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8 Basics of the Modern Data Stack

The modern data stack exists for primarily only one reason, for large companies to efficiently manage the data they have in their possession. Depending on what each company needs, the process could either be extremely long or reasonably short. Modern data stack is a term used to encapsulate: a cloud-based data warehouse, multiple data pipelines, an analytics/business intelligence platform, and a data prep and transformation tool. However, in its simplest form, a modern data stack only requires an ingestion tool, a warehousing tool, a transformation tool, and a business intelligence tool.

From that, the modern data stack is supposed to aid companies in speeding up the time that it takes to arrive at critical insights. Because a company’s data stack is the key to scaling its data strategy and making business decisions confidently, this translates into companies building better products, a more competitive go-to-market strategy, and a new level of data maturity. This would mean that the modern data stack slightly differs from data platforms and data infrastructure. Data platform, which is the implementation of the modern data stack into infrastructure, for example, how each of the technologies and services connects to each other.

While data infrastructure is the underlying computing system that powers the modern data stack, it has a focus on networking, hardware resources, and low-level application programming interface (API). There are other functions of the modern data stack that need to be fulfilled for companies or organizations to truly benefit.

These are considered the basics of what constitutes a modern data stack and are as follows:

What Are The Basics of The Modern Data Stack?

  1. Data Collection – This includes the process of collecting behavioral data from mobile, internet, or internet of things (IoT) devices and transactional data from backend services. The modern data stack is supposed to be equipped with tools that reduce the occurrence of poorly tracked data.
  2. Data Ingestion – data is transported from various sources (databases, server logs, among others) into a storage medium. A modern data stack has pipelines bringing in raw data from hundreds of first and third-party sources into the company’s data warehouse. New ingestion pipelines need to be constantly laid out all the time to meet growing business demands. Modern data stacks have data ingestion tools geared towards improving productivity and ensuring data quality.
  3. Data storage – a cloud-based solution that is used to store all the collected data sent from the data ingestion tool. It serves as the organization’s historical record of truth for all behavioral and transactional data. A modern data stack storage system focuses on providing serverless auto-scaling, lightning-fast performance, and economies of scale.
  4. Data transformation – Once the raw data has been moved into storage, it will need to be transformed into user-friendly data models. This is done by cleaning, normalizing, filtering, joining, modeling, and summarizing raw data to make it easier to understand and run queries. Modern data stack transformation tools focus on providing frameworks that enable consistent data model designs, promote reuse of codes, and facilitate testability. Data transformation tools also allow different types of data to collaborate. With multiple data sources, this is obviously an important component of the modern data stack.
  5. Business intelligence/Data analytics – This is where data is analyzed, and dashboards are created for users to explore the data. Modern data stack analytical tools have been designed with non-technical users in mind,  empowering domain experts to answer business questions without depending on developers and analysts. Modern data stack business intelligence (BI) tools are also made to concentrate on enabling data democracy by making it easy for anyone in the organization to analyze data quickly and build feature-rich reports.
  6. Data governance – Allows companies and organizations to keep track and make sense of their data which helps in data discoverability, lineage, cataloging, information security, quality, and sharing. Data governance also assists an organization in remaining legally compliant in terms of data protection. Problems such as sensitive data breaches are easily resolved. Modern data stack data governance tools enable transparency and high levels of data democracy and collaboration.
  7. Data orchestration – automating processes and building workflows within a modern data stack. With data orchestration, data teams can define tasks and data flow with various dependencies. Modern data stack orchestration tools focus on providing end-to-end management of workflow schedules, extensive support for complex workflow dependencies, and seamless integration.
  8. Data activation – democratizes the data within the warehouse using reverse extract, transform, and load (ETL). Data activation is the process of making data available in the tools used to create data-powered experiences, as well as creating those personalized experiences across customer touchpoints. People can go beyond looking at dashboards and use data in meaningful ways by activating data efficiently. Providing superior experiences requires behavioral data to be used in every customer interaction across all touchpoints. This is made possible when the data is made available in the downstream tools where it is activated to power those interactions, such as outbound emails and support conversations, as well as ads and in-app experiences.