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Data Warehouse and Data Analytics

Data warehouse analytics leverages large volumes of disparate data which has been centralized in a single repository, known as a data warehouse, for use in data analysis, data discovery, and self-service analytics.  The emergence of data warehouses has largely been driven by the need for a higher level view of business metrics and the need to de-compartmentalization business applications used in different departments.

A data warehouse can store and organize historical, operational, and transactional data for analytical use, improving data accessibility and enhancing a business’s ability to make bottom-line decisions.A data warehouse is a repository that stores current and historical data from disparate sources. It’s a key component of a data analytics architecture that creates an environment for decision support, analytics, business intelligence, and data mining.

A data warehouse holds data from multiple sources, including internal databases and SaaS platforms. After the data has been loaded, it can be cleansed, transformed, catalogued, and checked for quality before it’s used for analytics dashboards, reporting, machine learning, or anything else.

Historically, businesses used ETL tools to pipe data into expensive on-premises data warehouse systems. Due to the limited capacity of these expensive systems, business users needed to perform as much prep work as possible before loading data into the system. Today, however, cloud-based data warehouses — including Amazon Redshift, Microsoft Azure SQL Data Warehouse, Google BigQuery, and Snowflake — offer flexible infrastructure whose processing and storage capacity can quickly scale based on an organization’s data needs. More and more organizations are opting to skip preload transformations in favor of running transformations at query time — a process referred to as ELT. This lets business users transform raw data within a data warehouse at any time for any particular use case.

A data warehouse maintains strict accuracy and integrity using a process called Extract, Transform, Load (ETL), which loads data in batches, porting it into the data warehouse’s desired structure.

Data warehouses provide a long-range view of data over time, focusing on data aggregation over transaction volume. The components of a data warehouse include online analytical processing (OLAP) engines to enable multi-dimensional queries against historical data.

 

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