are available. A CEO may be represented as both a chief executive officer and a founder in a database. This might lead to incorrect identification. You can enhance categorization by normalizing data, ensuring that you reach the right prospects with your outreach campaigns.

Databases that are not organized and maintained may be complicated to analyze. It will be easier to sort through when you standardize your data and utilize a single organizational technique with correct capitalization. Translating complicated statistics into a straightforward list allows you to act on otherwise impossible or complex data.

Rules for database normalization

There are a few rules for database normalization called forms. A “normal form” is the term used to describe each rule. If the first rule is observable, the database is said to be in “first normal form.” When three of the first four regulations are followed, the database is said to be in “third normal form.” Although various degrees of normalization are conceivable, the third normal form is regarded as the most desirable level for most applications. The fourth, fifth, and sixth rules are concerned with maintaining the database in the third normal form.

Like many other formal standards and specifications, real-world situations do not always allow perfect compliance. In general, normalization necessitates extra tables, which some might find inconvenient. Ensure your application is prepared for any issues that may crop up due to violating one of the first three normalization rules, such as duplicate data or inconsistencies in dependencies.

First Normal Form

The most basic form of data normalization, 1NFm, ensures that a group does not contain duplicate entries. 1NF implies that each entry has only one single value for each cell and that each record is distinct. For example, recording a customer’s name, address, gender, and whether or not they have bought anything.

Second Normal Form

Data must first apply to all of the 1NF criteria in the 2NF rule. Data must then have only one primary key after that. All subsets of data placed in multiple rows should be kept in separate tables, and new foreign key labels should be used to connect them. For example, recording a customer’s name, address, gender, if they bought a product, and the product type. Product types are kept on a separate table with a unique foreign key to each individual’s name.

Third Normal Form

Data must first fulfill all of the 2NF criteria in this rule. Then, only data linked to the primary key should be dependent on it. If the primary key is updated, all impacted data must be moved to a new table. For example, recording a customer’s name, address, and gender but going back and changing a customer’s name. This may also alter the gender. A foreign key and a new table for gender are used in 3NF to avoid this. 

· Elementary Key Normal Form 

All tables must be in the third normal form to be in the elementary key normal form. All EKNF elements must begin at whole keys or terminate at elementary key features characteristics.

· Boyce-Codd Normal Form 

In 1974, Raymond F. Boyce and Robert Codd developed the Boyce-Codd normal form to remove redundancies more effectively in the third normal form. Functional dependency is not present in a BCNF relational schema. Within a BCNF schema, however, there may be additional hidden redundancies.

The fourth, fifth, and sixth rules are concerned with
maintaining the database in the third normal form

Fourth Normal Form 

Ronald Fagin first discovered the fourth normal form in 1977. The Boyce-Codd framework is expanded on in this model. The fourth normal form pertains to multivalued dependencies. A table is in the fourth normal form if X is a superkey for all its nontrivial multivalued dependencies.

Essential Tuple Normal Form

The essential tuple normal form (ETNF) sits between the fourth and fifth normal forms. This is for functional dependencies and constraints from joins in a relational database. ETNF requires that the schema is in Boyce-Codd’s normal form. Furthermore, each explicitly stated join dependency of the schema must include

Fifth Normal Form

The fifth normal form, or project-join normal form (PJ/NF), eliminates redundancy in a relational database. The point of the fifth normal form is to isolate semantically related multiple relationships. Every nontrivial join dependency in the table must be implied by the candidate keys in the fifth normal form.

Domain-Key Normal Form

The domain-key normal form (DK/NF) is the second most normalized form, a level beyond the fifth normal form. The database can’t have any additional constraints to achieve DK/NF. Every constraint on the relation must be a logical consequence of defining domains and keys.

Sixth Normal Form

The sixth normal form (6NF) is the highest degree of a normalized database. A relation must be in the fifth normal form to reach the sixth normal form. It can’t have any significant join dependencies either. Finally, a 6NF database maintains six or fewer primary keys across its tables.

A foreign key helps connect the table and references a primary key

Challenges of database normalization

It is advised that you do not implement a normalization strategy until you have given its implications careful thought.

