Why Benefits of Data Warehouse Outweigh the Financial Cost and How to Reduce the Cost of Development

Any organization that’s invested in an Analytics tool like Tableau, Power BI, Looker, etc. knows that they’re only as good as the data you feed them. Challenges such as disparate sources, inconsistent data formats, and slow legacy systems are just some of the roadblocks that stand in the way of getting the insights you need from your BI reporting and analytics tool.

A common solution to this challenge is a data warehouse that enables data management, analytics, and advanced data science. The data warehouse helps organizations to facilitate data-driven decision making, find cost savings, improve profitability, and the list goes on. No matter the industry, size of the organization, technology involved, or data savviness, our clients always ask us the same question: how do the benefits of a data warehouse justify the cost?

What are the costs of building a modern data warehouse?

Before diving into the many reasons why the benefits of a data warehouse are worth the costs to build it, let’s spend a bit of time discussing the two main investments.

The first major investment will be either hiring a consulting firm to develop your modern data warehouse or dedicating internal resources to the task. By hiring data consultants, you introduce additional costs in consulting fees, but yield results much quicker and therefore save time. If you choose to create an internal task force for the job, you reduce upfront costs, but altering day-to-day functions and the inevitable learning curve lead to longer development timelines.

data warehouse cost benefit analysis

The second investment is almost always necessary as you will need a tech stack to support your modern data warehouse. This may simply involve expanding or repurposing current tools or it could require selecting new technology. It’s important to note that pricing is different for each technology and varies greatly based on your organization’s needs and goals.

It typically involves paying for storage, computing time, or computing power, in addition to a base fee for using the technology. In total, this can typically incur a yearly starting cost of $25,000 and up. To make it easier, each of the major data warehouse technology stacks (Amazon Redshift, Snowflake, and Microsoft Azure SQL Database) offer cost estimating tools. With a clearer understanding of what your costs will look like, let’s jump into why they are worth it.

Why consultants are worth the additional costs.

While your current IT team likely has an intimate knowledge of your data and the current ecosystem, consultants offer benefits as well. Since consultants are dedicated full time to building out your modern data warehouse, they are able to make progress much quicker than an internal team would. Additionally, since they spend their careers developing a wide variety of analytics solutions, they may be more up to date on relevant technology advancements, have experience with various forms of data modeling to evaluate, and most importantly, they understand how to get business users to actually adopt the new solution.

At Aptitive (a 2nd Watch company), we have seen the most success by bringing in a small team of consultants to work side by side with your IT team, with a shared goal and vision. This ensures that your IT department will be able to support the modern data warehouse when it is completed and that the solution will address all of the details integral to your organization’s data. Considering the wealth of experienced consultants bring to the table, their ability to transfer knowledge to internal employees, and the increased speed of development, the high ROI of hiring consultants is unquestionable.

 

Download Now: A Holistic Approach to Cloud Cost Optimization [eBook]

 

Using a Modern Data Warehouse costs you less than using a traditional data analytics system you may currently have in place.

While this is a considerable amount of money to invest in data analytics, many of your current technology investments will be phased out, or the costs will be reduced using modern technology. These solutions alleviate your IT team from cumbersome maintenance tasks through automatic clustering, self-managed infrastructure, and advanced data security options. This allows your IT team to focus on more important business needs and strategic analytics.

With the volume and variety in data organizations track, it’s easy to find yourself stuck with messy data held in siloed systems. Modern data warehouses automate processes to eliminate duplicate information, reduce unnecessary clutter, and combine various sources of data together which enables you to save money by storing data efficiently. Think of it this way, if your data experts struggle to find key information, so does your technology. The extra compute time and storage costs more than you would expect, implementing a system that stores your data logically and in a streamlined manner greatly reduces these costs.

Advanced analytics unlocks insights, enables you to respond to events quicker, and optimizes key decision-making activities.

While it is more difficult to quantify the ROI here, dashboards and advanced analytics greatly enhance your employee’s ability to perform well in their job and save money. Regardless of your industry, using a modern data warehouse to drive analytics that empowers employees to perform better in several ways:

  • Dashboards dramatically decrease the time employees spend finding and organizing the data. For many of our clients, reports that once took analyst weeks of effort to are now able to automatically aggregate in seconds.
  • Accurate data empowers better decision-making and yields creative problem-solving. You have the right information quicker.
  • Real-time analytics enables you to quickly respond to significant business events. This gives you a competitive edge since you can easily retain customers, spot inefficiencies, and respond to external influences.
  • Predictive analytics save you money by finding opportunities before you would need to act.

Developing a full-scale data warehouse requires time and money that may not be available at the moment. That being said, the benefits of a data warehouse are necessary to remain competitive. To address this discrepancy, Aptitive has found a solution to help you build a modern data warehouse quicker and without the large upfront investment. A modular data warehouse contains key strategic data and ensures that you gain advantages of analytics almost immediately. On top of that, it provides a scalable foundation that you can add data to overtime until you incorporate all the data necessary for your business functions.

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For more details about implementing a modular data warehouse, check out this link or reach out to us directly to get started on your modular data warehouse.

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Amazon Web Services (AWS) Outage Constitutes Multi-Region Infrastructure

When Amazon’s cloud computing platform, Amazon Web Services (AWS), suffered an outage this past Tuesday (December 7, 2021), the magnitude of the event was felt globally. What happened, and how can your business learn from this significant outage?

Why was there an AWS outage?

Reported issues within the AWS infrastructure began around 12:00 ET/17:00 GMT on Dec. 7, according to data from real-time outage monitoring service DownDetector.

Amazon reported that the “US-East-1” region went down in North Virginia on Tuesday, which disrupted Amazon’s own applications and multiple third-party services that also rely on AWS. The issue was an “impairment of several network devices” that resulted in several API errors and ultimately, impacted many critical AWS services.

AWS outage

What were the effects of the AWS outage?

The effects of the AWS outage were massive because any problem affecting Amazon impacts hundreds of millions of end-users. AWS constitutes 41% of the global cloud-computing business, and many of the largest companies in the world are dependent on AWS’s cloud computing services. These businesses rent computing, storage, and network capabilities from AWS, which means the outage prevented end-users ‘ access to a variety of sites and apps across the Internet.

The major websites and apps that suffered from the outage are ones we turn to on a daily basis: Xfinity, Venmo, Google, and Disney+, just to name a few.

On Tuesday morning, users were reporting that they couldn’t log on to a variety of vital accounts. Most of us were going through our normal daily routine of checking the news, our financial accounts, or our Amazon orders, only to frustratingly realize that we couldn’t do so. 

With so many large organizations relying on AWS, when the outage occurred, it felt like the entire Internet went down. 

Benefits of a High Availability Multi-Region Cloud Application Architecture

Even though the outage was a major headache, it serves as an important lesson for those who are relying on a cloud-based infrastructure. As they say, you should learn from mistakes.

Amazon AWS OutageSo how can your business mitigate, or even avoid, the effects of a major failure within your cloud provider?

