Data Warehousing Consulting

By taking advantage of data consulting services, you may gain total control and a clear vision of business-critical information. As a rule, such services encompass data strategy elaboration, data governance, data management, data analysis, and data visualization.

A data warehouse is a centralized repository that stores both real-time and historical data extracted from multiple sources (internal and external). Unlike a database that supports Online Transactional Processing (OLTP), it employs an Online Analytical Processing (OLAP) system and is a key component of Business Intelligence. Also, you should not mix a data warehouse solution with data lakes that store information taken from disparate data sources in a raw format.

As a rule, data warehouse services involve consulting, implementation, testing, and maintenance, as well as migration of existing infrastructure to cloud data warehouses, and Data Warehouse as a Service (DWaaS).

The five pillars that ensure a smooth performance of a data warehouse are:

• ETL (Extract, Transform, Load) that adjusts data to a format required by a data warehouse;
• Metadata that provides descriptions for the stored data and simplifies its search;
• SQL Query Processing that allows analysts to derive insights from data;
• Data layer that is responsible for access management;
• Security policies that define data security mechanisms.

A data warehouse that is managed by a third-party service provider and delivered over the internet is considered a cloud data warehouse project. Here at Qulix, we carefully handpick each cloud data warehouse consultant, so that you may enjoy a scalable, feature-rich solution.

When comparing several DWHs, take into consideration the integration options, storage capacity, supported data types, security mechanisms, and subscription models offered by a cloud data warehousing provider and pick the one that better answers your data needs.

For starters, you may pay attention to such vendors as Google BigQuery, Amazon Redshift, Snowflake, Azure Synapse Analytics, and IBM.

Among the most well-known data warehouse vendors you may find Snowflake, Firebolt, Google BigQuery, IBM Db2 Warehouse, Amazon Redshift, and Azure Synapse Analytics.

If your company is mired in unstructured data that is stored in various locations, and you fail to get an overall picture of business performance or track KPIs, it's time to make use of data warehouse consulting services. As a result, you will get perfectly arranged analytics insights on the most crucial business processes and empower your teams with easy-to-grasp reports and dashboards.

A competent data warehousing specialist is the one who can develop, configure, and maintain a robust data warehouse. Also, such professionals should:

• Create functional and technical documentation for DWHs;
• Define methods and techniques for data warehousing procedures;
• Ensure troubleshooting support for DWHs;
• Keep control of the data stored in a DWH;
• Have a good command of several computer languages and data analysis techniques;
• Have experience with various BI tools.

To become a data warehousing specialist, you need to have at least a bachelor's degree in the IT field, pass DWH-related courses, and be a quick learner. Hands-on experience with BI tools and business analytics techniques will also be a competitive edge.

Data warehousing consultants are well-versed specialists who have deep expertise in the business analytics domain and a keen understanding of databases.

Business Intelligence (BI) is a set of tools and techniques a company employs to scrutinize business data and turn it into valuable insights. BI encompasses big data analytics, mining, and visualization, as well as advanced infrastructure and tools.

The major BI categories are:

• Descriptive analytics (interprets the historical data);
• Prescriptive analytics (helps business users build further strategies based on the analysis of historical data);
• Predictive analytics (enables specialists to anticipate possible trends and risks).

By leveraging BI tools, business users may effectively analyze and process real-time, historical, internal, external, semi-structured, and unstructured data.