Editor’s note: Learn about the current trends in the DWH market and check our data warehouse services to see which approach to implementing a data warehouse ScienceSoft’s team follows.
Today companies insist on more rigorous business requirements to data warehouses. In addition to comprehensive reporting, the companies seek DWH scalability and flexibility, constant data auditability, easy DWH management, predictive and prescriptive analytics. In the article, ScienceSoft’s data analytics experts show how the requirements in demand can be obtained with DWH trends.
- DWH scalability and flexibility
As your business grows, so does the volume, variety and velocity of your data. The inherent scalability of a cloud data warehouse allows you to adapt to the changing amount of data. Additionally, a cloud-based DWH allows quick changes in the processing capacity. Thus, scaling the data volume up or down won’t impact the performance of your data warehouse.
- Flexible pricing options
Cloud providers offer diverse pricing models and unique discount opportunities to meet their clients’ technical needs and budgets. Prices are determined by various factors: storage usage, compute usage, number of nodes, query performance, etc. For example, with Amazon Redshift, you are charged according to the amount of stored data and the number of nodes. The on-demand pricing option starts from $0.85/hour for storage and $0.25/hour for compute. You may address our article on cloud data warehouses for more details.
- Data availability
Nearly all cloud DWHs perform consistent backups automatically, which results in 99.9% data availability and fault tolerance. It means that in case of network latency, failures, be they hardware or software, there is no risk of bringing down your data warehouse solution.
Data Warehouse-as-a-Service (DWaaS) addresses the challenge of data warehouse implementation and management as its main benefit is:
- Minimizing data administration efforts
If you go for a DWaaS, your provider eliminates your hardware and software acquisition, configuration and maintenance costs. Because the provider performs DWH administration and management, you don’t have to worry about having a data warehouse team as well. You give your data to the provider, who aggregates, optimizes and stores it and saves you from having to manage your data storage infrastructure on your own.
Integrating big data tools in the data warehouse architecture allows:
- Shifting from reporting to advanced analytics
Companies can ingest vast amounts of raw data to perform advanced analytics. By combining historical business data with less structured data from big data sources (machine data, transactional data, public data, etc.), you can uncover hidden patterns, correlations and get insights that can drive business-improving actions. It’s a huge step towards accurate forecasting and boosting profit.
What Data Warehouse Solution Do You Need?
Turn to ScienceSoft to choose the best option for implementing a data warehouse in vein with trends. We will provide you with tailored recommendations to better suit your current needs and the nature of your data.
A DWH vendor with 14 years of experience, we can develop, migrate, and support your data warehouse or consult on any issue concerning your DWH.