Actian

Figure 2 - The Actian Avalanche architecture

Figure 2 illustrates the Actian Avalanche architecture. However, this will take some explanation. For example, DataFlow is a high-performance integration engine that was originally developed by Pervasive, while Zen (which was previously PSQL and before that Btrieve) is used in Internet of Things environments as an edge or mobile database, hence the gateway provided. In this context, it is worth commenting that time-series data is not currently supported by Avalanche, though it is supported in Zen. Similarly, geospatial data has not yet been implemented in Avalanche though it is supported in the Actian X database (which is targeted at hybrid transactional and analytic environments).

Vector is the database engine, and it has some notable characteristics. For instance, it exploits Intel’s vector instruction set (hence the Vector name) to process more data elements per instruction (SIMD: single instruction, multiple data) and optimises for L1 and L2 cache. Indeed, there has been considerable academic work involved in the development of Vector, which was created initially at CWI, the Dutch National Research Institute for Mathematics and Computer Science, and the technology incorporates patented algorithms for optimising the use of memory. A further patented technology known as Positional Delta Trees enables real-time updates without impact to query performance. For what it is worth benchmarks suggest that Avalanche has significant performance benefits compared to other cloud data warehouses.

The warehouse understands Spark datatypes and the optimiser treats third-party storage such as Amazon S3 and Hadoop as external tables with the ability to push-down predicates so that Avalanche supports full data virtualisation across these sources rather than mere query federation. User defined functions are available to support machine learning and there is support for R, Scala, Python, and all the usual business intelligence and analytics tools, and TensorFlow will be supported later this year. The ingestion of JSON documents is supported and the company plans to introduce features to optimise performance for the analysis of these.

Avalanche is a relatively new offering, having only been released in March 2019. As such it lacks some features that we would like to see, notably geospatial and time-series capabilities, which will be important for some IoT applications. That said, this sort of functionality are on the roadmap and expected to be released in the second half of 2020. Leaving that aside, the product shows significant promise. The company has a significant history in the database market and knows what it is talking about when it comes to performance, database optimisation, administration and management (Actian FlexPath cloud resource management) disaster recovery, high availability, security (multi-factor authentication) and so forth.

We especially like the approach Actian has taken towards migration from legacy data warehousing implementations, both in the tools that the company makes available to ease this process, and in the way that the hybrid nature of the environment will allow you to migrate to the cloud at your own pace.

The Bottom Line

Actian has always had excellent technology. It has, however, struggled with marketing and visibility. With HCL fully committed to Actian Avalanche, and the ability to leverage that company’s worldwide salesforce, there is every possibility of Avalanche being recognised as a market-leading offering within the cloud/hybrid data warehousing space.

Fig 02 - The main elements of DataConnect

DataConnect has four main components, as can be seen in Figure 2. What this doesn’t mention is Actian’s UniversalConnect, which is patented technology for developing and implementing connectors, either those that are available directly from Actian (more than 200 of them) or that you can develop yourself.

Fig 03 - The Capabilities of Actian Integration Manager

Integration Manager is being enhanced (2021) to support containerised deployments via Kubernetes. Its capabilities are illustrated in Figure 3. In addition there is Actian Studio, which is an Eclipse-based environment for developers, while the company should also have released Web Designer by the time this report is published. This is a browser-based no-code interface intended for use by citizen developers that will not require IT input. Finally, Actian plans (2021) the release of an actionable metadata repository with support for streaming data.

Actian DataConnect has been available, under a variety of names, for more than twenty years. It has the sort of reliability that you would expect from a product with this sort of pedigree. It is also one of the few products in this space that has focused, at least in part, in providing embedded capability. Historically – and this does not appear to have changed – it has had significant total cost of ownership benefits when compared to many of its competitors. However, data integration is a mature technology and DataConnect has languished somewhat over the last few years without significant new development. But it is now clear that that has changed and that Actian is putting significant efforts into extending DataConnect’s functionality and reach. This is especially associated with the launch, earlier in 2020, of Actian Avalanche.

