The Dark Side of Open Source Data Tools

Cost-effective, supported by a community of developers, and highly customizable—who wouldn’t want data tools with these characteristics? These are the main benefits of open source data tools that make them attractive to startups and enterprises. But there are some big challenges to implementing an open source data stack and most companies aren’t prepared to overcome them.

Data engineers and data scientists commonly use tools like Apache Hadoop, Apache Spark, Apache Kafka, and MongoDB. They’re a powerful set of open source resources for organizations looking to manage and analyze large amounts of data. Your DataOps team can use them for data integration, data cleansing, data storage, and data analysis.

You just need the technical expertise to set up and maintain the tools in a way that fits with the rest of your company’s technology stack. It’s a challenge, but is it worth taking on? Let’s take a look at the light and dark sides of open source data tools so you can decide for yourself.

What is an open source data tool?

An open source data tool is a software application or framework that's developed and made available to the public under an open source license. This allows anyone to use, modify, and distribute the code for any purpose—making these data tools a flexible and cost-effective resource for managing and analyzing big data.

While most use cases for open source data tools require a high level of technical expertise, they offer the advantages of flexibility and cost-effectiveness. They also often have a large community of developers contributing to the project.

But you need to ask yourself if the benefits of open source outweigh the challenges for your organization.

What are the benefits of open source data tools?

If you manage large amounts of data, open source data tools could be more cost-effective than proprietary software. Data scientists and data engineers can use them for free and modify them to fit your specific needs. That saves you money on software licensing fees and run costs.

High levels of customization also make open source tools desirable. They can be configured to run everything from data analytics to company dashboards and artificial intelligence apps. When you need a flexible and interactive option for your modern data stack, open source data tools are a great fit. Your DataOps and DevOps teams both have access to the source code and can make changes as needed to add new features or integrate with other software systems. This could be the secret to improving overall data quality at your company.

Whenever your data team needs support, they can access the community of developers and users, potentially steering clear of paying for expensive consultants. This community acts like an ecosystem that can make open source tools robust and secure.

Further, open source data tools are designed to work together—this makes it easier for your business to create an end-to-end data management and data analytics platform. This helps reduce the complexity and cost of integrating different software systems.

Before we get into specific types of open source data tools (and eventually their dark side), here’s a summary of the business benefits.

• Cost-effective: Open source data tools are often free to use and save your business money on software licensing fees and maintenance costs.
• Customizable: Open source data tools can be modified to fit your organization's specific requirements—providing a flexible and tailored data solution.
• Robust and secure: The large community of developers and users of open source data tools provides support, bug fixes, and updates, which helps your business stay up-to-date.
• Integration: Open source data tools are often designed to work together—making it easier for businesses to create an end-to-end data storage, analysis, and visualization platform.
• Open standards: Open source data tools often use open standards, which make it easier to integrate with other software systems and reduce vendor lock-in.
• Innovation: Open source data tools often have a faster development cycle, which means that new features and improvements are sometimes added more quickly than proprietary solutions.

Some of the best open source data tools

Some of the most popular open source data tools include Apache Hadoop, Apache Spark, Apache Kafka, Apache Cassandra, Apache Storm, and MongoDB. These tools are highly scalable and can handle high volumes and many types of data processing, making them an ideal solution for businesses that deal with big data.

Open source data tools provide a powerful and flexible solution for managing, analyzing, and processing large amounts of data—whether it’s coming from APIs, business applications, or Microsoft Excel docs.

Here are some of the most popular open source data tools that are used for data science and analytics workflows:

• Hadoop: A software framework designed to process and analyze large datasets using the MapReduce programming model. Hadoop is built on Java and can run on a cluster of computers, making it highly scalable. It provides support for SQL, making it easier to analyze and process data. With Hadoop, your business can process vast amounts of data, use machine learning algorithms, as well as analyze and visualize data in real time.
• Spark: A distributed computing system designed to process large datasets in memory. Spark is built on Java and Python and can run on a combination of on-site hardware and cloud resources. It provides support for real-time data processing, data analysis, and data processing. With Spark, your data team can process vast amounts of data in real time, use machine learning algorithms, and analyze and visualize data.
• Kafka: A distributed streaming platform designed to process and stream data in real time. Kafka is built on Java and can run on self-hosted servers or cloud containers. With Kafka, your business can manage complex datasets, use data analysis and data processing, and visualize data.
• Cassandra: A popular open-source, distributed NoSQL database management system designed for handling large volumes of structured and unstructured data. It was initially developed by Facebook and later became an Apache Software Foundation project. Cassandra was created to handle the massive amounts of data generated by social networks and other online applications, where scalability and high availability are essential.
• MongoDB: A NoSQL document-oriented database designed to store and manage big data. MongoDB is built on Java and can run on a cluster of computers. It provides support for automation and business intelligence and is capable of storing and processing large amounts of data. With MongoDB, your business can handle machine learning algorithms, data analysis, data processing, and data visualization.

What are common use cases for open source data tools?

Data teams use open source tools for everything from cloud data warehousing to data transformation and big data analytics tools. If you can imagine a type of DataOps need, open source software can do it—as long as you have the technical expertise to build, configure, and maintain the tools.

