Building Your Big Data Team in 2015 – Top 5 Pieces of Real-World Advice

January 27, 2015

There’s lots of advice out there on building a big data team, from industry or expert analysts and leading publications. But we wanted to see how this is being implemented in real life, so we talked to the real world big data mavericks – those who’ve faced the challenge of gaining true business value from big data and succeeded.  They shared real-world insights into how they made it happen and the advice they’d give to those ready to take the plunge. (Scroll to the bottom to meet our mavericks.)

1. Clearly define your business goal, and don’t be afraid to start small.
When you work with big data, you have to know first what you’re going to do with that data” – Marc Hayem, VP of Platform Transformation, RichRelevance

It may seem obvious but is often overlooked.  Whether you’re a data-driven company whose entire business model revolves around crunching big data, or a manufacturer looking to optimize your operational efficiency using machine data, you need to be clear about the challenge you’re trying to tackle with big data. Omitting this step, you risk ending up with inappropriate technologies, a lack of executive support, and an ill-prepared team. Saad Khalid, a product manager at Paytronix, echoes the advice about starting small:

Starting small to get into big data can be useful, because you can get lost in a lot of technical jargon: Hadoop, Hive, MapReduce. My advice to people considering big data as a project would be to take it slow, have a smaller project in mind where you can actually think about the questions that you want to answer and achieve results…. Have a team that is dedicated towards that goal, and those results.  Start slow and then grow big and then scale your project. ” Saad Khalid

Andrew Robbins is CEO of Paytronix, a company that helps restaurants build brand loyalty and get rich, big-data driven insights into their customers’ behavior for better sales and marketing.  The questions that big data could answer for them were endless – but in the end, zeroing in on one small, simple question – “Who had breakfast for dinner?” – helped them define the scope of their entire project:

“For us, we sat around and thought of so many ideas and it became so big and we boiled it down to a single question and it was who had breakfast for dinner?  In that question, it seems kind of simple.  The “who” is pretty complicated.  Who are the people?  Can you give me the collection and what are they like?  What are their demographics?  “The “had breakfast,” what does that mean?  You got to get into the details of a check.  Is it scrambled eggs?  …All of those pieces led to a simple thing that we could all shoot for and that was our minimal viable product and you can get to it quicker and then the team goes, “Aha.  That’s success.” Andrew Robbins

Finally, as you define your scope, make sure the projects have a measurable return to achieve your business goals.  Because big data projects can be complex, people need to be motivated to work through the challenges and that happens when your project impacts the business in a demonstrable way. Marc Hayem is VP of Platform Transformation at RichRelevance, a company that helps retailers provide personalized recommendations to shoppers.

“I think the important thing when you get into big data is to be able to prove the value rapidly, which is to really pick the right problem and demonstrate very rapidly that you can find solutions to that problem. That you can create value around that problem… If you have identified that something that will give you a competitive advantage and the technology is applied right, then the payoff can be monumental.” Marc Hayem

2. Choose your technologies carefully, based on the challenge you’re trying to address and your organizational culture.
“Pick the tools that work and ignore all the religion that’s out there.” – Andrew Robbins, CEO, Paytronix

You should only start to investigate your technologies once you define your problem.  Many of the big data leaders we spoke to acknowledged that the big data technology ecosystem can be complex, and cautioned against being driven by the current frenzy to adopt a particularly hot technology.  Their advice is unanimous: start with one problem, start small, and work backwards from there in picking your technologies.  Always pick the tools that solve the problem at hand and find tools increase your teams’ productivity, not create obstacles.  Andrew Robbins discussed how heated the debate can be:

I think one of the things that surprised me the most was just how fragmented the tool sets are and it really seems like the wild west of different components and how religious people are that you’re using a component .… ‘If you’re using Hive, you must be crazy.  You must use Impala.  Anybody who is not using Impala is just … that doesn’t make sense’. Pick the tools that work and ignore all the religion that’s out there.  Be practical.  Pick the tools that work.  You can always switch them out in the future if you need to” Andrew Robbins

Marc Hayem shares his perspective on what makes a good fit:

