Beyond Modern: The Evolution of Data Stacks

23 February 2024 | 5 minutes of reading time

In the last few years, the data landscape has been subject to many changes, with the Modern Data Stack (MDS) at the heart of these changes. Initially welcomed as a revolutionary approach to data management and analytics, the need for having an MDS has lost its significance over the need for having data activation. Now, as this evolution unfolds in front of the eyes of the data community, a debate arises over the appropriateness of the term 'modern' to describe a practice that has become commonplace. The emerging consensus is trending toward a new descriptor: the “Analytics Stack”, which signals this shift toward more activation-driven data practices.

Tracing the Evolution of the Modern Data Stack

The rise of the Modern Data Stack started with the departure from traditional, monolithic data management systems towards more flexible, cloud-based alternatives. This shift was driven by the need for scalability, agility, and the ability to leverage diverse data sources seamlessly. The MDS achieved this by integrating a suite of cloud based solutions, each serving a unique function in the data lifecycle from ingestion and storage to analysis and visualization.

Embracing Modern Data Architecture

Having cloud based solutions was a major leap forward. Organizations were able to enhance their flexibility and adaptability, ensuring it remained robust in the face of rapidly evolving data analytics demands. Yet, the MDS still depended a lot on running services provided by Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP). It still required strong technological knowledge for developing, implementing and maintaining of a modern data stack, making data teams strongly dependent on the availability of data engineers. With the rise of self-service cloud based Software as a Service (SaaS) solutions for ingesting, transforming, storage and exporting of data this dependency decreased significantly.

The Shift to Analytics Stack

The transition from a 'modern' data architecture to an analytics architecture is not just semantic. It reflects the architecture's broader role in generating actionable data products, such as insights in dashboards, analysis, and input for activation tools, rather than just storing and processing data. This demonstrates the industry's progress toward activation-driven analytics, which focuses on extracting and leveraging data to activate business tactics that support strategies and use insights to optimize operational efficiency.

As a result, the use of SaaS tools for data processing has increased. The utilization of such tools makes implementations more effortless and less complex, which enables data teams to focus on working with the data instead of working on the data. The selection of the right tool for the job remains crucial for the outcome of the analytics stack and the adoption of such an activation platform in your organization, from usage and from a learning curve perspective.

Our Preferred Tools

At i-spark, we welcome the evolution from pure insights to activation driven data analytics because this creates real value for our clients in the field of eCommerce and content providers. As pioneers in this field of data activation, we have been offering our customers actionable data products using best-in-class analysis tools for several years.

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Our preferred tools that typically make up the majority of the composable data platforms that we deliver, represent a blend of functionality, scalability, and innovation. Each of these tools are selected based on our experience and their proven distinctive benefits, the flexibility to meet various use cases and their fit in the data ecosystem:

  • Databricks - Databricks offers a unified platform that facilitates storage based on the lakehouse principles, governance controls, collaborative data engineering and data science. Its integration with other tools such as dbt Cloud enables seamless data flow and transformation, serving as the backbone for advanced analytics and machine learning projects.
  • Snowflake - As cloud based data warehousing solution, Snowflake provides a highly scalable, performance-efficient platform for storing and querying vast amounts of data. Its unique architecture allows for seamless data sharing and elastic scaling, making it an indispensable component of many data stacks.
  • Google BigQuery - Complementing the data warehousing solutions, Google BigQuery stands out as a serverless, highly scalable, and cost-effective data warehouse. It is optimized for speed and ease of use. BigQuery's ability to process vast datasets at lightning speed, along with its integrated machine learning capabilities, boosts analytical workflows and enables accessible data analysis directly within the data warehouse.
  • Fivetran - For data ingestion, Fivetran automates the movement of data into cloud data platforms like Databricks or Snowflake, ensuring that the data is continually updated with the latest information from various sources. This automation simplifies the data integration process, saving development time and allowing to process data faster and more effectively.
  • Dataddo - Similar to Fivetran, Dataddo enables flexible, no-code integration. It ensures that data from various SaaS platforms and databases is readily accessible for analysis, enhancing the analytics stack's versatility.
  • Hightouch - Operating at the intersection of data warehousing and operational systems, this sophisticated tool plays an important role in activating your data for business operations. Hightouch enables event data collection from but also the sync of processed data back into SaaS applications, enabling data-driven decision-making across all levels of the organization.
  • dbt Cloud - At the core of the data platform dbt Cloud functions as the transformation layer within the data stack, turning raw data into structured, analytics-ready formats. This tool enables analytics engineers and analysts to clean and prepare data themselves, without the need for data engineers, from different storage solutions. It is essential for maintaining data integrity and readiness for analysis, ensuring that insights are based on reliable, clean data.
  • Looker - For data visualization and business intelligence, Looker offers a powerful platform for building dynamic dashboards and reports. Connected to databases in Snowflake or Databricks, it allows users to explore and visualize data in an intuitive, accessible manner, turning insights into actionable intelligence.
  • Looker Studio (formerly Google Data Studio) - Looker Studio is known for its ease of use, seamless integration with Google's ecosystem (including BigQuery), and the ability to blend data from various sources. This light-weight tool allows users to create informative, easily understandable visualizations. Looker Studio helps to deliver insights quickly, making data accessible and actionable for everyone.
  • Klipfolio - Complementing the dashboarding tools, Klipfolio provides additional flexibility in dashboard and report creation. What sets Klipfolio apart is its ability to connect with virtually any data source, from databases and cloud services to spreadsheets and online services, enabling a holistic view of business performance. Its intuitive design interface allows users to create detailed, insightful visualizations without requiring deep technical expertise, making it accessible to users at all levels.

To be clear: an analytics stack does not consist of all these tools, but is often a carefully chosen selection of tools from this list tailored to the customer's requirements and wishes. These often relate to the functional and non-functional requirements and of course the available budgets.

Our use of the terms Analytics Stack & Data Platform

Our journey with the actionable data started years ago, even before the term “Modern Data Stack” became popular. And over the years we used various terms, from data stack to data pack to data platform to composable, to indicate these modern data platforms. Nowadays we started to use the term Analytics Stack to indicate the full suite of tools, from ingestion to activation, and to emphasize the importance of having actionable analytics over having data. The data platform itself is an essential part of the Analytics Stack and refers to platforms such as Databricks and Snowflake where efficient data storage is combined with the abilities for data/analytics engineering together with the scalability of these platforms.

The Future of the “Modern” Data Stack

The evolution from the Modern Data Stack to the Analytics Stack signifies a shift in the data management and analytics landscape. It reflects a deeper integration of data analytics into business decision-making and more importantly the activation of data in operational processes - a trend that demands an agile, insight-driven approach to data stack construction. Our preferred tool stack embodies this philosophy, offering a comprehensive, flexible solution designed to meet the challenges of today's data-driven world.

By carefully selecting tools that not only address current needs but are also adaptable to future demands, organizations can build a data infrastructure that anticipates the evolution of data analytics. As the debate over terminology continues, one thing remains clear: the power of effective use of data is still a game-changer for businesses seeking to grow.

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