This is the second of three posts about the new LiveView 1.4 release.
StreamBase LiveView is a radical departure from traditional database and business intelligence products. On the surface, it’s like a database – users pose ad-hoc queries, and LiveView provides a data set that matches those queries.
But LiveView is different in two fundamental ways: first, queries are run against live, streaming data – like stock market data, operational analytics, or even Twitter data streams. Second, these queries are run continuously and query results are updated continuously. This continuous processing and re-evaluation of queries is what gives LiveView its name – you get a live view of data in motion.
LiveView 1.4 continues to expand the capabilities of this continuous query processing model, fusing historical data with real-time continuous queries of live data.
Fusing Live and Historical Data for End Users
LiveView provides real-time analytical visibility into data in motion. But once you give a head of trading a live view of his risk and P&L, the first question he will ask is, “How is my risk now different than yesterday’s risk? Than last week’s risk? Than last month’s risk?” Answering these kinds of questions requires the fusion of real-time and historical information. LiveView 1.4 makes integration of live and historical data easier.
LiveView 1.4 adds a new table type - JDBC tables. JDBC table support lets IT administrators connect to a historical database and define queries to be executed against that database. To an end user, LiveView makes JDBC tables look just like live tables, which allows users to access both types of data – live and historical – in the same way, with one user interface.
Historical data is often stored in data warehouses with support for SQL access, and it is with this kind of stored historical data that LiveView 1.4 is designed to integrate.
Fusing Live and Historical Data for Back Testing
Historical contextualization of real time data drives better decisions. For example, an order may not be “large” on an absolute scale, but it might be large for that client. A new algorithmic order that is underperforming the benchmark may represent the first time that client has ever used that algorithm. Looking at historical trading volumes, by client, or by security, can highlight changes in client behavior that may represent opportunities for relationship building.
JDBC table support in LiveView 1.4 helps LiveView customers test these assumptions by overlaying historical data with live data.
Using LiveView to Populate Historical Data Stores
Not all organizations have existing historical data repositories. In some cases new systems are created to store this data, and need to be populated. LiveView can easily populate historical databases based on the real time data it has captured, either with batch end of day loads or parallel capture.
In the batch scenario, a revolving set of data is removed from the live LiveView system (e.g., closed orders) and dumped into the historical store.
In a trickle scenario, the same LiveView publisher that delivers data to LiveView trickle loads the historical store using the JDBC table.
Live and Historical Data Fused to Provide Insight
The purpose of LiveView is to collect, analyze, and stage live data, enabling every decision to be made with up-to-date data. With the new ability to fuse historical data with live, streaming data, IT has new options to slice and dice both types of data, with one platform.