Evaluating the tools can be overwhelming. There are new tools that come out constantly. There is a tendency to look at always the next shiny thing that comes out and think this will solve even more magical problems. At some point, you have to settle. You have to choose your tools. The tools that you’re comfortable with. That you have the tools for. That you have the staff for, more importantly. This is basically your tool set. That’s what you’re going to use. There is definitely with this ecosystem of open source tools, a tendency to go after the next big thing, constantly. It’s something that you have to fight a little bit. We have used a lot of open sourced software…Essentially, we believe that when you use open source solutions there is a community behind those tools. The tools get better over time, very, very rapidly.” Marc Hayem

Marc’s comments illuminate that in evaluating technologies, vendors, and platforms it’s important to consider what’s a good fit for your organization based on common values like transparency and innovation. Paytronix’s head of technology, Stefan Kochi, also believes this is an important factor:

Once we decide to implement a big data solution then we started looking at different providers, different vendors. The initial guiding principles were the ones that we use for other decisions we have made, such as they have to feel like an extended part of our company. … Some of the things we look for are – what was the technology based on? Open source versus private? How easy it for them to innovate? Innovation is critical. Do they serve things that we need? We have some guiding principles that we apply in general, the transparency of the company, how easy it to communicate with, and how solid and mature the product is. Pentaho was an attractive options early on. They use open source technologies, and that was very attractive to us. Paytronix uses a lot of open source technologies, so right there you have a connection with the approach that Pentaho has taken.”  Stefan Kochi

3. Identify key players on a cross functional team

While in some cases, a big data implementation can be done with one person or a very small team, the general consensus is that having a dedicated, cross functional team will ensure success. This is critical to ensuring that business needs are understood and data is successfully prepared and accessible to meet the defined the business needs. So what roles are needed?  We asked our big data leaders and internal big data services team to comment on what is working and compiled the results.  While structures vary from organization to organization, here are some key roles to consider.

  • Executive Sponsor- This senior level person understands the business needs, rallies support, and funds the solution. Andrew Robbins is an example:

“Paytronix is full of bright, curious, empathetic people. I wasn’t the star of this …we have a really bright engineer who is at the forefront of thinking about [big data] and I probably just provided some air cover so that we’re safe to go after it and be successful.”  Andrew Robbins

  • Business User – This individual defines and prioritizes the business requirements and then translates them into high level technical requirements.

“My favorite part about what I do currently is gathering requirements and actually really thinking about what our next product’s going to be.  What our next feature’s going to be.  Talking to our clients, and talking to my internal clients, which is the rest of the team here.  Really start to think about a new feature, a new product, and gathering those requirements, and thinking about design.  I love working with the engineering team, and really trying to think about how to approach problems in several different manners, and really try to come up with a creative solution so our clients can benefit from it.” Saad Khalid

  • Subject matter expert – Especially important in non-technical industries where the gap between a data developer and the Business user can be very large, this person knows the business intimately.
  • Data scientist – This individual understands the data and can extract information from that data to meet the business requirements. The data scientist ideally has both domain knowledge, statistical analysis background, and basic understanding of computer science.

“As I mentioned earlier, we have hundreds of algorithms that basically constantly try to decide what is best for our customer. You have to be able to build those algorithms. You have to understand the mathematics behind it. you have to understand the technologies. You also need very good data scientists. You need people who understand very well the mathematics behind the predictive modeling that takes place in personalization.” Marc Hayem

  • Data Engineer/Software Engineer – This individual has a software engineering background and experience in developing software for distributed or multi-threaded applications. This person typically is a server side Java developer who can implement ETL at scale using various Big Data technologies. Someone with experience in statistics and machine learning is a plus.

“Paytronix has a small engineering group. We’re not a large firm, but we’re fortunate to have a very talented engineering team. Those engineers who have done a lot of existing development of the product are also able to explore and go from an idea and a concept to a real product….There is a lot to manage when it comes to big data.  We have a dedicated team that looks after our structure and architecture.  There is an architecture that oversees big data and we also have 2 software developers. You need to have a dedicated team to take care of this structure.  It is extremely important. ” Saad Khalid

  • Data journalist – We’re hearing more and more about a data journalist – someone who looks at the data from a storytelling aspect. Forbes even predicts that storytelling will be the hot new job in big data analytics in 2015. This person serves as the link to the larger audience for the data, making it understandable to the audience consuming the data.
  • Platform/Systems Architect – This is a senior technical architect responsible for designing the entire end-to-end solution that meets the business requirements for both short-term deliverables and long-term needs. Typically this person has a software engineering background in large scale clustering/distributed processing systems and is responsible for technology selections and implementation process.  The architect defines the big data blueprints, or architectural model, that an organization will implement.

