Data warehouse solutions

Today’s complex analytics workloads involve a diverse array of data sources and types. These range from structured transactional data residing on premises to unstructured, born-on-the-cloud data flowing in from Internet of Things (IoT) sensors and mobile devices. For the most impactful insights, your business analytics teams need all of this data integrated. Choosing the right data warehouse platform or combination of solutions can help optimize your results.

Cloud data warehouse
For analyzing data that is born in the cloud, a cloud-based data warehouse might be best. It allows you to analyze data where it resides to speed results and reduce complexity. You also gain the deployment speed, rapid scalability and budgeting flexibility of cloud solutions.

On-premises data warehouse

When data already exists on premises or when government regulations restrict moving data across state or country lines, an on-premises data warehouse might be the best choice. Again, you gain the efficiencies of analyzing data where it resides and avoid the costs of moving large amounts of data to another environments. You can also retain tight control over your data while minimizing analytics latency.

Integrated data warehouse appliance

An integrated analytics solution that combines hardware and software can offer high performance while minimizing the management burdens of operating a traditional “software-defined” data warehouse. These solutions support a variety of data sources and types as well as fast-growing data volumes. They may include the latest data science technologies, such as machine learning or AI, to support your advanced analytics initiatives.

Hybrid environments

Many businesses can benefit from a combination of platforms. The key to capitalizing on this approach is to make sure the solutions have a common underlying platform. It  could share a common SQL engine, embedded analytics capabilities, common tools and underlying data software.

You could also consider an integrated data and AI platform such as IBM Cloud Pak for Data, which modernizes how you collect, organize and analyze data. Built on the Red Hat® OpenShift® open source platform to support hybrid multicloud deployments, it includes the IBM Db2 Warehouse among its numerous data management, integration and analytics capabilities designed to fuel innovation with AI.

The different data storage systems align with the types and volume of data you need to store as well as how the data will be used.

A database houses structured data and is limited in the volume of data it can accommodate. It is used primarily for fast queries and transactional processing.

A data warehouse also houses structured data but can accommodate larger volumes of both current and historical data from multiple sources. Data is organized into schemas to be used for operational data analysis.

Finally, a data lake houses massive volumes of raw data – structured, semi-structured and unstructured – opening the door to deeper analysis of data not previously accessible. The data is simply stored, not organized into schemas. It is not transformed until needed. Data lakes are commonly built on big data analytics platforms such as Apache Hadoop.