Real-time Streaming Analytics for Enterprises based on Apache Storm

Are you tasked with finding the best way to build real-time analytics applications.With real-time streaming analytics, enterprises can cut preventable losses, gain operational insights, and seize new opportunities.

Leading enterprises have realized the huge potential in real-time streaming data analytics from sources like social networks, machine generated data, log files, click-streams, network, and IP Detail Record (IPDR) data. The opportunity is to get a competitive edge by putting all your data at work to uncover new insights and support high velocity decision making.

By viewing this webcast you will:

• Learn about proven approaches and architectural considerations for building a high performance Apache stream processing platform

•Conceive / visualize a solution to discover exceptions, patterns, and trends by continuously querying, filtering, correlating, integrating, enriching, and analysing data in real-time

•Receive technology comparison based on research work and benchmarks conducted at Impetus Labs

For more Visit: www.streamanalytix.com

Apache Spark Streaming Made Easy

Real-time streaming analytics and IoT seem to be the next big thing in the data and analytics industry. As enterprises adopt Apache Spark and Apache Spark Streaming widely, IT teams are facing the challenge to provide the tools and the framework needed to make Apache Spark Streaming an easy-to-use, robust, scalable and multi-tenant service.

Join this webinar from the StreamAnalytix team at Impetus Technologies to see how this problem is being solved at many Fortune 1000 companies.

This webinar will cover:

An overview of the stream processing landscape

The need for a "Streaming platform" integrated with the Hadoop data lake

A visual IDE approach for building applications on Apache Spark Streaming

The usage of various Spark Streaming operators in sample applications

Spark SQL, Window, ML Lib, Join, Custom-Scala-code etc.

Real-time analytics Dashboards, App Deployment & Monitoring

For more Visit: www.streamanalytix.com

Impetus Technologies to Host Webinar on Apache Spark Streaming

Complimentary Webinar Will Demonstrate How Large Enterprises Can Easily Build and Deploy Real-Time Applications Using Spark Streaming

LOS GATOS, Calif., May 16, 2016 -- Impetus Technologies, a big data software products and services company, today announced it will host a complimentary webinar titled "Spark Streaming Made Easy!" on Friday, May 20 from 9:30-10:15 a.m. PT.

The combination of real-time streaming analytics and the Internet of Things is gaining momentum in the data and analytics industry. As enterprises widely adopt Apache Spark and Apache Spark Streaming, IT teams are challenged to provide data scientists, developers, business analysts and operations teams with the tools to make Apache Spark Streaming an easy-to-use, robust, scalable and multi-tenant service.

The webinar will illustrate how this problem is being solved at many Fortune 1000 companies. Specifically, Anand Venugopal, head of product for StreamAnalytix and Punit Shah, a StreamAnalytix solution architect, will cover:

An overview of the stream processing landscape;

The need for a streaming platform integrated with the Hadoop data lake;

A visual, integrated development environment approach for building applications on Spark Streaming;

The use of various Spark Streaming operators in sample applications (e.g., Spark SQL, Window, MLlib, Join, custom Scala code, etc.); and

Real-time dashboards, application deployment and monitoring.

"Adoption of Apache Spark Streaming is on the rise, but many enterprises feel the need for visual tools for rapid application development so they can launch and manage production grade use cases quickly with their current talent pool and not have to wait for deep Spark coding ability to be developed or hired," said Venugopal. "The purpose of this webinar is to enable the Spark user community to accelerate their streaming analytics projects. For more info: www.streamanalytix.com

Vendor Comparison Report - StreamAnalytix Recognized as a 'Strong Performer' by Independent Research Firm

In Forrester's March 2016 report titled, "The Forrester Wave™: Big Data Streaming Analytics Platform, Q1 2016", Impetus Technologies' StreamAnalytix solution - an enterprise-class, Streaming analytics platform, based on best-of-breed Open Source technology - has been cited as a strong performer.

In the report, Forrester defines Stream Analytics and why it is fundamental, Mainly for enabling real-time contextual intelligence to applications. 