The primary challenges of database normalization, such as using table joins and indexing, increase the length of reading times. It’s also difficult to know when you should normalize data and when you should avoid doing so. To put it another way, you don’t always have to choose one or the other. It is feasible to mix both normalized and denormalized data in the same databases.

Every application has unique requirements. You must first figure out what’s appropriate for the specific application and then choose how to structure your data. One of the biggest problems facing DevOps professionals is the time it takes to clean and prepare data. Most of the time is spent on activities like normalization rather than real analysis. This ultimately leads to wasting time and causing experts to divert their attention away from other duties.

Because of the amount of data organizations are handling, preparation takes a long time. It comes from all over the world and is never consistent. This is because more businesses choose to automate this time-consuming and laborious process. In recent years, the ability to normalize and merge data from many sources has improved, allowing for consistent access to huge and complex data sets. Data professionals may substantially decrease the amount of time they spend cleaning and preparing data by automating normalization. This allows them to focus more of their attention on higher-level analysis.

When is denormalized data useful?

It is not always necessary to normalize data. It’s sometimes a good idea to take the opposite approach and expand redundancy in a database. In some cases, database denormalization improves application performance and data integrity by adding redundant databases.

Denormalization might also help you get query results faster. You may sometimes discover information quicker by adding extra redundancy. Granted, it won’t have the same level of integrity. However, if you must choose between quality and speed, denormalized data is usually preferable.

Denormalization is frequently used in online analytical processing (OLAP) systems to simplify search and analysis. It’s also worth noting that denormalization consumes extra disk space since it allows for duplicate information. This also requires more memory and is costly to maintain.

DataOps and DevOps are collaborative approaches between developers and IT operations teams. The trend started with DevOps first. This communication and collaboration approach was then applied to data processing. Both methods argue that collaboration is the primary approach for application development and IT operations teams, but they target different operation areas.

DataOps methodology

DataOps is an agile method for building and implementing a data architecture that supports open-source tools and platforms in production. The goal is to extract benefits from big data. It focuses on IT operations and software development teams with data engineers, scientists, and analysts. The data scientists might collaborate to develop ways to increase desired business outcomes with their data. At the same time, other team members can point out what the company needs.

This approach utilizes several IT fields, including data creation, transformation, extraction, data quality, governance, and access control. There are no special software tools available, but frameworks and toolkits to support this methodology.

Comparison: DataOps vs DevOps

DataOps and DevOps are approaches that apply similar techniques in different fields.

In DevOps, all teams come together by sharing common goals. Both teams have similar priorities and expertise; they can more easily focus on creating high-quality products. DevOps and DataOps have a shared commitment to break up data silos and focus on inter-team communication. The latter is a subset of DevOps that includes members who deal with data, such as data scientists, engineers, and analysts. These approaches are complementary, not opposed.

The main difference between DataOps and DevOps is their maturity. DevOps has been around for over a decade, with organizations widely adopting and using this model for development. While the data version of it is a relatively new model and strategy, this field is subject to the rapidly changing nature of data.

The DataOps principles

DataOps includes both the business side and the technical side of the organization. The importance of data in the business requires almost the same audibility and governance as other business processes; therefore, greater involvement of other teams is required. These teams have different motivations, and it is essential to consider the goals of both teams. This approach enables data teams to focus on data discovery and analytics while allowing business professionals to implement appropriate governance and security protocols.

Optimizing code structures and distribution is only a part of the big data analytics puzzle. DataOps aims to shorten the end-to-end cycle time of data analytics, from the origin of ideas to creating charts, graphs, and models that add value. The data lifecycle depends on people in addition to tools. To be effective, collaboration and innovation must be managed. To this end, data operations incorporate agile development practices into data analytics so that data teams and users work together more efficiently and effectively.

What problem does DataOps solve?

DataOps is not just DevOps applied to data analytics. It promises that data analytics can achieve what software development achieved with DevOps. In other words, when data teams use new tools and methodologies, they can deliver massive improvements in quality and cycle time.

DataOps focuses on an organization’s data and getting the most out of it. The focus of this data can target anything from identifying marketing areas to optimizing business processes. Statistical process control (SPC) monitors and validates the consistency of the analytical pipeline. By doing this, SPC improves data quality by ensuring that all anomalies and errors are caught immediately. Breaking down the communication and organizational walls is not just the responsibility of one team or the other. Both teams need to work together to get more out of data with common goals.