At 2nd Watch, we are in favor of a high availability multi-region cloud approach. We advise our clients to build out multi-region application architecture not only because it will support your mission-critical services during an outage, but also because it will make your applications more resilient and improve your end-user experiences by keeping latencies low for a distributed

user base. Below is how we think about a multi-region cloud approach and why we believe it is a strong strategy

1. Increase your Fault Tolerance

Fault tolerance is the ability of a system to endure some kind of failure and continue to operate properly. 

Unfortunately, things happen that are beyond our control (i.e. natural disasters) or things slip through the cracks (i.e. human error), which can impact a data center, an availability zone, or an entire region. However, just because a failure happens doesn’t mean an outage has to happen.

By architecting a multi-region application structure, if there is a regional failure similar to AWS’s east region failure, your company can avoid a complete outage. Having a multi-region architecture grants your business the redundancy required to increase availability and resiliency, ensure business continuity and support disaster recovery plans.

2. Lower latency requirements for your worldwide customer base

The benefits of a multi-region approach goes beyond disaster recovery and business continuity. By adopting a multi-region application architecture, your company can deliver low latency by keeping data closer to all of your users, even those who are across the globe.

In an increasingly impatient world, keeping latency low is vital for a good user experience, and the only way to maintain low latency is keeping your users close to the data.

3. Comply with Data Privacy Laws & Regulations

“Are you GDPR compliant?” is a question you probably hear frequently. Hopefully your business is, and you want to remain that way. With a multi-region architecture, you can ensure that you are storing data within the legal boundaries. Also, with signs that there will be more regulations each year, you will stay a step ahead with data compliance if you utilize a multi-region approach.

How Can I Implement a Multi-Region Infrastructure Deployment Solution?

A multi-region cloud approach is a proactive way to alleviate potential headaches and grow your business, but without guidance, it can seem daunting in terms of adoption strategy, platform selection, and cost modeling. 

2nd Watch helps you mitigate the risks of potential public cloud outages and deploy a multi-region cloud infrastructure. Through our Cloud Advisory Services, we serve as your trusted advisor for answering key questions, defining strategy, managing change, and providing impartial advice for a wide range of organizational, process, and technical issues critical to successful cloud modernization.

Contact us today to discuss a multi-region application architecture for your business needs!

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5 Benefits Gained from Cloud Optimization

When making a cloud migration, a common term that gets tossed around is “cloud optimization”. If your organization is new to the cloud, optimizing your environment is essential to ensuring your migration pays off quickly and continues to do so in the long term.

If your organization is already established in the cloud, you may observe higher costs than expected due to cloud sprawl, under-utilized resources, and improper allocation of resources. Cloud optimization helps your organization reduce these costs and improve overall efficiency in the cloud

Cloud Optimization

What is cloud optimization?

The definition of cloud optimization may vary from one cloud service provider to another, but generally, cloud optimization is the process of analyzing, configuring, provisioning, and right-sizing cloud resources to maximize performance and minimize waste for cost efficiency. The reality is that many organizations’ cloud environments are configured in an inefficient manner that creates unnecessary cloud spend. With proper cloud optimization tools and practices, these unnecessary costs can be eliminated.

While cloud optimization is mostly discussed in terms of cloud spend, cost optimization is simply a faucet of cloud optimization and can extend to overall performance and organizational efficiency. Some examples of cloud optimization practices that your organization can adopt right now include:

  • Right-sizing: Matching your cloud computing instance types (i.e. containers and VMs) and sizes with enough resources to sufficiently meet your workload performance and capacity needs to ensure the lowest cost possible.
  • Family Refresh: Replace outdated systems with updated ones to maximize performance.
  • Autoscaling: Scale your resources according to your application demand so you are only paying for what you use.
  • Applying Discounts: Reserved instances (RIs) allow companies to commit to cloud resources for a long period of time. The longer the discount and the more a company is prepared to pre-pay at the beginning of a period, the greater the discount will be. Discounted pricing models like RIs and spot instances will drive down your cloud costs when used according to your workload.
  • Identity use of RIs: Identifying the use of RIs can be an effective way to save money in the cloud if used for suitable loads.
  • Eliminate Waste: Regulating unused resources is a core component of cloud optimization. If you haven’t already considered cloud optimization practices, you are most likely using more resources than necessary or not certain resources to their full capacity.

Why is cloud optimization important?

Overspending in the cloud is a common issue many organizations face by allocating more resources to a workload than necessary. Integrating cloud optimization practices can reap many benefits for your cloud infrastructure and your organization, including the following:

  • Cloud Efficiency: When workload performance, compliance, and cost are continually balanced against the best-fit infrastructure in real-time, efficiency is achieved. Implementing cloud optimization practices will eliminate as much cloud resource waste as possible, increasing the performance of your cloud environment.
  • Cost Savings: Although cloud optimization comes in a variety of forms, cost optimization is the most important component for many organizations. By reducing waste in the cloud, costs are reduced as a byproduct.
  • Greater Visibility: Cloud optimization practices utilize analytics to provide visibility into your cloud environment to make data-driven decisions. Implementing optimization tools also provides cost visibility, so your organization has a better perspective on cloud spend.
  • Increased Productivity: Once a cloud optimization strategy is implemented, IT teams will spend less time trying to solve problems because an optimized environment prevents problems before they occur.
  • Organizational Innovation & Efficiency: Implementing cloud optimization often is accompanied by a cultural shift within organizations such as improved decision-making and collaboration across teams.

Benefits of cloud optimization

What are cloud optimization services?

Public cloud services providers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud have over 500,000 distinct prices and technical combinations that can overwhelm the most experienced IT organizations and business units. Luckily, there are already services that can help your organization achieve the cloud optimization it needs to drive business outcomes. Cloud optimization services help your organization identify areas of improvement in your cloud for cost savings and efficiency, create an optimization strategy for your organization, and can manage your cloud infrastructure for continuous optimization.

At 2nd Watch, we take a holistic approach to cloud optimization. We have developed various optimization pillars based on real-time data to ensure your cloud environments are running as efficiently as possible. Behind our solutions for cloud optimization is a team of experienced data scientists and architects that help you maximize the performance and returns of your cloud assets. Our services offerings for cloud optimization at 2nd Watch include:

What are cloud optimization services

  • Strategy & Planning: Define your optimization strategy with our proven methodology, tailored to meet your desired business outcomes and maximize your results.
  • Cost Optimization Assessment: Gain the visibility necessary to make data-driven decisions. Identify opportunities across our Pillars of Optimization to maximize cost savings and cloud environment efficiency.
  • Spot Instance & Container Optimization: Save up to 90% compared to traditional cloud infrastructure by running both Instances/VMs and Containers on spot resources for relevant workloads.
  • Multi-Cloud Optimization: Cloud optimization on a single public cloud is one challenge but optimizing a hybrid cloud is a whole other challenge. Apply learning from your assessment to optimize your cloud environment for AWS, Microsoft Azure, Google Cloud, and VMware on AWS.
  • Forecasting, Modeling, & Analytics: Understand your past usage, and model and forecast your future needs with the analytical data needed for visibility across your organization.

Our cloud optimization process starts with data, and you have a lot of it. But data alone can lead you astray yielding wasted resources and overspend. There are many other factors to evaluate, such as EDP/EA agreements and Savings Plans/RI Purchases, to ensure you choose the most cost-effective option for your business. Strategically, our data scientists and architects map connections between data and workloads. We then make correlations between how workloads interact with each resource and the optimal financial mechanism to reach your cloud optimization goals.