As part of the vendor profile section, the report states: "Impetus takes a different approach from the other vendors in this Forrester Wave. Instead of providing a core streaming engine, Impetus' solution abstracts many of the details of deploying, management, and building applications that run on Apache spark streaming and Esper (a CEP engine). Its pleasant and efficient user interface hides the gory details underneath, but it also provides a mechanism for developers to add custom code. StreamAnalytix's strategy and architecture is to make its solution pluggable with other open source processing engines as they become popular." For more information: www.streamanalytix.com

Streaming Big Data ETL with Impetus StreamAnalytix and Syncsort DMX

Today we are announcing a partnership between Syncsort and Impetus Technologies, and our entry into an integration of batch processing and real-time stream processing that we call “Streaming ETL”. The mix of batch and real time analytics has also been referred to as the Lambda Architecture. Streaming ETL allows a mixing of the best batch and streaming technologies under the umbrella of tools which abstract the complexity of the underlying platforms.

The huge increase in types and sources of data has placed pressure on companies to blend and summarize that data quickly to create actionable information. A combination of real time analytics and batch processing is needed to meet the new demands.

There’s a grab bag of technologies that excel in specific aspects: Hadoop Mapreduce, Storm and Spark for massively parallel processing; Kafka and Apache Spark Streaming along with traditional messaging and queuing software for real time data movement; Mesos and YARN for cluster management. These components can be mixed and matched, but there are many APIs to learn and different skill sets needed to leverage them well.

Syncsort and Impetus Technologies abstract away the complexity

Impetus Technologies developed StreamAnalytix to ease the process of building, deploying and monitoring real-time Big Data Streaming Analytics applications. StreamAnalytix provides an abstraction over the complex technologies used in Big Data platforms (like Storm and Kafka).

Syncsort DMX-h gives users an easy way to specify data transformations and data enrichment, and provides an extremely efficient, high performance run time engine. We have provided this engine to be used in StreamAnalytix streaming pipelines. The DMX engine is called whenever the user needs to transform or enrich the data.

Common use cases for Streaming ETL

• A clickstream analyst requires an IP address in a web log to be converted to a decimal or hexadecimal value for downstream processing
• A mobile app sends a product identifier used on a web retail site, and that identifier has to be transformed to the product ID used in the Enterprise Data Warehouse using a lookup
• A hardware manufacturer sends real time testing data from many sites, which needs to be correlated with historical data
  

On the Syncsort side, we made only minor adjustments to enable the DMX run time engine to allocate less memory than it does for large batch operations, and to expose a Java interface to integrate with StreamAnalytix. The Impetus engineering team quickly added a component in their excellent Web UI to allow users to add one or more DMX transformations in any stream. The teams worked together to publish and interpret metadata from the two products. In the coming months, we will be working to add features and enhance the integration.

I will be producing a screencast of this new Streaming ETL software, and both web sites will have more information about this exciting joint solution. We look forward to hearing from you with any questions and feedback. For more information: www.streamanalytix.com

GUI tool for streaming data analytics processing adds support for Spark Streaming to its support for Apache Storm.

I'm a sucker for a tool that adds a layer of abstraction and corrals quality into one thing manageable and a lot of simple. As such, Impetus' StreamAnalytix product has been on my radar for a few time, StreamAnalytix allows you to build graphical data pipelines that mix the employment of electronic communication platforms like Apache Kafka, Big Data Streaming Analytics platforms like Apache Storm, and prognosticative analytics technologies, like R and SAS.

But this morning, Impetus is taking things a step more, asserting the discharge of StreamAnalytix 2.0, that adds support for Apache Spark Streaming. we have a tendency to area unit finally commencing to see the triumph of smart tools over platform in-fighting.

Easier assembly

StreamAnalytix already lets data developers integrate a streaming data engine with Apache Kafka and RabbitMQ as publish/subscribe message buses. It conjointly allows the integration of HDFS, Amazon S3, Apache HBase, Cassandra, Solr and ElasticSearch. All of this can be done through a mixture drag-and-drop visual programming associated an array of declarative functions that permit you are doing things sort of a code operation in HBase or Cassandra, or perhaps MySQL.

StreamAnalytix conjointly supports versioning; a SQL syntax for common CEP (complex event processing) tasks and therefore the ability to push real time analytics updates via WebSockets. It conjointly permits you to method streaming data analytics through your own Java functions (just offer StreamAnalytix a category, entry purpose and parameter info) and it'll beware of replicating your code across nodes in an exceedingly cluster and corporal punishment it in an exceedingly data processing configuration.

And if that were not enough, StreamAnalytix includes its own dashboard authoring tools that let you show of knowledge that updates and changes in real time analytics.

One, the other, or both?

