Discover what a modern stack is, why you should implement this process, and FAQs for building a robust and efficient data stack.
A growing trend among businesses is to set up a so-called modern data stack to handle the increasing amounts of data from eCommerce and marketing platforms and quickly get a comprehensive and real-time view of their business. You will surely ask yourself the following questions upon hearing about it the first time:
What is a modern data stack?
Why should you start using it?
What are its parts?
And what should you consider when building a modern stack for your business?
Read on below to discover the answers to these questions and more..
The term data stack refers to the combination of different software that are combined to build products and services for handling data. This includes the products and services for storing, managing, and accessing data. The modern data stack maximizes the benefits of cloud-based software-as-a-service (SaaS) products in order to provide a fast, optimized system for its users.
Some features of the modern data stack include the following:
There are four main reasons you should start using a modern data stack.
In the past, when software used by businesses were hosted on their own servers and computers, set-up required the expertise of a software engineer to ensure that the entire system worked as intended. Nowadays, the emergence of SaaS means that one can simply pay for a subscription and get access to a SaaS that is already set up for them by the software engineers behind that service.
Most of the SaaS used in modern data stacks are also designed to follow most of the conventions used by mainstream software. For example, both Excel and Google Sheets, seen as the dominant spreadsheet software in the market, use the same function names for a number of basic commands. Other competitors in the market also use the same conventions to ease the conversion from the mainstream competitors to their own products.
The two reasons we highlighted for the ease of use of a modern data stack in the previous section also lead to wide adoption. There is another one: as modern data stacks are a collection of different software integrated for the purpose of handling data, they work best when the individual software follows industry and market standards. This makes it easier to be adopted and integrated with other related software. One example is SQL, which is currently considered as the standard for databases. SQL is so ubiquitous that even non-database software uses its syntax and standards for some of its data handling-related processes.
Creating a modern data stack does not only involve selecting a set of software and then integrating them together. The crucial feature of modern data stacks is that the process should be automated. Once the data enters the stack, it will be automatically processed by the softwave, with the final output stored in the database for analysis by its users.
Automation is important because it significantly cuts the time needed to process and analyze the data. In highly-competitive markets, minimizing the processing time of data through automation allows businesses to make snap decisions that take advantage of the current market trends.
Finally, most cloud-based services have subscription plans that can be scaled up with the growth of your business. Unlike setting up an in-house system which requires a high budget upfront, you only pay what you need, thus keeping your software budget reasonably small.
There are three main components of a modern data stack: the data warehouse, the data pipeline, and the data analytics platform.
A robust database that can store the different types of data coming from different sources is, of course, essential. For this purpose, you should work with a data warehouse. The different types of data warehouses vary in their configuration, advantages, and disadvantages:
You can learn more about databases through our previous articles:
The data pipeline serves to channel the data from the sources to the data warehouse. It has several processes included in it. The major parts and processes are listed below.
Data ingestion: Depending on the diversity of your sources, you may need a service to handle the amount and type of data that enters your stack. This service is also necessary for ensuring that automation works seamlessly when you have different sources sending data at different times.
Data transformation: Data transformation is the process of changing the format, structure, or values of data. This is necessary because the wide variety of sources of the data stored in the data warehouse means that they also come in different formats, and thus need to be formatted first.
One method that involves data transformation is data munging. It is employed in order to ensure that the incoming data is well-formatted and does not have missing or improperly-formatted data. You can read more about data munging here.
Data validation: No computer system is perfect; issues with the output appear from time to time. For that purpose, procedures for data validation should be implemented to automatically flag possible output discrepancies for manual inspection. This may sound like data munging; it's because they are essentially the same process. Data munging is applied to raw data before it is stored in databases; data validation is applied to the output after data analytics and modeling.
Validated data can either be stored to a database or sent to its intended users in a process called data operationalization, which will be discussed next.
Data operationalization: Finally, the data stored in data warehouses can be analyzed and also become useful to your users in real-time. This process is called data operationalization. Modern data stacks can automate the data analysis and modeling processes as well as the delivery of the resulting metrics to users.
One way to do this is to construct data dashboards. Dashboards consolidate relevant data and metrics from different sources and display them to a screen. The layout of dashboards are such that they display as much relevant data as possible without looking too crowded or overwhelming.
A modern data stack should contain a comprehensive set of data modeling and analysis tools. These tools should be automated so that you can see how the metrics actually change in real-time and quickly make business decisions.
We have an entire set of articles priming you about data analysis, the foundation of data analytics:
As you create your own modern data stack, you should answer the following questions.
Data sources are the platforms and services that generate your raw data. They include the following:
Whatever the nature of your business, you can have multiple sources of data that you can process and analyze. Most of the popular platforms today have an API that you can use to automatically import the latest data to your modern data stack. You should select a data ingestion platform that covers all of the eCommerce and marketing platforms that you use and plan to expand to in the future.
To get a grasp of how an efficient data ingestion platform works, you should try Lido. It has built-in capabilities to import data without the need for add-ons or custom scripts that may only work for specific cases. In fact, you can even set up your spreadsheet to also do real-time analytics and create an easy-to-read dashboard, a key component of a modern data stack.
The nature of your business also dictates the types of data that you receive from the eCommerce and marketing platforms you use. Most of the time, they can be structured into neat tables. For this case, an SQL-based relational database that can scale well will be sufficient.
However, some businesses will need to store data that cannot be conventionally structured into neat tables. These so-called unstructured data require that you use a non-relational database.
Some of the examples of data warehouses services that you can use are BigQuery, Redshift, and Snowflake.
The rising star of analytics methods today in the modern data stack is business intelligence (BI). BI combines tools for data analytics, data mining, data visualization, and other relevant tools to give you a comprehensive overview of your business in real-time. Unlike the traditional methods of analysis, BI is integrated with the modern data stack so that it automatically analyzes the data as it enters your modern data stack.
The data analytics platform you select for your modern data stack should be able to handle the diversity of data that your modern data stack will handle every second. Some of the examples of analytics packages are Looker, Mode, and Tableau.
The users who need to access the data dictate whether you need to set up a data operationalization package or not. They need to be directed from the data warehouse to the users who need to see the data in real-time. While the implementation as part of the modern data stack is a novelty, this is not a novel idea as several manufacturing and utilities facilities use it all the time to monitor the operation of the machinery. Some of the platforms you can use for this purpose are Census, Hightouch, and Grouparoo.