Data Engineers Archives - Thomas Henson

Spark vs. Hadoop 2019

June 12, 2019 by Thomas Henson Leave a Comment

Spark vs. Hadoop 2019

In 2019 which skill is in more demand for Data Enginners Spark or Hadoop? As career or aspiring Data Engineers it makes sense to keep up with what skills are in demand for the market. Today Spark is hot and Hadoop seems to be on it’s way out but how true is that?

Hadoop born out the web search era and part of the open source community since 2006 has defined Big Data. However, Spark’s release into the open source Big Data community and boosting 100x faster processing for Big Data created a lot of confusion about which tool is better or how each one works. Find out what Data Engineers should be focusing on this episode of Big Data Big Questions Spark vs. Hadoop 2019.

Transcript – Spark vs. Hadoop 2019

Hi folks. Thomas Henson here with thomashenson.com, and today is another episode of Wish That Chair Spun Faster. …Big Data Big Questions!

Today’s question comes in from some of the things that I’ve been seeing in my live sessions, so some of the chats, and then also comments that have been posted on some of the videos that we have out there. If you have any comments or have any ideas for the show, make sure you put them in the comments section here below or just let me know, and I’ll try my best to answer these. This question comes in around, should I learn Spark, or should I learn Hadoop in 2019? What’s your opinion?

A lot of people are just starting out, and they’re like, “Hey, where do I start?” I’ve heard Spark, I’ve heard Hadoop’s dead. What do we do here? How do you tackle it?

If you’ve been watching this show for a long time, you’ve probably seen me answer questions similar to this and compare the differences between Spark and Hadoop. This is still a viable question, because I’ve actually changed a little bit he way I think about it, and I’m going to take a different approach with the way that I answer this question, especially for 2019.

In the past I’ve said that it really just depends on what you want to do. Should you learn Spark? Should you learn Hadoop? Why can’t you learn both? Which, I still think, from the perspective of your overall learning technology and career, you’re probably going to want to learn both of them. If we’re talking about, hey, I’ve only got 30 days, 60 days. “I want the quickest results possible, Thomas.” How can I move into a data engineer role, find a career? Maybe I just graduated college, or maybe I’m in high school, and I want to get an internship that maybe turns into a full-time gig. Help me, in the next 30 to 90 days, get something going.

Instead of saying depends, I’m really going to tell you that I think it’s going to be Spark. That’s a little bit of a change, and I’ll talk about some of the reasons why I think that change too. Before we jump into that, let’s talk a little bit about some of the nomenclature that we have to do around Hadoop. When we talk about Hadoop, a lot of times that we’re talking about Hadoop, and MapReduce and Htfs [Phonetic 00:02:10] in this whole piece. From the perspective of writing MapReduce jobs or processing our data, Spark is far and clear the leader in that. Even MapReduce is being decoupled, has been decoupled, and more and more jobs are not written in MapReduce. They’re more written with Flink [Phonetic 00:02:28], or Spark, or Apache Beam, or even [Inaudible 00:02:32] on the back-end. That war has been won by Spark for the most part. Secondly, when we talk about Hadoop, I like to talk about it from an ecosystem perspective. We’re talking about Htfs, we’re talking about even Spark included in that, and Flume, all the different pieces that make up what we call the big data ecosystem. We just call that with the Hadoop ecosystem.

