Our last installment of Big Data & Brews with Anil touches on a cool topic. Of course, I like that we get to use the chalkboard but we also had a chance to break down how Informatica sees the ecosystem (hint, the data intelligence layer is the most promising). We also talked about what he sees happening in the next 10 years that will really accelerate change in the industry.
The full conversation is just a click away – tune in!
Stefan: What would be interesting to see is in this ecosystem really of data technologies, right, where are you guys are sitting and then where you see Hadoops, Teradatas, Microstrategies, Datmeers. I kind of see you as the fabric that brings it all together. Is there a central brain of that fabric?
Anil: Right. You know, we believe so. Let me just take a stab at how we think of the word. This is obviously a logical view and it has to be translated based on … We see the world as start with this is — think of this as data persistence. This world is obviously is changing very rapidly. It was basically the databases of the world. Could be anything from mainframe database to relational database, etc. Now Hadoop and NoSQL and this world could be either on the framework or in the cloud or a combination.
Then we see the world or what we think of as data infrastructure. So this is the world, which we have traditionally played in and this world is also changing rapidly because it obviously, when this changes, this has to change here. You have things like data ingestion, which is changing very rapidly. Somebody once joked to me that that whatever IBM worked on in the 1970s always will be useful at some point so it’s like that. Things, concepts like changes and capture. The concepts like real time, streaming, etc. so all of those are coming back, right?
You have ingestion. You have data integration. Obviously that’s where you put it together, the aggregation etc. I think you have a lot of work around data quality, which is increasingly, “How do you do quality, especially on unstructured data” and things like that. That becomes a lot of work to …read more
The advent of connected manufacturing has ushered in an era where low-cost machine sensors take thousands of measurements per second at many points across the manufacturing process. This stream of sensor data enables manufacturers to quickly detect emerging anomalies and solve issues before they impact yield and quality.
Big Data insights enable predictive analytics for those rapid, proactive process adjustments. Manufacturers can capitalize on this opportunity by following an approach that combines the power of Teradata with Hortonworks Data Platform’s storage and compute efficiencies at extreme scale. Working together, our technologies enable big data insights that can dramatically improve existing manufacturing processes.
Register for the Teradata Partners Event
On Wednesday October 21st from 12:00-12:45, I will be presenting a webinar along with Dale Glover, Teradata VP of Industry Consulting. Join us for 45 minutes to learn more about how manufacturing companies are utilizing Hadoop to:
Establish a Single View of data on products throughout their entire lifecycles
Build a 360° view of lifetime customer value
Optimize manufacturing quality and yield
Proactively maintain equipment to minimize the risk of downtime
Presentation Title: Hadoop for Manufacturing Innovation & IoT
Session Number: 3719
Date & Time: Wednesday October 21st from 12 to 12:45PM PST
Location: 202 AB
About the Speakers
Grant Bodley: Hortonworks GM for Global Manufacturing Solutions
As General Manager of Global Manufacturing Industry Solutions at Hortonworks, Grant Bodley brings over 25 years of manufacturing experience in working with leading Automotive, Industrial, High Tech, and Aerospace Manufacturers in leveraging Big Data Insights and high impact use-cases to transform their businesses. Prior to Hortonworks, Grant was Vice President of Manufacturing Industry Solutions at SAP for more than 10 years.
Dale Glover: Vice President of Industry Consulting for Teradata
Dale Glover is a Vice President of Industry Consulting for Teradata. His Industry Consulting team is responsible for helping clients successfully implement Business Intelligence and Analytics to drive business process impact and value. He is leading the transformation of this organization to support an analytic consulting focus across a broad ecosystem of platforms and tools. His advanced Applied Analytic Team is helping organizations move from Big Data insights into the realization of value from advanced analytics in day to day operations.
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I recently had the pleasure of visiting with Arvind Battula, Sr. Data Scientist at Schlumberger. We discussed his background as a chemical and mechanical engineer and his move onto the Data and Analytics team as a data scientist. The following is a transcript of my conversation with Arvind. We discussed his background, his interesting focus areas for data science in oil and gas, and technologies that he believes will help transform the industry.
