Data Journey – Our Path to Structured Data
We offer customized solutions for all requirements.
In today's world, we collect data from many different places, and it's stored in at least as many locations. But where does all this data come from, how can it be structured and presented in a way that it can be understood, dependencies identified, and even used as a basis for decision-making? To best support our clients in the area of Data Analytics, a process model and workflow has been developed to ultimately not only answer all questions but also implement them.
We are experts in the entire process
Through a structured approach, high-quality data solutions are created that enable informed decisions.
The first step is the conception of data warehouse solutions to create a solid foundation for storing and managing large amounts of data. This involves creating appropriate data models that enable efficient organization and structuring of existing data. Additionally, workshops are used to analyze and evaluate existing data and business intelligence (BI) processes. These serve to identify requirements and align data analysis with the needs of our customers.
01. Data Modeling/Analysis
In this step, source systems are connected to integrate the data into the central system. Data pipelines are used to.
02. ETL Process (Extract Transform Load)
Databases and data lakes serve as central repositories that are set up and maintained both in the cloud and on-premises. Special attention is paid to comprehensive data security during setup and maintenance. In addition to meeting data protection requirements, encryption technologies, access controls, and regular security updates are implemented. Furthermore, regular monitoring ensures that the systems are always available and that potential security vulnerabilities are identified and addressed early on.
03. Data Storage/Management
This step ensures data quality to meet high data quality requirements. This includes the management and creation of metadata, which facilitates data access and organization. The data will be prepared for further purposes:
I. Usage in Reporting & Visualization – This preparation ensures that the data is available for analysis and predictions. AND/OR
II. Starting out in AI and Machine Learning – with the resulting Data Journey, we are laying the foundation for the AI and ML field in this process step.
I. Usage in Reporting & Visualization – This preparation ensures that the data is available for analysis and predictions. AND/OR
II. Starting out in AI and Machine Learning – with the resulting Data Journey, we are laying the foundation for the AI and ML field in this process step.
04. Data Preprocessing/Enrichment
Data visualization is carried out through dashboards, which enable a simple and clear presentation. In addition, key performance indicators (KPIs) are set up and an alerting system is integrated to quickly identify deviations or problems. Furthermore, the business department is empowered to independently access relevant data and perform analyses through self-service BI, without relying on technical support. These visualizations support the company in making informed decisions based on the analyzed data.
05. reporting & data visualization
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Learn how Denodo enables real-time data integration, governance, and AI readiness.
Technologies for successful implementation
For our solutions, we have a broad spectrum of technologies to cover the entire process.
An excerpt on the technologies currently in use, already applied in practice and projects, is presented in the overview. Due to expertise and diverse possibilities, solutions can be implemented individually here – no matter the basis. We offer the necessary know-how in every area!
| Process step | Technology |
|---|---|
| ETL processes | Azure Data Factory – Sequence Server Integration Services |
| Data storage/management | Data Lake Gen 2 Azure Blob Storage – Azure SQL Database – Azure Cosmos DB – Azure Files |
| Data processing | – Azure Databricks Synapse |
| Datenvisualisierung/ Data Analysis | – Power BI - Sequel-Server-Reporting-Services |
| Total Solutions | – Azure Synapse Analytics – Azure Fabric |
| Process step | Technology |
|---|---|
| ETL processes | AWS Glue |
| Data storage/management | – AWS Simple Storage Service (S3) – Amazon DocumentDB Amazon DynamoDB – Amazon Elastic File System (EFS) - Amazon Relational Database Service (RDS) |
| Data processing | – AWS Databricks – AWS Lambda |
| Datenvisualisierung/ Data Analysis | Amazon QuickSight |
| Total Solutions | – Amazon Athena – Amazon Redshift |
| Process step | Technology |
|---|---|
| ETL processes | - Google Cloud – Dataflow Data Fusion |
| Data storage/management | Cloud Storage – Filestore – Cloud SQL – Spanner Bigtable |
| Data processing | Google Dataproc |
| Datenvisualisierung/ Data Analysis | Looker |
| Total Solutions | BigQuery |
| Process step | Technology |
|---|---|
| ETL processes | Apache Hop |
| Data storage/management | PostgreSQL SQLite MySQL |
| Data processing | Python (Numpy, Pandas, Matplotlib, TensorFlow, Scipy) |
| Datenvisualisierung/ Data Analysis | Tableau |
| Total Solutions | Snowflake Data Platform |
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