The way that I’m answering this question today is, hey, I’m looking for something in 2019 that could really move the needle. What do you see that’s in demand? I see Spark is very, very much in demand, and I even see Spark being used outside of just Htfs as well, too. That’s not saying that if you’ve learned Hadoop or you’ve learned Htfs you’ve gone down the wrong path. I don’t think that’s the case, and I think that’s still viable. You’re asking me, what can you do to move the needle in 30 to 90 days? Digging down and becoming a Spark develop, that opens up a career option. That’s one of the quickest ways that you can get, and one of the big things we’ve seen out there with the roles. Roles for data engineers. Another huge advantage, we’ve talked about it a little bit on this channel, but the big announcement for what Data Bricks is going from the perspective of investment and what their valuation is. They’re an $2.5 billion advancement, and they’re huge in the Spark community. They’re part of the incubators and on a lot of steering committees for Spark. They have some tools and everything that they sell on top of that, but it’s just really opened my eyes to what’s out there. I knew Spark was pretty big, but the fact that Data Bricks and where they’re going, I think that’s a lot of what we’re seeing. Another point, too, you’ve heard me talk about it a good bit, but where we’re going with deep learning frameworks and bringing it into the core big data area. Spark is going to be that big bridge, I believe. People love to develop in Spark. Spark’s been out there. It gives you the opportunity now with Project Hydrogen and some of the other things that are coming to be able to take and do ETL over GPUs, but also import data and be able to implement and use Tensorflow or PyTorch, or even Caffe 2. It you’re looking in 2019 to choose between Spark and Hadoop to find something in the next 30 to 90 days, I would go all in with Spark. I would learn Spark, whether it be from Java, Scala, or Python, but be able to learn, and be able to start doing some tutorials around that, being able to code. Being able to build out your own projects, and I think that’s going to really open your eyes, and that can really get the needle moving. At some point, you want to go back, and you want to learn how to navigate data with Htfs. How to find things. They’re going on from the Hadoop ecosystem, because it’s all a big piece here, but if you’re asking me, the one thing to do to move the needle in 30 to 90 days, learn Spark.

Thanks again. That’s all I have today for Big Data Big Questions. Remember, subscribe and ring that bell, so you never miss an episode of Big Data Big Questions. If you have any questions, put them in the comment section here below. We’ll answer them on Big Data Big Questions.

Certifications Required For Hadoop Administrators?

June 11, 2019 by Thomas Henson Leave a Comment

Hadoop Certifications

Data Engineers looking to grow their careers are constantly learning and add new skills. What kind of impact do Hadoop Certifications have during the hiring process?

Data Engineers, Developers, and IT in general are known for their abundance of certifications. Everyone has an opinion as well about how much those certifications mean to real skills. On this episode of Big Data Big Questions find out what my thoughts are for Hadoop Admin Certifications and if Enterprises are requiring those for Data Engineers.

Transcript – Certifications Required For Hadoop Administrators

It’s the Big Data Big Questions show! Hi folks, Thomas Henson here with thomashenson.com. Today is another episode of… Come on, I just said it. Big Data Big Questions. Today’s question comes in from a user here on YouTube. If you have a question, make sure you put it in the comments section here below or reach out to me on thomashenson/big-questions. I’ll do my best to answer your questions here, live on one of our shows, or in one of our live YouTube sessions that we’re starting to do on Saturday mornings. Throw those questions in there. Let me know how are doing with this channel, and also if you have any questions around data engineering, machine learning. I’ll even take some of those data science questions, as well. Today’s question comes in around certifications in the Hadoop ecosystem. Are certifications required for Hadoop administrators/Hadoop developers? Absolutely, positively not. They’re not required, right?

Now, there may be some places where they’ll require you to. I did see that back in my day, in software engineering, but in general, they’re not going to be, not going to require you to have that before gaining entry. Now, they might be nice to have. Especially if you’re talking about going into an established team or an established group within your organization that, hey! We’re on the Horton Works stack, and we like to have everybody up to par from a certification perspective.

I haven’t seen that a lot specifically in the data engineering field, but it is something I’ve seen over the years in software engineering, but just not as much here lately. Now, does that mean that I’m saying that you shouldn’t go get a certification? That’s not what I’m saying at all. Especially if you’re learning and trying to get into the Hadoop ecosystem, and you’re like, hey, where do I really start?

First, you start with Big Data Big Questions.

Really, I can use the certifications. Whether it be from Azure, AWS, Cloudera, Horton Works, or Google GCP, Google’s Cloud Platform, you can take any of their certifications and really see, and build out your own learning path. That’s an opportunity there. Even if you’re not going to go down the path of trying to get that certification, if you’re trying to gain information and learn some of the things that you need to know as a good data engineer, whether it be on the developer side, whether it be on the administrative side, that’s definitely where I would start. That’s an opportunity there.

When we look at the question, it poses more of a philosophical question, if we will, in the data engineering and IT world, meaning how do we feel about IT certifications? I’ve answered this question. I had myself and Aaron Banks [Phonetic 00:02:31]. We were talking about specifically around IT certifications, and are they worth it, and we have a full-length video where we really dip into it. I’ll give you a little bit of a preview of my thought process around it.

