What Is A Streaming Data Warehouse?
Traditional data warehouses are designed to store batches of data, made available for analysis after the fact. Enterprises are no longer focused primarily on batch data because data-driven business decisions must be made in real time. Now they must incorporate streaming, location, and machine learning data into their data warehouse architecture. While the traditional data warehouse was not designed for real-time query and data analysis, today’s streaming data warehouse can support a broad range of data sources and real-time analytics on data as it is ingested, informing business choices.
The Kinetica Streaming Data Warehouse is fully SQL 92-compliant, while providing a full set of APIs to embed analytics into applications. By taking an API-first approach to building data-driven applications, a streaming data warehouse is able to present data at any point of user interaction, giving the business the flexibility to use the tools, apps, and platforms it prefers across departments.
A traditional data warehouse is a system for storing and reporting on batches of data. It is a passive analytical process because it does not take place in real time; rather, the data typically originates with multiple sources, and then is moved to the data warehouse for long-term storage and analysis.
A streaming data warehouse can ingest and store data, while analyzing that data in real time as it is received. The key difference is that traditional data warehouses can perform analytics, but cannot run them in real time and are limited in the type of analytics they support. With a streaming data warehouse, businesses get up-to-the-second results that incorporate all their data in a powerful, unified platform that can transform data into immediate, usable insight. The Kinetica Streaming Data Warehouse uses streaming analysis to capture the latest information from all different types of data, and combines it with the power of location intelligence and machine learning-powered predictive analytics.
The Advantages of a Streaming Data Warehouse
Traditional data warehouse results are slowed down by the time it takes to move data between separate systems and to modify data pipelines when analysis needs to change. But now, with a single platform to ingest and analyze your data, you can accurately detect new patterns and glean new insight.
Typical streaming analytics are too shallow, and traditional data warehouses take too long to load data and query it after the fact. With a streaming data warehouse, you can analyze as fast as you can stream, with up-to-the-second results, and event detection with near-zero latency.
Deploying machine learning models for real time analysis requires infrastructure that can calculate features with streaming data in real time, and geospatial analytics and visualization are slow, reducing accuracy. A streaming data warehouse offers machine learning at scale, and displays up-to-date, rich geospatial data at interactive speed.