In this context it is worth commenting on the potential of Actian Avalanche with Avalanche Connect, which will allow its data integration capabilities, as well as the functionality of the forthcoming Actian DataFlow, to leverage the elastic scaling offered by Avalanche. Moreover, as far as we know, this will represent the only cloud-based data warehouse on the market that also includes its own integration capabilities built into the environment: something that must be appealing to potential users.

The Bottom Line

Actian DataConnect, and particularly Avalanche Connect, have significant potential. The former is a comprehensive and competitive data integration product. Though it appears to be lacking any remarkable feature or functionality, Actian Data Connect has a strong following of ISVs that embed the technology including ADP, Xactly and FICO. The company’s future plans for extending further with its integration hub service, where has been proven in its Healthcare Claims and Service exchanges, make the company a definite “one to watch”.

One notable feature of Actian X is its support for a patented technology known as Positional Delta Trees. The following is an extract from the original academic research paper: “our goal is that read-only queries always see the latest database state yet are not (significantly) slowed down by the update processing. To this end, we propose the Positional Delta Tree (PDT), that is designed to minimize the overhead of on-the-fly merging of differential updates into (index) scans on stale disk-based data.” In other words, this is about improved query performance regardless of whatever updates are being made, while preserving consistency. It is also symptomatic of Actian’s approach, in that it leverages a number of patented technologies to maximise performance. In particular, there has been considerable academic work involved in the development of Vector, which was created initially at CWI, the Dutch National Research Institute for Mathematics and Computer Science. As another example, Actian X (the X100 engine) incorporates patented algorithms for optimising the use of memory.

Fig 02 The Actian X ecosystem

The broader ecosystem in which Actian X operates is illustrated in Figure 2. Not shown is support for Kafka and for PMML (predictive modelling mark-up language) though TensorFlow is not supported. Also not shown in this diagram is the fact that there is a managed cloud service available for storing and managing Active X backups; there are a number of geospatial capabilities, including 3D support for R-Tree indexes; and there are database health monitoring and capabilities. Also included with Actian X is DataConnect for Actian X, which provides a development engine for designing and testing integrations, plus a production engine for deployment purposes.

Ingres has a long-established pedigree for supporting mission critical transactional environments, while VectorWise has proven high-performance characteristics in supporting analytics, so we have no question marks over the performance and capabilities of the two acting in combination. And the integration tools and other functions provided by Actian are attractive. For existing Ingres users wanting to add analytic capabilities to their existing environment, Actian X is an obvious choice.

Non-Ingres users fall into two camps: existing users of other relational database systems and greenfield opportunities. We do not envisage that Actian is targeting the former. For greenfield environments, it is important to bear in mind that Actian X is an SMP-based system that scales up rather than scales out. It is not likely, therefore, to be cost-effective for the very largest hybrid environments where you may have hundreds of terabytes of data to process. Where that is not an issue then Actian X may well be worth consideration. If the company wants to address the extreme end of this market then it will need to extend the use of Vector within Actian X to Vector Cluster.

The Bottom Line

If you are an existing Ingres user then Active X should be a trivial decision to support hybrid analytic and transactional processing within the same environment. It is also a strong contender for mid-sized and smaller deployments, especially where SQL and traditional relational approaches are preferred.

Actian Zen provides a NoSQL API for local data processing and analytics. It allows you to leverage a large number of popular programming languages (C, C++, C#, Java, Python, Perl, PHP, and so on), and developers can choose between several methods of direct access to data without going through a relational layer. Zen databases (except Zen Core) also offer SQL access for reporting, querying, and local or remote transactions. This enables fast read and quick insert, update, and delete performance alongside full ACID response on writes and ANSI SQL queries. Zen supports SQL access via JDBC/ODBC and NoSQL access via the Btrieve and Btrieve 2 APIs.

Embedding Actian Zen into your system provides a variety of value-adding features and functionality, including end-user personalisation and multichannel context, decision support, hybrid cloud support, provisioning, management, and security and governance. Moreover, with both relational and direct data access for JSON, BLOB, and time-series data, self-tuning, reporting, data portability, exceptional reliability, easy upgrades, and backward compatibility, developers can use Zen to deliver applications at scale across on-premise data centers, hybrid cloud, branch, and remote field environments.