The way that I look at certifications is, if you’re looking to be able to prove out, especially if you’re outside of a field, then hey, getting a certification might benefit you to make yourself more desirable to getting your application and getting your brand in there. Having a certification does lend some credence in those situations. However, if you’re established in the role, and you’ve been doing data engineering, and you have a lot of experience in it, necessarily you’re not going to really need to have that certification. You’ve been proven. You’ve down the due diligence of being in that role, and you’re applying for a role as a data engineer. You don’t necessarily have to go through that certification process.

Like I said, I really think that certifications are really good. Whenever we’re talking about, hey, maybe I don’t have that experience in that role, and I want to prove to you that, hey, I’m coming from, maybe you’re a web developer like I was. You’re a web developer, and you’re like, “Man. I’d really like to get into this,” Hadoop and this data engineering side of things. Where can I start, or how can I really identify myself to be somebody who wants to take on that next role? That’s where a certification is really going to help. You can get that certification. You can walk through it, but, you’re not going to walk in though, and say, “Hey, I’ve got the certification,” and you, Mr. Data Engineer, Miss Data Engineer, that’s been in that role for six years, “I know more than you do, because I have the certification.” That’s not really the case, and that’s probably not what you want to do, especially if you’re new to an organization.

Be honest, and be gentle in your interview process if you have a certification, but you don’t have the experience, and just say, “Hey, you know, I’m really passionate about it.” I’ve been following Big Data Big Questions for some time, and I thought that it’d be good to get into the data engineering field. To show how serious I am, I actually went through and got, walked through some of the certification process in there, too. Just an opportunity there for you to stand out from the crowd and show your experience, when you don’t really have experience. Let me know how you feel about this question and this answer here. Put it in the comment section below. Love to hear feedback. Also, if you have any questions, make sure you put them in the comments section here below, and never, never forget to subscribe and ring that bell, so that you’ll never miss an episode. Thanks again, and I’ll see you on the next episode of Big Data Big Questions.

Data Engineer in 2019

May 31, 2019 by Thomas Henson Leave a Comment

What’s a Data Engineer Career Like in 2019?

Times change and keeping up with maintaining skills while managing day to day projects can be exhausting.

Should I learn Hive or Tensorflow?

Which is better Flink or Spark?

How as a Data Engineer will I focus on Containers?

Questions like these come up all the times when I speaking with aspiring and career focused Data Engineers. Find out my thoughts around skills and career outlook for Data Engineers in 2019 on this episode of Big Data Big Questions.

Transcript – Data Engineer in 2019

Hi folks, Thomas Henson here with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question comes in around, what does data engineering in 2019 look like? What are some of the trends? What are some of the things that are going on? Has this question come in from a comments section here on YouTube, so if you have a question, make sure you put it in the comments section here below or reach out to me on thomashenson.com. And, I’ll discuss it in an orderly fashion as they come in, provided I have the time. I’ve been getting a ton of questions, so I really appreciate it. Thank you for this community here.

Today’s question, before we jump into it, I want to give you my three top trends to watch for in 2019. Before we did, I did want to credit with an article that they did for their 10 trends in big data. I talked about them on my YouTube live session, so if you’re ever around Saturday mornings, jump on. Throw me a question in the chat. Let’s get to cracking. I try to answer as many questions as I can there, and try to do that Saturday mornings. Jump in there.

The 10 here, you can check in the comments section here below, where I have some of the link to the [Inaudible 00:01:13] trend here. I’m going to read some of them real quick. The first one they said for the top 10 trends in 2019. Data management and [Inaudible 00:01:22]. They’re talking a little bit about ETL and how ETL’s not going away. I’ve said that for a while, but we did read an article not too long ago that’s saying, “Hey, you know, there’s some tools out there that are really going to make ETL kind of a thing of the past.” We’ll see. Hopefully, right?

I’m not for ETL, I just, man. Started out there, and it seemed like I was never going to get out of it. Number two, data siloes continue to proliferate. This goes into what we saw when Hadoop emerged as this huge, big data lake, where the data’s only going to exist there. We’ve been talking about it, especially on this channel, over the past few years where, hey, data has a lot of gravity to it. There’s going to be data out on your edge. There’s going to be data in the cloud. There’s going to be data still in core data centers.

The idea of a fluid data lake is a little bit more consolidated. You still have those main areas, but you still have to do analytics and place in some area. number three, streaming analytics has a breakout year. Talked about streaming analytics on this channel for the last couple years. Actually did a session about the future architectures of streaming analytics at the 2017, was it Hadoop Summit? They call it Data Works, now.

