A data platform is the technological basis of a modern data stack and provides the functions for capturing, storing, processing and analyzing data.
A modern data platform is designed to be democratic, proactive, scalable, and flexible to respond to future technologies and the evolving needs of modern data teams. It is the technological basis of a modern data stack. areto plans and builds cloud-based software architectures (areto reference architectures) that combine different applications into a software or solution stack. This modern data stack is a layered system of automated services that collect, merge, model, and analyze data and finally present it to decision makers in an individualized way. A modern cloud-based data platform is the foundation of a data-driven company.
“How do I build my data platform?”
For most companies, building a data platform is a “need-to-have,” because many companies differentiate themselves from their competitors by their ability to extract actionable insights from their data.
But to build a data platform from the ground up is easier said than done. Every company is at a different stage of its Digital Journey, making it harder to prioritize which parts of the Data Platform to invest in first.
Before building a Data Platform you should set expectations for what the Data Platform should and should not do; and plan for both the long-term and short-term ROI of the Data Platform.
To simplify the process of building a Data Platform, we have outlined the 6 layers of a Data Platform.
Data cannot be processed, stored, transformed, and applied until it has first been ingested. As data infrastructures become more complex, data teams face the difficult task of ingesting structured and unstructured data from a variety of sources. This is often referred to as the Extract Transform Load (ETL) and Extract Load Transform (ELT) stages.
Some popular tools and services that we also use in our reference architectures are:
After you have built your ingestion layer for your data platform, you need a place to store and process your data. With many organizations currently moving their data landscape to the cloud, cloud-native data warehouses, data lakes, and data lakehouses have taken over the market, offering more accessible and affordable options for data storage compared to many on-premise solutions.
Whether you choose a data warehouse, a data lake, or a combination of both depends entirely on the needs of your business. There’s been a lot of discussion lately about whether you should choose open-source or closed-source solutions when building a modern data stack.
But no matter which approach your company chooses: To build a scalable, flexible data platform, you should invest in cloud storage and computing power.
Here are some of the leading solution providers:
The terms data transformation and data modeling are often used interchangeably, but they are two very different processes. When you transform your data, you take raw data and cleanse it with business logic to prepare the data for analysis and reporting. When you model data, you create a visual representation of the data for storage in a data warehouse.
Here is a list of common tools to transform and model data:
Easy-to-use, user-friendly data analytics & visualization tools are a key feature of a modern data platform. The use of self-service analytics in particular allows users to create queries and reports with little or no assistance from IT or data specialists, enabling them to make informed decisions quickly.
These solutions are the top solutions on the market:
When building a data platform, it is important to look very closely at the availability, usability, integrity, and security of the data throughout the data stack. Effective data governance ensures that data is consistent, trustworthy and not misused. This is becoming increasingly important due to data protection regulations and policies.
Here are our favorites:
areto Data Platform architectures are designed for longevity and sustainability, so that they will still represent a state-of-the-art infrastructure several years from now.
Cloud computing and AI can support to use resources more efficiently and reduce carbon footprint by improving data.
Cloud tools offer concrete possibilities for optimizing CO2 emissions, e.g. Power BI or SAP.
A Modern Data Platform optimizes resource utilization through elasticity of cloud technologies.
The areto reference architectures for building a modern Data Platform are based on five pillars: operational excellence, security, reliability, performance efficiency, cost optimization.
Optimal design of systems operation and monitoring as well as continuous improvement of supporting processes and procedures
Protection of information, systems, assets, risk assessments and mitigation strategies.
Maximizing ROI through the continuous process of improving the system throughout its lifecycle.
Ensure security, disaster recovery, for business continuity as data is mirrored across multiple redundant sites.
efficient use of computer resources, scalability to meet short-term peaks in demand, sustainability
Become a data-driven company with the areto Data Platform experts!
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