Companies rely on behavioral data to make critical business decisions on a day-to-day basis. Data is a valuable resource for organizations, and as data volume grows, managing the information becomes a challenge.
When choosing a data stack, businesses can either buy proprietary tools or build them using open source alternatives. While there are pros and cons to both solutions, companies are exploring the benefits of using open source alternatives. With data protection laws such as EU’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) in full force, businesses have grown increasingly aware of data privacy.
Open-sourcing your data stack enables you to gain full ownership of your data without having to rely on other vendors to keep your customers’ information safe. Take back control of your data infrastructure with open source tools that can be integrated into each layer of your data stack.
Here, we’ll discuss the advantages of switching to open source alternatives and how companies can find the right open source tool for each part of their data stack.
Three advantages of switching to open source alternatives
There are a few common advantages that compel teams to transition to open source alternatives for their data tech stack.
1. You control your Data
Open source alternatives allow you to own your data and how it is processed. They make data compliance with GDPR and CCPA easier, allowing you to control what personal information you collect, where it is stored, and who can access it.
Data ownership lets you control what happens to your data when you move it into your data warehouse, so you can trust that the information is accurate. The biggest advantage of owning your data is controlling how it is collected, processed, and modeled. When you control all three phases, you can begin to build assurance in the quality of your data. With open source, your data stack runs inside your cloud or local environment, enabling you to control who can access and use your data.
2. It’s cost-effective
Open source tools are free and cost-effective solutions upfront. While the source code is free, the resources required to integrate and maintain them are not. To integrate open source tools into your stack, you’ll likely need software engineers, who will have to invest a good amount of time setting up the tools to run properly. While this is a lot of work, engineers can rely on the rest of the open source community for help to get the tools up and running. But the long-term benefits of having control over your data stack may outweigh the initial burden of implementing the solution.
3. It is flexible for your business
Open source solutions allow you to build out a data stack that fits your business needs, not the industry standard. Take advantage of building out what you want to see from the data and its format across your business. This is especially true when you are building out particular use cases. With the right tools in place, you can use the same data stack to drive marketing attribution, product analytics, and customer journey analytics. Open source allows you to unlock the benefits of your data and use it to your advantage.
With open source tools, you are not locked into any contracts, so if the tool doesn’t work out for your business, you don’t have to worry about losing your data.
Consider open source alternatives for every part of your modern data stack
Before getting started with an open source tool, it is important to decide your use case for that tool and how it fits into your data stack. Once this is determined, you can move forward with selecting the right tools for your pipeline.
Here is an example of what a modern data stack might look like:
Pic Credit: dataform.co
Collecting data from multiple sources is crucial for data-informed organizations. A recent survey found that 55% of businesses have begun relying on data to boost their efficiency. Here are a few questions you should consider when choosing a data collection tool:
- What event data do you want to collect from your users?
- Are you collecting data from internal resources such as CRM, finance or support tickets?
- Are you collecting data from your own application(s)?
- Are you collecting data from third-party applications?
Answering these questions with your team will help you select the right data collection tool. Here are a few open source data collection tools we recommend for your data stack:
- Snowplow Trackers allow you to collect events from the web, mobile, desktop, server, and IoT.
- Matomo allows you to collect relevant data from your users that you fully control.
Once you collect your customers’ data, you need a data warehouse to store this data to perform data transformations and analyses. According to Amazon, “a data warehouse is a central repository of information that can be analyzed to make more informed decisions.”
While the three most popular data warehouses aren’t open source (Amazon Redshift, Snowflake, Google BigQuery), you should still have an open source analytics tool to load and extract data from these warehouses.
Here are a few questions to consider when choosing an open source tool for this layer of your data stack:
- What happens when data volume increases? Will the warehouse be able to scale as your volumes increase?
- What service provider am I already using for my site?
Here are a few open source databases and their use cases that we recommend for your data stack:
- Apache Druid: For real-time database querying
- TimescaleDB: For time-series querying
- PostgreSQL: An object-relational database system that has been around for over 30 years
- MySQL: A relational database management system backed by Oracle
- CockroachDB: A cloud-native SQL database
- MongoDB: A NoSQL database that uses JSON-like documents to store data
Data transformation is essential for generating insights from your data. This is where business logic is applied to data and later transformed into something that can easily be analyzed. Data transformation is essential for empowering internal teams with the data they need to make data-informed decisions.
Here are a few questions to consider when choosing open source tools for this layer of your data stack:
- Are you constantly dealing with incomplete and wrong data?
- Are employees complaining about data quality?
We recommend using dbt for your data transformations. dbt is an open source data transformation tool that allows data analysts to perform data transformations in separate, collaborative environments without impacting users. The open source analytics engineering tool enables users to use software engineering principles, such as version control and testing, to allow for easy collaboration.
Image Source: dbt
For organizations to be truly data-informed, they need to be able to generate insights from the data. Here are a few questions to ask yourself before moving forward with a data analysis open source tool:
- Do only a limited number of users have access to data that the entire company needs?
- Are end users struggling to get access to the data they need?
- Do end users not trust the data?
Putting it all together
The biggest reason behind open sourcing your data stack is gaining full control of your data and data infrastructure. From data collection to processing, storing, and analyzing your data, open source tools give you the ability to do all of that while owning your data.
Image Credit: Snowplow Docs
With Snowplow BDP, you can own your data and manage it within your cloud environment. Snowplow BDP allows you to have open source flexibility without the headaches of implementing and managing the infrastructure.Snowplow’s technology allows you to leverage Snowplow data for an endless number of use cases, from marketing attribution to product analytics, personalization, and many more.