Businesses are making data analytics a priority like never before — so much so that, according to a recent TDWI study, 82% of businesses are prioritizing their budgets around new technologies and services for analytics and business intelligence.

To derive value from their data, however, companies must efficiently sort through it. 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.

Data warehouse characteristics

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.

The overall data analytics process involves several stages:

  1. Extraction: Information is collected from different areas of the business, including company financial records, customer transactions, and inventory.
  2. Loading and storage: Data is loaded in the warehouse area where it can be accessed.
  3. Transformation: The data may be checked for duplications or discrepancies, and organized for further use.
  4. Analysis: The data is available for use in business intelligence, machine learning, and analytics.

Data warehouses vs. data lakes vs. data marts

Although a data warehouse is an effective and useful way to store 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.

A data mart is similar to 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.

Data warehouses, data lakes, and data marts perform different duties. Businesses may use all three for different purposes.

Gaining the benefits of a data warehouse

Data warehouses — both on-premises and in the cloud — let businesses analyze complex data, yielding benefits in several areas.

Once you’ve accepted the value of having a data warehouse, you need a way to populate it with the data you have in your existing databases and SaaS tools. That’s where Stitch comes in.

Stitch can load data to your data warehouse from more than 90 data sources. Stitch provides a secure, easy-to-use data pipeline that’s also a bridge to business intelligence. Sign up for a free trial and get data into your data warehouse in minutes.

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