Kohlleffel: Arvind, you entered the data science world recently on the Schlumberger Data and Analytics team and have a very interesting background coming from both chemical engineering and mechanical engineering disciplines. Tell me about your experience and engineering background.
Battula: Certainly, my background is diverse. I started my formal training as a chemical engineer. After my bachelors, I applied for graduate school in mechanical engineering to deal mostly with computational fluid dynamics. I wanted to pursue a Ph.D. in the same area, but my doctoral work changed direction to focus on nanophotonics, which is the interaction of nanometer-scale objects with light.
Kohlleffel: That makes for quite a compelling base of experience for your data science work. Now that you’ve moved to the Data and Analytics team, where have you focused so far?
Battula: My mechanical engineering background has been very helpful at Schlumberger since we are dealing with designing products that are used in the harshest conditions imaginable on the planet. In everything we do, we must consider very minute design details to ensure the most robust end product. Before we design and build parts and assemblies, we are very thorough in our calculations and modeling–to quantify our engineering and physics assumptions. This is where we leverage data and analytics to bring a new rigor to the process and move beyond some standard linear assumptions which can be obstacles to efficiently model complex phenomena across all variables.
For example, factors like high temperature, high pressure, stress, vibration, corrosion, aging all act in parallel on the mechanical systems. We can look deeply into that data to better understand the combinations of these variables that are causing mechanical failures and then we can bring together the data streams for both physics and engineering.
This non-linear root cause analysis shows us the real world we deal with on a daily basis. It is ideally suited to leveraging big data and analytics and it benefits multiple groups within our company including engineering, manufacturing, sustaining and maintenance.
In …read more
What I saw at Strata in New York! For whatever reason, these are the things that struck me immediately: Kafka has gained a lot of momentum, and I might wager that 6 months from now, you will be using it…
There’s excitement in the air as one of Benelux’s largest Big Data conferences “Big Data Expo”, comes to Utrecht in The Netherlands.
We’re sponsoring and you’ll find our experts Chris Harris and Jhon Masschelein presenting such topics as “5 Steps for Effective use of Apache Spark in Hortonworks Data Platform 2.3” and “Lessons Learned: 5 Common Hadoop Use Cases”. You can register here.
As Hortonworks continues to extended its footprint in Europe, we’re seeing some exciting use cases and an increasing momentum of enterprise adoption of Hadoop. The Hadoop Summit that we organized in Brussels early this year showcased some of the great European use cases. Here’s a short overview of one my favorites:
ING Bank: Destroying Data Silos for Creating a Predictive Bank
Hellmar Becker a Utrecht resident discusses breaking down Data Sillos and creating a centralized Datalake at ING. He also discusses the modernization of their data centers, migrating away from legacy systems within their governance and security framework.
Bart Buler, Hellmar’s co-presenter discusses the banks steps into becoming a truly predictive bank. Bart also provides some do’s, don’ts and difficulties in this journey and talks about the future for the bank including “integrating analytics as part of data flows”, “showing interactive results to individuals without access to the cluster” and many more.
You can more videos listed here
To conclude, Big Data Expo, will showcase an array of new technologies, exciting case studies and organizations making the most out of data. Come visit us at Stand 21 as my colleague Alfie Murray-Dudgeon pictured below awaits.
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If the sky was the limit and we had unlimited storage and compute, what would the future of the data world look like? In part 4 of my interview with Informatica, acting CEO, Anil Chakravarthy, says we’re already seeing a preview of it in the consumer world. What does he mean? Watch below to find out more:
Stefan: Let me switch gears here a little bit. Where do you see the future really in the data world? If sky’s the limit, and we have unlimited storage on compute and, you know, Ray Kurzweil is right and we have chips are faster than our brains in something like five years. Where is this going?
Anil: Yes, to me actually I think we already see a preview of the future. I’m talking about enterprise data right now. I think we see a preview of that feature already in the consumer world. I mean think of the Apple App Store for example – what are there, over a million apps right now at this point? But the apps are already separated from the data. Your data that the apps operate on is kind of under your control; you may have a separate repository that you use for it, either your own or iCloud, etc, and the apps are extremely modular and the apps come and go very quickly, the data lives a lot longer.