What is a DataOps engineer?

DataOps engineers establish and maintain the data sourcing and usage cycle by defining and supporting the work processes and technologies that others employ to source, transform, communicate, and act on data.

DataOps engineers are responsible for the company’s information architecture. They’re in charge of creating an environment where data development can occur. They develop the technologies that data engineers and analysts use to build their products. Engineers also help data engineers with workflow and information pipeline design, code reviews, as well as all-new processes and workflows for extracting insights from data.

What is DataOps as a Service?

DataOps as a Service is a managed services platform that combines DataOps components with multi-cloud big data and data analytics management software. These components construct scalable, purpose-built big data platforms that adhere to stringent data privacy, security, and governance standards.

DataOps as a service entails real-time data insights. It shortens the time to develop data science applications, allowing for improved communication and collaboration across teams and team members. Increasing transparency necessitates the use of data analytics to predict all potential scenarios. This service aims for processes to be repeatable and reusable code utilized whenever feasible, resulting in improved data quality.

How are dynamic IT operations affecting company culture? What do businesses need to understand about data driven AI to successfully drive their operations into the future?

Risks that previously stayed inside organizational units, such as IT Ops, now leak across domains, influencing decision-making for the entire company. These factors, including process and system change, will try to pull the organization in multiple directions at once; only a unified data management system with the intelligence to extract operational insights can drive positive business change.

Culture has got to change, too. 

Agile processes such as DevOps and AIOps offer mechanisms to control the flow of data and turn it into actionable results. For example, continuous application development and deployment gathers data about functional use, user sentiment, efficiency, and various other metrics to inform the overall process and improve the product. Such rapid dev cycles let organizations identify and fix issues more quickly and with fewer resources, ultimately delivering greater business value.

The resulting streamlined collaboration amongst IT, development, and customer teams improves capabilities, but also introduces dependencies that must be effectively managed. It changes the culture to adapt to the system, and thereby, the system of service delivery will fundamentally affect how organizations are structured, built, and run. Eventually, there will be increased digitization of every human process, and that means human impact will slowly be replaced by AI impact. This doesn’t mean that machines are coming to take our jobs. Instead, it means the modern enterprise IT organization must address a new future. How will it look?

Retraining and Re-Skilling is a Fact of Life: In a world of dynamic IT, requirements are no longer static, and teams need to be prepared. Organizational units must be able to work in different ways with new technology, with new skills that include robotic emotional intelligence, cloud-native literacy, and infinite adaptability.

System operators become stewards of the business because system operations directly impact business outcomes. It won’t be enough for workers to merely deploy, configure, and manage IT while isolated from the goals of the company, because those goals are inextricably linked to how objectives are achieved through innovation. Employees will need to develop new skills for functioning in a dynamic system. Training staff in agile methodologies, service-oriented software design, and six sigma-style approaches for process improvement will become critical for success.

Automation Is Everything: Automation by policy won’t be enough; even runbooks will become obsolete. Data and results are always changing, so simple automation will not drive meaningful change. Artificial intelligence can be used to drive automation policies and frameworks, with the potential to reduce errors and integrate disparate systems which previously required multiple points of oversight.

The trend towards using AI for data understanding is driving intelligent services into other parts of the business, as well. Rather than just for correlating system and operational data, AI is being adapted to business process optimization. Moving forward, IT operations can begin to run themselves, led by deep data analytics. Alerting and response processes will automatically use their own feedback to update the system’s intelligence, identify emergent trends, and take actions to deliver improved operational results.

Risk Becomes Universal: Increasing connectivity and generalized frameworks (like DevOps and site reliability engineering) mean that risk is no longer siloed to one department or function. It’s spread everywhere, and cascading effects become commonplace.

In general, successful engineering outcomes rely on coordinated processes and policies that dictate operational needs, even after deployment. Introducing connectivity between formerly disparate systems can create instability that potentially affects operations in other, unintended areas. Thus, broad risks arise if different systems can’t interoperate once unified into a single IT ecosystem. Addressing requirements under a generalized framework helps prevent gaps between application implementations.