Another lesson that Paytronix has learned is the importance of building a working model first. You can get caught up in the big picture, being very strategic, but you have to build the working model first. If you have a billion transactions that you want to ETL, you should probably ETL a thousand. You get an idea how the systems are working with a thousand transactions. Another important thing that we learned is that you have to be very focused on system integration and architect should be always present as you connect. Systems talking to each other is like building many bridges. You have people focus on each bridge, but someone needs to oversee all the bridges together.” Stefan Kochi

  • IT/Operations manager – This person operationalizes, deploys, manages, and monitors the systems. They should understand Hadoop and big data to successfully deploy across systems and scale to hundreds or thousands of servers, instead of just a few.  Yug Muppala, a software engineer at RichRelevance, points out the critical nature of this role:

We at RichRelevance have a really good operations team that keeps our servers up and running all the time. That is really important they make the cluster available to us and keep the health of the cluster up and running.”  Yug Muppala

4. Be creative to make the most of your human and technology resources
“Instead of search for the mythical people, we would take people we know and create a team that could be successful”  - Andrew Robbins, Paytronix

While the above list provides general guideline for a big data team, it’s only a starting point.  There’s a well-known meme about how looking for the perfect data scientist – who combines analytics with business savvy  and development skills and mathematics – is like looking for a unicorn: it doesn’t exist.  Companies who’ve successfully launched big data initiatives haven’t used unicorns – they’ve been innovated and are clever with how they resource their project and leverage their team.  Andrew Robbins acknowledges this:

When you make the move the Big Data, what are you concerned about?  What we’re concerned about in Paytronix and probably the biggest one is can you be successful, and then you go back from that and you say, “Where are the people?  What people are going to implement this solution?”  Is it internal people or are we going to go hire people?  Then people talk about data scientists.  Have you seen a data scientist?  Do you live next to one?  Can you find them on the street? I think one of the things that made us successful at Paytronix was to say we would, instead of search for the mythical.  To us, a data scientist is a function, not a person.  Data science might include a strategist, an analyst and an engineer.  In between them, they can satisfy the need of data science.” Andrew Robbins

Creative thinking and innovative technologies offer other options to remove the need for unicorns.  There are many emerging technologies that help minimize the dependence on coding and other hard to find skillsets – for smaller companies that can’t afford data scientists, these technologies are attractive options. Yug Muppala, a software engineer at RichRelevance, talks about why they use Hive:

Hive is very easy for anyone with SQL knowledge to start writing, querying the Hadoop cluster. That’s a big advantage. Not many people have knowledge around Pig scripts and stuff like that and most of our data science team is very comfortable with writing SQL queries. Hive gives them that advantage so that they could just go write queries themselves instead of having to wait for someone else to write the extraction for them.” Yug Muppala

Pentaho’s own visual interface helps here, by reducing the amount of code needed to join data, and reducing the time Paytronix spent on this task from two weeks to a mere hour and a half:

“We have some data in our transactional database and we have some data in Hadoop. Joining these two together was a hassle before and Pentaho helped us solve this problem. . . .It’s a simple step within Pentaho. ..We don’t have to write a lot of code which we were doing before and it’s a simple process of dragging and dropping steps to connect these different data sources.” Yug Muppala

5. Look to the future
Last – as you look ahead to building a team in 2015, there are a few thing to keep in mind:

  1. Consider the cloud. More and more companies are running all or part of their big data environment in the cloud.  As cloud becomes more widely adopted and becomes more mature and secure.  Look for team members with experience in the cloud, in addition to those who have dealt with data governance and compliance issues.
  2. Consider self-service analytics. Whether the end user is a customer or an internal user, you’ll need to consider how to make the insights created from your big data environment available for consumption both inside and outside your firewalls.  How will you deliver high-quality governed data to end users for analysis? Will you embed analytics in customer-facing software, or perhaps within an enterprise application?
  3. Consider the profile of people willing to tackle these big data challenges. In addition to experience with the relevant technologies and having people to embrace and learn from the challenge that big data provides. Marc Hayem says, “The people I’ve worked with are very much start-up people. They are adventurous a little bit more than your average IT person.”

