Data Platform

A data platform is the technological basis of a modern data stack and provides the functions for capturing, storing, processing and analyzing data.

What is a "Modern Data Platform"?

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.

"We are creating a communication platform based on which decisions are supported by data."

How to build a modern Data Platform ?

“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 Ingestion

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. (ETL/ELT)

Data Processing

We design and implement robust processes to make data-driven decisions repeatable, automatable, reliable, and thus manageable....

Data Analytics & Visualization

With Data Analytics & Data Science solutions, you discover the opportunities hidden in your data. areto helps you generate information from existing data. Knowledge for successful decisions.

Data Storage

Cloud-native data warehouses, data lakes, and even data lakehouses are optimal storage solutions and offer more accessible and affordable options for data storage compared to many on-premise solutions.

Transformation, Modelling & Train

Data transformation and data modeling to cleanse raw data using business logic and prepare for analyses, reports, visualizations.

Security, Governance

Data is queried, stored, processed and presented on a data platform using a variety of tools and technologies. Therefore, the consideration of security and governance and operation is indispensable.

Data Platform - Data Ingestion / Data Integration

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:

  • Matillion – Matillion offers powerful data transformation and integration solutions for cloud data warehouses with its Matillion Data Loader & Matillion ETL products.
  • Azure Data Factory: Azure Data Integration Service and Orchestrator. Provides full integration into Azure based Data Platform and multiple connectors for data connectivity. Centralized job control of all processes.
  • Apache Kafka – An open source event streaming platform for streaming analytics and data ingestion.
    Despite the numerous ingestion tools available in the market today, some data teams choose to build custom code to ingest data from internal and external sources, and many organizations even develop their own custom frameworks to accomplish this task.

Data Platform - Data Storage & Processing

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: 

  • Snowflake – Snowflake, the first native cloud data warehouse, offers a whole host of benefits for data teams in terms of cost, elasticity, scalability, ease of use, etc.
  • Amazon Redshift – Amazon Redshift, one of the most widely used options, is based on Amazon Web Services (AWS) and integrates easily with other data tools in the space.
  • Microsoft Azure – The Azure cloud platform consists of more than 200 products and cloud services and is designed to help you develop new solutions.
  • Amazon S3 – An object storage service for structured and unstructured data. S3 gives you the compute resources to build a data lake from the ground up.
  • Databricks – Databricks, the Apache Spark-as-a-Service platform, pioneered the building of a data lake, offering users the ability to leverage both structured and unstructured data, and provides the cost-effective storage capabilities of a data lake.

Data Platform - Data Transformation / Data Modelling

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:

  • dbt (data build tool) is a leading open source tool for transforming data once it has been loaded into your warehouse.
  • Azure Data Factory is Azure’s ETL cloud service for serverless data integration and data transformation with horizontal scaling.
  • Datavault Builder – with Datavault Builder, you can design and maintain your conceptual and logical data model.
  • WhereScape – WhereScape® offers WhereScape® Red and WhereScape® 3D software for building, extending, and managing data warehouses, data marts, and Big Data solutions.
  • SSIS (Sequel Server Integration Services) from Microsoft enables your organization to extract and transform data from a variety of sources.
  • Python code and Apache Airflow – For ambitious data engineers using custom code for data transformation. 

Data Platform - Data Analytics & Visualization

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:

  • Microsoft Power BI – With Power BI, you connect to and visualize any data using the unified, scalable platform for self-service and business intelligence (BI).
  • Tableau is a powerful, secure, and flexible end-to-end analytics platform for your data.
  • SAP Analytics Cloud – With SAP Analytics Cloud, business departments can simulate, plan, and evaluate business-relevant operations.
  • Cognos Analytics – For organizations that need high scalability and comprehensive analytics functionality for their business needs, on-premise or in the cloud, IBM Cognos® Analytics is the ideal solution.
  • Pyramid Analytics enables data-driven decision making for everyone in the organization.
  • Thoughtspot – Search your cloud data warehouse in a whole new way. Provide all employees* with a search experience familiar from Google, for instant analytics and insights from your cloud data.

Data Platform - Data Governance & Data Security

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:

  • Microsoft Purview Governance Portal – Microsoft Purview solutions in the Governance Portal provide a unified data governance service that helps you manage your on-premises, multicloud and software-as-a-service (SaaS) data.
  • Alation – Alation’s active data governance puts people at the center, giving employees access to the data they need and workflow guidance on how to use it.

Data Platform & Sustainability

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.

areto Data Platform reference architectures

The areto reference architectures for building a modern Data Platform are based on five pillars: operational excellence, security, reliability, performance efficiency, cost optimization.

Operational Excellence
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.

Cost optimization
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.

Performance efficiency
efficient use of computer resources, scalability to meet short-term peaks in demand, sustainability

areto customers

Become a data-driven company with the areto Data Platform experts!

Overtake the competition by making faster and better decisions!

Find out where your company currently stands on the way to becoming a data-driven company.
We analyze the status quo and show you what potential exists.
How do you want to get started?

Free consulting & demo appointments

Do you already have a strategy for your future data platform solution? Are you already taking advantage of modern cloud platforms and automation? We would be happy to show you examples of how our customers are already using areto’s agile and scalable architecture solutions.

Workshops / Coachings

Our workshops and coaching sessions provide you with the know-how you need to build a modern data platform architecture. The areto TrainingCenter offers a wide range of learning content.

Proof of Concepts

What architecture is the best for us? Are the framework conditions suitable for it? Which prerequisites must be created? Proof of concepts (POCs) answer these and other questions so that you can then make the right investment decisions. This way, you start your project optimally prepared.

We look forward to hearing from you

till sander areto

We look forward to talking with you!

Till Sander
phone: +49 221 66 95 75-0