Companies are actively seeking faster and more cost-effective methods to manage their data. The advent of cloud data warehouses, which employ massively parallel processing (MPP) and SQL, has sparked a revolution in data processing. Now, we enter a new era with the Modern Data Stack (MDS), a suite of cloud-native tools that are user-friendly, scalable, and affordable. This transformative suite empowers organizations by revolutionizing data management and analysis.
The realm of digital data is experiencing explosive growth. In 2010, the annual data generation stood at 1.2 zettabytes. As per forecasts by data experts and observers, the milestone of 64 zettabytes was surpassed in 2020, and it is projected that by 2035, an almost unimaginable threshold of 2,142 zettabytes will be crossed. This exponential surge in data profoundly impacts the technical resources that companies must acquire to fully harness its potential.
In this context, it becomes necessary to redefine the parameters of the Data Stack. A Data Stack encompasses a range of tools, technologies, and platforms utilized to manage and analyze data within an organization. Typically, a Data Stack comprises various functional layers that cover all aspects of a data project, including data collection, storage, processing, analysis, and visualization.
A standard Data Stack may include components such as relational or NoSQL databases, tools for data transformation and cleansing, machine learning frameworks, solutions for data flow integration and management, as well as tools for data visualization. The selection of tools primarily depends on the specific needs and objectives of each company’s data requirements. However, as data volumes continue to soar, the traditional Data Stack must give way to the Modern Data Stack.
Differences between Modern Data Stack and Legacy Data Stack
The primary distinctions between a Modern Data Stack and a Legacy Data Stack stem from the technologies and methodologies employed for data management and analysis.
A Legacy Data Stack refers to an older, traditional collection of technologies and tools, typically built on proprietary solutions and monolithic architectures. These systems can be expensive to maintain, challenging to adapt, and limited in terms of advanced analytical capabilities. They may also lack flexibility when it comes to integrating new data sources or working with large volumes of data.
In contrast, the Modern Data Stack embraces a more agile approach, leveraging contemporary technologies. It harnesses the power of open-source solutions, cloud computing, and service-oriented architecture (SOA) to provide enhanced flexibility, scalability, and agility. Moreover, the Modern Data Stack often incorporates tools such as cloud data warehouses, data lakes, automated data pipelines, self-service analytics platforms, data discovery platforms, and interactive visualizations. This enables businesses to delve deeper into data utilization and drive further advancements within their operations.
Promises of a Modern Data Stack in 2023
First and foremost, A Modern Data Stack offers enhanced agility for your company. It contributes to the rapid and flexible implementation of data flows, transformations, and analyses. It simplifies the process of adding or modifying data sources, allowing for easy adaptation to changing business needs. Another promise of the Modern Data Stack is scalability. Benefiting from the advantages of cloud technologies, it easily adapts to exponentially growing data volumes without requiring major investments in infrastructure.
This native scalability also translates into the seamless integration of various data sources, whether structured or unstructured, internal or external to the enterprise.
Thanks to self-service analysis platforms and interactive visualizations, a Modern Data Stack enables a greater number of users to take advantage of data, even without in-depth technical knowledge. Finally, a Modern Data Stack automates data collection, transformation, and management tasks, reducing manual effort and improving operational efficiency.
Foundations of a Modern Data Stack
When building a modern data stack, there are several fundamental components that need to be brought together.
First are the data sources, which can originate from within the company, such as transactional databases, flat files, business applications, sales tracking tools, and sensor data. Additionally, external data sources like public APIs, social networks, and market data can also play a crucial role. It is important to identify and integrate these data sources meticulously into the Modern Data Stack to ensure comprehensive coverage of data relevant to the company’s analytical needs.
Data storage is another vital element within the modern data stack. This encompasses various options such as cloud data warehouses, data lakes, relational or NoSQL databases, and distributed file systems. The primary objectives are to provide scalable, high-performance, and secure storage for the data. To facilitate data transformation, a dedicated brick within the modern data stack is required. This involves cleansing, preparing, and transforming raw data into a more structured format suitable for analysis.
Data analysis encompasses a range of techniques, including machine learning, statistical analysis, SQL queries, interactive dashboards, and data visualization. The ultimate aim is to extract actionable insights and knowledge from the data.
Finally, monitoring the data and performance of the Modern Data Stack is essential to ensure optimal operation and align with data governance efforts. This aspect plays a vital role in overseeing the functionality and effectiveness of the data stack.