Data governance builds steam, talked about some of that here. Soft skills start to emerge as tech evolves. Just talking about the soft skills of understanding the business, talks about that with the book, the big data MBA here. Deep learning gets a little bit deeper. Hm. Have we talked about deep learning on this channel? Special K expands footprint. They’re talking about Kubernetes and what’s going on with the doctorization. Clouds are hard to ignore. New tech will emerge, talking about how Silicon Valley and a lot of open source, and closed source, tools have been emerged, and they don’t see that stopping anytime soon. Then, smart things everywhere. I’ve talked about those a good bit here, too.

Without further ado, let’s jump into my three trends for 2019. My three trends to watch for in 2019. The first one, deep learning and Hadoop. How are these ecosystems going to interact with each other? A lot of project out there have talked about it last year, around project hydrogen, submarines, another project, and NVIDIA’s Rapid. It’s all about being able to use GPU and also be able to use those deep learning libraries with data that’s in your Hadoop ecosystem or just for some ETL. That’s one of the things that NVIDIA Rapid’s… Maybe I should do a video just specifically on that. Watch that trend. Start watching what’s going on with TensorFlow and being able to use integrated in with Spark and some of your other tools that are more traditional in the Hadoop ecosystem. That was number one. Number two. Two? Yep. Number two, containerization of the world overtakes data engineering. Similar to what they were talking about it [Inaudible 00:04:11], with their trends, with Special K being special. I think the containerization, we’ve seen it a lot, a lot of announcements here lately with cloud native applications and cloud native experiences on the Cloudera side, and you even saw in Hadoop 3.0 where they were laying the groundwork to be able to containerize your Yarn, schedule your engine, and some of the other components there. We’re going to continue to see that, and that’s one skill that you’re going to be looking for. If you’re in data engineering right now, you want to know what’s coming up down the pipe for you, I would look into doing some things and getting more familiar with the containerization. That’s actually in my roadmap for the end of the year for me, to understand a little more around docker, and Kubernetes, and that whole ecosystem. That is a big trend we will see for data engineering. It’s not going to slow down. It’s been picking up steam a lot here lately, but it’s going to go full force. My third trend, thing that I’m looking for, for data engineers in 2019, streaming analytics. I was doing some research and looking around some IDC numbers around where we’re talking about from a data perspective. We’re gonna be, one of the interesting tidbits that they were talking about is how streaming analytics will take up anywhere from around 30% of all the analytics and things that are going on in Azure. Think about all these different devices bringing in data here by 2025. 30% of that’s going to have to be streaming analytics. That’s a huge number. There’s a number of tools out there that are helping to try to deal with what’s going on from a streaming analytics perspective.

We’ve got , we’ve got Kafka. On the cloud side we’ve got Kinesis. A lot of different tools. We had [Inaudible 00:05:42] on this channel here, but there’s a lot of tools in place, a lot of tools being created, because streaming analytics is a huge beast of data to handle. It’s a different kind of problem than what we’ve seen, and it’s only going to get worse as we start bringing in more data, more devices. Really cool opportunities for you as data engineers. Outside of my goals for 2019, if you’re looking for some things to jump into and some educational paths for yourself as a data engineer in 2019, I would look into those three trends. Deep learning, containerization, and then streaming analytics. That’s all I have for today. Make sure to subscribe and ring that bell so that you never miss an episode of Big Data Big Questions. Throw a comment in the comments section here below if you have any questions. If you like the video, if you hated it, just let me know how you feel about this, and I will see you next time on this episode of Big Data Big Questions.

Data Engineering Courses Without Java

February 11, 2018 by Thomas Henson Leave a Comment

There are a ton of resources out there for Data Engineers to learn to develop/administrate Hadoop/Big Data Ecosystem. Do all these courses require basic Java Knowledge? When you are starting out learning to become a data engineer it can seem everywhere you look basic Java skills are required, but I’m telling you that is not the case. In this video I’ll explore the options and thoughts on learning big data without Java skills.

Video – Data Engineer Courses without Java

Landing a Data Engineer Internship

February 10, 2018 by Thomas Henson Leave a Comment

What are the skills required to land a Big Data Engineer Internship? Is it possible to gain those skills in under 3 months?