Meet the Mavericks:

Andrew Robbins, Paytronix2
Andrew Robbins, CEO, Paytronix
Learn more about Andrew’s journey with big data here.

Marc Hayem, RichRelevance2
Marc Hayem, VP of Platform Transformation, RichRelevance
Learn more about Marc’s journey with big data here.

Saad Khalid, Paytronix2
Saad Khalid, Product Manager, Paytronix

Stefan Kochi, Paytronix2
Stefan Kochi, Head of Technology, Paytronix

Yug Muppala, RichRelevance2
Yug Muppala, Software Engineer, RichRelvance


Big Data in 2015—Power to the People!

December 16, 2014

Last year I speculated that the big data ‘power curve’ in 2014 would be shaped by business demands for data blending. Customers presenting at our debut PentahoWorld conference last October, from Paytronix, to RichRelevance, to NASDAQ, certainly proved my speculations to be true. Businesses like these are examples of how increasingly large and varied data sets can be used to deliver high and sustainable ROI. In fact, Ventana Research recently confirmed that 22 percent of organizations now use upwards of 20 data sources, and 19 percent use between 11 – 20 data sources.[1]

Moving into 2015, and fired up by their initial big data bounties, businesses will seek even more power to explore data freely, structure their own data blends, and gain profitable insights faster. They know “there’s gold in them hills” and they want to mine for even more!

With that said, here are my big data predictions for 2015:

Big Data Meets the Big Blender!

The digital universe is exploding at a rate that even Carl Sagan might struggle to articulate. Analysts believe it’s doubling every year, but with the unstructured component doubling every three months. By 2025, IDC estimates that 40 percent of the digital universe will be generated by machine data and devices, while unstructured data is getting all the headlines.[2] The ROI business use cases we’ve seen require the blending of unstructured data with more traditional, relational, data. For example, one of the most common use cases we are helping companies create is a 360 view of their customers. The de facto reference architecture involves the blending of relational/transactional data detailing what the customer has bought, with unstructured weblog and clickstream data highlighting customer behavior patterns around what they might buy in the future. This blended data set is further mashed up with social media data describing sentiment around the company’s products and customer demographics. This “Big Blend” is fed into recommendation platforms to drive higher conversion rates, increase sales, and improve customer engagement. This “blended data” approach is fundamental to other popular big data use cases like Internet of Things, security and intelligence applications, supply chain management and regulatory and compliance demands in Financial Services, Healthcare and Telco industries.

Internet of Things Will Fuel the New ‘Industrial Internet’

Early big data adoption drove the birth of new business models at companies like our customers Beachmint and Paytronix. In 2015, I’m convinced that we’ll see big data starting to transform traditional industrial businesses by delivering operational, strategic and competitive advantage. Germany is running an ambitious Industry 4.0 project to create “Smart Factories” that are flexible, resource efficient, ergonomic and integrated with customers and business partners. The machine data generated from sensors and devices, are fueling key opportunities like Smart Homes, Smart Cities, and Smart Medicine, which all require big data analytics. Much like the ‘Industrial Internet’ movement in the U.S., Industry 4.0 is is being defined by the Internet of Things. According to Wikibon, the value of efficiency from machine data could reach close to $1.3 trillion dollars and will drive $514B in IT spend by 2020.[3]The bottlenecks are challenges related to data security and governance, data silos, and systems integration.

Big Data Gets Cloudy!

As companies with huge data volumes seek to operate in more elastic environments, we’re starting to see some running all, or part of, their big data infrastructures in the cloud. This says to me that the cloud is now “IT approved” as a safe, secure, and flexible data host. At PentahoWorld, I told a story about a “big datathrow down” that occurred during our Strategic Advisory Board meeting. At one point in the meeting, two enterprise customers in highly regulated industries started one-upping each other about how much data they stored in Amazon Redshift Cloud. One shared that they processed and analysed 5-7 billion records daily. The next shared that they stored a half petabyte of new data every day and on top of that, they had to hold the data for seven years while still making it available for quick analysis. Both of these customers are held to the highest standards for data governance and compliance – regardless of who won, the forecast for their big data environments is the cloud!

