The modern data stack has become popular over the last decade and companies are scrambling to implement one. This popularity is far from a fluke, especially since modern data stacks help businesses save time, boost the reliability of data, and become better aligned with their goals. Still, the modern data stack is a relatively young concept that might leave older business leaders stressed when they try to wrap their heads around this. Allow that stress to melt away because we explain the ins and outs of the modern data stack below.
What is a Modern Data Stack?
Data stacks have been around for ages, but have slowly evolved and become more modern overtime with new developments in technology. In 2012, the data warehouse landscape dramatically changed when businesses began adopting the first cloud infrastructure known as Amazon Redshift. This was the first time that a data warehouse was ran entirely in a cloud, which made data analytics much more affordable for several different companies and increased the reliability of data. In 2016, Snowflake was a new data warehouse on the data scene that was much faster and cheaper than its predecessor. Data engineers did away with conventional ETL tools, and were now able to utilize a new process known as ELT (extract, load, transform. This became the birth of the modern data stack, which many businesses rely on today.
Legacy data stacks are hosted on the premise of a business and a team has to manage and scale the hardware. On the other hand, modern data stacks are hosted in a cloud and the management of hardware is done automatically. A modern data stack is a suite of products hosted in the cloud used for data integration and analysis. This allows business leaders and data analysts the opportunity to better allocate their time to gather actionable insights rather than focus on managing the technology itself.
Benefits of the Data Stack
The modern data stack comes with a multitude of benefits including saving time, resources, and money. Data analysts and data engineers are able to dedicate more time and energy into high-value analytics and data science projects. The modern data stack is easier to scale, boosts the reliability of data, and drives business outcomes. Also, the modern stack comes with an assortment of resources and tools that maximize the true potential of the data across a business.
How Does the Modern Data Stack Work?
The modern data stack cultivates efficiency, increases the reliability of data, and highlights areas of potential. Several different layers stacked on top of one another create the modern data stack many businesses use today. The overall architecture and tools used vary from company to company, but the core components remain the same. We’ll explore the layers below.
Data integration is a process that takes data from several sources and turns it into a single, cohesive view. This process involves data being extracted and loaded into a centralized warehouse using various tools and databases known as ELT. Businesses accomplish this task through the use of SaaS tools such as Fivetran, Segment or Apache Kafka.
Data governance commonly referred to as data organization is the practice of categorizing, classifying, and arranging raw data in order to make it more useful and understandable. Part of this practice includes analyzing both unstructured and structured data. This data is sorted into tables, fed into databases, and analyzed by different types of data. Many consider data governance as a preliminary step in the process of data management.
Many companies store massive amounts of data inside data warehouses, which reside in the cloud. Data storage is a method of collecting and retaining digital information. Data warehouses also known as data lakes are considered the heart of the modern data stack. Proper data storage leads to increased reliability of data, a boost in data accessibility, and auto-elastic scalability.
Data transformation is the process of converting data from one format to another in an effort to make it more usable. This allows businesses to integrate, cleanse, store, and mine data for actionable insights, increase in the reliability of data, and make more intelligent decisions moving forward. This process can be accomplished through techniques such as data normalization, data generalization, data smoothing, and more.
Every business has a unique set of problems and utilizes modern data stacks to come up with data-driven solutions. It’s a bit of a complex process that relies on several different layers that make up the stack. Companies use data stack all in an effort to increase the reliability of data, save time, save money, and activate the true power of their data. Many of the core components that make up the modern data stack include data integration, data storage, and data transformation. Some companies use additional layers, tools, and resources depending on their goals. Either way, the modern data stack is a valuable asset in an ever-evolving space that’ll be here for years to come.