Today’s question comes in from YouTube: “Please make a video on how much time is required to become big data engineer ?? is it possible in 3 months and also try to mention about minimum skills required for internship.”

In this episode of Big Data Big Questions we will explore what the basic skills are for landing an Data Engineer Internship and tips for excelling after landing that internship. Watch the video below to find out what skills Data Engineers should focus on before applying for a Data Engineer Internship.

Video – Data Engineer Internship

Talking Heron Real-Time Analytics with Streamlio

January 31, 2018 by Thomas Henson Leave a Comment

To say Streaming Analytics is popular is an understatement. Right now Streaming Engineering is a top skill Data Engineers must understand. There are a lot of options and development stacks when it comes to analyzing data in a streaming architecture. Today I sat down with Lewis Kaneshiro (CEO & Co-founder) and Karthik Ramasamy (Co-founder) of Streamlio to get their thoughts on Streaming Analytics and Data Engineering careers.

Streamlio Opensource Stack

Streamlio is a full stack streaming solution that handles the messaging, processing, and stream storage in real-time applications. The Streamlio development stack is built primary from Heron, Pulsar, and BookKeeper. Let’s dicuss each of these opensource projects.

Heron

Heron is real-time processing engine used/incubated by Twitter. Currently Heron is going through the transition of moving into the Apache software foundation (learn more about this in the interview). Heron is at the heart of real-time analytics by processing data before the time value expires.

Pulsar

Pulsar is an Apache incubated project for distributed publishing and subscribing messaging real-time architectures. The origin of Pulsar is similar to that of many opensource big data projects in that it was used first by Yahoo.

BookKeeper

BookKeeper the scalabale, fault-tolerant, and low-latency storage service used in many development stacks. BookKeeper is under the Apache Software foundation and popular in many opensource streaming architectures.

Interview Questions

Have we as a community accepted Hadoop related tools to be virtualized or containerized?
How do Data Engineers get started with Streamlio?
What are the biggest real-time Analytics use cases?
Is the Internet Of Things (IoT) the primary driver behind the explosion in Streaming Analytics?
What skills should new Data Engineer focus on to be amazing Data Engineers?

Checkout the interview to learn answers to these and more questions.

Video Streamlio Interview

Links from Streamlio Interview

Learning Roadmap for Data Engineers?

December 19, 2017 by Thomas Henson Leave a Comment

Is there a learning Roadmap for Data Engineers?

Data Engineers are highly sought after field for Developers and Administrators. One factor driving developers into that space is the average salary of 100K – 150Kwhich is well above average for IT professionals. How does a developer start to transition into the field of Data Engineering? In this video I will give the four pillars that developers/administrators need to follow to develop skills in Data Engineering. Watch the video to learn how to become a better Data Engineer…

Transcript – Learning Roadmap For Data Engineers

Thomas: Hi, Folks. I’m Thomas Henson with thomashenson.com. Welcome back to another episode of Big Data, Big Questions. Today we’re going to talk about some learning challenges for the data engineer. And so we’re actually going to title this a roadmap learning for data engineers. So, find out more right after this.

Big Data Big Questions

Thomas: So, today’s question comes in from YouTube. And so if you want to ask a question, post it here in the comments, have these questions answered. So, most of these questions that I’m answering are questions that I’ve gotten from the field that I’ve met with customers and talked about, or I get over, and over, and over. And then a lot of the questions that I’m answering are coming in from YouTube, from Twitter. You can go to Big Data, Big Questions on my website, thomashenson.com…Big Data, Big Questions, submit your questions there. Anyway that you want to use it, use the #bigdatabigquestions on Twitter, and I will pull those out and answer those questions. So, today’s question comes in from YouTube. It’s from Chris. And Chris says, “Hi, Thomas. I hold a master’s degree in computer information systems. My question is, is there any roadmap to learn on this course called data engineer? I have intermediate knowledge of Python and Sequel. So, if there’s anything else I need to learn, please reply.”

Well, thanks for your question, Chris. It’s a very common question. It’s something that we’re always wanting to understand is how can I learn more, how can I move up in my field, how can I become a specialist. So, a data engineer is in IT. It’s a sought out field with high demand, but there’s not really a roadmap for these, so you can see what some people are learning, what other people are saying is a specification. So, you’re asking what I see and what I think are the skills that you need based off your Python and your Sequel background. Well, I’m going to break it down into four different categories. I think there’s four important things that you need to learn. And there’s different ways to learn them. And I’ll talk a little bit about that and give you some resources for that. And all the resources for this will be posted on my blog. So, I’ll have it on thomashenson.com. Look up Roadmap Learning for Data Engineer. And under that video, you’ll see all these links for the resources.

