Data Warehouse Modernization with areto DWH Experts
The process of digitalization, the importance of new data types, and the explosion of data volumes are leading to major challenges for data warehouse architectures.
Modern, agile data warehousing is the basis for data science and data analytics and helps companies make quick and informed decisions to stay one step ahead of the competition.
We specialize in guiding companies step-by-step through our proven DWH approach model and building high-performance, future-proof data warehouse solutions for our customers.
Data Warehouse
Data Vault 2.0 // Cloud DWH // Data Warehouse Automation
With System and Proven Procedure Model to the Data Warehouse
What is a Data Warehouse?
A data warehouse (DWH) is a central database optimized for analysis purposes that brings together data from several, usually heterogeneous sources. The structured data warehouse often functions as a single point of truth and is thus the basis of a company-wide information strategy. The goal of building a data warehouse is to integrate data from distributed and differently structured databases. In this way, a consistent view of the source data and thus comprehensive evaluations can be made possible. In summary, aggregation and evaluation (by means of data analytics or data science) of distributed company data are of central importance with regard to process optimization, competitiveness and strategic orientation of a company.
Reasons for Building a Modern Data Warehouse
Faster queries for analytics and reporting
Consolidation of data from multiple sources
Informed decision making
Historization, historical data analysis
Data quality, consistency and accuracy
Separation of analytics processing from transactional databases to improve performance of both systems
How to Start? The areto DWH Procedure Model
At the beginning of a DWH project, it is recommended to establish a project-based agile DWH/BI Competence Center. The requirements for the new data warehouse solution must be collected and defined in close cooperation between BICC, IT and the business units. The implementation and integration of existing data sources should be agile, in short iterations. This procedure reduces the development effort to a minimum and increases the delivery speed (“time-to-deliver”). The areto DWH approach model enables requirements to be transformed into results and presented in a short period of time (“time-to-market”) through close business interaction. It includes an iterative implementation of small project contents with a high degree of automation and short release cycles.
Data Integration - ETL vs ELT
With Extract-Transform-Load (ETL) solutions, organizations have traditionally moved data into the data warehouse to prepare it for data analysis. Another way is Extract-Load-Transform (ELT). ELT extracts data from the source, loads it unmodified into a target platform – such as a cloud data warehouse – and then transforms the data by harnessing the power of an analytic database such as Exasol or Snowflake. The ELT approach streamlines and overcomes the challenges of previous ETL solutions – such as complexity, inflexibility, low speed and high cost.
Data Modeling
Data modeling is the foundation for performance and flexibility of a data warehouse. It plays a crucial role in all layers of the system and connects ETL processes with data analytics and data science. Since each of these areas has its own unique modeling requirements, we take special care to create powerful structures. Our structured approach and the many years of experience of our experts, even with complex business intelligence systems, guarantee the best results with high data quality.
Data Warehouse Automation
Traditional data warehouse solutions can no longer keep up with current challenges such as real-time analysis, new data types and Big Data. By the time new user requirements are implemented, information needs have changed, become redundant, or new aspects have been added. However, standardization and automation make DWH processes more effective. In the interest of our customers, we ensure that data integration is standardized as far as possible. The increasing adoption of Data Vault as a data modeling method for DWH has led to the development of numerous Data Warehouse Automation (DWA) solutions. By combining leading DWA tools with areto’s technical expertise, our customers can realize immense time and cost benefits.
Data Warehouse Automation Solutions
DWH Architecture and Modeling Approach Data Vault 2.0
The Data Vault architecture and modeling approach enables quick understanding of data with its simple and understandable modeling paradigms as well as naming conventions. Data Vault combines the best of dimensional and normalized modeling. This makes Data Vault modeling scalable, flexible, and inherently consistent. Data Vault modeling is adaptable to an organization’s unique needs and provides optimal support for agile process models.
Data Vault revolutionizes the architecture of the data warehouse through its new way of data integration and data provisioning. Due to the high standardization of the processes, it is possible to automate data provisioning to a very high degree.
With Data Vault you create new opportunities and perspectives to grow your business and lead it into the future.
Cloud Data Warehouse
As we have seen, increasing data volumes, new data types, more complex processes, and increasing business unit requirements are leading to new challenges for DWHs and analytics. At the same time, IoT and cloud solutions are changing the way data is stored, structured and accessed.
Cloud data warehouses make it possible to respond adequately to the changing requirements. Using cloud data warehousing, enterprises today can scale horizontally to meet either compute or storage requirements, depending on their needs. This can significantly reduce the costs associated with over-provisioning servers for traditional DWHs.
Another advantage of the cloud data warehouse is flexibility. In the past, IT teams had to estimate how much storage capacity was needed for their business units. The easy scalability of cloud data warehouses almost completely eliminates the risk of miscalculations that cannot be revoked in the long term.
Benefits Cloud Data Warehouse
Scalability
Scaling data warehouses in the cloud is much easier compared to on-premise warehouses.
Costs
Cloud-based warehouses are cheaper to set up because there are no hardware or upfront licensing costs.
Time-to-Market
It is quick and easy to get a cloud data warehouse up and running. Deploying an on-premises data warehouse takes much longer.
Performance
Cloud data warehouses are optimized for analytics. High performance for complex queries through massively parallel processing (MPP).
Improving of Reporting and Analytics through Data Warehousing
Up to now, attempts to make company-wide data-supported decisions have failed in many companies due to a simple and yet very human phenomenon: Each department works for itself, collects its own data, and when it comes to cross-departmental coordination or reporting to the management, it also only refers to its own data.
So it often happens that departments believe they are talking about the same events and key figures and yet mean completely different processes. This can lead to serious misunderstandings and simply thwart business planning across all channels in which the company is active. Not only controlling, business intelligence or cross-channel marketing depend on clear values, but management in particular can only make valid decisions when it has a realistic picture of the situation. All too often, valuable time is wasted with lengthy attempts to explain the situation – in the worst case, without any results.
A data warehouse built by areto provides a single point of truth (SPoT), a clear common data basis for all departments. With the areto process model, companies manage to get all departments talking about the same content. The advantage: If you start from a single truth, you avoid frictional losses. Time and energy previously spent explaining differences in data results and defining terms are then free for more productive considerations. Those who don’t have to rack their brains over data-related differences in results can make more realistic forecasts and act more responsibly.
With business intelligence tools from Tableau, MS Power BI, Pyramid Analytics, IBM Cognos, ThoughtSpot, and SAP BI, you’ll be well-positioned to analyze and visualize data.
areto's DWH Experts Support
- big data warehouse architectures
- cloud data warehouses
- near real-time data warehouses
- GDPR compliant data warehouses
- data warehouse modeling
- master data management (MDM) - integration of MDM into your data warehouse
- the design and integration of a data science platform into the data architecture
Data Warehousing Know-how Video Library
Strategische Entscheidungen schneller treffen - Datavault Builder - Exasol - areto
Data Vault Automatisierung mit Matillion und areto
Snowflake Cloud DWH - Don´t be a Snowflake - use Snowflake!
Logical DWH - Datenvirtualisierung bei hessnatur
Snowflake Cloud DWH - Datenversorgung mit Kafka und dem areto Data Chef
Logical DWH - Datenvirtualisierung bei Springlane
DWH Modernisierung - Data Vault verleiht Flügel!
Become a data-driven company with areto data warehouse 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 DWH-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 DWH-solutions.
Workshops / Coachings
In our Microsoft workshops and coaching sessions, you will gain the necessary know-how for setting up a modern DWH. The areto DWH-rainingCenter offers a wide range of learning content.
Proof of Concepts
areto Data Warehouse Customers
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