A recent International Data Corporation (IDC) DataSphere forecast report notes that the compound annual growth rate of global data creation and replication will reach 23% between 2020 and 2025.
Another study suggests global data creation will grow to over 180 zettabytes during that same period.
Fortunately, companies of all sizes and types can use data warehouses to collect, store, and analyze data on demand.
To help you get started, we have put together this blog post. This blog aims to help you understand the concept of a data warehouse, its role in today’s business landscape, and 9 top data warehousing tools that you can consider to set up your first data warehouse.
What is a data warehouse?
Google defines a data warehouse as an enterprise system used for the analysis and reporting of structured and semi-structured data from multiple sources, such as point-of-sale transactions, marketing automation, customer relationship management, and more. A data warehouse is suited for ad hoc analysis and custom reporting. A data warehouse can store both current and historical data in one place and is designed to give a long-range view of data over time, making it a primary component of business intelligence.
Data warehouses can be deployed on-premises, in the cloud, or a hybrid environment. However, traditional data warehouses or on-premises warehouse solutions often require substantial up-front investment and longer lead times.
In comparison, cloud data warehouses can be set up rapidly and are managed and hosted by independent cloud service providers such as Google Cloud and Amazon Web Services (AWS). Businesses also benefit from a cloud environment's inherent flexibility, resiliency, and more predictable cost and data security features.
Cloud-based warehouses are also easier to set up and manage than their on-premises counterparts since companies do not need to purchase hardware and incur additional infrastructure expenses.
The role of data warehousing
Data warehousing tools have been in existence for more than three decades. However, in recent years, data warehouses have grown in popularity and usage due to the emergence of new data types and data hosting methods.
Today’s data warehousing tools are built to enable organizations to unify all data types from different data sources, such as data from the supply chain, customer relationship management (CRM), and enterprise resource planning (EPR).
Data warehouses are also sometimes confused with databases. However, both systems are different and serve different purposes.
While both are relational data systems, databases store current data, whereas data warehouses store both current and historical data. In addition, databases often extract information from one source, while data warehouses pull all types of data from multiple sources.
Industry-leading data warehouse tools:
- Amazon Redshift
Amazon Redshift is a cloud-based, fully managed, petabyte-scale data warehouse service. The cloud-enabled DW tool allows you to start with a few hundred gigabytes of data and seamlessly scale to a petabyte or more. Amazon Redshift is a feature-rich data warehouse service that data-driven companies use to collect, store, and analyze data from different sources to gain new insights and drive business growth.
- Google BigQuery
Google BigQuery is a powerful and cost-effective data warehousing service that allows you to unify, manage, and govern all types of data (structured, semi-structured, and unstructured data). With built-in capabilities to ingest large amounts of data and make it immediately available to query. BigQuery’s cloud-based DW tool enables data scientists and analysts to access and analyze data across clouds quickly and securely.
- IBM Db2 Warehouse
IBM Db2 is an analytics data warehouse that enables businesses to consolidate all their data in a central repository and use its in-database analytics to drive business results. You can consider a Db2 warehousing solution in a range of scenarios: when your organization needs its data to stay on-premises due to privacy concerns or intends to leverage the flexibility and scalability of the cloud without compromising on the data integrity or if it plans to use a hybrid architecture to manage and secure its data workloads. Db2 warehouses provide scalability and performance through their MPP architecture and can be deployed in a range of environments.
Snowflake is a cloud-based data warehouse that can be set up within minutes to accelerate analytics to drive business intelligence. Companies prefer Snowflake for its incredible performance and ability to seamlessly scale up for larger data volumes and scale out to support a growing number of users.
Both startups and large enterprises use PostgreSQL as their primary data warehouse to unify data types and generate business insights. PostgreSQL is an advanced, open-source object-relational database system with robust feature sets, including granular access controls, online/hot backups, and point-in-time recovery.
In addition to being free and open-source, PostgreSQL is also known for its proven architecture, extensibility, reliability, and data integrity, making it the open-source database system of choice for organizations across many industries.
- Azure Synapse Analytics
Azure Synapse Analytics provides more than just a data warehousing solution; it’s way more comprehensive and helps organizations meet their needs for data integration, enterprise data warehousing, and big data analytics.
With Azure Synapse, teams can query data on their terms and gain immediate access to data and analytics for their business intelligence (BI) and machine learning needs. Organizations and data teams choose Azure Synapse to gain an end-to-end view of their business and democratize data access across business lines.
- Oracle Autonomous Data Warehouse
Oracle Autonomous data warehouse is a cloud-based tool that is simple, fast, and elastic. This tool helps organizations avoid the need to undergo the complex and time-consuming process of setting up a data warehouse and provides numerous in-built self-service tools to help data scientists, data analytics, and non-experts to discover business insights using data of any volume and type.
The Autonomous data warehouse keeps data secure by encrypting it at rest and in motion. An IDC report also noted that using the Autonomous tool helps companies reduce operational costs by an average of 63 percent.
- Amazon S3
Amazon S3 or Amazon Simple Storage Service provides cloud-based object data storage for various use cases such as websites, backups, cloud-native applications, and analytics. S3 uses the same storage infrastructure Amazon.com uses for its e-commerce network and helps data-driven teams retrieve data from anywhere. This tool manages data with an object storage architecture that leads to industry-leading scalability, security, data availability, and performance.
- Teradata Vantage
Teradata Vantage, formerly known as Teradata Database, is built on a Massively Parallel Processing (MPP) architecture that helps effectively manage large volumes of data and gain immediate access to business insights.
Leading organizations across communications, media and entertainment, financial services, retail, and more embrace Vantage capabilities to inform their mission-critical decisions. Teradata is cloud-agnostic, too, and is compatible with Microsoft Azure, Google Cloud, Amazon Web Services (AWS), Teradata Cloud/Customer Cloud, and commodity hardware running VMware virtualization software.
To stay ahead of the competition, today’s businesses need to bring all their disparate data sources together to unlock value. However, traditional data storage and management tools require substantial upfront investment and resources to manage the infrastructure.
Cloud-based data warehouses emerge as a better alternative to meet ever-increasing data challenges. Organizations can leverage these cloud warehousing solutions to rapidly create centralized data repositories to serve as a single source of truth and increase efficiency across business lines.
Fortunately, companies have various options for choosing a DW tool, such as Amazon Redshift and Google BigQuery; however, you should carefully compare each solution, weigh the pros and cons, and choose the tool that best aligns with your company’s data strategy and teams’ requirements.