Ingesting Data

The first thing is you need to be able to move data in and out. And so most likely you’re going to want to know how to move into HDFS. So, you want to know how to move that data in, how to move it out, and how to use the tools. You can use Flume, just using some of the HDFS commands. You also want to know how to do that maybe from an object perspective. So, if in your workflow, you’re looking to be able to move data from an object based and still use that in Hadoop or the Hadoop ecosystem, then you’d want to know that. And then also I mix in a little bit of Kafka there, too. So, understanding Kafka. So, the important point there is being able to move data in and out. So, ingest data into your system.

ETL

The next one is to be able to perform ETL. So, being able to transform that data that’s already in place or as it’s coming into your system. Some of the tools there… If you watch any part of my videos, you know that I got my start in Pig, so being able to use Pig, or use MapReduce jobs, or maybe even some Python jobs to be able to do it. Or Spark just to be able to transform that data. So, we want to be able to take some kind of maybe structured data or semi structured data and transform it, being able to move that into a MapReduce job, a Spark job, or transform it maybe with Pig and pull some information out. So, being able to do ETL on the data, which rolls into the next point which is being able to analyze the data.

Analyze & Visualize

So, being able to analyze the data whether you have that data, you’re transforming it, maybe you’re moving it into a data warehouse in the Hadoop ecosystem. So, maybe you move it into Hive. Or maybe you’re just transforming some of that data, and capturing it, and pulling into HBase, and then you want to be able to analyze that data maybe with Mahout or MLlib. And so there’s a lot of different tutorials out there that you can do, and it’s just kind of getting your feet wet, understanding, “Okay, we’ve got the data. We were able to move the data in, perform some kind of ETL on it, and now we want to analyze that data.” Which brings us to our last point. The last thing that you want to be able to do and be familiar with is be able to visualize the data. And so with visualizing the data, you have some options there. So, you can use Zeplin or some of the other notebooks out there, or even some custom built… If you’re familiar with front end development, you can kind of focus in on some of the tools out there for making some really cool charts in really cool different ways to be able to visualize the data that’s coming in.

Four Pillars – Learning Road Map for Data Engineers

So, the four big pillars there, remember, are moving your data – so being able to load data in and out of HDFS – object based storage, and then also I’d mix a little Kafka in there, performing some kind of ETL on the data, being able to analyze the data, and then being able to visualize the data. In my career, I’ve put more emphasis around the moving data and the ETL portion. But for whatever you’re trying to do… Or your skill base may be different. Maybe you’re going to focus more on the analyzing of the data and the visualization of the data. But those are the four keys that I would look at for a roadmap to becoming a better data engineer or even just getting into data engineering. All that being said, I will say… I did four. Kind of draw a box around those four pillars and say as we’re doing those, make sure we’re understanding how to secure that data for bonus point. So, as you’re doing it, make sure you’re using security best practices and learning some of those pieces because we start implementing and put these into the enterprise, we want to make sure that we’re securing that data. So, that’s all today for the Big Data, Big Questions. Make sure you subscribe, so you never miss an episode, all this awesome greatness. If you have any questions, make sure you use the #bigdatabigquestions on Twitter. Put it in the comment section here on the YouTube video or go to my blog and see Big Data, Big Questions. And I will answer your questions here. Thanks again, folks.

Show Notes

HDFS Command Line Course

Pig Latin Getting Started

Should Data Engineers Know Machine Learning Algorithms?

November 10, 2017 by Thomas Henson Leave a Comment

How involved should Data Engineers be in learning Machine Learning Algorithms?

For the past few years Data Scientist are one of the hottest jobs in IT. A huge part of what Data Scientist do is selecting Machine Learning Algorithms for projects like SmartHomes and SmartCars. What about the Data Engineer, should they know Machine Learning Algorithms as well? Find out in this episode of Big Data Big Questions.

Transcript – Should Data Engineers Know Machine Learning Algorithms?

Hi, folks. Thomas Henson here, with ThomasHenson.com, and today is another episode of Big Data, Big Questions. And so, today’s question that comes in is, “Should data engineers know machine-learning algorithms?” So, we’re going to tackle that question right after this.

