GigaOm Radar for Data Lakes and Lakehousesv3.0

1. Executive Summary

Data lakehouses are systems that collect, store, manage, and process data for use in analytics workloads such as business intelligence (BI), reporting, data science, machine learning (ML), and artificial intelligence (AI)—all from a single platform. These systems play a crucial role in an organization’s ability to derive meaning from its data, make predictions, and answer questions about the business.

These platforms are important to all business levels and personas. Data scientists and data engineers develop and train ML models on the significant quantities of data that these platforms can store and manage. Business users, business analysts, and data analysts access the data in the lakehouse through client tools, such as BI applications, for on-the-fly exploration or ad hoc analytics in order to answer questions about their businesses. These users are also able to carry out more formal analytics and reporting workloads to extract insights that can be leveraged when making strategic decisions about the business.

Organizations need to manage all their data in a way that is cost-effective and facilitates fast analysis for competitive time to insight. To achieve these goals in the analytics landscape, organizations increasingly found that they needed the capabilities of both data lakes and data warehouses. Data lakes and data warehouses each have their own strengths and shortcomings, and neither was originally created to work smoothly in tandem with the other. In a competitive business environment, organizations can’t afford to deal with the complexity, latency, governance concerns, and inefficiencies that can arise when trying to cobble together a unified solution from these two separate data platform types.

Data lakehouse technology combines elements of both data lakes and data warehouses. They were created specifically to address the pain points of trying to build a reliable solution from separate data lakes and data warehouses. Data lakehouses blend the structure, curation, performance, and governance advantages of the data warehouse with the flexibility and cost-effectiveness of the data lake into a single platform.

This is our third year evaluating the data lake and lakehouse space in the context of our Key Criteria and Radar reports. This report builds on our previous analysis and considers how the market has evolved over the last year.

This GigaOm Radar report examines nine of the top data lake and lakehouse solutions and compares offerings against the capabilities (table stakes, key features, and emerging features) and nonfunctional requirements (business criteria) outlined in the companion Key Criteria report. Together, these reports provide an overview of the market, identify leading data lake and lakehouse offerings, and help decision-makers evaluate these solutions so they can make a more informed investment decision.