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BigData SV 2017: (San Jose, CA)


Big Data Silicon Valley


Watch theCUBE’s Coverage of Big Data Silicon Valley LIVE


Event Details:

  • Date:
        March 13th, – March 16th 2017
  • Location:
         The Fairmont San Jose
         170 S Market St
         San Jose, CA

Guests on theCUBE:

Itamar Ankorion
CMO, Attunity

Many data streams, one data lake: the new design for efficient processing
Data is like water — heavy, expensive to move and countless ways to store it. Working efficiently with data means moving the processing to where the data lives, but if a company’s information comes hundreds or thousands of sources, that can be tricky. Read the full blog post with highlights from their panel at SiliconANGLE.com.


Bruno Aziza
CMO, AtScale

Serving customer needs in the evolving business intelligence realm
As data management and analysis continues to mature in the context of utilization by businesses other than those primarily focused on technology, business intelligence tools are becoming more common-place, and enterprises are consequently looking to find the most flexible toolsets to meet their needs. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Darren Chinen
Senior Director, Data Science & Engineering, Malwarebytes

Going real-time in the war against malware
Horrible things lurk on the internet, and they all want to live inside a company’s computer network. Viruses, malware, ransomware and more are out there, and all good people wage war against these threats. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Oliver Chiu
Sr. Product Marketing Manager, Big Data, Microsoft

Microsoft’s partnership with Hortonworks evolves in a cloud-first world
The future of the tech world is too complex for any one company to navigate alone. Industry giant Microsoft Corp. knows this better than most. It has reached out to other companies and other ecosystems to help drive its future innovations. One of those companies is Hortonworks Inc., a big data enterprise software business. Together, they’re looking toward the future of the cloud. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Ravi Dharnikota
Chief Enterprise Architect, SnapLogic

How companies are taking aim at ever-changing datasets
As big data expands its applicability to more business efficiencies, the dialogues between connected devices are generating their own developments and studies, though the highly automated nature of their processes creates a high volume of filler for analysis. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Gaurav Dhillon
Chairman & CEO, SnapLogic

Taking advantage of enterprise tech at consumer prices through the cloud
Buying powerful technology is expensive. The most powerful tools are considered enterprise-grade because only large businesses can afford them. However, more and more companies are discovering that renting powerful technology is cheap. Cloud computing allows these smaller businesses to wield enterprise-level tools at a fraction of the cost, and that’s unleashing untold innovation, according to Gaurav Dhillon, chairman and chief executive officer of SnapLogic Inc. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Basil Faruqui
Solutions Marketing Manager, BMC Software

Data automation and scheduling becoming mission critical to big data
Over the years companies have explored many avenues to handling and manipulating large amounts of data. However, with the advent and growth of the cloud and big data, the resulting explosion in the quantity of data being analyzed and the seemingly endless number of data sources, the need for fast, flexible and smart automation of the data workflow that can adapt and handle changes is now more important than ever. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Tony Fisher
Senior SVP, Strategy, Zaloni

Can the enterprise afford to be in the ‘ignore state’ with its data lake?
The data lake is evolving, and one company is nudging the enterprise into extracting value from data that’s stored in this increasingly complex paradigm. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Scott Gnau
CTO, Hortonworks

IoT data is moving target; computing requires new artillery
Anyone working on a software (or hardware) solution to help data trek to the edge and back in Internet of Things applications may be wasting their effort, said Scott Gnau, chief technical officer of Hortonworks Inc., during BigData SV 2017 in San Jose, CA. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Yaron Haviv
Founder & CEO, iguaz.io

New tech allows real-time big data processing between the cloud and the edge
Faster is better. That’s as true in big data as anywhere else in business. Unfortunately, data is heavy, slow and expensive to move. The most efficient play is to process data at the source, but that’s not always possible. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Joe Hellerstein
Chief Strategy Officer, Trifacta

The value of keeping metadata free and open
Too much data to crunch? The solution may be yet more data — cataloged with machine learning to make recommendations for use cases, says Adam Wilson, chief executive officer of Trifacta Inc. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Holden Karau
Principal Software Engineer, IBM

Spark ML: getting closer to the edge to improve latency
Going mainstream in the data-driven enterprise is Apache Spark, the open-source analytics engine. As prominent industries move to the Internet of Things markets and machine learning technologies to capitalize on data, Spark ML (which provides a uniform set of high-level application program interfaces that help users create and tune practical machine learning pipelines) offers companies the ability to build real-time streaming solutions that provide fast, advanced analytics to gain insights that drive business. Read the full blog post with highlights from Karau’s interview at SiliconANGLE.com.


Josh Klahr
VP of Product, AtScale

Serving customer needs in the evolving business intelligence realm
As data management and analysis continues to mature in the context of utilization by businesses other than those primarily focused on technology, business intelligence tools are becoming more common-place, and enterprises are consequently looking to find the most flexible toolsets to meet their needs. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Martin Lidl
Director, Deloitte

Many data streams, one data lake: the new design for efficient processing
Data is like water — heavy, expensive to move and countless ways to store it. Working efficiently with data means moving the processing to where the data lives, but if a company’s information comes hundreds or thousands of sources, that can be tricky. Read the full blog post with highlights from their panel at SiliconANGLE.com.


