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.

Actian Data Platform’s core differentiator, at least in terms of data integration, is that it offers effective, proven integration as a service, alongside robust data quality and warehousing functionality, within a much broader data ecosystem solution provided by a single, unified platform. This provides a great deal of flexibility in how you can handle your data during the integration process, while ensuring that it remains trusted and high quality.

The data integration process itself is designed to be easy to use, both in and of itself and alongside accompanying technologies like data ingestion, quality, and preparation. To this end, it includes real-time, event-driven data ingestion via an extensible library of prebuilt connectors, REST and SOAP APIs, and desktop data loading for on-premises data; data preparation and transformation actioned using a no-/low-/pro-code transformation builder, a drag-and-drop transformation canvas, and automated transformation scheduling; the integrated application of data quality; and secure, real-time data delivery. The data integration processes themselves are designed using templates and/or guided workflows, and there is a management interface for centrally monitoring all of your ongoing and completed processes, regardless of where those integration processes are occurring or have occurred.

Actian’s data quality capability, which is frequently leveraged in concert with its data integration functionality, uses data quality rules to drive data remediation, with sufficiently poor-quality data remediated automatically based on provisions described in each rule. Automated rule generation is available, as are a selection of industry-specific packs of predefined quality rules. Rules can vary in their methods: they can check whether the data exists at all, they can check if it matches a particular pattern, and so on. Data can be moved to a specific location automatically after a given result from these checks (which ties into data integration in an obvious way) and the platform can send out a notification to the relevant users when a check fails. Data quality can be run on demand or on an automated schedule and is supported by drag and drop data profiling as well as a data quality dashboard, which features – among other things – a run history and a results summary.

Actian also provides an impressive range of connectivity options, enhanced by its in-platform data interoperability solution, Actian Connect. More than 300 connectors are provided out of the box, alongside REST API integration. Actian Connect is supported by Actian’s newly released Connector Factory, a means for users to quickly and easily create their own connectors, over and above the ones Actian already provides. Actian Connect also provides automated data pipeline deployment and workload scheduling.

Actian has a significant history in the database market. Accordingly, the Actian Data Platform benefits from the sort of reliability that you can only get from a product and company that has been in the market, in one form or another, for decades.

More concretely, the combination of data integration and data quality that Actian provides should not be sniffed at. Not only are these capabilities often vital in and of themselves, but it is frequently beneficial to deploy them together by building data quality profiling and remediation into your data integration processes, thereby ensuring that the data that lands is of high quality. This is particularly important for supporting data analytics – one of the primary use cases for data integration – due to how deleterious poor-quality data can be (and frequently is) to statistical analysis. This is likewise the case for providing training data to machine learning and (generative) AI processes: these technologies rely on high quality data to function well, so it is in your best interest to make sure they get it.

It is also important to remember that data integration and data quality are only two of many areas of data functionality that the Actian Data Platform offers. The flexibility in managing your data that this provides is substantial.

Finally, it is worth reiterating that the Actian Data Platform’s connectivity and integration options are rather impressive, especially with the addition of the Connector Factory, as is its single pane of glass management layer.

The bottom line

The Actian Data Platform provides a robust and versatile solution for a range of data management use cases, particularly data integration and data quality, that offers a vast array of connectivity options as well as significant ease of use features. In short, it is very much worth your consideration.

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.