Embedded Analytics is the New BI

Although “classic BI,” which involves a business analyst looking at data with a separate tool outside the flow of the business application, will be around for a while, a new wave is rising in which business users increasingly consume analytics embedded within applications to drive faster, smarter decisions. Gartner’s latest research estimates that more than half the enterprises that use BI now use embedded analytics.[4] Whether it’s a RichRelevance data scientist building a predictive algorithm for a recommendation engine, or a marketing director accessing Marketo to consume analytics related to lead scoring or campaign effectiveness, the way our customers are deploying Pentaho leave me with no doubt that this prediction will bear out.

As classic BI matured, we witnessed a final “tsunami” in which data visualization and self-service inspired business people to imagine the potential for advanced analytics. Users could finally see all their data – warts and all – and also start to experiment with rudimentary blending techniques. Self-service and data visualization prepared the market for what I firmly expect to be the most significant analytics trend in 2015….

Data Refineries Give Real Power to the People!

The big data stakes are higher than ever before. No longer just about quantifying ‘virtual’ assets like sentiment and preference, analytics are starting to inform how we manage physical assets like inventory, machines and energy. This means companies must turn their focus to the traditional ETL processes that result in safe, clean and trustworthy data. However, for the types of ROI use cases we’re talking about today, this traditional IT process needs to be made fast, easy, highly scalable, cloud-friendly and accessible to business. And this has been a stumbling block – until now. Enter Pentaho’s Streamlined Data Refinery, a market-disrupting innovation that effectively brings the power of governed data delivery to “the people,” unlocking big data’s full operational potential. I’m tremendously excited about 2015 and the journey we’re on with both customers and partners. You’re going to hear a lot more about the Streamlined Data Refinery in 2015 – and that’s a prediction I can guarantee will come true!

Finally, as I promised at PentahoWorld in October, we’re only going to succeed when you tell us you’ve delivered an ROI for your business. Let’s go forth and prosper together in 2015!

Quentin Gallivan, CEO, Pentaho

120914-Quentin-Big-Data-2015-Prediction-Graphic

[1] Ventana Research, Big Data Integration Report, Tony Cosentino and Mark Smith, May 2014.

[2] IDC, The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things, Gantz, Minton, Turner, Reinsel, Turner, April 2014.

[3] Wikibon, Defining and Sizing the Industrial Internet, David Floyer, June 2013.

[4] Gartner, Use Embedded Analytics to Extend the Reach and Benefits of Business Analytics, Daniel Yuen, October 3, 2014.


Attention Retail Banks, Its Time for Change!

July 18, 2014

Walhalla_(1896)_by_Max_Brückner

Retail banks, which have been wracked by scandals relating to PPI fraud, LIBOR rigging, unpopular bonus schemes and IT failures, need to think beyond upselling and cross-selling and consider how big data analytics can repair trust and improve the whole customer experience. In the article, Montetising Big Data in Retail Banks Starts with a Better Customer Experience, Davy Nys, VP of EMEA & APAC at Pentaho shares how retail banks can achieve  the ‘Valhalla’ of customer value pricing (CVP), or maximising the total value of a customer to a bank throughout all interactions and transactions. He explains how big data integration and analytics supports CVP in five ways:

  1. Supporting a two-way, 360-degree view
  2. Lower costs
  3. Smarter offers
  4. Customer friendly fraud detection
  5. Measuring customer sentiment

Learn more about how to achieve the ‘Valhalla’ of CVP read the full article here and register/attend the live webinar featuring Forrester Analyst Martha Bennett on the topic: Making the Most of your Data in the Financial Sector on July 22nd at 11am GMT


Pentaho 5.1 in LEGO

July 16, 2014

Two weeks ago we launched Pentaho Business Analytics 5.1. The new capabilities in Pentaho 5.1 support our ongoing strategy to make the hardest aspects of big data analytics faster, easier and more accessible to all. In honor of our Chief Architect, Will Gorman (also a LEGO Master Builder), we decided to have some fun with LEGO and now present to you the LEGO explanation of new features and functionality in Pentaho 5.1:

Lego_5.1

Direct Analytics on MongoDB – Unleash the value of MongoDB analytics for IT and Business Analysts with no coding required.

MongoDB5.1_2

Data Science Pack – Operationalize predictive models for R and Weka, drastically reducing data preparation time and effort.

