Liaison’s Data Platform as a Service Includes Data Mapping

Loraine Lawson

Liaison Technologies is now offering what it calls the first dPaaS. That’s short for data platform as a service, but integration is part of the solution as well. It’s all offered in a multi-tenant cloud platform, which is generally translated as “scales well.”

Liaison sees this new offering as a fit for hybrid environments, B2B supply chain integration and, of course, Big Data. One of the key bragging points is the “Contivo automated mapping solution.” That feature reduces the time it takes to map the data, but it also incorporates a data profiling and cleansing algorithm. That seems to be a common theme as vendors seek to address some of those cloud data governance and data quality pain points.

Oh, and of course there’s API management, because nobody offers a cloud data service without it these days. It also leverages Hadoop and Cassandra for data processing, storing and management.

“This creates a massively scalable platform that can make sense of big data in virtual real time,” the press release notes. “With scalability no longer an issue, businesses can take data integration to any level they wish.”

It sounds a lot like an integration platform as a service (iPaaS) offering, but actually, Liaison Technologies plays in the integration brokerage space. Basically, it’s coming out of the B2B space, which is different from the enterprise integration heritage from which many iPaaS vendors evolved. As the name suggests, integration brokerages incorporate managed services into their offerings, and that’s true for the Alloy Platform as well.

Liaison has more than 7,000 customers worldwide. It also has long roots in the cloud integration space; in 2012, it acquired cloud-based integration vendor Hubspan.

ClearStory Offers Support for Data Lakes

Earlier this week, I profiled ClearStory, which recently expanded the capabilities of its cloud-based data processing tool built on Spark.

But I neglected to mention one key piece of recent news: The company also launched a new solution to help enterprises manage data lakes.

A core part of ClearStory’s solution is its ability to ferret out metadata and relationships between data sets. Kumar Srivastava, the senior director of product management at ClearStory Data, explained via phone that ClearStory’s algorithms can determine if data sets have a sibling relationship or a child/parent relationship, as well as whether the data contains location, time, categories, names and so on — the metadata about the data.

If you’ve read anything about data lakes, you’ll know that’s one of the concerns experts have about porting large amounts of data into a Hadoop store. And sure enough, Srivastava said they are hearing from enterprises clients who are unable to govern and manage their data lakes.

Data Lakes

“What we’ve seen in big enterprise customers is they are creating data lakes - or what we call a modern data architecture - where creators don’t have to worry about how data will be used in analysis, just pooling all the data in central storage,” Srivastava said. “We realized it’s getting hard to discover the data that exists in the data lakes. That limits to me the value you can get out of it.”

The company realized its algorithms could help, so it’s now offering an API platform that has opened those functions as services. Now enterprises can access and use ClearStory’s APIs to support their internal applications.

Additionally, the company is working to support deeper integration with Hortonwork’s distribution of Hadoop. That effort will complement Hortonwork’s own governance initiative, Srivastava added.

If you’d like to read more, Information Week compared ClearStory to another Spark-driven start-up, Databricks Cloud.

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.

Add Comment      Leave a comment on this blog post
Apr 4, 2015 12:24 PM Ilya Geller Ilya Geller  says:
Hadoop is obsolete: it does not structure data, work with Internal statistics. I discovered and patented how to structure any data: Language has its own Internal parsing, indexing and statistics. For instance, there are two sentences: a) ‘Fire!’ b) ‘In this amazing city of Rome some people sometimes may cry in agony: ‘Fire!’’ Evidently, that the phrase ‘Fire!’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrase weights: the first has 1, the second – 0.12; the greater weight signifies stronger emotional ‘acuteness’. First you need to parse obtaining phrases from clauses, for sentences and paragraphs. Next, you calculate Internal statistics, weights; where the weight refers to the frequency that a phrase occurs in relation to other phrases. After that data is indexed by common dictionary, like Merriam, and annotated by subtexts. This is a small sample of the structured data: this - signify - : 333333 both - - once : 333333 confusion - signify - : 333333 speaking - - once : 333333 speaking - - both : 333333 place - - in : 250000 higher - - best : 250000 Reply

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