Cloud Optimization with 2nd Watch

Working with a managed cloud service provider like 2nd Watch will give your organization the expertise needed for cloud optimization. If you want to learn more about cost savings or are interested in fully optimizing your cloud infrastructure, contact us to take your next steps.

 

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6 Cloud Consulting Services Benefit your Organization’s Cloud Infrastructure

Cloud computing is a complex process that requires proper planning and continuous management. Whether you are just getting started with the cloud or have been in the cloud for years, you might find yourself asking questions regarding running your cloud infrastructure. Which cloud provider is best for my organization? Should I have one or more public cloud providers? How will I ensure financial transparency and efficiency in the cloud? To tackle these questions, there are a variety of cloud consulting services that make these challenges much easier to overcome for a successful cloud journey.

cloud consulting benefits

What is cloud consulting?

 For any business, getting expert advice guides operations and efficiency for a business to expand. With the cloud as a relatively newer concept for many businesses, a cloud expert is essential to ensure cloud efficiency.

A cloud consultant is someone who specializes in the cloud and can help answer questions, recommend clients with the right architecture that meets their client’s business needs, and can even maintain the cloud applications for their clients. By engaging in cloud consulting services, any questions you have about the cloud can be answered by an expert so you can ensure you are taking an approach that uses the cloud to its full potential. For example, a cloud consultant can recommend the cloud platform that suites your business needs or recommend a hybrid cloud solution.

Cloud consulting service types 

Cloud consulting services vary from one company to another, and there are different services and cloud solutions for different business needs. Although cloud services may vary depending on who your cloud consultant is, we like to break up our services into six different categories.

  • Cloud Advisory: If you are considering a transition to the cloud, cloud advisory services help answer key questions, define strategy, manage change within your organization, and provide impartial advice for a wide range of organizational, process, and technical issues related to cloud modernization.
  • Cloud Migration: When making a transition to the public cloud, there are many different aspects to consider for a successful migration. A cloud consultant can formulate a holistic migration strategy, whether you are migrating an individual workload or an entire data center.
  • Application Modernization & DevOps: A DevOps transformation provides your company and team members with tools and strategy for modernizing your applications. This can be as simple as helping your organization identify strengths and opportunities through an assessment or can include a fully managed DevOps pipeline with ongoing cultural guidance.
  • Data & Analytics: According to a 2nd Watch survey of 150 enterprises, 57% of organizations do not have the analytics expertise necessary to meet business needs. Data and analytics services transforms your organization to be data driven. If you are just starting out in the cloud or are interested in utilizing data, a cloud consultant can help implement an initial set of analytic processes. If your organization is more mature when it comes to data, a cloud consultant can design, build, or enhance your analytic architecture.
  • Compliance, Security, & Business Continuity: Security should be a top priority at every layer of your cloud environment, yet many businesses do not prioritize the security and compliance required when running a cloud environment. A cloud advisor can provide services that monitor your cloud environment 24/7 so that you do not have to.
  • Cloud Operations & Optimization: Optimization ensures your cloud environments are running as efficiently as possible. Handing that responsibility over to a cloud consulting firm helps your organization maximize the performance and returns of your cloud assets.

What are the benefits of cloud consulting?

 Upfront, the working with a cloud consulting firm may seem costly, but the benefits reaped from working with the right cloud consultant greatly justifies the associated costs. Some of the resulting benefits include:

  • Knowledge: Working with a cloud consultant for will give your organization the advisory needed to confidently go about your cloud adoption and journey.
  • Efficiency: Handing over some of the tasks needed to run your environments to a cloud consultant can reduce your time managing the cloud and increase organizational efficiency to drive business outcomes.
  • Reduced Costs: Cloud experts will set up your cloud infrastructure in the most efficient way possible to save your organization from unnecessary cloud spending. Additionally, hiring a cloud consulting company reduces the need for a fully staffed IT department.
  • Enhanced Security: Managing a public cloud infrastructure requires continuous security and compliance to ensure the safety of your data. Working with a cloud consulting firm allows your infrastructure to be managed 24/7.

Consult with 2nd Watch

Cloud has the potential to revolutionize business, but without guidance, it can prompt some daunting decisions in terms of adoption strategy, platform selection, and cost modeling. At 2nd watch, we have the expert knowledge to advise you on these topics and help you get started with your cloud journey. Beyond our planning phases, our team can help you with the migration, optimization, and transformation of your cloud environment . Contact us to learn more or to take your next steps.

-Tessa Foley, Marketing

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Don’t Be Afraid of Learning New DevOps Tooling

DevOps is defined as a set of practices that combines software development (Dev) and information-technology operations (Ops) to shorten the systems development life cycle and provide continuous delivery with high software quality. (DevOps, n.d.) The idea of tools, or tooling, is not mentioned, likely due to misconceptions or their sheer number.

Learning New DevOps Tooling

Indeed, there are a lot of DevOps tools that span across a multitude of categories. From application performance monitoring tools to configuration management tools to deployment tools to continuous integration and continuous delivery tools and more, it’s no wonder IT organizations are hesitant to learn new ones.

Misconceptions about DevOps tooling are a common problem in software development and are essential to highlight. When 2nd Watch guides organizations along their DevOps transformation journey, common misconceptions encountered include:

  • “We already have a tool that does that.”
  • “We have too many tools already.”
  • “We need to standardize.”
  • “Our teams aren’t ready for that.”

However, these statements contradict what DevOps represents. Although DevOps practices are not about running a particular set of tools, tooling is undoubtedly an essential component that helps DevOps teams operate and evolve applications quickly and reliably. Tools also help engineers independently accomplish tasks that generally require help from other groups, further increasing a team’s velocity.

Crucial DevOps Practices

Several practices are crucial to a successful DevOps transformation and counter the objections mentioned above.

Experimentation

Humans learn by making mistakes and adjusting their behavior accordingly to improve continuously. IT practitioners do that day-in and day-out when they write code, change a configuration setting, or look at a new dashboard metric. All the above statements work against this idea, hindering experimentation. A new tool may open new avenues of test automation or provide better tracking mechanisms. An experimentation mindset is crucial to the development process for developers to get better at it and improve their understanding of when, where, and how something will or will not fit into their workflow.

Shifting Left

“Shifting left” is a common DevOps practice and is important to understand. Many of the above statements stifle this idea due to siloed, gated workflows. For example, a product’s development workflow starts on the left, then flows along different stages and gates toward the right, eventually making it into production. By moving feedback as far left as possible, implementers are alerted to problems faster and quickly remedy the issues.

Servant Leadership

Servant leadership is critical in handling a changing culture. It is another aspect of shifting left where, rather than “managing” specifics and giving orders, servant leaders encourage and guide decisions toward a common goal. Ultimately, the decision-making process is moving to the left.

One tool to rule them all and, with automation, bind them!

Described here is a specific tool that helps developers be more efficient in their software delivery which, in turn,  drives organizations to embrace the DevOps culture. It is a unified tool that provides a single interface that everyone can understand and to which they can contribute.

