This post first appeared on The New Stack.
While its precise definition may be elusive, one thing is clear about the modern data stack: It's not your traditional, monolithic approach favored by giants of the past. The modern data stack is a dynamic ensemble of specialized tools, each excelling in a specific facet of data handling. It's a modular, shape-shifting ecosystem that accommodates the fluidity of technology and ever-changing business needs.
Despite or perhaps because of this fluidity, the modern data stack does have some defining characteristics. It is cloud native, modular, performant, compatible with RESTful APIs, features decoupled compute and storage, and is open. Let’s look at those in a little more detail:
- Cloud native: Cloud native tools deliver unparalleled scalability, allowing organizations to seamlessly process and analyze vast data sets while maintaining high performance across diverse cloud environments. Whether it's the public clouds or private ones, the modern data stack is multicloud compatible, ensuring flexibility and avoiding vendor lock-in.
- Modular: The modern data stack offers a buffet of specialized tools, each optimized for a specific data task. This modularity allows organizations to craft a customized data infrastructure tailored to their unique needs, promoting agility and adaptability in a rapidly evolving data landscape.
- Performant: Performance is at the core of the modern data stack. Its components are engineered for high performance, enabling organizations to process and analyze data efficiently.
- RESTful API compatibility is employed for smooth and standardized communication between stack components, promoting interoperability and for the creation of microservices that break up the stack into manageable components. An example of this is the all pervasiveness of the S3 API inside the stack.
- Decoupled compute: Decoupling compute from storage is a fundamental architectural principle of the modern data stack. This separation allows organizations to independently scale their computational resources and storage capacity, optimizing cost efficiency and performance. It also enables dynamic resource allocation, ensuring that computational power is matched to specific workloads.
- Open: The modern data stack champions openness by embracing open source solutions and open table formats, dismantling proprietary silos and eradicating vendor lock-in. This commitment to openness fosters collaboration, innovation and data accessibility across a wide spectrum of platforms and tools, reinforcing the stack's adaptability and inclusivity.
The Shape of the Modern Data Stack
Picture the modern data stack as a symphony orchestra, with each instrument playing its part while following the conductor, Kubernetes, to create a harmonious data experience. While the players may change, the components remain constant: data integration, storage, transformation, data observability, data discovery, data visualization, data analytics and machine learning and AI. Let's delve into each of these areas.
Object storage plays a crucial role in the modern data stack. Object storage offers a scalable, performant and flexible storage solution for the ever-increasing volume of data. The stack's agility is enhanced by object storage, as the best-of-breed object storage can be deployed across diverse infrastructures, underscoring the importance of software-defined storage.
Storage increasingly performs an active role, seamlessly integrating with elements in the rest of the stack and serving as the backbone for lakehouse architectures. Lakehouses, like those built using MinIO and Iceberg, Hudi and Delta Lake, exemplify this use case perfectly.
Ingest is the bridge that connects disparate data sources. Modern data integration tools embrace the ethos of flexibility and democratization. They don't hoard data in proprietary silos; instead, they facilitate data accessibility, irrespective of where data resides. Whether it's in the public cloud, private cloud, on bare-metal infrastructure or at the edge, data integration tools break down the barriers that once kept data isolated.
One noteworthy player in this realm is Apache NiFi, an open source data integration tool that orchestrates data flows with ease. It's object storage-friendly, ensuring your data can seamlessly traverse various environments. Airflow is another obvious performer in this space. Airflow is an open source platform designed for orchestrating, scheduling and monitoring complex data workflows, making it easier to manage and automate data-related tasks
The older pattern of data integration involving actual data movement has been largely unseated by the concept of integrating in place. This paradigm shift represents not just a change in the way we manage data but a fundamental transformation in how we approach data freedom, accessibility and agility. Data in the modern data stack belongs to you, not to proprietary systems. The entity that reaps the benefits must be you and your organization, not a multinational company selling an outdated relational database management system.
While there may be some overlap between transformation and data integration applications, it's important to note the existence of highly specialized transformation tools like Apache Spark and DBT. These tools serve a distinct purpose, allowing data engineers and analysts to modify and refine data before it's used by downstream applications within the stack. With object storage as both the source and destination for data, these tools ensure that data remains consistent, accessible and reliable throughout the transformation process.
Ensuring data reliability and quality is paramount in the modern data stack. Data observability tools act as the watchful guardians, offering insights into the health and behavior of your data pipelines. These tools not only monitor but also detect anomalies, helping you maintain data integrity.
Prometheus, a popular observability tool, empowers you to gain deep insights into your data infrastructure, providing the necessary observability along with the S3 compatibility that is the standard for the modern data stack. Grafana, while often associated with infrastructure and application monitoring, can also be extended to monitor data pipelines.
Tools like Apache Atlas and Collibra provide the means to catalog and discover data assets across the organization. Integrating with object storage repositories ensures that all data, regardless of its location, can be discovered and used.
Data visualization tools turn raw data into meaningful and actionable insights. They enable users to craft compelling stories, uncover patterns and make data-driven decisions. These tools thrive on accessibility, ensuring that data is within reach for everyone, not just data scientists or analysts. Here again, we see the prevalent use of RESTful APIs used to connect to data in the stack.
Tools like Tableau, Power BI, Looker and Apache SuperSet lead the way in this category, offering insights on data wherever it is.
Object storage is primary storage for online analytical processing (OLAP) analytical databases. This forward-looking approach, adopted by analytical giants like Snowflake, SQL Server and Teradata hinges on the concept of queryable tables that eliminate the need for data migration and allows these highly performant databases to focus their energies on query performance instead of storage. This trend follows the next logical step with smaller, lightweight analytics engines like DuckDB that have completely ceded storage and instead rely only on in-memory processes to further accelerate data analytics workloads.
Cloud native analytics platforms that pursue the advantages of object storage of scale, performance and cost effectiveness are revolutionizing the way enterprises extract value from their data. It's not just a technological shift; it's a strategic imperative for organizations seeking to stay competitive in today's data-driven world.
Machine Learning and AI
Now more than ever, Machine Learning (ML) and AI have a prominent place in the modern data stack, driving transformative insights and decision-making capabilities. ML frameworks like TensorFlow and PyTorch take center stage, showcasing their capacity to hyperscale when integrated with highly performant object storage. This powerful synergy not only accelerates the training and inference phases of ML models but also amplifies the agility of AI-driven applications, allowing organizations to harness the potential of their data for anomaly detection, natural language processing, computer vision and more. In this era of data-driven innovation, MI and AI have become indispensable pillars, reshaping industries and unlocking new possibilities for businesses willing to explore the frontiers of intelligent automation and data-driven decision support backed by powerful object storage.
These contenders for the modern data stack aren’t the end-all be-all options for the enterprise architect. There are plenty that have been left out and plenty more we have yet to explore, but the categories should be the takeaway for readers. The modern data stack will continue to evolve, embracing new tools and technologies. The constant, however, is its requirements around scale, performance, data accessibility, modularity and flexibility.
At MinIO, we view these pillars as engineering-first principles. In fact, we think of ourselves as more of a data company than a storage company. We aim to be part of the overall data orchestra, enabling large-scale pieces as well as improvisation.
Keep exploring, keep innovating, and keep unlocking the limitless potential of your data. The modern data stack is your symphony, and you are the composer. You can drop us a note on Slack or send us an email at firstname.lastname@example.org if you have any questions or ideas on what belongs in the modern data stack.