Lego_RWeka

Full YARN Support – Reduce complexity for big data developers while leveraging the full power of Hadoop

YARN_5.1

Visit the 5.1 landing page to learn more about this release and access resources such as videos, data sheets, customer profiles and download.

 


Dinosaurs Have Had Their Day

June 16, 2014

dinosaur

Once upon a time, (not so) long ago in 2004, two young technologies were born from the same open source origins – Hadoop and Pentaho. Both evolved quickly from the market’s demand for better, larger-scale analytics, that could be adopted faster to benefit more players

Most who adopt Hadoop want to be disruptive leaders in their market without breaking the bank. Earlier this month at Hadoop Summit 2014, I talked to many people who told me, “I’d like to get off of <insert old proprietary software here> for my new big data applications and that’s why we’re looking at Pentaho.” It’s simple – no company is going to adopt Hadoop and then turn around and pay the likes of Informatica, Oracle or SAS outrageous amounts for data engineering or analytics.

Big data is the asteroid that has hit the tech market and changed its landscape forever, giving life to new business models and architectures based on open source technologies. First the ancient dinosaurs ignored open source, then they fought it and now they are trying to embrace it. But the mighty force of evolution had other plans. Dinosaurs are giving way to a more nimble generation that doesn’t depend on a mammoth diet of maintenance revenue, exorbitant license fees and long-term deals just to survive.

In this new world companies must continually evolve to survive and dinosaurs have had their day. It’s incredibly rewarding to be  part of a new analytics ecosystem that thrives on open standards, high performance and better value for customers. So many positive evolutionary changes have taken place in the last ten years, I can’t wait to see what the next ten will bring.

Richard Daley
Founder and Chief Strategy Officer
Pentaho

Image: #147732373 / gettyimages.com


Good news, your data scientist just got a personal assistant

June 3, 2014

personal asstIf you are or have a data scientist in house you’re in for good news.

Today at Hadoop Summit in San Jose, Pentaho unveiled a toolkit built specifically for data scientists to simplify the messy, time-consuming data preparation, cleansing and orchestration of analytic data sets. Don’t just take it from us…

The Ventana Research Big Data Analytics Benchmark Research estimates the top two time-consuming big data tasks are solving data quality and consistency issues (46%) and preparing data for integration (52%). That’s a whopping amount of time just spent getting data prepped and cleansed, not to mention the time spent in post processing results.  Imagine if time spent preparing, managing and orchestrating these processes could be handed off to a personal assistant leaving more time to focus on analyzing and applying advanced and predictive algorithms to data (i.e. doing what a data scientist is paid to do).

Enter the Pentaho Data Science Pack, the personal assistant to the data scientist.  Built to help operationalize advanced analytic models as part of a big data flow, the data science pack leverages familiar tools like R, the most-used tool for data scientists and Weka, a widely used and popular open source collection of machine learning algorithms. No new tools to learn. In the words of our own customer, Ken Krooner, President at ESRG “There was a gap in the market until now and people like myself were piecing together solutions to help with the data preparation, cleansing and orchestration of analytic data sets. The Pentaho Data Science Pack fills that gap to operationalize the data integration process for advanced and predictive analytics.”

Pentaho is at the forefront of solving big data integration challenges, and we know advanced and predictive analytics are core ingredients for success. Find out how close at hand your data science personal assistant is and take a closer look at the Data Science Pack.

Chuck Yarbrough
Director, Big Data Product Marketing


Cloudera Stamp of Approval

April 3, 2014

logo_cloudera_certifiedYesterday, Cloudera announced the general availability of Cloudera 5 (C5), the latest generation of Cloudera’s unified data platform for the enterprise data hub. Pentaho engineers have been working on certification since the beta was available in early February to make sure we are certified on day one of the GA.

Cloudera and Pentaho  have a long standing strategic relationship with tested joint technologies that have been deployed time and time over again. By using Cloudera Certified products, enterprises significantly reduce risk while taking advantage of the worlds most complete, tested and popular platform powered by Apache Hadoop. This stamp of approval should put customers at ease when deploying C5 knowing that Pentaho and Cloudera have worked together to ensure the highest level of capabilities and compatibility.

Learn more about Pentaho and Cloudera’s joint solution benefits, access the download, resources and recordings.

Paul Vasquez
Senior Product Manager, Technology Partners
Pentaho
@BigDataPaul


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