GNU Make

GNU Make is a tool which controls the generation of executables and other non-source files of a program from the program’s source files.” – gnu.org

GNU Make generates artifacts from source files by running local commands. It’s prevalent in the UNIX-like (*nix) world, and for a good reason. In the past, developers primarily used GNU Make to build Linux source files and compile C and C++. However, its absolute dominance in that realm made it readily available in nearly every *nix flavor and with minimal variance. It’s a tried-and-true tool that’s relatively simple to use and available everywhere.

The Basics

There are two things needed to use GNU Make.

  • the GNU Make tool
  • a Makefile

Because installation of the tool is outside of the scope of this article, that step will be skipped. However, if a *nix system is used, Make is likely already installed.

Rather than using the GNU Make tool to create an executable, make is strictly used here to set up automation, significantly simplifying the code. Terraform is the tool used to demonstrate how GNU Make works.

The Makefile goes directly into the root of the project…

…and looks like this:

The Makefile

The GNU Make tool will look inside the current working directory of the “Makefile” and use that as its set of instructions. Although the tool will look for Makefile or makefile, it is a firmly accepted standard practice to use the capitalized form.

The Makefile defines a set of rulestargets, and dependencies. Since the Makefile is a wrapper, the targets are omitted and listed as .PHONY, telling make that a file for the rules we will not be output.

Defining Rules
  1. The initrule:

In this example, make is being told that it is not building anything with the rule (.PHONY: init), is defining the rule (init:), and which commands make is to execute when the init rule is executed (terraform init).

The following command is run in the root of the project to execute the rule:

  1. The plan rule

Once the project is initialized, Terraform displays the changes it wants to make to the infrastructure before implementation to verify expectations.  (NOTE: terraform plan prints out a description of what Terraform is going to do, but does not make changes)

.PHONY is used again because, like before, nothing is being created. Here, the init rule is set up as a dependency for the plan rule. In this case, when ]% make plan is executed, the init rule will run first. Any number of these rules can be strung together, helping to make them more atomic. (NOTE: it is not necessary to run init before plan with Terraform.)

With a little more complexity, make can codify important steps in an operation. In the above, an environment variable is required (ENV), then the appropriate workspace is selected before running the plan rule with the appropriate variables set for that environment. (-var-file).

3.     The test rule

Create a simple test rule for the Terraform project:

Here, the code is formatted to canonicalize it, and then the validate Terraform command is run to ensure the templates are valid.

Concepts of Unifying Tools

Central to the idea of a unifying tool are self-documenting source code and the adapter pattern. Each concept is detailed below to provide a better understanding of why they are so important.

Self-Documenting Code

The idea behind self-documenting code is to have code written in a way that’s explanatory to its purpose. Variable names, function names, classes, objects, and methods should all be named appropriately. Volumes of documentation are not needed to describe what a piece of code is doing. As it’s code, the underlying understanding is that it’s executable, so what is read in the code is what will happen when it is executed.

In the above examples, executable documentation for the project was also created by using make as the central “documentation” point. Even when things got more complex with the second plan example, any editor can be used to see the definition for the plan rule.

In cases where Terraform isn’t used often, it isn’t necessary to remember every command, variable, or command-line argument needed to be productive. Which commands are run, and where, can still be seen. And, if more details are required, execute the command in a terminal and read its help information.

The Adapter Design Pattern

In software engineering, the adapter design pattern is a well-known use pattern to allow different inputs into a standard interface. The concept may be better understood through the following analogy.

There are many different types of outlets used throughout the world. Europe uses a round, 2-pronged plug. The United States (US) uses a 2-pronged flat or 3-pronged (2 flat, one round) outlet. One solution for US plugs to work in European outlets is to jigger the outlet itself, allowing for both US and European plugs. However, this “solution” is costly. Instead, an easier and less expensive solution is to use an adapter that understands the inputs of the US plug and converts it to the European inputs that we can then plug into the wall.

This is seen when documentation for command-line tools is accessed using the help argument. Nearly every command-line tool understands the –help switch or help command. It’s ubiquitous.

The earlier examples (init, plan) are more specific to Terraform, but the test rule is not. The test rule is an excellent example of being able to use make as an adapter!

Development testing is a critical aspect of all engineering, including software delivery. Writing tests ensure that any changes introduced into a project don’t cause any unintended problems or bugs.

Every language and many tools have individual testing frameworks—multiple frameworks per language, many times with different commands for each. By using make as the user interface to the project, workflows can stay the same—or very similar—across multiple projects utilizing multiple tools. For example, make test can be run in any project, and the tests for that project will execute, regardless of the language. Python? Golang? .NET? Ruby? make test can be used for each one in every project.

Another example is the init rule in the above Makefile. There’s typically some setup or configuration for every project that needs to happen to get an environment ready for use. The init rule can be used to set up any project, whether for running pip for python or npm for Node.

Conclusion

The fear of tooling burnout is not apocalyptic. By utilizing a wrapper tool like GNU Make to provide a standard interface, many problems that might be encountered when implementing a breadth of tools can be mitigated. However, cultural changes are essential, and tools will not be able to solve them. But a piece of tech like GNU Make can be used to alleviate the perceived notions of “Too many tools.”

Contact 2nd Watch to discuss how we can help further your DevOps transformation.

-by Craig Monson, Principal Cloud Consultant, 2nd Watch

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The Importance of Leadership in Moving to a DevOps Culture

Why DevOps?

DevOps is a set of practices that improve the efficiency and effectiveness of IT operations. Utilizing many aspects of agile methodology, DevOps aims to shorten the systems development life cycle and provide continuous improvement. As you consider incorporating DevOps into your operations, understand the effect DevOps has on processes and culture. Successful implementation is about finding the right balance of attention on people, processes, and technology to achieve improvement.

devops culture

The ultimate goal is continuous improvement through processes and tools. No amount of tooling, automation, or fancy buzz words can cause any greater effect on an organization than transforming their culture, and there’s no other way to do that than to focus on the change.

Understanding What You Are Trying to Accomplish with DevOps

Ask yourself what you are trying to achieve. It may seem obvious, but you may get on the wrong track without thinking about what you want your development and operations teams to achieve.

Often, when clients approach 2nd Watch wanting to incorporate DevOps, they are really asking for automation tools and nothing else. While automation has certain benefits, DevOps goes beyond the benefits of technology to improve processes, help manage change more effectively, and improve organizational culture. Change is difficult. However, implementing a cultural shift is particularly challenging. Often overlooked, cultural change is the greatest pain 2nd Watch consultants encounter when working with companies trying to make substantial organizational changes. Even implementing things as simple as sharing responsibility, configuration management, or version control can cause turmoil!

From IT Management to Leadership

There is a distinction between what it means to be a manager versus being a leader. And, in all industries, being a manager does not necessitate being a good leader.

It’s helpful to consider the progression of those in technical roles to management. Developers and operations personnel are typically promoted to managers because they are competent in their technical position—they excel at their current software development process, configuring a host or operating a Kubernetes cluster. However, as a manager, they’re also tasked with directing staff, which may put them outside of their comfort zone. They are also responsible for pay, time and attendance, morale, and hiring and firing. They likely were not promoted for their people skills but their technical competencies.

