Jags Ramnarayan, SnappyData, at Spark Summit 2017
#SparkSummit #theCUBE
https://siliconangle.com/2017/07/25/snappydata-enriching-spark-hybrid-database-sparksummit/
How SnappyData is enriching Spark as a hybrid database
Enriching Apache Spark so it’s not just a platform but also a store is just part of the innovation occurring at SnappyData Inc., according to Jags Ramnarayan (pictured), founder and chief technical officer of SnappyData.
Built on Spark’s open-source in-memory data processing engine, SnappyData is a high-performance in-memory data platform for mixed workload applications. The platform complements Apache Cassandra, an open-source distributed NoSQL database management system, allowing real-time work with Spark, Ramnarayan explained.
“… A single data store that’s capable of taking Spark streams, doing transactions, providing mutable state management in Spark, but most importantly being able to turn around and run analytical queries on that state that is continuously merging,” is how Ramnarayan described SnappyData to George Gilbert (@ggilbert41) and David Goad (@davidgoad), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during this year’s Spark Summit event in San Francisco, California.
Solving the open source paradigm encapsulating Spark, Mesos, Akka, Cassandra and Kafka Stack for the ability to get insight and responses on live data, SnappyData complements Cassandra, enabling real-time working in Spark giving “Google-like speeds to live data,” Ramnarayan said. “If stuff changes in Cassandra, I can have an immediate, real-time reflection of that mutable state in Spark on which I can run queries rapidly.”
Spark isn’t the reason, but it is the solution
SnappyData is pulled in to work on Hybrid Transaction/Analytical Processing use cases because SnappyData is a hybrid database, and in turn they pull Spark in because it’s part of the SnappyData solution.
“All of a sudden it’s not just a Spark API, but what we provide looks like a SQL database itself,” Ramnarayan said.
Machine learning models traditionally learn on historical data search, but parlance is evolving very rapidly so being able to sort and order the model itself incrementally and at near real-time speeds is becoming more and more important, he added. SnappyData is working with the University of Michigan on incrementally augmenting a model on the edge or even inside the cloud, and even venturing into re-tweaking model unsupervised. But that’s “all in the future,” Ramnarayan stated.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Spark Summit 2017. (* Disclosure: DataBricks Inc. sponsored this Spark Summit 2017 segment on SiliconANGLE Media’s theCUBE. Neither DataBricks nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
Spark Summit 2017 | San Francisco. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Register For Spark Summit 2017 | San Francisco
Please fill out the information below. You will recieve an email with a verification link confirming your registration. Click the link to automatically sign into the site.
You’re almost there!
We just sent you a verification email. Please click the verification button in the email. Once your email address is verified, you will have full access to all event content for Spark Summit 2017 | San Francisco.
I want my badge and interests to be visible to all attendees.
Checking this box will display your presense on the attendees list, view your profile and allow other attendees to contact you via 1-1 chat. Read the Privacy Policy. At any time, you can choose to disable this preference.
Select your Interests!
add
Upload your photo
Uploading..
OR
Connect via Twitter
Connect via Linkedin
EDIT PASSWORD
Share
Forgot Password
Almost there!
We just sent you a verification email. Please verify your account to gain access to
Spark Summit 2017 | San Francisco. If you don’t think you received an email check your
spam folder.
In order to sign in, enter the email address you used to registered for the event. Once completed, you will receive an email with a verification link. Open this link to automatically sign into the site.
Sign in to gain access to Spark Summit 2017 | San Francisco
Please sign in with LinkedIn to continue to Spark Summit 2017 | San Francisco. Signing in with LinkedIn ensures a professional environment.
Are you sure you want to remove access rights for this user?
Details
Manage Access
email address
Community Invitation
Jags Ramnarayan | Spark Summit 2017
Jags Ramnarayan, SnappyData, at Spark Summit 2017
#SparkSummit #theCUBE
https://siliconangle.com/2017/07/25/snappydata-enriching-spark-hybrid-database-sparksummit/
How SnappyData is enriching Spark as a hybrid database
Enriching Apache Spark so it’s not just a platform but also a store is just part of the innovation occurring at SnappyData Inc., according to Jags Ramnarayan (pictured), founder and chief technical officer of SnappyData.
Built on Spark’s open-source in-memory data processing engine, SnappyData is a high-performance in-memory data platform for mixed workload applications. The platform complements Apache Cassandra, an open-source distributed NoSQL database management system, allowing real-time work with Spark, Ramnarayan explained.
“… A single data store that’s capable of taking Spark streams, doing transactions, providing mutable state management in Spark, but most importantly being able to turn around and run analytical queries on that state that is continuously merging,” is how Ramnarayan described SnappyData to George Gilbert (@ggilbert41) and David Goad (@davidgoad), co-hosts of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during this year’s Spark Summit event in San Francisco, California.
Solving the open source paradigm encapsulating Spark, Mesos, Akka, Cassandra and Kafka Stack for the ability to get insight and responses on live data, SnappyData complements Cassandra, enabling real-time working in Spark giving “Google-like speeds to live data,” Ramnarayan said. “If stuff changes in Cassandra, I can have an immediate, real-time reflection of that mutable state in Spark on which I can run queries rapidly.”
Spark isn’t the reason, but it is the solution
SnappyData is pulled in to work on Hybrid Transaction/Analytical Processing use cases because SnappyData is a hybrid database, and in turn they pull Spark in because it’s part of the SnappyData solution.
“All of a sudden it’s not just a Spark API, but what we provide looks like a SQL database itself,” Ramnarayan said.
Machine learning models traditionally learn on historical data search, but parlance is evolving very rapidly so being able to sort and order the model itself incrementally and at near real-time speeds is becoming more and more important, he added. SnappyData is working with the University of Michigan on incrementally augmenting a model on the edge or even inside the cloud, and even venturing into re-tweaking model unsupervised. But that’s “all in the future,” Ramnarayan stated.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of Spark Summit 2017. (* Disclosure: DataBricks Inc. sponsored this Spark Summit 2017 segment on SiliconANGLE Media’s theCUBE. Neither DataBricks nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
