Managed Warehouse Services

While a database is employed to collect huge amounts of structured real-time data generated by a particular piece of software, a data warehouse stores both current and historical data taken from multiple sources (internal and external).

Databases (e.g., MongoDB, Cassandra, or Redis) are rather simple solutions that support Online Transactional Processing (OLTP). Data warehouses, on the other hand, use an Online Analytical Processing (OLAP) system and are more complex and functional.

One more notion related to information storage is a data lake. Data lakes like AWS S3 or Google Cloud Storage store data in a raw format. It can be both real-time and historical data extracted from disparate sources.

A data warehouse is a cutting-edge approach to data analytics and storage. Moreover, it's a key Business Intelligence component. Amazon Redshift, Google BigQuery, Oracle Autonomous Data Warehouse, and Snowflake are data warehouse solutions.

As a rule, data warehouse services encompass consulting, implementation, testing, and maintenance. But here at Qulix, we also offer managed warehouse services, migration of existing infrastructure to cloud data warehouses, and DWaaS.

The DWaaS model presupposes that a cloud service provider keeps control over a data warehouse (i.e., configures and manages it) and offers it to customers on a subscription basis.

Great scalability and lower maintenance costs are the major benefits unlocked by DWaaS solutions.

The major data warehouse components involve:

• ETL (Extract, Transform, Load) that turns data into a format compliant with those of a data warehouse;
• Metadata that describes the stored data and facilitates its search;
• SQL Query Processing that allows analysts to extract insights from data;
• Data layer that ensures access management;
• Security policies that predetermine data protection mechanisms.

Amazon Web Services (AWS) offers a wide range of cloud-based solutions. Amazon Redshift is one of them. It employs SQL and machine learning to analyze various types of data across data warehouses, databases, and other data sources.

A data warehouse is the backbone of thorough data analysis and Business Intelligence. It allows enterprises to collect and store sensitive data in one place, which streamlines its management. Besides, when leveraging data warehousing solutions, company leaders get a clear vision of business processes, make better-informed decisions, and increase ROI.

Among the most well-known data warehouse service providers you may find Microsoft Azure Synapse, Snowflake, Amazon Redshift, IBM Db2 Warehouse, Oracle Autonomous Data Warehouse, and Google BigQuery.

Snowflake, Firebolt, Google BigQuery, IBM Db2 Warehouse, Amazon Redshift, and Azure Synapse Analytics head the top list.

If your enterprise is drowned in semi-structured data that is scattered across several storage places, it's high time to create a single data warehouse. In this way, you will effectively keep and process huge volumes of information, adjust all the data to an easy-to-grasp format, and empower your teams with intuitive reports and dashboards.

Business Intelligence (BI) is a set of technologies and strategies a company leverages to meticulously analyze business data and turn it into valuable insights. BI involves big data analytics, mining, and visualization, as well as special infrastructure and tools.

With BI tools, business users may effectively work with real-time, historical, internal, external, unstructured, and semi-structured data.

The key BI categories are:

• Descriptive analytics aimed at interpreting the historical data;
• Prescriptive analytics that allows business users to build further strategies based on the analysis of historical data;
• Predictive analytics employed to anticipate possible trends and risks.