Many enterprise organizations make the mistake of believing employees who have outstanding technical skills will naturally excel at people management once they get that promotion. Unfortunately, this mistake breeds many managers who fall short of potential, often negatively affecting corporate culture.

Leading the Change

It’s imperative to understand the critical role leadership plays in navigating the amount of change that will likely occur and in changing the organization’s culture.

Whether you’re a manager or leader matters a lot when you answer the question, “What do I really want out of DevOps?” with, “I want to be able to handle change. Lots and lots of change.”

Better responses would include:

  • “I want our organization to be more agile.”
  • “I want to be able to react faster to the changing market.”
  • “I want to become a learning organization.”
  • “I want to embrace a DevOps culture for continuous improvement.”

The underlying current of these answers is change.

Unfortunately, when bungled management occurs, it’s the people below that pay the price. Those implementing the changes tend to take the brunt of the worst of the change pain. Not only does this cause lower morale, but it can cause a mutiny of sorts. Apathy can affect quality, causing outages. The best employees may jump ship for greener pastures. Managers may give up on culture change entirely and go back to the old ways.

However, there is light at the end of the tunnel. With a bit of effort and determination, you can learn to lead change just as you learned technical skills.

Go to well-known sources on management improvement and change management. Leading Change by John P. Kotter[1]  details the successful implementation of change into an organization. Kotter discusses eight steps necessary to help improve your chances of being successful in changing an organization’s culture:

  1. Establishing a sense of urgency
  2. Creating the guiding coalition
  3. Developing a vision and strategy
  4. Communicating the change vision
  5. Empowering broad-based action
  6. Generating short term wins
  7. Consolidating gains and producing more change
  8. Anchoring new approaches in the culture

It’s all about people. Leaders want to empower their teams to make intelligent, well-informed decisions that align with their organization’s goals. Fear of making mistakes should not impede change.

Mistakes happen. Instead of managers locking their teams down and passing workflows through change boards, leaders can embrace the DevOps movement and foster a culture where their high-performing DevOps team can make mistakes and quickly remedy and learn from them.

Each step codifies what most organizations are missing when they start a transformation: focusing on the change and moving from a manager to a leader.

The 5 Levels of Leadership

Learning the skills necessary to become a great leader is not often discussed when talking about leadership or management positions. We are accustomed to many layers of management and managers sticking to the status quo in the IT industry. But change is necessary, and the best place to start is with ourselves.

The 5 Levels of Leadership by John C. Maxwell[1] is another excellent source of information for self-improvement on your leadership journey:

  • Level 1 – Position: People follow you only because they believe they have to.
  • Level 2 – Permission: People follow you because they want to.
  • Level 3 – Production: People follow you because of what you have done for the organization.
  • Level 4 – People Development: People follow you because of what you have done for them.
  • Level 5 – Pinnacle: People follow because of who you are and what you represent.

Leadership easily fits into these levels, and determining your position on the ladder can help. Not only are these levels applicable to individuals but, since an organization’s culture can revolve around how good or bad their leadership is, this ends up being a mirror into the problems the organization faces altogether.

Conclusion

When transforming to a DevOps culture, it’s essential to understand ways to become a better leader. In turn, making improvements as a leader will help foster a healthy environment in which change can occur. And there’s no better catalyst to becoming a great leader than being able to focus on the change.

2nd Watch collaborates with many different companies just beginning their workflow modernization journey. Contact us to discuss how we can help your organization further adopt a DevOps culture.

-Craig Monson

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What to Expect at AWS re:Invent 2021

Welcome back friends! AWS re:Invent turns 10 this year and once again 2nd Watch is here to help you navigate it like a pro. As we all know now, AWS re:Invent 2021 is back in person in Las Vegas. One addition this year, Amazon Web Services is also offering a virtual event option… well, kind of…. As it currently stands, only the keynotes and leadership sessions will be live streamed for the virtual attendees. Breakout sessions will only be live for in person attendees, but will be available on-demand after the event.

What to Expect at AWS re:Invent 2021

For the rest of this blog I will try to focus on my thoughts and limit my regurgitation of all the information that you can get from the AWS re:Invent website, such as the AWS Code of Conduct, but I think it’s worth noting what I think are some key highlights that you should know. Oh, and one more thing. I have added a small easter egg to this year’s blog. If you can find a Stan Lee reference, shoot me an email: dustin@2ndwatch.com and call it out. One winner will be picked at random and sent a $25 Amazon gift card. Now let’s get to it.

Some important things to note this year

Now that AWS re:Invent is (mostly) back in person, AWS is implementing proper health measures to prevent the spread of COVID. Make sure to review the health guidelines published by AWS. (https://reinvent.awsevents.com/health-measures/). Here is the summary for those that don’t enjoy more eye exercise than necessary. Refer to aforementioned link for more details and FAQ’s if you do.

  • All badge holders attending in person must be fully vaccinated for COVID-19 (2 weeks after final shot) which means you must provide a record of vaccination in order to receive your badge. AWS makes it clear that there are no ifs, ands or buts on this. No vax proof, no badge. ‘Nuff said!
  • Masks will be required for everyone at the event. Real ones. Unfortunately face lingerie and train robber disguises will not count.

Keynotes at Glance

This year’s keynotes give you the best of both worlds with both a live option for in person attendees and on-demand viewing option for virtual attendees. The 2021 keynotes include:

  • Adam Selipsky, AWS CEO
  • Peter DeSantis, Senior Vice President, Utility Computing and Apps
  • Wener Vogels, CTO, Amazon.com
  • Swami Sivasubramanian, Vice President, Amazon Machine Learning
  • Global Partner Summit presented by Doug Yeum, Head of AWS Partner Organization, Sandy Carter, Vice President, Worldwide Public Sector Partners and Programs, and Stephan Orban, General Manager of AWS Marketplace and Control Services

2nd Watch Tips n’ Tricks

Over the last 9 years we have watched the AWS re:Invent conference morph into a goliath of an event. Through our tenure there we have picked up an abundance of tips n’ tricks to help us navigate the waters. Some of these you may have seen from my previous blogs, but they still hold strong value, so I have decided to include them. I have also added a couple new gems to the list.

  • App for the win – I cannot stress this one enough. Download and use the AWS Events app. This will help you manage your time as well as navigate around and between the venues.
  • Embrace your extravert Consider signing up for the Builder Sessions, Workshops, and Chalk Talks instead of just Breakout sessions. These are often interactive and a great way to learn with your peers.
  • Watch for repeats AWS is known for adding in repeat Breakout sessions for ones that are extremely popular. Keep your eye on the AWS Events app for updates throughout the week.
  • Get ahead of the pack After Adam Selipsky’s Keynote there will likely be sessions released to cover off on new services that are announced. Get ahead of the pack by attending these.
  • No fomo Most of the Breakout sessions are recorded and posted online after re:Invent is over. Fear not if you miss a session that you had your eyes on, you can always view it later while eating your lunch, on a break or doing your business.
  • Get engaged – Don’t be afraid to engage with presenters after the sessions. They are typically there to provide information and love answering questions. Some presenters will also offer up their contact information so that you can follow up again at a later time. Don’t be shy and snag some contact cards for topics relevant to your interests.
  • Bring the XL suitcase – Now that we are back in person, get ready to fill that swag bag! You will need room to bring all that stuff home so have extra room in your suitcase when you arrive.
  • Don’t just swag and run – Look, we all love stuffing the XL suitcase with swag, but don’t forget to engage your peers at the booths while hunting the hottest swag give-a-ways. Remember that part of the re:Invent experience is to make connections and meet people in your industry. Enjoy it. Even if it makes you a little uncomfortable.
  • Pro tip! Another option if you missed out on a reserving a session you wanted is to try and schedule something else that is near it at the same time. This will allow you to do a drive by on the session you really wanted and see if there is an open spot. Worst case, head to your back up session that you were able to schedule.