Murthy Mathiprakasam
Director of Product Marketing, Informatica

Forget reinventing the wheel, companies get agile by embracing abstraction
There’s a strange sickness in the tech world. It loves to reinvent the wheel. Yet, if the real goal is shipping product, there’s no reason to build up from raw code. In development, abstraction layers let people build on pre-made code, so they don’t have to reinvent the wheel. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Katharine Matsumoto
Data Scientist, eero

How companies are taking aim at ever-changing datasets
As big data expands its applicability to more business efficiencies, the dialogues between connected devices are generating their own developments and studies, though the highly automated nature of their processes creates a high volume of filler for analysis. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Stephanie McReynolds
VP, Alation

Monetizing muck: Can metadata see into data lakes and extract value?
Metadata is earning shout-outs at BigData SV 2017 in San Jose, CA, as a practical first step to monetizing massive, murky data lakes. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Abhishek Mehta
CEO, Tresata

Is big data truly transforming, or is history repeating itself?
While the exploration of possibilities enabled by the continuing sophistication of tools for handling big data has some seeing brave new worlds on the horizon, other analysts and developers are perceiving it as yet another case of history repeating itself. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Chris Murphy
IT Solution Architect

Many data streams, one data lake: the new design for efficient processing
Data is like water — heavy, expensive to move and countless ways to store it. Working efficiently with data means moving the processing to where the data lives, but if a company’s information comes hundreds or thousands of sources, that can be tricky. Read the full blog post with highlights from their panel at SiliconANGLE.com.


Lee Paries
VP, Think Big Analytics

Monetizing muck: Can metadata see into data lakes and extract value?
Metadata is earning shout-outs at BigData SV 2017 in San Jose, CA, as a practical first step to monetizing massive, murky data lakes. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Arik Pelkey
Senior Director, Product Marketing, Pentaho

Can machine learning streamline the path from messy data to insight?
As much as companies would like silver bullet analytics to manifest insight from data, the only way out of the data jungle is through it, according to Arik Pelkey, senior director of product marketing at Pentaho Corp. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Donna Prlich
Sr. VP of Products & Solutions, Pentaho


Frederick Reiss
Chief Architect, IBM Spark Technology Center (spark.tc)

Bringing the power of artificial intelligence to real-world applications
IBM Corp. has spent years developing its artificial intelligence platform, Watson, looking to the open-source Apache Spark platform for innovative machine learning capabilities that can help push Watson into business verticals for manufacturing, healthcare, cybersecurity and retail. Having made the IBM Spark Technology Center a part of its AI-driven ecosystem, IBM has opened up the analytics capabilities of Watson in an effort to simplify the machine learning for progressive neural networking using Apache SystemML. The idea is to help businesses move quickly into the new domains of machine learning. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Josh Rogers
CEO, Syncsort

Bridging legacy and next-gen systems in the big data age
As the methods for collecting, organizing and analyzing colossal amounts of business data continue to diversify, some of the former champions of the big data world have found their glory fading, while fresh challengers are quickly rising in prominence.Read the full blog post with highlights from his interview at SiliconANGLE.com.


Ben Sharma
Founder & CEO, Zaloni

Can the enterprise afford to be in the ‘ignore state’ with its data lake?
The data lake is evolving, and one company is nudging the enterprise into extracting value from data that’s stored in this increasingly complex paradigm. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Raymie Stata
SVP, Big Data Services, SAP

Monetizing big data with Hadoop as a Service: SAP’s story
Back in September 2016, SAP SE scooped up a company called Altiscale Inc. to add high-performance and scalability to its Big Data-as-a-Service solution. Altiscale brought a highly optimized cloud infrastructure that offers Hadoop as a Service and Spark. These services complement the SAP Cloud Platform, offering a more dynamic enterprise solution that allows companies to focus on operating the business, according to Raymie Stata, senior vice president of big data services at SAP. Read the full blog post with highlights from his interview at SiliconANGLE.com.


Yuanhao Sun
Co-Founder & CTO, Transwarp


Amit Walia
EVP, Chief Product Officer, Informatica

Can artificial intelligence cataloging be the Google for enterprise big data?
Despite uncertainty about its usefulness, companies continue hoarding masses of data. Does this mean data scientists are doomed to shovel through dreck looking for rare nuggets, or is there any easier way? Read the full blog post with highlights from his interview at SiliconANGLE.com.


Wei Wang
Senior Director, Product Marketing, Hortonworks

Microsoft’s partnership with Hortonworks evolves in a cloud-first world
The future of the tech world is too complex for any one company to navigate alone. Industry giant Microsoft Corp. knows this better than most. It has reached out to other companies and other ecosystems to help drive its future innovations. One of those companies is Hortonworks Inc., a big data enterprise software business. Together, they’re looking toward the future of the cloud. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Adam Wilson
CEO, Trifacta

The value of keeping metadata free and open
Too much data to crunch? The solution may be yet more data — cataloged with machine learning to make recommendations for use cases, says Adam Wilson, chief executive officer of Trifacta Inc. Read the full blog post with highlights from their interview at SiliconANGLE.com.


Tendü Yogurtçu
GM, BigData, Syncsort

Draining the swamp: how seamless data management is becoming mission critical
A few years ago, Dave Vellante, co-host of theCUBE, made the suggestion that “data lakes” — the vast amount of data that companies store while not currently in use — are turning into “data swamps” because the technology used to manage them is not being leveraged properly. Read the full blog post with highlights from their interview at SiliconANGLE.com.



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