The System Drags the Company Forward: Insights-driven action will transform digital business well in advance of executive priorities. Change will happen before anyone is ready. Indeed, with so much complexity, so many tools and processes, and so many competing business demands, IT changes now impact culture in entirely new ways.

Business impacts will come from system “intelligence” rather than manual processes. Efficiency in operations management is thus derived from the entire system, including the people themselves. What was previously the IT culture is rapidly replaced with a combination of self-adapting processes and workers who focus more on value than on plumbing.

Transforming the enterprise from silos into integrated platforms will propel the business forward. The resulting system will also drive cultural change at an accelerated rate, perhaps faster than the worker community is prepared to accept, but informed by real data instead of executive intuition.

Pursuing DevOps ROI (return on investment) is compelling for organizations that adopt this approach to agile development practices. With the evolution toward cloud and mobile apps that run on converged infrastructures companies that implement DevOps processes can realize significant benefits in the three components of ROI. These include reduced costs, enhanced productivity and faster time to revenue. DevOps can also help mitigate risks, such as customer loss due to poor user experience, operational inefficiencies, and non-compliance with GRC (governance, regulatory, compliance) mandates.

In contrast, enterprises with legacy systems that adhere to conventional development and operations processes jeopardize being overcome by modern applications and the new computing architectures they require.  Legacy systems cannot accommodate the loosely coupled, frequently-changing, stateless nature of the many components that comprise composite apps.  They cannot scale adequately nor meet the more demanding latency and performance requirements of these apps.  By continuing to do things the old way, these organizations risk competitive disadvantage in the marketplace and in returns to their investors.

DevOps is Strategic

DevOps should be viewed as a strategic initiative that drives business growth and builds value for all stakeholders.  As more organizations recognize this, they pursue DevOps ROI.

Application availability and reliability are the ultimate measures of IT success.  DevOps applies agile and lean practices throughout the software lifecycle to achieve high-quality application development and faster deployment.  DevOps also mitigates rising complexity caused by new computing trends such as virtualization, cloud and mobility through increased automation.

The collateral impact on the enterprise product life cycle is profound: companies accelerate the time-to-market of their products and services, creating sustainable efficiencies and value.  These efficiencies drive ROI – both within IT and at the corporate level.

The more significant benefits of DevOps are depicted in the accompanying graphic:

Legions of users have become accustomed to engaging with SaaS and other cloud-based apps through their work and social networking.  And they are increasingly accessing these apps with a mobile device.  The required agility to deliver these apps and services makes high-availability, performance reliability and user experience paramount.

Accelerating time to value is predicated on a combination of culture, practices and automated processes that drive efficiency, availability and reliability from software creation to production.  When DevOps is adopted as a strategic business initiative, the holy grain of continuous operations becomes more attainable.

Benefits of a Holistic Approach

The most effective transformations to DevOps take a holistic approach.  This means including all application constituents, including business owners, line managers, development, operations and quality assurance teams.  Constituents may also extend to security and compliance teams.  Cross-functional inputs for systems requirements and software functionality creates an ongoing feedback loop that promotes engagement and helps alleviate complexity.

Hence, the lateral benefits of DevOps are improved communications and collaboration across the organization.  Closer collaboration between developer, operations and quality assurance teams follows a “done once, done right” approach.

Application testing should emulate a production system environment.  DevOps teams can then discover dependencies, learn how the application will perform when it goes “live”, and make adjustments to the compute environment accordingly.  With practice and automation, these processes become iterative and repeatable, allowing for more consistent development, testing and deployment.

By moving the testing forward in the process, performance monitoring and analytics also comes earlier in the life cycle.  Rather than waiting for post-production performance data to analyze what went wrong, DevOps can build performance analytics models that can anticipate operational and quality problems before deployment.

These metrics can then be used to establish key performance indicators (KPIs) against which the production environment can be measured.  As production metrics more consistently adhere to KPIs, application performance and user experience improves.  Sharing the data with business teams at this stage accelerates the feedback loop and allows for adjustments to be made faster and with less stress.

Continuous integration enables better collaboration and agility to improve code validation, which reduces risks.  This drives continuous delivery, which facilitates smaller releases to occur more frequently.  Automated processes make for higher quality releases that are easier to manage once they are in production. While continuous delivery is most often associated with DevOps, it is the end of a series of processes that go into application launch.