Welcome back. So, today, we’re going to talk a little bit about algorithms, right? So, you know, put your math hat on, and let’s dive into this question today. And so, today’s question, it’s one I get a lot. It’s about the role of a data engineer in machine-learning. And basically, it is… You know, I’ve taken this question from a couple of different sources that I’ve seen, where they’ve asked, you know, “Should data engineers know machine learning algorithms?” And kind of where some of that falls into is, you know, what is the role of the data engineer, and what is the role of the data scientist? And so, really, this question, for me, is really simple. I’m going to go off of my experience and kind of share with you what I’ve done around machine-learning algorithms and how I’ve approached it in my career as a data engineer, software engineer, you know, Hadoop administrator.

There’s a couple of different ways to look at it, but basically, the way that I’ve approached it is I haven’t really learned it. And when I say, “Learned it,” or, “Know it,” I’ve not been in…you know, I’m not going to make a recommendation on it. So, you know, the way I look at it is you should be familiar with them. So, you should be familiar with them, especially familiar with them as far as, like, what’s involved in the package? So, are you using Mahout? You know, what are the algorithms in there, what are the algorithms in your workflow? And then, all the other libraries too.

So, if you’re evaluating other libraries…so, maybe you guys are looking to…you know, maybe you haven’t used Spark and you want to look at the e-mail library that’s there, and you’re kind of going back and forth through those, you want to understand from a basic very high-level, you know, what those algorithms are, and for sure, what algorithms you’re using in your environment, so you can make an educated recommendation saying, “Hey, you know, I think we should move this. Let’s still have the data scientists involved, and have them, you know, look and make sure that the algorithm that we’re going to be using from those packages are going to fall in line with what we’re really using,” because that’s one of the things too, you’ll find that they will differentiate a little bit, so, you know, what we’re using in my house may not be exactly the same, you know, version in, you know, MadLib, or, you know, the ML library.

And so, just be able to understand kind of for sure what’s in your workflow. Be familiar with them too. Another thing that I did…so, like I said, be familiar with them from a high level, but not be making a recommendation, I actually did, you know, picked one, so I would say, you know, be familiar with them, but pick one that you really want to…you know, really want to understand and learn. I picked Singular Value Decomposition, because that’s something that we used a lot in our workflow, and so, I was just kind of…had a natural curiosity for it, and it…you know, it had a really cool story too around it. So, you know, I found some stories around it, you know, it was made really popular with the Netflix Challenge. So, back…Netflix had a challenge for…you know, to, “Beat our data scientists with your algorithm.” And so, SVD was used to, you know, do some of the sorting there, and it was kind of made famous from that perspective, and so, you know, I was familiar with it, but I made sure that I understood one, just for natural curiosity.

Now, if you are looking to, you know, at some point, make a jump, right, to data scientist, if you’re a data engineer, and at some point down the road, you’d like to be…you know, “I want to be the data scientist. I want to say, ‘Hey, this is the algorithm we should use.’” You know, maybe you just want to be a data scientist because, you know, for a couple years running, it’s been the…you know, the sexiest career, you know, in IT for a while, and so, if that’s kind of your approach, you know, definitely start to know them.

Obviously, learn the ones that are in your environment first, because that’s going to be the easiest, because you’re going to have the access to, you know, why you’re using it, how you’re using it, and you have access to the data scientists too, to kind of, you know, take you under their wing, to some extent, and, you know, show you the ins and outs of why you’re using what you did and, you know, kind of why you didn’t use other ones too. For an aspiring data scientist, then yes, for sure, you want to jump in and, you know, start to understand and start to know them. But for a data engineer, I don’t think you have to learn the algorithms, right? I think you have to be familiar with them, I think, you know, for natural curiosity, you know, maybe learn one or two.

But really, our role is not to recommend and say, “Hey, you know, these are the algorithms I think we should use,” or even, like, to pick packages and say, “Hey, these packages here, we’re going to…you know, we’re going to standardize on that and that’s the only thing we’re going to use.” That’s…you know, that’s not really our role, right?

If you have any questions, make sure you submit them to Big Data, Big Questions. You can do it from the website, go to Twitter, use the hashtag #BigDataBigQuestions, in the comment section there, however you want to get in touch with me and get those questions answered. Also, make sure you subscribe so that you never miss all these Big Data, Big Questions goodness, and so that you can always, you know, learn more. Thanks again, folks!

