Actian

Last Updated: 24th June 2019
Analyst Coverage: Philip Howard and David Norfolk

What was originally Relational Technologies Incorporated, founded in 1980, became Ingres Corporation in 1989. It was acquired by the ASK Group in 1990 which was itself acquired by CA in 1994. Ingres was spun out of CA in 2005 and was renamed as Actian in 2011. In 2018 the company was acquired by HCL Technologies, which is a company headquartered in India but with offices around the globe. The company has more than 120,000 employees.

Actian, which will continue to operate separately from HCL, markets a number of different database and data integration products as well as the OpenROAD 4GL. As far as databases are concerned these include Vector (previously VectorWise) and Vector Cluster (previously VectorH or Vector on Hadoop), which provide in-memory columnar processing for analytics; the Zen (previously PSQL and, before that, Btrieve) embedded database; a NoSQL Object database (previously Versant) and Actian X, which combines the capabilities of Ingres and Vector. Actian X is regarded as the next release of Ingres, which means that every Ingres user – of which there are thousands – with an active subscription is entitled to upgrade to Actian X as the next step on their upgrade path. Vector is available both on premises and in the cloud, and a managed service offering running on AWS has recently been introduced.

Company Info

Headquarters: 2300 Geng Road, Suite 150, Palo Alto, CA 94303
Telephone: +1 650 587 5500

Actian Analytics Platform

Last Updated: 18th December 2014

What it does

The Actian Analytics Platform consists of a number of components designed to provide an environment suitable for supporting big data analytics. In such an environment you need to:

Be able to ingest all sorts of data—whether structured, unstructured or machine generated—and from whatever source is appropriate, whether inside or outside the organisation.
Be able to prepare data for analysis efficiently. This is particularly important where a discovery process (the sort of thing that data scientists do) is required, because data preparation can take as much as 80% of this time.
Have a platform for the initial discovery process and data science analytics.
Be able to then conduct low latency analytics that are time-sensitive using results from the discovery process. Requirements 3 and 4 may or may not be in the same place.
Once you have performed your analytics you need to be able to distribute the results to users and applications that require the information produced by your analyses.

Actian delivers capabilities for requirements 1 and 2 through the DataConnect offering, it provides 1, 2, and 3 via Actian DataFlow, and for 4 it has three offerings, which may be used separately or in conjunction. Most commonly, you would use either Vector or Matrix for low latency analytics depending on the sophistication of the analytics workload and scalability requirements. Or, if you have a combination of traditional and complex analytics workloads, then you might want to deploy both, depending on the scale at which you are operating. And, last but not least, Vector in Hadoop would be used to provide high performance SQL access natively in Hadoop to data and analytic results stored in HDFS.

Markets and channels

Actian has both an extensive channel marketing program (primarily for its historic database products) and a significant direct sales force. The company is not limited to any particular vertical sector or geography. DataConnect has a particular emphasis on integration with cloud-based (SaaS) applications but this is by no means uncommon today.

Users

We doubt whether even Actian knows exactly how many companies are using its technologies. This is because Ingres and PSQL in particular are widely embedded by OEM partners and ISVs. The total number of users (many of which are no doubt unaware that they are using these products) will certainly run into tens of thousands and could easily be in six figures.

Technology

There are three elements to the Actian Analytics Platform: data integration, data preparation and what is sometimes referred to as the Extended Data Warehouse (XDW).

A traditional data warehousing environment has been considered to consist of a conventional data warehouse plus data marts. The key element that has changed with big data and The Internet of Things is the requirement for a discovery platform where large amounts of data can be explored for interesting insights but in a relatively inexpensive manner. In the case of the Actian Analytics Platform this would be its new Hadoop SQL edition offering, which is a combination of DataConnect for high performance connectivity, DataFlow for visual data science and analytics, and the implementation of Vector on HDFS (the storage system for Hadoop) via YARN. Vector is a column-based analytic database for single server implementations that owes its performance advantages (it currently holds the TPC-H benchmark record for the fastest non-clustered database solution) to the fact that it uses vector processing that exploits CPU parallelism. What Actian has now done is to implement this on top of HDFS so that it will run on low cost Hadoop clustered hardware. Initial benchmarks suggest that Vector in Hadoop is better than an order of magnitude faster than Impala running on Hadoop.

Once interesting nuggets of information are discovered the typical process is that relevant data will be loaded into a high performance analytics database for low latency analytic processing and operational business intelligence. Here Actian offers Vector, Matrix and SPARQLverse. Vector we have already discussed. Matrix (formerly ParAccel) is an MPP (massively parallel processing) column-based database. In technical terms the product’s biggest strength is its optimiser, which has a number of advanced capabilities (for example, for processing correlated sub-queries). It is worth noting that Matrix is what powers Amazon Redshift. SPARQLverse is an MPP-based graph database running on top of HDFS developed by SPARQL City (of which Actian is a major shareholder).

For data preparation, blending and enrichment, and visual discovery analytics and data science, the company offers Actian DataFlow (previously Pervasive DataRush), which is a massively parallel engine (it is extremely fast) that allows you to profile, cleanse and run other data preparation tasks, and/or execute data mining algorithms on the fly: that is, while loading data. It is worth commenting that the market for data preparation and analysing data while in motion is burgeoning and rapidly evolving into a sub-market of its own (several new and existing vendors are introducing markets in this area) but Actian got here first.

Finally, Actian DataConnect not only includes the data profiling and data quality capabilities that may be instantiated on Actian DataFlow but it also provides the data integration (ETL: extract, transform and load) requirements necessary to ingest and output data from and to both external and internal sources and targets. It is noteworthy that Pervasive, prior to its acquisition, had a history of developing connectors to a very wide range of sources and targets and, over the last few years in particular, it has focused on developing adapters to connect to cloud-based and SaaS (software as a service) providers. It has a particularly close relationship with Salesforce.com.

Services

Actian provides conventional training, consulting and support services. Some of its partners also offer consulting services.

Actian X

Last Updated: 28th June 2019

What is it?

Fig 01 The Query Processing element

Actian X is a hybrid database that combines what used to be known as Ingres and what was previously VectorWise. It is SMP (symmetric multi-processing) based with parallel processing of the data based on a configurable number of CPUs. Its architecture is illustrated in Figure 1 where the X100 query engine is derived from VectorWise.

As one might expect from a product with its pedigree, the Ingres part of this equation provides ACID guarantees and support for two-phase commit within transactional processing environments. The X100 engine, on the other hand, exploits Intel’s vector instruction set (hence the Vector name) to process more data elements per instruction (SIMD: single instruction, multiple data). It is ANSI SQL 2003 compliant and maintains ACID compliance. Columnar storage exploits compression capabilities to minimise storage requirements.

The “Query Processing” element shown in Figure 1 is aware of where data is held and directs queries, or parts of queries, to the appropriate data store. Synchronisation between the two data stores is either defined through rules using stored procedures, or you can use conventional replication technology if latency is not an issue.

Customer Quotes

“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.”

How does it work?

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.

Why should you care?

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.