To get started using SQLstream Blaze, install SQLstream s-Server following the steps in the Installation Guide. s-Server installs with everything you need to view data visualizations, including s-Dashboard and the s-Server WebAgent. The installation guide also describes using the Mochi demo, which uses both of these tools to visualize an alert system for bank login failures. Once you have viewed the Mochi demo, you can install SQLstream StreamLab to start creating your own visualizations based on the Mochi data.
This guide offers an overview of SQLstream s-Server concepts including streams, the system architecture, application models. It also compares streaming SQL with RDMS SQL.
This guide provides instructions on how to install SQLstream s-Server, SQLstream s-Studio, SQLstream client tools, and SQLstream StreamLab.
This guide provides instructions for building a pipeline in s-Server.
This guide describes how to read data into s-Server from the file system, network sockets, Apache Kafka topics, AMQP messages, Amazon Kinesis streams, RDBMS systems, and other sources.
This guide describes how to write data from s-Server into the file system, network sockets, Apache Kafka topics, AMQP messages, Amazon Kinesis streams, RDBMS systems, and other locations
This guide describes how to transform data in s-Server, using SQL, User Defined Transforms and User Defined Functions.
This guide describes how to use StreamLab to parse, analyze, and display s-Server data graphically.
This document explains how to use SQLstream's integrated development environment, s-Studio, to parse, analyze, and output s-Server data.
This document describes how to use s-Dashboard to display s-Server data graphically.
This document explains how to use a customized version of a console based utility to execute SQL in s-Server.
This guide offers comprehensive documentation of SQLstream s-Server streaming SQL, including the particular implementation of CREATE, ALTER, SELECT, and DROP statements.
This guide explains how to configure, maintain, and monitor s-Server once you have installed it.
Defines key terms used for SQLstream s-Server.
SQLstream Blaze 5.1.0 introduces a number of key improvements in the way s-Server reads and writes to data systems. This wider range of data systems also appears in SQLstream StreamLab, allowing you to incorporate such systems into StreamLab analytics as well as write to these systems directly from StreamLab. StreamLab also features a wider range of data analytics.
SQLstream Blaze 5.1.0 introduces the Extensible Common Data Framework for reading and writing data. Available as both an adapter--for data streams local to the machine running s-Server--and an agent--for data streams remote to the machine running s-Server, the Extensible Common Data Framework provides a flexible, extensible way to parse and write to common industry file formats (CSV, XML, JSON) over the most popular i/o formats, including the file system, network sockets, AMQP, Kafka, and IBM MQ (formerly MQ Series). You can also define custom parsers for both the ECD adapter and agent.
SQLstream s-Server provides a plugin implementing SQL/MED access to any foreign DBMS for which a JDBC driver is available. You can use this plugin to select, insert, merge and delete on foreign tables for Oracle, PostgreSQL, MySQL, Teradata, and Microsoft SQL Server. This allows you to, for example, tail a database table and incorporate its data into s-Server analytics, or archive stream data to an external database. Merge currently does not function for Postgres and MySQL.
StreamLab 2.1 is a browser-based interface for parsing, analyzing, and displaying data streams.You can now read and write to CSV, XML, and JSON files from StreamLab. StreamLab 2.1 also lets you read and write to external data sources, including Oracle, PostgreSQL, MySQL, Teradata, and Microsoft SQL Server.StreamLab 2.1 also lets you create sinks. Sinks can serve as data sources for other StreamLab projects. StreamLab 2.1 also adds a number of new analytics, including pivot, time sort , window, partition window, and GroupRank.
For a tutorial on using new StreamLab features, please see http://thyrd.org/sqlstream/tutorials.html. This tutorial walks you through the process of installing s-Server, installing StreamLab, and creating a PostgreSQL table. It then walks you through the process of using StreamLab to analyze a log file (in this case, data on the location and speeds of regional buses), create visualizations based on this analysis, and then store this data in a PostgreSQL database.