Our re:Invent Predictions

Now that we have you well prepared for the conference, here are a couple of our predictions for what we will see this year. We are not always right on these, but it’s always fun to guess.

  • RDS savings plans will become a reality.
  • Specialty instance types targeted at specific workloads (similar to the new VT1 instance they just announced focused on video).
  • Security hub add-ons for more diverse compliance scanning.
    • Expanded playbooks for compliance remediation.
    • More compliance frameworks to choose from.
  • Potential enhancements to Control Tower.
  • Virtual only attendees will not get the opportunity for the coveted re:Invent hoodie this year.

In Closing…

We are sure that after December 3rd there will be an overwhelming number of new services to sift through but once the re:Invent 2021 hangover subsides, 2nd Watch will be at the ready and by your side to help you consume and adopt the BEST solutions for your cloud journey. Swing by our booth #702 for some swag and a chat. We are giving away Gretsch Guitars we are super excited to see you!

Finally, don’t forget to schedule a meeting with one of our AWS Cloud Solution Experts while you’re at re:Invent. We would love to hear all about your cloud journey! We hope you are as excited as we are this year and we look forward to seeing you in Las Vegas.

-Dustin Snyder, Director of Cloud Infrastructure & Architecture

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9 Helpful Tools for Building a Data Pipeline

Companies create tons of disparate data throughout their organizations through applications, databases, files and streaming sources. Moving the data from one data source to another is a complex and tedious process. Ingesting different types of data into a common platform requires extensive skill and knowledge of both the inherent data type of use and sources.

Due to these complexities, this process can be faulty, leading to inefficiencies like bottlenecks, or the loss or duplication of data. As a result, data analytics becomes less accurate and less useful and in many instances, provide inconclusive or just plain inaccurate results.

For example, a company might be looking to pull raw data from a database or CRM system and move it to a data lake or data warehouse for predictive analytics. To ensure this process is done efficiently, a comprehensive data strategy needs to be deployed necessitating the creation of a data pipeline.

What is a Data Pipeline?

A data pipeline is a set of actions organized into processing steps that integrates raw data from multiple sources to one destination for storage, business intelligence (BI), data analysis, and visualization.

There are three key elements to a data pipeline: source, processing, and destination. The source is the starting point for a data pipeline. Data sources may include relational databases and data from SaaS applications. There are two different methods for processing or ingesting models: batch processing and stream processing.

  • Batch processing: Occurs when the source data is collected periodically and sent to the destination system. Batch processing enables the complex analysis of large datasets. As patch processing occurs periodically, the insights gained from this type of processing are from information and activities that occurred in the past.
  • Stream processing: Occurs in real-time, sourcing, manipulating, and loading the data as soon as it’s created. Stream processing may be more appropriate when timeliness is important because it takes less time than batch processing. Additionally, stream processing comes with lower cost and lower maintenance.

The destination is where the data is stored, such as an on-premises or cloud-based location like a data warehouse, a data lake, a data mart, or a certain application. The destination may also be referred to as a “sink”.

What is a Data Pipeline and How to Build One | 2ND Watch

Data Pipeline vs. ETL Pipeline

One popular subset of a data pipeline is an ETL pipeline, which stands for extract, transform, and load. While popular, the term is not interchangeable with the umbrella term of “data pipeline”. An ETL pipeline is a series of processes that extract data from a source, transform it, and load it into a destination. The source might be business systems or marketing tools with a data warehouse as a destination.

There are a few key differentiators between an ETL pipeline and a data pipeline. First, ETL pipelines always involve data transformation and are processed in batches, while data pipelines ingest in real-time and do not always involve data transformation. Additionally, an ETL Pipeline ends with loading the data into its destination, while a data pipeline doesn’t always end with the loading. Instead, the loading can instead activate new processes by triggering webhooks in other systems.

Uses for Data Pipelines:

  • To move, process, and store data
  • To perform predictive analytics
  • To enable real-time reporting and metric updates

Uses for ETL Pipelines:

  • To centralize your company’s data
  • To move and transform data internally between different data stores
  • To Enrich your CRM system with additional data

9 Popular Data Pipeline Tools

Although a data pipeline helps organize the flow of your data to a destination, managing the operations of your data pipeline can be overwhelming. For efficient operations, there are a variety of useful tools that serve different pipeline needs. Some of the best and most popular tools include:

  • AWS Data Pipeline: Easily automates the movement and transformation of data. The platform helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available.
  • Azure Data Factory: A data integration service that allows you to visually integrate your data sources with more than 90 built-in, maintenance-free connectors.
  • Etleap: A Redshift data pipeline tool that’s analyst-friendly and maintenance-free. Etleap makes it easy for business to move data from disparate sources to a Redshift data warehouse.
  • Fivetran: A platform that emphasizes the ability to unlock faster time to insight, rather than having to focus on ETL using robust solutions with standardized schemas and automated pipelines.
  • Google Cloud Dataflow: A unified stream and batch data processing platform that simplifies operations and management and reduces the total cost of ownership.
  • Keboola: Keboola is a platform is a SaaS platform that starts for free and covers the entire pipeline operation cycle.
  • Segment: A customer data platform used by businesses to collect, clean, and control customer data to help them understand the customer journey and personalize customer interactions.
  • Stitch: Stitch is a cloud-first platform rapidly moves data to the analysts of your business within minutes so that it can be used according to your requirements. Instead of focusing on your pipeline, Stitch helps reveal valuable insights.
  • Xplenty: A cloud-based platform for ETL that is beginner-friendly, simplifying the ETL process to prepare data for analytics.

 

How We Can Help

Building a data pipeline can be daunting due to the complexities involved in safely and efficiently transferring data. At 2nd Watch, we can build and manage your data for you so you can focus on BI and analytics to focus on your business. Contact us if you would like to learn more.

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Data Center Migration to the Cloud: Why Your Business Should Do it and How to Plan for it

Data center migration is ideal for businesses who are looking to exit or reduce on-premises data centers, migrate workloads as is, modernize apps, or leave another cloud. Executing migrations, however, is no small task, and as a result, there are many enterprise workloads that still run in on-premises data centers. Often technology leaders want to migrate more of their workloads and infrastructure to private or public cloud, but they are turned off by the seemingly complex processes and strategies involved in cloud migration, or lack the internal cloud skills necessary to make the transition.

