Data Modelling for Analytics: Transforming Raw Data into Actionable Insights in Your Data Stack    

Data Modelling for Analytics: Transforming Raw Data into Actionable Insights in Your Data Stack

A data stack is an assemblage of several technologies that facilitate the pre-processing of unprocessed data before its utilization. The specialized tools needed to acquire, organize, store, and manipulate data make up a modern data stack (MDS). These tools make it possible to convert data from “inedible data,” or information that cannot be used, to “edible data,” or information that can be utilized.

Data applications and their utilization are widely recognized: Among many other instances, frequent data breaches draw attention to security issues, social media platforms make considerable use of personal data, and the developing area of artificial intelligence depends heavily on various data sets.

What about what goes on behind the scenes, though? How do businesses utilize the personal information obtained from websites or other sources within their system? The transformation process could appear to be a mysterious entity to individuals unfamiliar with the complexities of the digital world. This piece aims to deconstruct this procedure, emphasizing an important concept that should be understood: the data stack.

Primary uses for Data Stacks

Simple stages can be used to simplify the process of obtaining data, including data assets like your personal data, and transforming it into a format that businesses can use:

1. Pipelines for data

Here, the information is collected and transferred, or swallowed, to a location that allows for analysis. This is the unfit state to eat. Examples of pipeline products feeding data into or between sources include well-known technologies.

2. Storage of data

The data is kept somewhere after being fed through a pipeline, typically in a data warehouse or data lake. After that, this data platform can give users access to analysis, visualization, and transformation tools.

Large volumes of structured data are usually kept in data warehouses, so business intelligence (BI) tools may use them. However, a data lake works better for holding enormous volumes of unstructured, raw data. Find out more about data lakes vs. warehouses. It is essential to know about Data modelling best practices.

3. Transformation of data

Data transformation, which involves converting data from one format, structure, or value system to another, is an essential stage in data management and analysis. This procedure is crucial to prepare raw data for analysis and decision-making.

One data transformation technique is extraction, transform, and load (ETL), which combines data in a central repository or data warehouse. The enterprise’s business rules are used to collect the data, which is saved and processed using machine learning.

4. Information display

Data visualization is the last stage. Businesses frequently want to see visual representations of the data and any analysis done on it, especially stakeholders. Data visualization can help with this.

Data visualization is made incredibly simple, with products requiring minimal settings. Within your Atlas cluster, you can choose the data source, then from a variety of charting options, select the style of chart you want, the fields you want to include, and any aggregations (such sums, averages, or groupings) that you want to use to pre-manipulate the data before showing.

5. Data instruments

Each business employs diverse tools, but they should all be simple to integrate and serve specific purposes. Data warehouses, pipelining, data catalogues, data archiving, data lakes, and data quality are some of the tools. Technology stocks are the source of data stacks. Technology stacks are precisely what they sound like the layers that make up a company’s product—using a web application as an illustration. The front-end user interface (HTML, CSS, and JS for functionality and style) is required, and it sits on top of the back-end software—which runs the program itself.

Legacy Data Stacks versus Modern Data Stacks

The distinction between on-premise and cloud-based solutions is the primary source of the difference between an outdated data stack and a contemporary one. Since legacy data storage is housed in a single location, hardware will require unique provisioning, management, and scalability in response to changing business requirements. Since the data stacks are fully hosted in the cloud, all the necessary hardware maintenance for equipment management may be carried out automatically. Data transformation technologies that are cloud- and SaaS-based save a lot of money and let users concentrate on their business goals.

A legacy data stack preceded the current data stack. This approach of preparing data for analysis requires a lot of infrastructure. Legacy data stacks are still essential for enterprises despite the trend toward more contemporary data stacks becoming more widespread. They must be correctly incorporated into your MDS since they contain vital company information. The following lists the main distinctions between the two:

The Ability to Modularize

The ability to build different goods that can be divided into independent yet integrated components is known as modularity. This might be layer-by-layer developing your stack in a data stack, adding other technologies and solutions that are ideal for your company.


Since the contemporary data stack is a cloud-based solution, data processing speed has skyrocketed. With the current data stack, tasks that previously required hours can now be completed in minutes. This is also a faster option because cloud data warehouses are automated.

In summary, technology stacks are essential for developers in all types of businesses. These ideas are not novel, and the contemporary data stack is an addition to your organization’s background operations. Nearly all applications developed within an organization originate from a stack pipeline.

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