Navigating the Data Platform Dilemma: To Build or Buy?

As a business, you have a crucial decision to make when it comes to sourcing tools: build or buy? Both options have their pros and cons, and it's important to weigh them carefully to choose the right path for your organization.

Managing data involves the ingestion of data from different data sources, storing it in a data warehouse or a data lake, transforming it, and using it for visualization or analytics.

Many companies begin by considering building or buying a data ingestion or a data pipeline solution. The decision gets even more complex when you have to choose between building the whole end-to-end data stack yourself with help from open-source tools or going with a third-party vendor who has already put together an end-to-end data stack for you to start in 30 mins along with the required commercial, production-ready features, and support.

No matter what stage your business is at, it's important to consider the build vs. buy a data stack argument and make an informed decision. Let us explore all this in a bit more detail and especially on how Datazip makes it easy to resolve this dilemma for you.

What is a Data Stack or a Data platform?

A data stack refers to the various technologies, tools, and systems that are used to collect, store, process, and analyze data within an organization. It typically consists of several layers, including:

1. Data Collection: This includes technologies and tools used to gather data from various sources, such as sensors, websites, social media platforms, and more.
2. Data Storage: This refers to the technologies and systems used to store data, such as databases, data warehouses, and cloud storage solutions.
3. Data Processing: This involves the tools and technologies used to transform raw data into a form that can be analyzed and used for various purposes, such as ETL (extract, transform, load) tools and data lakes.
4. Data Analysis: This includes the tools and technologies used to analyze and gain insights from data, such as data visualization tools, statistical analysis software, and machine learning platforms.

The components of a data stack can vary depending on the specific needs and goals of an organization and can be customized to support different types of data and use cases.

What is a Modern Data Stack and Why is it Getting Popular?

As companies are looking to leverage data and respond to customer demands faster, there is a growing need to unlock the power of data.

Modern Data Stack is a comprehensive suite of tools that streamline the data integration process, making it easier than ever to extract, load, and transform data. MDS starts with a fully managed ELT data pipeline, which quickly and efficiently collects data from various sources. The data is then sent to a cloud-based columnar warehouse or data lake, where it can be easily stored and accessed. From there, a powerful data transformation tool helps to clean, filter, and transform the data as needed. Finally, a business intelligence or data visualization platform allows you to analyze and visualize the data, uncovering valuable insights and helping to inform data-driven decision-making.

There are several reasons why modern data stacks are becoming increasingly popular:

1. The increasing volume and variety of data: Modern organizations are generating and collecting more data than ever before, from a wide range of sources, including sensors, websites, social media platforms, and more. This requires a data management system that is able to handle large volumes of data and support different types of data.
2. The need for real-time data processing and analysis: Many modern organizations rely on data-driven decision-making, which requires real-time access to data and the ability to analyze and gain insights from it quickly. A modern data stack is designed to support this need.
3. The rise of data-driven business models: Many modern businesses are adopting data-driven business models, which rely on the ability to collect, analyze, and use data to drive business decisions and operations. A modern data stack is essential for supporting these types of business models.
4. The increasing importance of data privacy and security: With the increasing importance of data privacy and security, modern data stacks often include technologies and systems that are designed to protect data and ensure compliance with relevant regulations.
5. The increasing adoption of cloud computing: Modern data stacks often include cloud-based technologies and tools that offer scalability, flexibility, and cost-effectiveness. The increasing adoption of cloud computing has made it easier for organizations to implement and manage modern data stacks.

​What is Segmentation and how it helps in marketing​

Build vs. Buy Data Platforms: What are the Factors to Consider?

There are several important factors to consider when evaluating the build vs. buy decision.

1. Cost

Building a data platform can be a costly and time-consuming process, especially if you need to purchase or develop new technologies and tools. On the other hand, purchasing a data platform from a third-party vendor can be more cost-effective, but it's important to carefully evaluate the total cost of ownership, including any ongoing subscription or maintenance fees.

There are several major cost categories to consider when building or buying a data platform, including:

1. Development and Implementation Costs: This may include the cost of designing, developing, and testing the data platform, as well as any necessary training or onboarding.
2. Hardware and Infrastructure Costs: This may include the cost of purchasing or leasing servers, storage, and other hardware and infrastructure to support the data platform.
3. Software and Tool Costs: This may include the cost of purchasing or developing any necessary software and tools, such as databases, data visualization platforms, and more.
4. Maintenance and Support Costs: This may include the cost of ongoing maintenance and support for the data platform, including updates, bug fixes, and technical support.
5. Salaries and Benefits: This may include the cost of salaries and benefits for any engineers or other employees who are responsible for building, maintaining, or using the data platform.

The estimated total annual costs, for example, can be upwards of $150k for series A and B companies based in India and upwards of $500k for companies based in the US.

2. Level of Customization

Building a data platform in-house can allow for greater customization and flexibility, but it may require more resources and expertise. Purchasing a data platform from a third-party vendor may offer fewer customization options, but it may be easier and faster to implement. Another advantage of working with a paid data platform is the ability to take advantage of the technology and feature updates such a platform offers. This will allow the company to be on-par with industry standards. Employees also benefit from this as their learning is transferable.

