Building a Data Stack on a Budget: An Affordable Guide to Data Management Sujeet Pillai January 17, 2023

Database management

A data stack is a combination of various tools and technologies that work together to manage, store, and analyze data. It typically consists of a data storage engine, an ingestion tool, an analytics engine, and BI visualization tools. In recent years, data stacks have become quite central to an organization’s operations and growth.

Data management is an essential part of any organization, and the way data is managed has evolved over the years. Data lakes and data warehouses were once only affordable by larger organizations. However, with the growth of the open-source data stack ecosystem, this has changed. The open-source data stack ecosystem has grown significantly in recent years, providing powerful alternatives for every layer of the stack. This has pushed the envelope for data stacks and reduced entry barriers for organizations to adopt a data stack.

One of the main reasons why data stacks have become more accessible is the availability of open-source alternatives. For every layer of the data stack, open-source alternatives are available that pack a serious punch in capability. These alternatives are often just as good, if not better, than their commercial counterparts. They also tend to be more flexible and customizable, which is essential for organizations that need to tailor their data stack to their specific needs.

Another reason why data stacks have become more accessible is the availability of cheap cloud resources. Cloud providers such as Amazon Web Services, Google Cloud, and Microsoft Azure provide low-cost options for organizations to set up and run their data stacks. This has made it possible for even smaller organizations to afford a data stack.

Organizations need to seriously consider this framework over a patchwork of point-to-point integrations. A patchwork of point-to-point integrations is often a result of an ad-hoc approach to data management. This approach is not only difficult to manage but also limits the organization’s ability to gain insights from its data. On the other hand, a data stack framework provides a more structured approach to data management, making it easier to manage, and providing the organization with the ability to gain insights from their data.

An Affordable Data Stack

One affordable data stack that organizations can consider is the following:

Storage Engine: Clickhouse

Clickhouse is a column-oriented database management system that can handle large data loads and has great query performance. It runs on commodity hardware and can be self-hosted using Docker. Clickhouse is designed to process large amounts of data, and its columnar storage model also makes query performance great.

Ingestion Engine: Airbyte

Airbyte is an open-source data integration platform that automates the ingestion of data sources and can be monitored and managed from a UI. It can also be self-hosted using Docker and has the ability to use Clickhouse as a sink. Airbyte automates the ingestion of data sources, making it easy to bring data into the data stack.

Analytics Engine: DBT

DBT is a powerful analytics engine that helps organize data models and processing. It’s built on SQL with jinja templating superpowers, making it accessible to a lot more people. DBT is a hero in the data lakes space, helping organizations organize their data models and processing. When building out an analytics process in DBT it’s quite helpful to use a conceptual framework to organize your models. I found this blog excellent to provide a great starting point.

Visualization Engine: Metabase

Metabase is a powerful visualization tool that makes it easy for organizations to gain insights from their data. It has lots of visualizations that cover most bases. The SQL query builder or ‘question wizard’ in Metabase is quite powerful for non-SQL experts to get answers from their data. It also has a self-hostable open-source version and can be set up in Docker relatively easily.

Infrastructure

For infrastructure, we recommend using Amazon Web Services. This stack can be deployed on a single m5.large instance for smaller-scale data and can be scaled up to a cluster configuration for larger data sets. Additionally, the different components of the stack can be separated into different servers for scaling. For example, if many Metabase users are accessing the data, it may be necessary to move Metabase onto its own server. Similarly, if ingestions are large, it may be necessary to move Airbyte onto its own server, and if storage and queries are large, it may be necessary to move Clickhouse into a cluster formation. This allows organizations to scale their data stack as their data needs grow.

Production considerations

When it comes to taking the data stack to production, there are a lot of other considerations. Organizations should ensure reliable, fault-tolerant backups, set up security and role-based access, and build DBT models to cater to multiple use cases and normalize data values across sources. Other considerations may include monitoring and alerting, performance tuning, and disaster recovery planning.

Reliable, fault-tolerant backups are crucial to ensure that data is not lost in the event of a disaster. Organizations should have a well-defined backup and recovery plan in place. This should include regular backups, offsite storage of backups, and testing of backups to ensure they can be restored in case of an emergency.

Security and role-based access are also crucial considerations. Organizations should ensure that only authorized personnel have access to sensitive data. This can be achieved by setting up role-based access controls, which ensure that only users with the necessary permissions can access sensitive data.

Building the DBT models to cater to multiple use cases, normalizing data values across data sources, etc., are also essential. Organizations should ensure that their data is accurate, consistent, and reliable. This can be achieved by building DBT models that cater to multiple use cases and normalizing data values across data sources.

Finally, monitoring and alerting, performance tuning, and disaster recovery planning are also important. Organizations should ensure that their data stack is performing at optimal levels and that they are alerted to any issues that arise. Performance tuning is also necessary to ensure that the data stack is performing at optimal levels. Disaster recovery planning is also crucial to ensure that data can be recovered in the event of a disaster.

Conclusion

In conclusion, data stacks have become increasingly affordable and accessible for organizations of all sizes. The open-source data stack ecosystem has grown significantly, providing powerful alternatives for every layer of the stack. Organizations should seriously consider adopting a data stack framework over a patchwork of point-to-point integrations to drive growth and operations. A data stack framework provides a more structured approach to data management, making it easier to manage, and providing the organization with the ability to gain insights from their data

Deploying a data lake to production with all these elements is a non-trivial technical exercise. If you do not have this expertise in-house you should consider using the services of a consulting organization with expertise in this area like Incentius. Drop us an email at info@incentius.com and we’d be happy to help.