How to choose the right data stack for your business
Data comes in many shapes and forms, but two of its core structures are stacks and queues. TechTarget’s definition states the following; “In programming, a stack is a data area or buffer used for storing requests that need to be handled.”
And what’s inside that data stack? It’s not just a data warehouse. Data stacks are composed of tools that perform four essential functions; collect, store, model and report. But the stack itself and the data warehouse are the two we’ll focus on in this article since they have a high importance level.
To get the lowdown on why it is essential to focus on your data stack and warehouse, we talked with Archit Goyal, Solutions Architecture Lead at Snowplow Analytics to understand more.
What are the opportunities and challenges that arise when choosing and developing a data stack?
“Choosing a data stack will depend on multiple factors: the company’s core use cases, the size and capabilities of their data team, their budget, their data maturity, and so on,” Goyal said. “One of the key choices is choosing between packaged analytics solutions (think GA or Adobe) versus more modular components that combine to make up a data stack. The main advantage of packaged products is that they have a lot of analytics tooling ready to go right out of the box. However, the main drawback is that you sacrifice control and flexibility over your data management in favor of simplicity and ease-of-use. Picking and setting up multiple best-in-breed tools to make up the analytics stack is harder work, but will give you greater control over your data asset in the long term.”
So what is a data warehouse, and why do companies need it? For example, what’s the difference between a data warehouse and a MySQL database?
“A Data Warehouse is a centralized data repository which can be queried for business benefit,” Goyal said. “They can contain data from heterogeneous sources, such as SQL, CSV files, text files, and more. Comparatively, data warehouses are columnar databases, and MySQL is a relational database. This means that warehouses are optimized for historical analysis of data as it is easy to aggregate values across rows (e.g., count sessions over time), whereas MySQL databases are good for storing and retrieving individual entries as a transactional store in an app.”
What are some excellent examples of data warehouses?
“The big three (currently on the scene) are Google’s BigQuery, Amazon’s Redshift, and Snowflake,” Goyal said. “These are typically used to store a company’s data in a columnar format to allow for easy analysis and reporting. When used as the source of truth for a company to answer business questions, particularly about its users, it can be extremely powerful.”
So that covers warehouses, but what is our definition of a data stack, and what should be inside a good data stack?
“At Snowplow, we think about the data stack in four different stages,” Goyal said. “First, we collect. Data quality matters. With high-quality and complete data, attribution models are accurate, it’s easy to track and understand user behavior, and customer experiences can be optimized. That’s why our customers choose Snowplow, as we provide flexibility to collect data from multiple platforms and channels, as well as delivering clean and structured data.”
“Then, we store. Snowflake, BigQuery, Redshift, and S3 are all examples of tools for storing data that is collected.”
“The third stage is to model. Data modeling can help teams democratize their data – At Snowplow, our customers use tools like Snowplow SQL Runner, dbt, and Dataform to model their data.”
“Finally, we report. At this stage, data teams want to enable self-service of analytics within their organization. This includes the use of tools such as Looker, Redash, PowerBI, and Amplitude.”
“There is no one size fits all approach,” Goyal said.