DevOps Has a Big Future

Treating infrastructure configurations as code allows DevOps teams to manage provisioning on the fly in software, setting the stage for the eventual migration toward entirely software-defined environments.  Most operations teams have scripting experience.  This makes infrastructure-as-a-code technologies relatively easy to learn since they are mostly written in languages such as Ruby.

As big data and the Internet of Things emerge, DevOps becomes even more important.  In a software-defined environment, the precision of the software and services controlling networks, sensors and devices is critical as everything becomes inter-connected.

The more available and reliable software is the greater the insights from operational intelligence.  The greater the insights, the better the decision outcomes and ROI benefits to the organization.  This makes pursuing DevOps ROI compelling to all organizations.

Follow @DataconomyMedia

Gabriel Lowy is the Founder of Tech-Tonics Advisors. During the past 16 years, he has been consistently recognized as a leading technology analyst, including Forbes.com Best Analysts in America (#4 in 2010; #1 in 2004) and The Wall Street Journal Best of the Street (#2 in 2003). Gabriel has held senior technology research analyst positions with several Wall Street firms, including Mizuho Securities USA, Noble Financial Group, Inc., Collins Stewart LLC, Credit Lyonnais Securities USA and Oppenheimer & Co. His views can be found here.

(Image Credit: Matt Moor)

Darwin never actually said the following quote, but it’s truthy so I’ll use it:

It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change. —Darwin-ish

I was watching a talk by Josh Wills the other day. He was applying Lean engineering concepts to data science. To illustrate how important rapid learning is, he told a story about the team that built the Gossamer Condor and won the Kremer Prize for human-powered flight. They won because they failed more repeatedly than their competition. I’m sure their competition was brilliant, and they definitely had several years head start, but they lost because it took them maybe a year to iterate from design, to build, to test flight, to crash and destroy, and go back to design again. The Gossamer Condor team’s breakthrough insight was this: if the Condor could be repaired and improved in days, then they’d test 100 designs in the time their competition tested 1.

50 years on, and data science is following this same anti-pattern of the teams that didn’t get the Kremer Prize: come up with brilliant ideas, painstakingly move them out to the real world, watch them fail, and then slowly start the brittle process anew. This would be a problem for any profession—the faster you can iterate, the faster you can learn, and the more problems will be solved—but it is an especially pressing problem in data science because there is a huge shortage of data scientists, so inefficiencies mean that many critical problems are not getting solved.

What can we do about it? Luckily, software engineering, which is a sister to data science, has been working through these problems for the last two decades and has some pretty good patterns to build from. Devops is the area of software engineering concerned with moving software from development to real-world use quickly and safely. Devops lets software engineers try more things, and therefore learn faster. Here are the pieces I believe are necessary for data science:

1. Automated tests: these don’t have to be exhaustive, but there should be an automatic way to know that changes don’t horribly break your user-facing system
2. 1-Button Deploy: If releasing changes takes more than one step, it will break more frequently, and more importantly in the context of this article, releases will happen less frequently.
3. 1-Button Rollback: The counterpart to 1-Button Deploy, if an error is discovered in a user-facing system, reverting to a pre-error state must be swift and reliable.
4. Instrumented Infrastructure: Data science problems often require distributed architectures, non-obvious dependencies, and complex feedback loops. To successfully try many things quickly, it is necessary to spend a minimal amount of time understanding the infrastructure, tuning it, and correcting errors.

It’ll take some work, but I believe Devops is the next crucial frontier for Data Science—a massively underrated piece of this rapidly changing discipline.

Yes, I cofounded a company, Mortar, that amongst other things addresses these problems… because I think they are so important to solve.

Follow @DataconomyMedia

K Young has been CEO of Mortar Data since 2010. Mortar helps data scientists and data engineers spend 100% of their time on problems that are specific to their business—and not on time-wasters like babysitting infrastructure, managing complex deploys, and rebuilding common algorithms from scratch. Mortar’s platform runs pipelines of open technologies including Hadoop, Pig, Java, Python, and Luigi to provide out-of-the-box solutions that can be fully customized. Prior to founding Mortar Data, K built software that reaches one in ten public school students in the U.S. He holds a Computer Science degree from Rice University.