3. Integration

It's important to consider how well the data platform will integrate with your existing systems and technologies. Building a data platform in-house may offer greater flexibility in terms of integration, but it may require more resources and expertise. Purchasing a data platform from a third-party vendor may offer pre-built integrations, but it may not fit as seamlessly with your existing systems.

4. Scalability

Consider whether the data platform will be able to scale with your organization's needs. Building a data platform in-house may offer more flexibility in terms of scalability, but it may require more resources and expertise. Purchasing a data platform from a third-party vendor will offer pre-built scalability.

5. Support

Consider the level of support and documentation that is available for the data platform. Building a data platform in-house will require more resources and expertise to provide support, while purchasing a data platform from a third-party vendor may come with comprehensive support and documentation.

6. Time

Consider the amount of time and resources that will be required to build or purchase a data platform. Building a data platform in-house may be a longer and more resource-intensive process, while purchasing a data platform from a third-party vendor may be faster and easier.

7. Expertise

Building a data platform in-house may require specialized expertise and resources in order to design, develop, and maintain the platform. The right data engineering talent is very difficult to maintain and when such an engineer leaves, all the work comes to a standstill.

8. Opportunity Cost of Data

Building a data platform in-house can be a time-consuming and resource-intensive process, which may divert resources and attention away from other important projects and priorities. This can have an opportunity cost in terms of the potential benefits that could have been realized from those other projects. Purchasing a data platform from a third-party can reduce the opportunity cost of waiting for a data platform to be built in-house.

It's important to carefully evaluate these and any other relevant factors when deciding whether to build or buy a data platform.

How Good are Open Source Data Tools?

​Open-source data tools can be reliable, but it's important to carefully evaluate their quality and suitability for your needs before using them.

One advantage of open-source data tools is that they are typically developed and maintained by a community of volunteers, which can lead to a large user base and a high level of collaboration and innovation. This can result in a high-quality tool that is continuously improved and updated.

However, it's important to note that open-source data tools may not have the same level of support and documentation as proprietary tools, and they may not be as well-tested or stable as commercial software. It's also important to consider the licensing terms of open-source tools and ensure that you are using them in accordance with the terms of the license.

There are many open-source tools that can be used as part of a modern data stack, like Apache Spark, Airflow, DBT, ClickHouse, Metabase, etc. One major challenge with this way of going about it is that it will take considerable engineering efforts, time, and money to be about to get them working in a scalable manner and to continuously keep them up and reliable. And over and above this, you will have to build and maintain data observability solutions to effectively manage your pipelines from ingestion to the dashboard.

This is why a new category of all-in-one data platforms is emerging.

Build vs. Buy Flow Chart

Frameworks or flow charts make it easy for decision-makers to evaluate or navigate through the process of making a hard decision. Here is a flow chart that you can use to decide on if you want to build or buy a data platform, especially one that helps you with end-to-end data management capabilities.

One critical decision for you to seriously consider is how much advantageous would it be if you can build your analytics competitive advantage over a fundamental third party data platform that helps you get started quickly and is maintenance free.

Data engineers excel at building and maintaining effective data models that end users like analysts and business users can use to query and get their questions answered. If data engineers continue to spend about 80% of their time trying to build and maintain ingestion pipelines and fixing and texting for data quality, a lot of their valuable expertise is wasted.

Why do All-in-One Data Platforms Make a lot of Sense?

It's important to note that there is no one-size-fits-all solution when it comes to data management, and the decision to use an all-in-one data stack or a more modular approach will depend on the specific needs and goals of an organization. Here are a few potential benefits of using an all-in-one data stack:

1. Simplicity: An all-in-one data stack can be simpler to set up and manage compared to a more modular approach, as it includes all of the necessary components in a single package.
2. Integration: All-in-one data stacks are designed to work seamlessly together, which can make it easier to integrate different components and avoid potential compatibility issues.
3. Cost-effectiveness: An all-in-one data stack can be more cost-effective compared to a modular approach, as it includes all of the necessary components in a single package.
4. Support: All-in-one data stacks often come with comprehensive support and documentation, which can make it easier to troubleshoot issues and get help when needed.

​Learn how Datazip is transforming customer lives​

Why Datazip?

​Datazip is an all-in-one data platform that is hosted in your private cloud environment.

What can Datazip be used for?

• Data Ingestion with 150+ sources
• Data Warehousing Solution
• Data analytics (BI tool) Solution

What value can you expect:

1. Time to value from data reduced from a few weeks/months to hours
2. Simplified data reliability, and freedom from complexity of scaling different tools.
3. A fully end-to-end no/low code tool, to make data accessible to all the non-engineering folks
4. Quick support for all your queries

Below is a breakdown of what it would cost you to build and manage a data platform compared to using Datazip.

Conclusion

It is important to carefully evaluate the pros and cons of the build vs. buy consideration for your company. Data tools are increasingly becoming more capable and hence have the features to solve most of the use cases.

An all-in-one data platform solution like Datazip offers you not only ingestion but managed data warehouses, managed transformation flows, data quality checks, etc out of the box. This allows you to worry less about fundamental data management workflows and focus more on new data models, better dashboards and reports, and actionable data.

Hope this post has helped you with enough pointers to think and discuss about and be able to quickly arrive at a decision. If you have any questions or wish to talk to us for a more detailed discussion, feel free to reach out to us at [email protected].