In today’s highly competitive and data-driven environment, it is essential that companies can make strategic decisions exceptionally quickly based on all relevant data. This agility demands short cycle times – from collecting the data to gaining insights. Conventional analysis processes are not designed for this speed, which leads to delays.
Businesses stay competitive by automating analytics-related data processing and leveraging advanced analytics capabilities. This is precisely what a cloud-centric analytics infrastructure, also known as a modern data stack, does. Until recently, this goal was hardly achievable for many companies. More and more companies are now relying on a current data stack. Reason enough to look at the differences between traditional and modern analytics infrastructures.
Limits Of Conventional Data Analytics
Conventional business intelligence and data analysis functions do not meet faster availability and agility requirements. Often, teams are solely responsible for building data pipelines and managing on-premises storage and compute requirements. The teams put significant effort into manually coding, designing and maintaining processes for SQL-based data extraction, transformation and loading ( ETL ), building semantic layers and designing complex schemas.
In short, data teams are spending valuable time and resources managing legacy data integration infrastructures instead of turning relevant data into business insights. An outdated data infrastructure not only causes high personnel costs but is also problematic in the following aspects:
- Difficult procurement
- Complicated use
- Expensive maintenance
- Time-consuming construction (often in months-long projects)
Most importantly, legacy data infrastructures are difficult to adapt to change, which contradicts the needs of modern businesses. In today’s companies, reporting is constantly subject to new requirements. Data source schemas frequently change, as do the required APIs. New source data systems are continually being added, changed or deleted. Data-savvy executives are constantly formulating further data queries that need to be answered. In addition, development cycles, which often last 12 to 18 months anyway, can be interrupted by problems.
The Modern Data Stack
Today’s businesses, no matter the size, use dozens of applications. The data that is generated in the process provides valuable insights into business processes and can identify opportunities for optimization. To use the potential of data in companies, it is worth implementing a modern data stack.
The Modern Data Stack (MDS) is a collection of tools used to centralise, manage, and analyse data. The core components of the modern data stack include:
- An Automated Data Pipeline: Automated data pipelines transfer data from different sources into the respective data warehouse or lake. The correct implementation is not an easy task, although the focus here is on the technicalities of extracting and loading data. A data pipeline with predefined connectors can be set up quickly and enables scalable data integration. It is fully managed and honours API or schema changes.
- A Cloud-Based Data Warehouse Or Data Lake As The Destination: To create connections between data from disparate sources, companies need a platform that enables secure, permanent data storage while being easily accessible for analysts and data scientists. This platform can be relational and designed for structured data (data warehouse) or non-relational and contain both structured and unstructured data (data lake). The platform must be able to provide and scale both computing and storage capacity without long downtimes.
- A Data Transformation Tool: The data transformation tool should be compatible with the storage location and have features that allow for easy data lineage tracing, such as B. Version control and documentation that clarifies the consequences of the transformation on the respective tables.
- Business Intelligence Or Data Science Platform: Data is collected to generate insights that help companies make decisions. Progressive companies can use data to deploy artificial intelligence for automated decision-making in operational systems.
The Advantages Of A Modern Data Stack
In contrast to the legacy data stack, the modern data stack is hosted in the cloud and requires trim technical configuration by the user. This promotes accessibility for the end-user and scalability to quickly meet growing data demands. Long-lasting and costly downtimes caused by scaling local server instances can thus be avoided.
With a Modern Data Stack, data teams give decision-makers the data and insights they need right away. Due to the short provision times, companies can react better to the dynamic demands of the market.
The core of a modern data stack is a cloud data warehouse or a data lake. This includes cloud-based tools for analytical reports and visualisations and support in building or automating data pipelines.
This paradigm allows data engineers, data analysts, and data architects to focus on mission-critical projects that deliver business value. The underlying data engineering tasks, such as maintaining the data pipelines and designing the schemas, are handled by cloud services. For example, Fivetran offers pre-configured, maintenance-free data connectors for over 150 data sources, including databases, SaaS applications, files and APIs. The data is provided ready for the query at the destination. This significantly shortens the time it takes to obtain knowledge in data analysis.