Data Warehouse: Everything You Need to Know

A data warehouse (DWH) is a centralized repository of data integrated from one or more data sources. The main two approaches used to integrate data into the data warehouse are Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT). The data warehouse is a core component of business intelligence, which enables structured data storing, reporting and analysis.

The DWH implementation and management can be assigned to either a company’s in-house IT team or a professional consultancy. There is also a possibility to eliminate the burden of DWH design, implementation, maintenance and support by opting for DWaaS.

Data warehouse fundamentals

  • Trends in the Data Warehouse Implementation Market

See the benefits your company can obtain by moving your DWH to the cloud, integrating big data into the DWH and turning to DWaaS.

  • Data Warehouse Pricing: Things To Be Aware Of

Get insight into data warehouse price components and the ranges of DWH costs.

  • How to Build a Data Warehouse from Scratch?

Explore a step-by-step guide to risk-free data warehouse development.

  • Data Warehouse Design: How To Structure Your Data Assets

Check out what architectural approaches are employed to design a data warehouse and choose a beneficial DWH structure for your business.

  • A Big Data Warehouse – a Want or a Need?

Find out the definition and purpose of a big data warehouse and what benefits it brings to the decision-making process.

  • Data Lake vs. Data Warehouse: Why You Don’t Have To Choose

Learn about the difference and synergy between a data lake and a data warehouse, and define how to structure your big data solution in accordance with your business needs.

Data warehouse project examples

  • Big data warehouse implementation for advertising channel analysis in 10+ countries

Pine Analytical implemented a big data warehouse and analytics solution to allow a market research company to cope with the continuously growing amount of data and conduct faster big data analysis.

  • Implementation of a data warehouse and data analytics for a telecom company

Pine Analytical designed and implemented a data warehouse and data analytics solution to enable the customer to collect data (including big data) from multiple data sources and get valuable insights into customer behavior.

  • BI implementation for a multibusiness corporation

Pine Analytical delivered a DWH and analytics solution to allow the customer integrate data from multiple applications specific to their business directions and optimize business processes with company-wide analytics.

  • BI implementation for the producer of phytotherapy products

Pine Analytical implemented a DWH as a part of a BI solution to allow the customer consolidated disparate data sources under one roof and embrace company-wide reporting.

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