Data Center Migration

 

Though data center migration can be a daunting business initiative, the benefits of moving to the cloud is well worth the effort, and the challenges of the migration process can be mitigated by creating a strategy, using the correct tools, and utilizing professional services. Data center migration provides a great opportunity to revise, rethink, and improve an organization’s IT architecture. It also ultimately impacts business critical drivers such as reducing capital expenditure, decreasing ongoing cost, improving scalability and elasticity, improving time-to-market, enacting digital transformation and attaining improvements in security and compliance.

What are Common Data Center Migration Challenges?

To ensure a seamless and successful migration to the cloud, businesses should be aware of the potential complexities and risks associated with data center migration. The complexities and risks are addressable, and if addressed properly, organizations can create not only an optimal environment for their migration project, but provide the launch point for business transformation.

Not Understanding Workloads

While cloud platforms are touted as flexible, it is a service-oriented resource, and it should be treated as such. To be successful in cloud deployment, organizations need to be aware of performance, compatibility, performance requirements (including hardware, software, and IOPS), required software, and adaptability to changes in their workloads. Teams need to run their cloud workloads on the cloud service that is best aligned with the needs of the application and the business.

Not Understanding Licensing

Cloud marketplaces allow businesses to easily “rent” software at an hourly rate. Though the ease of this purchase is enticing, it’s important to remember that it’s not the only option out there. Not all large vendors offer licensing mobility for all applications outside the operating system. In fact, companies should leverage existing relationships with licensing brokers. Just because a business is migrating to the cloud doesn’t mean that a business should abandon existing licensing channels. Organizations should familiarize themselves with their choices for licensing to better maximize ROI.

Not Looking for Opportunities to Incorporate PaaS

Platform as a service (PaaS) is a cloud computing model where a cloud service provider delivers hardware and software tools to users over the internet versus a build-it-yourself Infrastructure as a Service (IaaS) model. The PaaS provider abstracts everything—servers, networks, storage, operating system software, databases, development tools—enabling teams to focus on their application. This enables PaaS customers to build, test, deploy, run, update and scale applications more quickly and inexpensively than they could if they had to build out and manage an IaaS environment on top of their application. While businesses shouldn’t feel compelled to rewrite all their network configurations and operating environments, they should see where they can have quick PaaS wins to replace aging systems.

Not Proactively Preparing for Cloud Migration

Building a new data center is a major IT event and usually goes hand-in-hand with another significant business event, such as an acquisition, or outgrowing the existing data center. In the case of moving to a new on-premises data center, business will slow down as the company takes on a physical move. Migrating to the cloud is usually not coupled with an eventful business change, and as a result, business does not stop when a company chooses to migrate to the cloud. Therefore, a critical part of cloud migration success is designing the whole process as something that can run along with other IT changes that occur on the same timeline. Application teams frequently adopt cloud deployment practices months before their systems actually migrate to the cloud. By doing so, the team is ready before their infrastructure is even prepared, which makes cloud migration a much smoother event. Combining cloud events with other changes in this manner will maximize a company’s ability to succeed.

Treating and Running the Cloud Environment Like Traditional Data Centers

It seems obvious that cloud environments should be treated differently from traditional data centers, but this is actually a common pitfall for organizations to fall in. For example, preparing to migrate to the cloud should not include traditional data center services, like air conditioning, power supply, physical security, and other data center infrastructure, as a part of the planning. Again, this may seem very obvious, but if a business is used to certain practices, it can be surprisingly difficult to break entrenched mindsets and processes.

How to Plan for a Data Center Migration

While there are potential challenges associated with data center migration, the benefits of moving from physical infrastructures, enterprise data centers and/or on-premises data storage systems to a cloud data center or a hybrid cloud system is well worth the effort.

Now that we’ve gone over the potential challenges of data center migration, how do businesses enable a successful data center migration while effectively managing risk?

Below, we’ve laid out a repeatable high level migration strategy that is broken down into four phases: Discovery, Planning, Execution, and Optimization. By leveraging a repeatable framework as such, organizations create the opportunity to identify assets, minimize migration costs and risks using a multi-phased migration approach, enable deployment and configuration, and finally, optimize the end state.

Phase 1: Discovery

During the Discovery phase, companies should understand and document the entire data center footprint. This means understanding the existing hardware mapping, software applications, storage layers (databases, file shares), operating systems, networking configurations, security requirements, models of operation (release cadence, how to deploy, escalation management, system maintenance, patching, virtualization, etc.), licensing and compliance requirements, as well as other relevant assets.

The objective of this phase is to have a detailed view of all relevant assets and resources of the current data center footprint.

The key milestones in the Discovery phase are:

  • Creating a shared data center inventory footprint: Every team and individual who is a part of the data center migration to the cloud should be aware of the assets and resources that will go live.
  • Sketching out an initial cloud platform foundations design: This involves identifying centralized concepts of the cloud platform organization such as folder structure, Identity and Access Management (IAM)  model, network administration model, and more.

As a best practice, companies should engage in cross-functional dialogue within their organizations, including teams from IT to Finance to Program Management, ensuring everyone is aligned on changes to support future cloud processes. Furthermore, once a business has migrated from a physical data center to the cloud, they should consider whether their data center team is trained to support the systems and infrastructure of the cloud provider.

Phase 2: Planning

When a company is entering the Planning phase, they are leveraging the assets and deliverables gathered in the Discovery phase to create migration waves to be sequentially deployed into non-production and production environments.

Typically, it is best to target non-production migration waves first, which helps identify the sequence of waves to migrate first. To start, consider the following:

  • Mapping the current server inventory to the cloud platform’s machine types: Each current workload will generally run on a virtual machine type with similar computing power, memory and disk. Oftentimes though, the current workload is overprovisioned, so each workload should be evaluated to ensure that it is migrated onto the right VM for that given workload.
  • Timelines: Businesses should lay out their target dates for each migration project.
  • Workloads in each grouping: Figure out what migration waves are grouped by i.e. non-production vs. production applications.
  • Cadence of code releases: Factor in any upcoming code releases as this may impact the decision of whether to migrate sooner or later.
  • Time for infrastructure deployment and testing: Allocate adequate time for testing infrastructures before fully moving over to the cloud.
  • Number of application dependencies: Migration order should be influenced by the number of application dependencies. The applications with the fewest dependencies are generally good candidates for migration first. In contrast, wait to migrate an application that depends on multiple databases.
  • Migration complexity and risk: Migration order should also take complexity into consideration. Tackling simpler aspects of the migration first will generally yield a more successful migration.

As mentioned above, the best practice for migration waves is to start with more predictable and simple workloads. For instance, companies should start with migrating file shares first, then databases and domain controlled, and save the apps for last. However, sometimes the complexity and dependencies don’t allow for a straightforward migration. In these cases, utilizing an experienced service provider who has experience with these complex environments will be prudent.

Phase 3: Execution

Once companies have developed a plan, they can bring them to fruition in the Execution phase. Here, businesses will need to be deliberate about the steps they take and the configurations they develop.

