Agile Data Warehouse with Data Vault
A Data Warehouse is a software architecture that for many years has been helping companies recover valuable knowledge from their different IT systems. The reality of the situation in which this technology is being implemented, however, has changed dramatically in recent years: These days, many companies are producing disproportionately more data and the associated reaction rates for analysing this information has been drastically shortened. Simultaneously, the thirst for knowledge on the part of companies and organisations has been increasing. This means classic Data Warehouse approaches are soon driven their limits. Data Vault promises to fulfil these new requirements and offers a promising approach to enhance and modernise the traditional Data Warehouse concept.
The limits of traditional Data Warehouses
When faced with extremely high data volumes, scaling a Data Warehouse can be very difficult. And for companies using commercial database software may also result in high licensing costs. This acts as a deterrent for many companies, which is why they fail to analyse their data and make use of the knowledge it contains.
Because more and more data in non-standard formats is becoming the focus of analysis, relational databases can quickly be pushed to their limits.
To satisfy these new demands, new technologies are being brought into play.
Data Warehouse modernization with data vault
Data vault is the current answer to many challenges that data warehouse architects face, because the innovative data warehouse modeling promises timely developments and an faster time-to-market.
Especially companies that have to load large volumes of data in a short time and companies that develop their business intelligence applications in an agile way benefit greatly from the data vault methodology.
The business value behind the data vault concept:
- Massive reduction in development time when business requirements are implemented
- Faster return on investment for DWH set-up / modernization
- Management and adherence of compliance requirements (Basel, BCBS 239, etc.)
- 100% auditability through historization and traceability of all data up to the source system
- Reporting date related evaluations: data from archived data can be displayed / restored for the desired reporting date. Report statuses can be compared with each other.
To take advantage of data vault, it is not necessary to completely rebuild an existing data warehouse architecture. It is possible to build new aspects of a data warehouse solution, with concepts and methods of the Data Vault and without losing the existing components. This is feasible by clearly assigning which areas are located where in the architecture. Also the partial use of data vault is possible.
Are you interested in a pilot project? We implement your special business case with Data Vault.
Enhancing Data Warehouse with Hadoop, NoSQL & more
A number of technological approaches have been developed to overcome the limitations of the RDBM system: NoSQL databases, Apache Hadoop, and analytical databases.
Hadoop as a Data Warehouse platform
Apache Hadoop takes up where other, traditional Data Warehouse systems have reached their limits. The essential problem with using conventional Data Warehousing technologies is the rapid rise in operational costs when processing large amounts of data. In addition, more and more unstructured data is being produced that just does not fit into the logic of a standard Data Warehouse. Hadoop is not a database. Instead, it consists of and relies on the distributed file system HDFS and the MapReduce framework for processing data.
Comprehensive view of enterprise data
Analyses of integrated sources of data
Time savings through standardised access to data
Automated and standardised determination of key metrics
Knowledge gains by linking information (data mining)
Company data comparable over time
Time Traveling (reporting date related evaluations)
Auditing capability due to historization of all data stored in the DWH
Near-real time loading thanks to high parallelizability
Seamless integration of NoSQL/unstructured data
Data model easily extendable for all subject areas
Automatable ETL templates available
Design and modelling of a Data Warehouse
As an experienced consulting company in the BI sector, we utilise powerful open source tools like Pentaho Data Integration (PDI) to develop the extraction, transformation and loading (ETL) processes you need. At the database level, we use interesting technologies such as NoSQL, PostgreSQL and Neo4j. These technologies enable us to implement complex requirements.
Development, deployment and operation of data vaults
With our Pentaho Data Vault Framework, we offer a quick and easy way to implement a data warehouse with Data Vault. You are 2-5x faster when building an agile data warehouse. Not only do you save time, you are also faster time-to-market.
Are you planning a DWH project or do you want to modernize your data warehouse?
Pentaho from Hitachi Vantara is the leading software for data integration and big data analytics. Hitachi Vantara offers a comprehensive solution portfolio for Big Data, Internet of Things and Cloud. it-novum is Hitachi Vantara's Big Data Insights and IoT partner and the largest Pentaho implementation and training partner in EMEA.