Here are some common data operations use cases for open source data tools:

1. Data ingestion: Open source data tools such as Apache Kafka and Flume allow data to be ingested in real time from various sources, including databases, message queues, and IoT devices.
2. Data storage and cloud warehouses: Open source data storage tools like Apache Hadoop, Cassandra, and MongoDB provide a cost-effective solution for storing large volumes of structured and unstructured data in relational databases and cloud containers (AWS, Azure, Google, etc.).
3. Data transformation: Open source data processing tools like Apache Spark and Flink allow organizations to process large volumes of data in real time, enabling near-instant analytics and insights.
4. Data analytics, dashboards, and visualization: Open source data tools and programming languages like R, Python, and Jupyter enable organizations to perform complex data analysis and visualization tasks.
5. Machine learning: Open source machine learning frameworks like TensorFlow, Keras, and PyTorch allow your organization to build and train machine learning models on large datasets to automate tasks and make predictions.
6. Data governance and security: Open source data governance and security tools like Apache Ranger and Knox provide a cost-effective way to manage data access and control data security—ensuring data privacy and compliance.
7. DevOps: Open source DataOps tools like Jenkins and GitLab allow organizations to manage their data operations pipelines, including version control, continuous integration, and deployment.

The flexibility and scalability of open source data tools make them a powerful and cost-effective solution for organizations of all sizes looking to manage, process, and analyze their data. But, there are some major challenges to getting those benefits out of your open source data tools.

What’s the dark side of open source data tools?

Open source data tools require a higher level of technical expertise than out-of-the-box solutions. Organizations need to have skilled and very expensive technical personnel who can set up and maintain the tools or work with third-party vendors who can provide support and assistance.

If you’re expecting polished user interfaces, get ready for command prompts and UI that aren’t so sleek. Think Linux vs. Windows. This can make it more difficult for new users to get up to speed and results in a steeper learning curve for everyone. Non-technical users will have trouble for a long time.

While open source tools are highly customizable, they can also lack some of the features and functionality that are available in proprietary solutions.

Here are the key challenges of open source data tools:

• Technical expertise: Using open source data tools often requires a higher level of technical expertise from your data scientists, data engineers, and data analysts. That means you need to hire and maintain a highly technical DataOps team made up of senior team members.
• Usability: Open source data tools can lack the polished user interfaces and documentation common in proprietary software, making it more difficult for new users to get up to speed.
• Features and functionality: Open source data tools may lack some of the advanced features and niche applications that are available in proprietary solutions.
• Support: While there is a large community of developers and users who contribute to open source projects, businesses will not have the same level of direct support as they would with a proprietary solution. You might have to wait a few days for an answer from a community member that you could get immediately from a vendor.
• Security: While open source data tools can be more secure than proprietary solutions, they can also be more vulnerable to security risks if not properly configured and maintained.

Open source data tools and privacy/security challenges

Like any software, open source data tools are not immune to privacy and security risks. Here are some of the key privacy and security challenges associated with open source data tools:

1. Vulnerabilities and exploits: Like all software, open source data tools may contain vulnerabilities that can be exploited by attackers. Since the source code is public, attackers can more easily identify and exploit open source software.
2. Data breaches: Open source data tools may be vulnerable to data breaches, where sensitive data is accessed or stolen by unauthorized users. These breaches can be caused by exploitable code, poor data access controls, or other factors.
3. Compliance issues:  Your company may face compliance challenges when using open source data tools. Depending on the nature of the data being processed or analyzed, organizations may need to comply with regulations such as HIPAA or GDPR, which can be challenging.
4. Lack of support and maintenance: While open source data tools are generally well-maintained, some may lack proper support and maintenance. This can lead to issues such as poor performance, outdated versions, and a lack of security updates.

Ways to mitigate risks with open source data tools

To combat these challenges, your company can take steps like monitoring the latest security advisories, performing regular vulnerability scans and penetration testing, ensuring data access controls are in place, and using tools that are compliant with relevant regulations. All these steps are added costs for organizations in the form of team members’ time and potentially new tools.

Here are the top five things you can do to avoid the downsides of open source data tools:

1. Stay up-to-date with security patches. This means monitoring security advisories and ensuring that updates are installed promptly.
2. Perform regular vulnerability scanning and penetration testing. This helps to identify vulnerabilities that may exist within your open source data tools and helps to ensure that they are properly secured.
3. Implement data access controls. By implementing role-based access controls, your data team can prevent data breaches and ensure that only authorized users have access to sensitive data.
4. Ensure compliance with regulations. Your company must comply with relevant regulations such as HIPAA, GDPR, and CCPA. To ensure compliance, it’s important to use open source data tools that can meet the requirements of all data regulations.
5. Evaluate the security of open source data tools before adoption. This helps to make sure that the tool meets your organization's security requirements and that any potential vulnerabilities are identified and addressed.

What next?

Most businesses run on a combination of open source data tools and proprietary data platforms—it’s rare that an organization is entirely open source. Data orchestration platforms like Shipyard can connect your existing open source data tools and fill in feature gaps.

It’s easy to find out how and where Shipyard works best for you. Sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start building data workflows in 10 minutes or less, automate them, and see if Shipyard can make open source easier for your business.