Data has become one of the most sought after commodities, and to many large corporations, it is the single most valuable resource. Data is so valuable that it has become integral to sustaining our current economy — becoming as necessary as oil, labor, capital, and land. Data drives so much of our day-to-day, but to understand what goes on behind the scenes, we need to first ask: What is a data stack? A data stack is a collection of various technologies that allow for raw data to be processed before it can be used. A modern data stack (MDS) consists of the specific tools that are used to organize, store, and transform data. These tools allow for the data to be taken from “inedible data” (data that cannot be worked with) to “edible data” (data that can be worked with).
The concept of what data can be used for is well known: We are all aware of data breaches, social media’s reliance on personal data, data being used for artificial intelligence, etc. But what about what happens behind the scenes? How can a company take personal data collected from a website and turn it into a well targeted advertisement? For those who are not familiar with the intricacies of data, the transformation process can be seen as a blackbox. This article will help break down this process and we will focus on a crucial term worth learning about: the data stack.
This process can be simplified down to four steps:
The tools used in each company are different, but they should be easily integratable and have distinct uses. Some examples of tools are: data pipelining, data catalogs, data quality, and data lakes. Data stacks originate from technology stacks: Exactly as it sounds, technology stacks are the layers that comprise a product produced by the company. Take a web application, as an example: The necessary layers are the front-end user interface (all the HTML, CSS, and JS that make the application pretty), on top of the back-end software that actually makes the application run. A modern data stack is very similar.
“Time is money.” A cliché, but true, especially when it comes to a data-driven corporation. The more efficient a data stack with transforming raw data, the faster data teams can monetize it. Having the proper tools in your modern data stack is critical for your company's overall success.
A legacy data stack is what came before the modern data stack. It’s an infrastructure-heavy method of preparing data for analytical use. Even though the move towards modern data stacks is gaining popularity, legacy data stacks are still vital for businesses. They hold essential company information and need to be integrated properly into your MDS. The key differences between the two are outlined below:
Legacy data stack:
Modern data stack:
The four main advantages of switching from an outdated stack to a modern data stack are:
Modularity is a term to describe a way to create various products that can be separated into standalone, but integratable, components. In a data stack, this would be seen as building your stack layer by layer, including various technologies and tools that are perfect for your organization.
The modern data stack is a cloud-based solution, meaning the speed of processing data has increased exponentially. The same amount of work that took hours with a legacy data stack can now take minutes. The automation involved has also made this a faster option.
Hardware and complicated infrastructure are no longer needed in a modern data stack. This cuts costs down drastically, while allowing more authority over your data processing methods.
Setting up a modern data stack can take as little as 30 minutes. Modern data stacks are also automated, meaning fewer working hours need to be involved in the data process.
As the requirement for more data storage space increased, new technologies (MongoDB being among them) found more efficient ways of dealing with data. Cloud technology skipped to the forefront of modern engineering in the early 2010s and dramatically changed Big Data forever. Amazon’s Redshift in 2012 pushed forward the modern data lake, and this truly paved the way for data optimization and transformation as we know it today. Cloud computing and storage allowed for data to now be loaded prior to being transformed (a process known as ELT: extract, load, transform) instead of its sister method (ETL: extract, transform, load), due to storage space being readily available due to cloud computing technology. Some examples of well known companies that provide proper data stack tools are: Snowflake, Google BigQuery, and AWS Redshift. These organizations help provide companies with data storage, data transformation, and the various business intelligence tools necessary to conduct data manipulation.
Technology stacks are crucial for developers in every sort of corporation. These are not a new concept, and the modern data stack is an addition to what should be going on in the background of your organization. Almost every application produced in a company is born through some sort of stack pipeline.
Here at MongoDB, some of the more better known technology stacks are MEAN and MERN. These stacks are not the extent of what MongoDB can do: MongoDB even allows integration with Apache Hadoop, so complex data analytics can be conducted on data stored in your MongoDB clusters. This combination, along with important business intelligence tools, allows for a deeper analysis of your raw data.
Every data-driven organization needs a personalized modern data stack. There are a multitude of companies offering competing services with pay-as-you-go methods, so integrating an efficient and elegant data pipeline into your organization is now easier than ever before.