In the Execution phase, companies will put into place infrastructure components and ensure they are configured appropriately, like IAM, networking, firewall rules, and Service Accounts. Here is also where teams should test the applications on the infrastructure configurations to ensure that they have access to their databases, file shares, web servers, load balancers, Active Directory servers and more. Execution also includes using logging and monitoring to ensure applications continue to function with the necessary performance.

In order for the Execution phase to be successful, there needs to be agile application debugging and testing. Moreover, organizations should have both a short and long term plan for resolving blockers that may come up during the migration. The Execution phase is iterative and the goal should be to ensure that applications are fully tested on the new infrastructure.

Phase 4: Optimization

The last phase of a data center migration project is Optimization. After a business has migrated their workloads to the cloud, they should conduct periodic review and planning to optimize the workloads. Optimization includes the following activities:

  • Resizing machine types and disks
  • Leveraging a software like Terraform for more agile and predictable deployments
  • Improving automation to reduce operational overhead
  • Bolstering integration with logging, monitoring, and alerting tools
  • Adopting managed services to reduce operational overhead

Cloud services provide visibility into resource consumption and spend, and organizations can more easily identify the compute resources they are paying for. Additionally, businesses can identify virtual machines they need or don’t need. By migrating from a traditional data center environment to a cloud environment, teams will be able to more easily optimize their workloads due to the powerful tools that cloud platforms provide.

How do I take the first step in data center migration?

While undertaking a full data center migration is a significant project, it is worthwhile. The migration framework we’ve provided can help any business break down the process into manageable stages and move fully to the cloud.

When you’re ready to take the first step, we’re here to help to make the process even easier. Contact a 2nd Watch advisor today to get started with your data center migration to the cloud.

 

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3 Advantages to Embracing the DevOps Movement (Plus Bonus Pipeline Info!)

What is DevOps?

As a result of the increase in cloud adoption across all industries, understanding practices and tools that help organizations’ software run efficiently is essential to how their cloud environment and organization operate. However, many companies do not have the knowledge or expertise needed for success. In fact, Puppet’s 2021 State of DevOps Report found that while 2 in 3 respondents report using the public cloud, only 1 in 4 use the cloud to its full potential.

Enter the DevOps movement

What is DevOps

The concept of DevOps combines development and operations to encourage collaboration, embrace automation, and speed up the deployment process. Historically, development and operations teams worked independently, leading to inefficiencies and inconsistencies in objectives and department leadership. DevOps is the movement to eliminate these roadblocks and bring the two communities together to transform how their software operates.

According to a 2020 Atlassian survey, 99% of developers & IT decision-makers say DevOps has positively impacted their organization. Benefits include helping advance their career, and better and faster deliverables. Given the favorable outcome for these developers and IT decision-makers, adopting DevOps tools and practices is a no-brainer. But here are three more advantages to embracing the DevOps movement:

1. Speed

Practices like microservices and continuous delivery allow your business operations to move faster, as your operations and development teams can innovate for customers more quickly, adapt to changing markets, and efficiently drive business results. Additionally, continuous integration and continuous delivery (CI/CD) automate the software release process for fast and continuous software delivery. A quick release process will allow you to release new features, fix bugs, respond to your customers’ needs, and ultimately, provide your organization with a competitive advantage.

2. Security

While DevOps focuses on speed and agile software development, security is still of high priority in a DevOps environment. Tools such as automated compliance policies, fine-grained controls, and configuration management techniques will help you reap the speed and efficiencies provided by DevOps while maintaining control and compliance of your environment.

3. Improved Collaboration

DevOps is more than just technical practices and tools. A complete DevOps transformation involves adopting cultural values and organizational practices that increase collaboration and improve company culture. The DevOps cultural model emphasizes values like ownership and accountability, which work together to improve company culture. As development and operations teams work closely together, their collaboration reduces inefficiencies in their workflows. Additionally, collaboration entails succinctly communicating roles, plans, and goals. The State of DevOps Report also found that clarity of purpose, mission and operating context seem to be strongly associated with highly evolved organizations.

In short, teams who adopt DevOps practices can improve and streamline their deployment pipeline.

What is a DevOps Pipeline?

What is a DevOps pipeline

The term “DevOps Pipeline” is used to describe the set of automated processes and tools that allow developer and operations teams to implement, test, and deploy code to a production environment in a structured and organized manner.

A DevOps pipeline may look different or vary from company to company, but there are typically eight phases: plan, code, build, test, release, deploy, operate, and monitor. When developing a new application, a DevOps pipeline ensures that the code runs smoothly. Once written, various tests are run on the code to flush out potential bugs, mistakes, or any other possible errors. After building the code and running the tests for proper performance, the code is ready for deployment to external users.

A significant characteristic of a DevOps pipeline is it is continuous, meaning each function occurs on an ongoing basis. The most vital one, which was mentioned earlier, is CI/CD. CI, or continuous integration, is the practice of automatically and continuously building and testing any changes submitted to an application. CD, or continuous delivery, extends CI by using automation to release software frequently and predictably with the click of a button. CD allows developers to perform a more comprehensive assessment of updates to confirm there are no issues.

Other “continuous” DevOps practices include:

  • Continuous deployment: This practice goes beyond continuous delivery (CD). It is an entirely automated process that requires no human intervention, eliminating the need for a “release day.”
  • Continuous feedback: Applying input from customers and stakeholders, and systematic testing and monitoring code in the pipeline, allows developers to implement changes faster, leading to greater customer satisfaction.
  • Continuous testing: A fundamental enabler of continuous feedback. Performing automated tests on the code throughout the pipeline leads to faster releases and a higher quality product.
  • Continuous monitoring: Another component of continuous feedback. Use this practice to continuously assess the health and performance of your applications and identify any issues.
  • Continuous operations: Use this practice to minimize or eliminate downtime for your end users through efficiently managing hardware and software changes.

 Embrace the DevOps Culture

We understand that change is not always easy. However, through our Application Modernization & DevOps Transformation process, 2nd Watch can help you embrace and achieve a DevOps culture.

From a comprehensive assessment that measures your current software development and operational maturity to developing a strategy for where and how to apply different DevOps approaches to ongoing management and support, we will be with you every step of the way. Following is what a typical DevOps transformation engagement with us looks like:

Phase 0: Basic DevOps Review

  • DevOps and assessment overview delivered by our Solutions Architects

Phase 1: Assessment & Strategy

  • Initial 2-4 week engagement to measure your current software development and operational maturity
  • Develop a strategy for where and how to apply DevOps approaches

Phase 2: Implementation

Phase 3: Onboarding to Managed Services

  • 1-2 week onboarding to 2nd Watch Managed DevOps service and integration of your operations team and tools with ours

Phase 4: Managed DevOps

  • Ongoing managed service, including monitoring, security, backups, and patching
  • Ongoing guidance and coaching to help you continuously improve and increase the use of tooling within your DevOps teams

Getting Started with DevOps

While companies may understand the business benefits derived from DevOps, 2nd Watch has the knowledge and expertise to help accelerate their digital transformation journey. 2nd Watch is a Docker Authorized Consulting Partner and has earned the AWS DevOps Competency for technical proficiency, leadership, and proven success in helping customers adopt the latest DevOps principles and technologies. Contact us today to get started.

-Tessa Foley, Marketing

 

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