Data warehousing concepts have evolved considerably from single stack repositories to logical warehouses, enabling real-time data virtualization and multi-dimensional data processing. Old school approaches for modern data warehousing initiatives are creating setbacks for enterprises. Conventional approaches like Enterprise Service Bus (ESB) don’t support the growing data, structured or unstructured data, and cloud-born data. Enterprises need to remodel their strategy to modernize their warehouse and harness more value out of the data.
Why is Data Warehousing Important?
Data warehousing enhances the quality of business intelligence, and so managers and executives do no longer need to make business decisions based on limited data or their gut. With all kinds of data stored in one place, data warehouses allow organizations to quickly make informed decisions on key initiatives with little or no IT support. IT can thus experience improved productivity levels as they get to focus on governance role rather than the daily operational role. This allows enterprises to offer delightful customer experiences and become easier to do business with. Additionally, companies that are well-versed with data warehouse concepts are likely to generate more revenue.
Conventional vs Modern Data Warehousing
Modern data warehousing has undergone a sea change since the advent of cloud technologies. Today’s data warehouses focus more on value rather than transaction processing. Modern data warehouses are primarily built for analysis.
It is primarily the design thinking that differentiates conventional and modern data warehouses. Conventional data warehouses cover four important functions:
- Data sources
- Infrastructure
- Applications
- Analytics
A modern data warehouse has four core functions:
- Object Storage
- Table Storage
- Computation and Processing
- Programming Languages
Here are some of the examples highlighting core differences between conventional and modern data warehouses:
Conventional data warehouse Systems
- Operational Systems:CRM, ERM, financial, and billing
- Extract Transform and Load (ETL) Systems:Decision analysis model and data
- Enterprise Data Warehouse:Operational, customers and IT data marts
- BI Platform:Knowledge Performance Index (KPI) summary systems
- Customer Value Analysis:Interactive data queries, OLAP, and static data analysis
- BI Collaboration Portal:wholesale, OEM, sales, employees and external
Modern Data Warehouse Systems
- HDFS: Hadoop Distributed File System
- HCatalog: Metadata, table, and storage management layer system
- HBase: Key-value database and columnar storage
- MapReduce: Scalable data processing framework for huge data sets
- Oozie: A MapReduce job scheduler
- ZooKeeper: Hierarchical key-value store for synchronization
- Hadoop: Open-source software framework for storage and processing of big data sets
- Hive: A high-level language built on top of MapReduce for analyzing large data sets.
- Pig: A high-level language factored into MapReduce for parallel data processing
Using the same approach for modern data warehousing leads to slow writes. Several difficulties arise when further databases need to be created to support the parallel processing of data. An enterprise-class integration tool can make the data migration more manageable and easier.
Evaluating the Data Warehouse Initiative
Enterprises should answer the following questions before embarking on a data warehousing initiative:
- 1.Can the IT Environment handle data from diverse sources?
- 2.Can the IT handle excessive volumes of data flowing through various machines and sensors?
- 3.Is there a multiplatform architecture to maximize scalability and performance?
- 4.Does the architecture supports real-time analysis of high-velocity data?
- 5.What capabilities does IT offer for data virtualization (cloud services) and data integration?
- 6.Is there a mechanism for automated orchestration, improved agility, and release life cycle?
- 7.Does the enterprise support a bi-model business intelligence model?
Answering these questions can help enterprises in envisaging a best-fit engineering solution that aligns multi-structure data into data warehouses. It enables polyglot persistence data strategy that enables organizations in taking advantage of diverse infrastructure.
Best Practices for Successful Adoption of Data Warehousing?
Define the Data Storage and Compression Formats: There might be more than one option for data storage. Each storage option might be offering distinct advantages and benefits. It is important to evaluate the data storage and formats that will work smoothly with the applications in an ecosystem.
Look out for Multi-tenancy Support: Multi-tenancysupport is important for the Business Intelligence (BI) environment. It gives the advantage of using a single software stack to serve thousands of partners & customers and make upgrades or customizations.
Review the Schema: Evaluate the nature of databases you are storing. Verify how it is being loaded, processed, and analyzed to optimize schema objects.
Ensure Metadata Management: Ensure end-to-end meta-data management for data warehousing initiatives. Metadata Management defines the success for data warehousing projects. It helps in capturing the necessary information to build, use, and interpret thedata warehouseelements.
Enterprise-Class Integration Solution for Agile Deliveries
An enterprise-class integration solution can make it easy to build data warehouse projects. However, the next big important task is to find the right solution that addresses start-to-end integration needs. Adeptia offers a cloud-based integration solution that helps you execute the right strategy for data warehousing. Know our capabilities for business intelligence data warehousing.