Selecting Apache Storm or Spark in StreamAnalytix unveil a designer with each common and platform-specific functions to incorporate in your pipeline. Meaning a given stream process style is coupled to a particular engine. however since the pipelines themselves area unit just persisted as JSON files, StreamAnalytix may in the future even yield the conversion of pipelines from one streaming engine to subsequent.

Folks from Impetus aforementioned that whereas this is not on the roadmap in any official sense, that it is a situation they've thought of and one they see as a logical progression from wherever they're. For more information visit: www.streamanalytix.com/spark-streaming-for-real-time-analytics

Impetus Technologies Offers Free Versions of its Streaming Analytics Platform

Several versions of StreamAnalytix, offered by Impetus Technologies, are currently accessible for complimentary, permitting enterprises to faucet into a streaming data analytics platform supported a range of leading open source technology parts.

The role we have a tendency to be taking part in within the marketplace initial of all is: we have a tendency to be creating open source way more expendable by the enterprise,” same Anand Venugopal, head of product for StreamAnalytix at Impetus Technologies.

StreamAnalytix, launched earlier this year, is constructed on associate open source stack together with Apache Storm, Kafka, Hadoop. The answer embeds a fancy event-processing engine for performing arts, real time analytics on streaming data analytics.

The platform works with all leading Hadoop distributions and provides enterprises with the pliability to simply integrate with any data source, message queue and target data source store of alternative. It conjointly permits passing on data and event triggers to different enterprise applications supported business rules applied over streaming data.

Three free versions of the platform, StreamAnalytix fat-free, StreamAnalytix Developer and StreamAnalytix Sandbox, are going to be accessible indefinitely and meet specific wants of its user.

“It’s a very compelling price,” Venugopal said. “I’m excited regarding what we are giving and what is turning out in terms of support for Spark Streaming Analytics some months down the road.”

StreamAnalytix fat-free is right for developers trying to ingest streaming data into Hadoop and use a robust visual toolkit for developing real time analytics applications supported Apache Storm and deploying at scale.

StreamAnalytix Developer is meant to produce more power and suppleness for developers to create real-life and sophisticated enterprise applications and perform practical testing before increasing to a full scale preparation.

And StreamAnalytix Sandbox provides developers with the quickest pathway to experiencing the ability of the StreamAnalytix platform to create and take a look at real time analytics applications with Apache Storm.

The free versions of StreamAnalytix can unleash the ability of a graphical user interface (GUI) -based speedy application development and preparation platform to a wider audience of streaming analytics developers to realize expertise, conduct pilots and take a large variety of applications into production.

“It could be a powerful way to get illustrious, giving one thing valuable for no price.” Venugopal said. “This platform can allow you to build an associate application freelance of the underlying stream engine. Once we have a tendency to get in we have a tendency to be able to not be obsolete. There's no risk of degeneration.” For more information please visit us at : www.streamanalytix.com

Analytics done wrong is creepy; doing it right is magic

 When it involves to Big Data analytics, the question isn’t simply “how much” however additionally “when?” Corporations that may kind helpful information quicker than their competition have a place. Further, operational intelligence interactions with client demand analytics and process within the now; minutes later is just too late.

To satisfy this would like, corporations are adopting Spark Streaming Analytics, the follow of operating with because it comes in, instead of drawing on processed Data already in storage. One amongst the players during this market is Impetus Technologies, Inc., with its product StreamAnalytix.

To take a glance at the globe of streaming data analytics, Jeff Frick and patron saint Gilbert, cohosts of theCUBE, from the SiliconANGLE Media team, joined Anand Venugopal at the complex event processing Summit East 2016 conference. Venugopal is that the Head of Product for StreamAnalytix with Impetus Technologies, Inc.

The new world of Business Analytics streaming

The language started with a summary of what StreamAnalytix well for corporations. Venugopal replied that it provides one common abstraction for streaming, permitting a business to make stream applications on any engine. “We wish to modify enterprises to specialize in the business price and application layer,” he said.

Venugopal then outlined many reasons why corporations were pushing toward quicker Spark Streaming Analytics. 1st was client analytics, permitting a Business Analytics to grasp and serve the requirements of its customers. He additionally mentioned victimization prognostic analytics to avoid losing customers. He then referred to the advantages of streaming once it involves optimizing operations to lower prices.

