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Big News from Big Data LDN

Big News from Big Data LDN

If you’ve been keeping up with our blogs, you’ll know that we exhibited alongside our partners Canonical at Big Data LDN earlier this month. We had no doubts it was going to be an exciting event but the size, scale and quality of Big Data LDN exceeded our expectations in every way.

For two full days across November 13th-14th, the massive Olympia exhibition centre in London was packed to the rafters with speakers, delegates and vendors eager to discuss, discover and demonstrate the latest advances in big data technology. We are very proud to have been a part of it and we’re especially encouraged by the huge number of people who visited the Spicule / Canonical stand to find out more about our revolutionary new ANSSR platform and watch our demos of Apache Hadoop, Apache Druid, Apache Kafka and Apache Superset. The response we received was overwhelmingly positive, with delegates immediately recognising the huge benefits that ANSSR can bring to their business, their productivity and their budget. To find out how keen they were to use ANSSR for themselves was enormously gratifying. It makes all the hard work that we and Canonical have put into developing the ANSSR platform very, very worthwhile.

ANSSR – A quick overview for the (as yet) unconverted

The ANSSR platform provides users with all the free-to-use software they need to affordably scale, manage and deploy big data applications. Before ANSSR, configuring, scaling and installing complicated data-based solutions was so prohibitively expensive and time-consuming that only the largest, most well-off companies and corporates could afford to do it. Now, thanks to ANSSR, the playing field is levelled – even the smallest company can manage their big data just as effectively as their richest competitor, scaling it up or down depending upon their requirements, eliminating wastage and taking greater control of their budget.

One of the biggest takeaways from Big Data LDN was that simplifying big data and making it more accessible, visual and economical is exactly what businesses are looking for – and that’s exactly what ANSSR is designed to do. Also, users appreciated how comprehensively Spicule and Canonical’s ANSSR platform is solving their big data issues, keeping them flexible and competitive while helping to save them money along the way. In every respect, ANSSR ticks the big data boxes of almost everyone who’s used it or attended our demo. If you’d like to know more about what ANSSR can do for your business, get in touch.

Our Anssr Demo

We demonstrated the capabilities of Apache Hadoop, Apache Druid, Apache Kafka and Apache Superset by creating a large set of telecommunications data, storing it into Hadoop, running queries and gaining insights with Druid, and then using Superset to visualise the results. Finally, we demonstrated how efficient Kafka is at live-streaming data and immediately recognising live faults within that data, meaning any anomalies can be removed or rectified straight away. Even though we used telecommunications data as an example, it was clear to all the delegates how the Anssr platform can benefit any company that manages and interrogates data on a large scale. The delegates weren’t only surprised by how extremely fast Anssr is (in many cases it takes just a sub-second to get results), they were also impressed that it is open-source and free-to-use. Unlike solutions offered by larger companies, there are no expensive licensing fees.

The Big Data LDN Buzz: Hadoop Lives!

Not surprisingly, the future of Hadoop and the highly publicised merger between Cloudera and Hortonworks were both major points of discussion at Big Data LDN. But, despite recent speculation that Hadoop’s days might be numbered, it was very apparent from Big Data LDN that Hadoop is very much alive and well and that the Cloudera/Hortonworks merger has brought it back to the forefront.

One of the most interesting conversations we had was with our friends at MapR, who highlighted the fact that the merger has really helped companies like them (and us) because, instead of competing against both Cloudera and Hortonworks, we’re all now competing against a single merged entity and a lot of fresh opportunities and possibilities are already appearing. We completely agree. Although the merger initially caused consternation in some quarters, we’re confident that it will lead to a more creative marketplace and greater benefits for customers, vendors, innovators and investors. The general mood of Big Data LDN definitely seems to support that opinion.

In other Big Data LDN news

We almost lost our junior developer Ryan to the world of Formula One motor racing when he tried out the Mercedes simulator and came sixth. Or sort of. Unfortunately, Ryan missed out on his second run because he was too busy talking with all our wonderful visitors about the big data solutions we offer so his sixth place on the leader board was a bit short-lived. Still, F1’s loss is our gain eh, Ryan? At least Lewis Hamilton can sleep easier tonight…

And, finally…

Probably the best way to sum up Big Data LDN is with this comment from our Technical Project Manager, Stephen Downie:

“Big Data LDN was a fantastic, beautifully organised event and a great opportunity to swap thoughts and ideas with the delegates and our industry peers. Most importantly, though, it was a pleasure to exhibit alongside Canonical and demonstrate what ANSSR can do for everyone who took the time to visit our stand. To all those people who saw us, thank you for dropping by. It was great to meet you, and we’re looking forward to working with many of you soon.”

Don’t worry if you missed Big Data LDN. Even though we don’t get out of the office as often as we’d like, you can easily find out more about ANSSR, Apache Hadoop, or any of the other innovative and cost-effective data management and private cloud solutions we provide by giving us a call on

01603 327762 or sending us an email at info@spicule.co.uk. But, if any of you have got an F1 simulator, please don’t tell Ryan or we might never see him again!


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