At a recent benchmark study by Mammoth Data Inc., Google’s Data Flow Services has beat Apache Spark by a huge margin. Of course there is a contention to this – as Mammoth’s study was sponsored by Google. However, Mammoth Data pointed out that since it uses Apache Spark for its own consultancy – the study is highly objective.
The startup darling AirBnB has ignited a spark for providing a perfect match between short term renters and landlords. Data analytics is at the heart of the home listing service for rendering personalized search results to users. With the intent to understand its users better, it has built a new matching engine for its mobile application on Apache Spark and its machine learning capabilities. AirBnB is a prime example for making the best use of big data technologies and tools for business growth.
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Spark 1.0 was launched almost two years ago and a change was due. Spark 2.0 is a result of the continuous effort to double down on the good things about Spark and mitigate the limitations. Spark 2.0 is better at SQL and streamlined APIs. Spark 2.0 works 10x faster than Spark 1.0.
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TIBCO’s analytics platform leverages big data analytics for augmented intelligence to enhance human experience through streaming analytics solutions that provide a practical approach to cognitive computing. TIBCO has announced several updates to its analytics platform -new data wrangling features in TIBCO Spotfire ,enhanced BI support in TICBO Jasper soft, code free operational intelligence dashboards in TIBCO Live View wen and a new accelerator package for Apache Spark and IoT . If you want to know more in detail about the accelerator package built on top of Apache Spark and IoT then you can read more in detail at appdevelopermagazine.
IBM is harnessing the power of Spark and other emerging big data technologies like Zeppelin, link, Storm for handling unstructured and streaming data in a single memory efficient platform. Apache IBM offers Spark-as-a-Service in the cloud and is embedding it into the Watson analytics platform. IBM has moved its ETL platform on top of Apache Spark. This has helped IBM reduce the code to 4 million lines instead of 40 million on ETL platform.
The leading vendor of SaaS based monitoring platform for cloud applications- Datadog has announced its support for using Hadoop and Spark. Datadog monitoring service brings data from servers, applications, databases, tools and services to present users with a unified view of the apps that are hosted and run in the cloud. Datadog has announced that the platform would now be integrated with technologies like HDFS, Hadoop MapReduce, YARN and Spark so that users can make the best use of Datadog’s rich dashboards, full stack visibility, targeted alerts, and collaborative tools and integrations.
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After Hadoop, Spark is in the big data elite and has the most active big data development community in the world but will it remain in elite is the big data question. With the new stream based data processing project Concord ,Apache Spark’s reign seems to be at threat.Concord.io built on top of Apache Mesos fills in the blank space left by Apache Spark in terms of event based streaming and low latency streaming. Will it be able to topple Spark? Let’s wait and watch.
Databricks has announced the technical preview of Apache Spark 2.0. However, the preview is just to gather the feedback from the community before it is available in production. The new release is based on the feedback from the big data community and emphasize on two major areas of improvement-SQL interface and programming API’s.