Astera

Fig 02 - Astera ReportMiner Marketecture

Data integration is a mature market and Astera Software offers all the basic requirements that one would expect, and we do not need to discuss these. However, there are a number of additional capabilities that are worth more detailed consideration. The first of these relates to data quality. The products include data profiling capabilities and the ability to develop (in a no-code environment) your own data quality rules. These leverage Astera’s rules engine (which has a look and feel similar to Microsoft Excel) and, while Astera does not offer a match engine per se, the rules engine does have the ability to do things like fuzzy matching. Moreover, a number of the rules that are delivered with the product (more than 500 of them) provide survivorship options. There are specific data quality capabilities – see Figure 2 – including matching in this instance, in the Astera ReportMiner product.

Beyond data quality there are three recently introduced modules: API Integration, Data Virtualization and Data Warehouse Automation, of which the first and third merit discussion. This isn’t to say that Data Virtualization isn’t nice to have, just that its principles (a virtual view across multiple data sources) are well known. As for API Integration this allows users to expose any data source, or a subset thereof, as a secure, managed, real-time RESTful API. You can publish these APIs to both internal and external users, and you can publish data either to a virtual database, to an API or OData. Service Orchestration is provided. Further enhancements to these capabilities are planned.

Data Warehouse Automation allows you to speed up the process of creating or migrating to a data warehouse. Astera does this by providing data modelling capabilities that allow you to map dimension and fact tables and then automatically load data based on those definitions. Where necessary Astera has the ability to reverse engineer relevant (for example, Snowflake) data warehouse schemas, or it can push a schema (including joins, lookups and so on) to the target.

Finally, we should mention that Astera plans to introduce Data Flow as a Service (DFaaS) in the near future.

There are a plethora of data integration solutions available across the marketplace and, while we are happy that Astera offers the sort of architecture that should provide the scalability, performance and resilience that enterprises expect, there is nothing in particular that stands out about the product’s core capabilities. However, most competitors cannot offer the specialised document-based capabilities that Astera offers through ReportMiner and EDIConnect, few offer API integration, even fewer offer data virtualization and none, as far as we know, offer data warehouse automation integrated into a general-purpose data integration solution. Moreover, all the Astera modules share the same core platform and architecture, providing user interface and functional consistency, something that cannot always be said for competitive “integrated” platforms.

We should add that while there is a significant amount of automation built into the offering, that doesn’t mean that we wouldn’t like to see more: recommendations for example. Also, the sooner Astera Cloud is available the better: this is clearly an area where Astera is currently behind some of its competitors. We would also like to see more technology partnerships in adjacent areas such as data governance and data cataloguing.

The Bottom Line

Astera Software is not one of the 800lb gorillas in this market. Nevertheless, we have been pleasantly surprised at how well it stacks up in comparison. Astera Centerprise is worth serious consideration even if you are contemplating one of the behemoths of this industry.

Figure 2 - Building a pipeline in Astera

At its most basic, Astera enables you to build data (integration) pipelines through an intuitive, no-code interface powered by drag and drop (shown in Figure 2). These pipelines include data orchestration and mapping, and while a wide variety of prebuilt transformations and other pipeline components are provided to you, you can also build your own (again, without needing to write code). Custom components can be shared and reused as you would expect.

Both structured and unstructured data can be ingested into your pipelines, courtesy of the range of connectors built into the product. In addition, Astera provides API connectivity through REST APIs, while also including tools for API lifecycle management and for building any of your data sources – or a subset thereof – into secure, managed, real-time REST APIs, publishable to both internal and external users. Notably, these APIs are treated like any other pipeline component from a user perspective, and can be implemented into your pipelines just as simply as that implies. The other end of your pipelines can connect to a variety of databases, visualisation tools, and ERP/CRM systems. Pushdown optimisation is used to ensure transformations are performed in their optimal locations.

Data warehouse automation is also available, and speeds up the process of creating or migrating to a data warehouse by providing data modelling capabilities that allow you to map dimension and fact tables and then automatically load data based on those definitions. It also allows you to reverse engineer relevant data warehouse schemas, or push a schema to a target environment. Similarly, Astera pipelines are “self-adapting”: they automatically react to changes in their source(s), such as a change in schema, and adjust themselves accordingly in order to keep functioning.

Astera Intelligence, as mentioned above, is the collective name for Astera’s AI-powered capabilities. These are primarily used for two purposes: directly improving productivity, perhaps by automating an existing process, and enhancing usability, enabling your users to work more quickly and effectively (improving productivity indirectly). Several particular examples of AI, as used by Astera, are worth highlighting:

• Intelligent Document Processing (IDP) uses AI to automate data ingestion for documents. This works by automatically identifying the type(s) of data within the document, then creating and/or applying an appropriate data extraction and mapping template. In this way, data present in the document will be intelligently parsed and mapped to matching data fields. This process would normally need to be carried out at least somewhat manually to extract the data held within your documents, so automating it like this stands to save a potentially huge amount of work, especially if you have a large quantity of
• documents to process (for instance, when digitizing an archive).
• Astera Intelligence features a natural language, chatbot-style interface for building and/or modifying your data pipelines. This includes functionality for reviewing and verifying the pipelines built using this interface. This has obvious applications in both automation and usability.
• You can incorporate generative AI directly into your data integration pipelines using Astera’s prompts-as-transformations feature. These are custom transformations that allow you to submit a curated but dynamic prompt – largely prewritten but parameterised based on your chosen input fields – to an LLM from within one of your pipelines, then have that pipeline act on predefined output fields contained within the response. This allows you to leverage generative AI systematically and automatically, without needing to be a data science professional. There is a caveat here: since LLMs are far from perfect – the hallucination problem, if it even can be solved, has certainly not been solved yet – it is highly recommended that any runtime use of this feature is either supervised by a human or extensively tested beforehand.
• RAG (Retrieval-Augmented Generation) support is available via a selection of purpose-built components that can be placed within your pipelines and leveraged as with any other component.

Astera promises the sort of scalability, performance, and resilience that you would expect from an enterprise data integration solution. Moreover, few of its competitors – if any – offer data warehouse automation alongside data integration, and its range of API functionality is impressive. That said, there are two areas where it really differentiates itself: usability on the one hand, and AI on the other.

In terms of usability, it is not unusual for products of this nature to feature drag and drop, no-code interfaces. Rather, what makes Astera stand out is the consistency with which it supports this kind of interface: almost everything, from pipeline components to APIs to LLM prompts, can be boiled down to an object to be placed on a canvas. This minimizes the number of interfaces that users need to learn while allowing them to take advantage of even advanced functionality quickly and easily.

Moreover, Astera’s usability is further enhanced by its AI capabilities, including the direct integration of LLM prompts into pipelines (as transformation components) and its natural language interface (although this is slightly at odds with the otherwise uniform application of its drag and drop interface). This frequently crosses over into enhancing productivity as well, such as with the aforementioned natural language interface, and most significantly with IDP, which stands to make your work much faster and easier if you are extracting data from documents.

The bottom line

Astera is an eminently easy to use, enterprise-ready data integration solution and data pipeline builder. It offers several broader data management capabilities – such as data quality, data profiling, and data warehouse automation – either within or without its pipelines, and its AI functionality, Astera Intelligence, is substantial and well-considered. In short, there is a lot to like about it.