If you contrast that with the enterprise world, the enterprise world has been one where the data has been very closely tied to the apps. You know you have ERP apps or CRM apps or other kinds of apps, or custom apps where the data models have been very closely tied. You still have some separation, that’s why you can reuse the data, but the data and the apps have been very closely tied together. To me, that world is going to go the same way as the consumer world already has gone. So if you ask me what’s the future, it’s like, the data models, the understanding of what different data types are, whether it’s schema-on-read or pre-defined schema and things like that, the data will be designed for durability and will be designed essentially to be used by a variety of apps, maybe cloud-based apps, maybe on-premise apps, etc, etc. The apps will become a lot more modular and the apps will come and go, and maybe apps may be …read more
Since the partnership between Hortonworks and SAS we have created some awesome assets (i.e., SAS Data Loader sandbox tutorial, educational webinars and array of blogs) that have enabled Hadoop and Big Data enthusiasts’ hands-on training with Apache Hadoop and SAS’ powerful analytics solutions. You can find more details around our partnership and resources here: http://hortonworks.com/partner/sas
To continue the momentum, we have Paul Kent, Vice President of Big Data at SAS, share his insights on the value of YARN and the benefits it brings to SAS and its users- this time around SAS Grid and YARN.
On my travels and in the SAS Executive Briefing Center, it has become more obvious that many folks have grabbed on to the idea that Hadoop will allow them two things:
to assemble a copy of all their data in one place
to provide enough processing horse power to actually make some sense (business value) of the patterns contained in a holistic view of said data
As they get closer to this goal they realize what a valuable resource the data lake has become. They need an effective means to “share nicely” – its not likely that every department is going to have the resources to establish their own data lake, and even if they do, you’ll be back to arguing about which version of the truth is the correct one.
YARN is the component in the Hadoop eco-system that helps folks share the value gained from building a shared pool of the organizations data.
Move the work to the Data
As the data volumes and velocities grow it has become important to find a strategy that minimized the number of hard (permanent) copies of data (and inherent reconciliation and governance). YARN allows Hadoop to become “the Operating System for your data” – a tool that manages and mediates access to the shared pool of data, as well as the resources to manipulate the pool.
Yarn allows the various patterns of work destined for your cluster to form orderly and rational queues, so that you can set the policy for what is urgent, what is important, what is routine, and what should be allowed to soak up resources so long as no one else requires them at the moment.
Expand then Consolidate
Disruptive technologies like Hadoop are often deployed “at the fringes” of an organization (perhaps in an Innovation Lab). Initial ROI is often …read more
My chat with Informatica’s Anil Chakravarthy touched on the subject of database schemas, ETL and dynamic mapping. With the growing number of data sources and complexity, Anil argues that a purely static schema has only limited use and that flexibility is critical. He also points out that technology doesn’t have to provide the perfect answer, but it should save time, which to me, is the most valuable asset.
Enjoy the next episode of Big Data & Brews!
Stefan: What’s your perspective? As you said, there’s a growing number of data sources and more insights shape up as you’re enriching more the data. Is it really hard to define the static schema that we used to do?
Anil: Yeah, absolutely. Let me actually, just because it’s good conversation, I’ll start with the other extreme because the schema discussion usually goes from either …
Stefan: Black to white.
Anil: Yeah. Either everything is fixed or nothing is fixed. As you mentioned earlier, when I was at Symantec, one of the businesses, product groups, that I ran was data loss prevention, the DLP business. There, there is no schema. It’s basically, how do you un-structure data, especially over email? Somebody might be sending social security numbers, etc. What do you do in those cases? DLP became a very successful category by just having essentially regular expressions. That you look for certain data.
Has that been enough? Clearly not because you look at what’s going on in the world of breeches etc. It’s necessary, but not sufficient. That’s what has shaped our world view. You don’t want to insist on schema everywhere. There will be many, many types of data where you can do perfectly good processing without schema. That is not sufficient by itself. Even in the world of security that we’ve been talking about now, like we just talked about, you need to understand metadata. You need to understand what is valuable data. You cannot combine it with other schemaless… You might, for example, have SharePoint documents where you’ll never get any schema, but they still contain valuable information in order to protect data and process it. You …read more