How to Create the Best Cloud Data Warehouse

In the 1990s, as businesses embraced information systems, they realized the need for a centralized Cloud-Based Data Warehouse (DWH) to harmonize data across various applications. The concept of a DWH, pioneered by IBM researchers Barry Devlin and Paul Murphy in 1988, presented a solution. A DWH is a specialized database that automates reporting and streamlines business intelligence, enabling informed management decisions. Let's find out how to create the best Cloud Data Warehouse.

Building a Solid Cloud Data Warehouse: Tackling Complexities Head-On

When aiming to create the best Cloud Data Warehouse, several complexities need to be considered. One primary challenge is pipeline design, where data must be efficiently transferred from various sources into a single storage unit. This process often involves dealing with imperfect APIs, upload limitations, and data parsing issues. You have the option to either manually set up all the connections or leverage pre-built solutions like Owox, Alooma, or Blendo. For larger datasets, creating custom scripts for daily data synchronization is advisable.

Small businesses lacking a Database Administrator (DBA) should opt for a pipeline platform, with costs varying depending on data volume and connection count.

Data quality poses another hurdle. The data transfer process may not generate errors, but it can result in incomplete information due to API limitations. Additionally, errors might occur during variable parsing, such as encoding issues from 1C data.

Following the creation of the data repository, the next crucial step is selecting frontend tools for data visualization. Businesses require user-friendly charts, comparisons, slices, and dynamic displays.

Investing in data analytics within the company yields noticeable results. With comprehensive data at your disposal, optimizing advertising campaigns, conducting A/B tests, and validating product hypotheses becomes significantly easier.

Diagram of data warehouse architecture

1. The data comes into Data Warehouse from various sources. For example, API, databases, and third-party components.
2. Data Warehouse itself consists of 3 schemes:? Stage- for intermediate storage of data from sources, DDS- a stable layer for storing detailed data, and SDS- a layer that contains new data obtained during data processing from DDS.
3. Data in DDS is processed quite often (from less than every 24 hours and up to 1-2 hours).
4. The DDS layer is used for online access to data, but for longer storage, there is Data Lake.
5. SDS contains Dimension Tables and Fact Tables.
6. BDS scheme, which may not exist permanently (maybe abstract). In fact, it contains ready-made results for visualization and demonstration.

1. DWH and analysts are transforming business management. They use data from DWH for optimal decisions, considering a range of factors like environmental impact, employee well-being, customer loyalty, and long-term stability.

Without DWH and analysts, business management is like a blind ride on ice. Corporate storage serves as a large Data Warehouse, unlocking opportunities for businesses.

If you want to know more about why Cloud Data Warehouse services are an effective analytics tool, check the Jelvix article .