How To Create A Sandbox Environment?

What is a sandbox environment? 

A sandbox environment is a controlled and isolated environment designed for testing, experimentation, and development purposes. 

It acts as a replica or twin of the production environment, offering a safe and separate environment for data teams in marketing and various other industries. 

Think of a sandbox as a playground for grown-ups. Here you can test, play, and experiment without messing up the real stuff. This setup gives you a free pass to try and break things without affecting the end users.

For example, in this testing environment, you can run quality assurance checks on your marketing dashboards before sharing them with your clients. And, as a development environment, it enables you to test new features (e.g. optimize customer churn prediction with a new machine learning algorithm) - without breaking the production features.  

How to use a sandbox environment?

Sandboxes are nifty tools that data teams in marketing (and other departments) can use to work their magic over multiple use cases.

#1 Test new data apps before production

Imagine you're developing a new customer loyalty app. Before rolling it out to your clients, you want to ensure it works flawlessly and doesn't cause any unexpected issues. 

In a sandbox environment, you can extensively test the platform, send loyalty emails, subscribe and unsubscribe customers from loyalty campaigns, and generally verify that all features function as expected. This way, you can avoid any potential mishaps and deliver a polished marketing solution to your clients.

#2 Run experiments on data

As a data scientist, you've come up with a brilliant idea for a machine-learning model that can predict customer behavior accurately. Before implementing it in your live marketing campaigns, you can test the model in a sandbox environment. 

You experiment with different algorithms, fine-tune the model's parameters, and assess its performance on sample datasets. This way, you can ensure your model is optimized and delivers accurate predictions when deployed in the production environment.

#3 Share data with clients

You're a part of a consultancy agency, and your client is a retail company interested in understanding their customer demographics better. 

In a sandbox environment, you can share a secure, anonymized dataset containing customer information. This allows your client to explore this data, conduct analysis, and provide feedback without accessing sensitive customer details. 

This collaboration ensures data privacy and enables your client to make data-driven decisions for their marketing strategies. And since this data is in a sandbox and isolated from your complete datasets, you don’t have to worry the clients will accidentally have permission to view sensitive data. 

The best part? You can automate this workflow and share data with multiple clients using the same sandboxing technology in real time. All while guaranteeing data governance, security, and privacy.

#4 Security research

Your team has developed a new landing page to handle customer sign-ups and logins. Before making it publicly available, you want to assess its security against potential cyber threats. 

Within a sandbox environment, you can simulate common cybersecurity attacks, such as SQL injection or cross-site scripting, to identify any vulnerabilities. This way, you can patch up any weaknesses and ensure your application remains secure when deployed in the real world.

Sandboxing is not used just in marketing. Cybersecurity researchers consistently use sandboxes as an additional layer of security when testing malicious code, new antivirus software, firewalls, malware-detection algorithms, or exploring other security threats.

#5 Quality assurance

Your marketing team is launching a revamped website with a new user interface and interactive features. 

To ensure a smooth user experience, you conduct quality assurance testing to see how it works in a sandbox environment. 

Your team checks the functionality of buttons, navigational elements, and data displays. By detecting and fixing any issues in the sandbox, you guarantee a seamless user experience when the website goes live.

Testing in sandboxes before deploying is a common best practice in software development. 

#6 Training and learning

You've introduced a new marketing automation tool to streamline your team's workflow. To get everyone up to speed, you provide access to a sandbox environment containing sample data. 

In this risk-free space, team members can practice creating automated marketing campaigns, setting up workflows, and analyzing performance metrics. This hands-on learning experience helps them become proficient with the new tool before using it in real marketing campaigns.

Impatient to try a sandbox use case yourself? Wait no longer. We’ll show you two approaches: the classic, more challenging way, and the new, simplified approach. 

How to create a sandbox environment (the traditional hard way)?

Traditionally, there have been three primary methods for creating sandbox environments:

1. Virtual Machines (VMs): Tools such as VirtualBox, Microsoft Hyper-V, Windows Sandbox, and VMware are used to establish VMs.  These VMs use technology to create a virtual new host device that acts as hardware separate from your own host machine.
2. Virtual Environments: Software technologies like the 'virtualenv' library in Python are popular choices, especially among data engineers and scientists.
3. Containerized Image: You can use platforms like Docker or Kubernetes to set up containerized environments.  

But traditional technologies posed several challenges for data engineers:

1. Choosing the right sandboxing tech: The ideal sandboxing technology often depends on various factors, including the user's operating system (be it Linux, Windows, MacOS, Android, etc.), their technological expertise (in areas like kernels, Hypervisors, inheritance and dependencies), and specific data governance issues (such as privacy and security concerns).
2. Keeping the producing and sandbox environment in sync.
3. Managing data, which includes backups, recoveries, and other data loss prevention mechanisms.
4. Provisioning infrastructure - from persisting storage to network configurations - sandboxing requires a lot of overhead.

Luckily, there’s a more straightforward and easier way to create a sandbox.

How to create a sandbox environment (the easy way)?

Keboola is the data stack as a service that automates all your data operations — including creating a new sandbox environment. 

Creating a new sandbox environment in Keboola is as simple as a three-click process:

Prerequisite: Ensure you have data in Keboola Storage. No data in Storage yet? Follow this easy tutorial to see how simple it is to load data into Keboola. 

1. Go to Transformations. 
2. Click Sandbox.
3. Choose your Sandbox backend.

4. Load data into the new sandbox environment and start experimenting.

Curious about the fine details and possible customizations? Check Keboola’s docs for details. 

Choosing Keboola for your sandbox environment has many benefits:

1. Out-of-the-box DataOps: Keboola takes care of security, syncing, sharing, networking, and all the other DataOps features. No need to waste precious engineering time on DataOps.
2. Multiple backend choices: With Keboola, you aren’t limited to the backend of your data storage. Feel free to select Snowflake, Redshift, RStudio for R, Jupyter for Python, or Julia (currently in Beta) for your backend of choice. 
3. Auto-cleaning: Forget about stale and outdated data. Keboola automatically removes sandboxes that aren’t active. 
4. Templetizable and shareable: Reuse and share your code across different sandbox environments and datasets using simple templates. 

And those are just on top of the benefits you unlock by using sandboxes.

6 benefits of a sandbox environment 

A sandbox environment brings many benefits to data teams:

• Collaborative exploration: Data teams can collaborate with clients or colleagues by sharing sandbox environments, facilitating joint analysis, reviewing data products together, and facilitating decision-making without exposing sensitive data.
• Learn new skills and technology: Teams can learn how to use new apps and technologies on copies of the data. Until they’re ready to work with the real data sets.
• Security: Sandboxes allow you to access datasets without worrying about exposing your production data to security threats or privacy leaks. 
• Templates: With sandbox environments, you can easily use other people’s source code and data to contribute to a project, without affecting the production code. 
• Lower errors: Increase quality assurance and lower error rates by testing solutions in sandboxes before pushing them to the production environment. 
• Time savings: Your data experts will save time on two fronts. First, they’ll spend less time debugging the production code. Second, spinning up a new sandbox environment for experimentation is quick and super easy with tools like Keboola.

Test your data products in a safe way with Keboola 

Keboola helps you unlock all the benefits of sandbox environments, including safety, quality assurance, experimentation, template ability, and time savings.

Moreover, unlike other sandboxing technologies, in Keboola you can set up a new sandbox environment in a couple of clicks — without worrying about the DataOps.

The best part? Keboola offers an always-free tier for every user, so you can play with data without breaking the piggy bank. 

Try Keboola for free today.