Doing analytics right

There’s a proverb that analytics done wrong is creepy, however, doing it right is magic. Venugopal touched on this subject, spoken language that in areas wherever the client provides plenty of information, banking, for instance, it’s sensible for corporations to use that information to reinforce the client expertise. He additionally took an instant to say the tradeoffs between quality and speed. Businesses, he said, are additional probable to trade speed for necessities that shield the business, like security. For more information Visit us: www.streamanalytix.com

StreamAnalytix 2.0 Industry’s Only Multi-Engine Streaming Analytics Platform

Enterprises are now rapidly moving to add Real Time Analytics and Stream Analytics as a strategy for becoming more agile and responsive to data available in Real Time Analytics. StreamAnalytix is a

platform to build and deploy streaming analytics applications for any industry vertical, any data format, and any use case.

StreamAnalytix 2.0 is architected to provide a level of abstraction that allows for the deployment of multiple streaming engines depending on the use-case requirements. This affords customers a new level of “best-of-breed” flexibility in their real-time architecture.

With StreamAnalytix, you can use the visual IDE and an enhanced set of powerful stream processing operators to easily construct data pipelines in a matter of minutes. You can then deploy them to a StreamAnalytics processing engine of choice.

Focus on your business logic. Leave the plumbing to StreamAnalytix

Support for Spark Streaming Analytics

A rich array of drag-and-drop Spark data analytics transformations including Machine Learning operations to analyze data using SQL queries and save the query output in a data store of choice. Built-in operators for predictive models with inline model-test feature and graphs to visually analyze data for models like Neural Networks and Tree.

• Proven Open Source Stack Ingest, store, and analyze millions of events per second with a pre-integrated package of industry-preferred Open Source components: Hadoop, NoSQL, Kafka, RabbitMQ,

ApacheStorm, Elastic Search, and Apache Solr.

• Rapid App Development

Integrate custom applications into the real-time data pipeline by visual drag and drop.

Rapidly port predictive analytics and machine learning models built in SAS or R via

PMML onto real-time data.

• Open, Flexible, & Extensible

Use any fast-ingest data store of your choice. Bring in any number of proprietary or standard data sources. Integrate the real-time data pipeline with other existing applications, based on configurable conditions.

• Real-time Visualization

View Spark pipeline data on enhanced self-service real-time dash-boards with user-editable widgets for various chart types. Create custom visualizations or integrate with third party dashboards using web socket connections. New Features Available in StreamAnalytix 2.0

• Support for Message Queueing: Additional support for industry standard message queue systems, including TIBCO, ActiveMQ, IBM MQ, Amazon Kinesis and S3.

• Varied Data Processing: A rich array of real-time data processing functions for string, time, date,numeric and other data types such as GeoPoint.

• Versioning: Create different versions of the pipeline and roll back to the older version for testing and debugging.

• Extensibility: Extensibility of stream-processing operators and libraries with user-defined functions.

• Complex Event Processing: Embedded Complex Event Processing engine enhanced for high-availability support.

• Multi-tenancy Controls: Restrict resources for specific tenants and pipelines with multi-tenancy controls.

• Smooth Blending: Code-free enrichment and blending of streaming data and static data with lookups and MVEL expressions.

• Data Encryption Support: Support for incoming/ outgoing data encryption through Kerberos and SSL for Storm pipelines. LDAP/ ActiveX based authentication for the web-based administration UI.

StreamAnalytix, a product of Impetus Technologies, enables enterprises to analyse and respond to events in real timeanalytics at Big Dataanalytics. Now featuring support for ApacheSpark Streaming. it is currently the industry's only platform that provides the powerful advantage of offering users with multi-engine support-which provides the flexibility to match the choice of stream processing engine to the requirements of a particular use case.

Website: www.streamanalytix.com | Email: inquiry@streamanalytix.com

Apache Storm | Stream Analytics

Are you tasked with finding the simplest things to build real time analytics applications.

With real time streaming analytics, enterprises will cut preventable losses, gain operational insights, and seize new opportunities.

Leading enterprises have complete the massive potential in real time streaming information from sources like social networks, machine generated information, log files, click-streams, network, and IP Detail Record (IPDR) information. The chance is to induce a competitive edge by swing all of your information at work to uncover new insights and support high rate higher cognitive process.


By viewing this webcast you will:


Learn about tried approaches and field of study concerns for building a high performance Apache stream process platform

Conceive / visualize an answer to get exceptions, patterns, and trends by endlessly querying, filtering, correlating, group action, enriching, and analysing information in period of time

Receive technology comparison supported analysis work and benchmarks conducted at Impetus Labs

For more Visit: - www.streamanalytix.com