Businesses are making data analytics a priority like never before. In fact, according to the Dresner Advisory’s Business Intelligence Market Study 2021, organizations in the retail/wholesale, financial services, and technology industries alone are planning 50% or more increases their annual business intelligence (BI) budgets.
Leveraging big data for data analysis is clearly a path that competitive businesses want to take to improve their decision making. But deriving value from an amount of data that grows every day requires companies to first be proficient in data management so that they can perform high-quality data analysis. A data warehouse stores and organizes various types of data — historical, operational, transaction processing, and metadata — from a variety of business processes for analytical use, improving data accessibility and enhancing a business's ability to make bottom-line decisions.
A data warehouse is a central repository that stores current and historical data from disparate sources. It's a key component of a data analytics architecture, providing proper data management that creates an environment for decision support, analytics, business intelligence, and data mining.
An organization’s data warehouse holds business data from multiple sources, including internal databases and SaaS platforms. After the data has been loaded, it can be cleansed, transformed, and catalogued. Data quality is also checked before it's used for analytics dashboards, reporting, machine learning, and any additional needs by decision makers and other end users.
Although a data warehouse is an effective and useful way to store large amounts of data for business analytics, it's best suited for structured data defined by a schema.
By contrast, a data lake can hold both structured and unstructured data, so in addition to sources defined by schemas, it can hold raw data such as log files, internet clickstream records, images, or social media posts. Data lakes are often built on a big data platform like Apache Hadoop.
A data mart is a subset of a data warehouse, but holds data for one specific department or line of business, such as sales or finance. A data warehouse can feed data to a data mart, or a data mart can feed a data warehouse.
In summary, data warehouses, data lakes, and data marts perform different duties. Businesses may use all three for different purposes depending upon their data flows, workloads, and operational systems.
Typically, data warehouses are created with a three-tier architecture:
Historically, businesses used ETL (extract, transform, load) tools to aggregate 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 management system.
Today, however, cloud-based data warehouses from most providers — including Amazon Redshift from AWS, Microsoft Azure SQL Data Warehouse, Oracle, Google BigQuery, and Snowflake — offer flexible infrastructures with processing and storage capacity that can quickly scale based on an organization's data needs. More and more businesses are opting to skip preload transformations in favor of running transformations at query time — a process referred to as ELT (extract, load, transform). This lets business users transform raw data within a data warehouse at any time for any particular use case.
The overall data analytics process using ELT involves several stages:
Data warehouses — whether on-premises or cloud data warehouse — allow businesses to optimize their analysis of complex datasets, yielding benefits in several areas.
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