Guavus

The most notable thing about SQLStream is its use of ANSI SQL – though Java, Python and Scala are also supported – for executing continuous queries over arriving data streams, whereas most other vendors use proprietary languages. The query planner and optimizer is the powerhouse behind the offering. SQLstream has invested in automatic query optimisation, and with performance features such as lock-free scheduling of query execution, has removed the need for manual tuning whilst delivering excellent throughput and latency performance on a small hardware footprint. The architecture supports distributed processing over server clusters, with redundancy and recovery options.

A geospatial analytics library is included, as is a library of data collection and enterprise integration connectors, including support for Hadoop, data warehouses and messaging middleware. There is an SDK for third parties to build new connectors and data processing operators. Native support for operational data issues is included, for example handling delayed, missing or out of time order data in a way that is invisible to the user or application.

Figure 2 – SQLStream StreamLab showing connectivity options

StreamLab is aimed at embracing the business analyst with a set of drag-and-drop and visual tools for data preparation and application development. No coding is required – SQL is generated dynamically by the software – and there is an intelligent recommendation engine that will make suggestions to the user to help in building applications. With StreamLab, the user interacts directly with live data streams (which may include Kafka – see Figure 2 – or Kinesis-based data, the latter being a significant differentiator), building pipelines of stream processing and streaming analytics operations, the results of which are pushed to integrated real-time dashboards or connected to external systems and storage platforms. Note, however, that as far as machine learning and artificial intelligence are concerned, SQLStream is a platform that will host these capabilities but it does not offer the option to build these functions within the SQLStream Blaze environment. The company partners with DataRobot but does not support PMML. There are no facilities to combine real-time with historic data.

The streaming analytics space is all about speed. By making the analytics process faster, organisations seek to make more responsive, more effective and more timely decisions, ultimately trying to react in seconds, not hours, to the data that is entering their system. This is necessary not just to meet the demands of consumers, but also in competitive environments.

Of course, the big advantage that SQLStream offers is precisely that it is based on SQL and therefore requires no re-training of personnel, who are relatively easy to find and hire. However, this is not the only advantage of focusing on a declarative language and the company’s work on SQL optimisation has paid off, with SQLStream Blaze having a reputation for high performance.

The industry accelerators also represent significant advantages over the company’s competitors and we like the data preparation and auto-suggestion capabilities built into the platform. However, many of SQLStream’s competitors directly offer support for building machine learning models, in some cases supporting the training of those models in-platform. Provided you are happy using a third-party environment (such as DataRobot) to support artificial intelligence, this shouldn’t be a problem.

The Bottom Line

SQLStream is in a position on its own within the streaming market. Its use of SQL makes it unique and, given the enduring popularity of SQL, is a significant strength compared to the offerings provided by rival vendors.