Show Notes

Mahout

Spark MLlib

MADlib

Big Data Big Questions

Netflix Prize

Singular-Value Decomposition

Python vs. Scala Freelance Data Engineers

November 9, 2017 by Thomas Henson Leave a Comment

Which is better for Freelance Data Engineers Scala or Python?

Picking up freelance gigs can be a challenge especially when just starting out. So which language is better for getting freelance gigs Scala or Python? In today’s episode Big Data Big Questions I answer a question about freelance options for Data Engineers. Watch the video below to find out about Scala and Python Freelance options.

Transcript – Python vs. Scala Freelance Data Engineers Video

Hi. I’m Thomas Henson with thomashenson.com. Today is another episode of Big Data Big Questions. Today’s question focuses around Python versus Scala for freelancers. Today, we’re going to tackle that question. Find out more right after this.

Today’s question comes in from our Big Data Big Questions series. If you have any questions, go to thomashenson.com Big Data Big Questions. Submit your questions. That’s where today’s is coming from.

Today, our question’s coming in from Gill Taso [Phonetic]. I hope I’m saying your name right. I hope I’m not mispronouncing it. Thanks for following, and thanks for asking a question, Gill.

Today, Gill’s question is, “I saw your video on learning how to become a data engineer, and it was very interesting.” Thank you, Gill. “I subscribe to your Twitter and your YouTube channel.” Well, thanks again. That’s awesome. That’s how you can make sure that you never miss an episode. Currently Gill is a junior data engineer who works mainly with technologies like Impala and Hive, with a little bit of Spark SQL.

Said, “I would like to improve my skills in Spark. I have intermediate skills in Python, but as I review the industry, it seems it’s better to learn Scala, which is very tough, at least for me. Do you think it’s better for me to sharpen my skills in Python or transition to Scala now? Also, I’m looking for ways to be a freelance data engineer in the short-term, maybe two to three years, because I’m looking more for more flexibility. Do you have any advice to give me for training, skills, and technology?”

Thanks for the question, Gill. That’s actually a two-part question, I feel like, so I feel like there’s a couple different ways that we can look at it. First, the question is, for a junior data engineer that’s working a really good intermediate skills in Python, but should you transition more to Scala? The second part of the question is, what should you do for freelancing?

There’s two different ways to look at it. If we look at it in your current career, where the industry’s going, is Python a viable option, or should you start to do Scala? Or, where’s that market going to be at for freelancing? I think those are two different things. What we see for freelancing and then what we see for industry and in your career progression, and especially if you’re in the Spark community. It seems like you’ve got, hinted around that, you’ve been working with Spark for a little while, and starting to get into more of the Spark, and that’s why you’re starting to look at Scala.

We’ll break it down real quick. First, my thoughts on Python versus Scala, just in the industry, I think that, with you saying that you have intermediate Python skills, if you’re looking at Spark, and that’s what you’re really wanting to specialize a little bit more in, I don’t think it would hurt you. I think it would be beneficial for you to start going ahead and looking into Scala, and maybe doing some research, there.

I definitely wouldn’t push back, and say, “I don’t need those Python skills anymore.” Both of them are interpreters. You can write both of those inside of Spark, so you’re covered there. There a little more functionality, it seems like, and more documentation around Scala. Part of it is because it’s expanding out that Java, so it keeps a lot of people from having to write those Java jobs versus just using the Scala interpreter ripple or just using the functions there.

From where I see it, and where you want to go, and especially if, you being a junior, saying that you’re a junior, data engineer, if you want to progress, I don’t think there’s a problem with you learning Scala. I just wouldn’t say that you really need to jump to it now.

It’s hard for us to, as technologists, as engineers, as developers, we love playing with new toys. Any time there’s a new language or a new toy, a new kind of technology, whether it be some kind of project with dockers containers, or Mesos, or Yarn, anytime there’s something new, we always look at that, like, “Oh, man!” That’s a shiny new object we want to play with.

What should we do from our career perspective? I don’t think you have to go all in on Scala. I think that, even with Python, and where we see Python now, and just look at the different open source projects and everything that’s going on with Python. I think you’re set there. One recommendation that I was given early in my career, that I want to pass along to you, is being a junior engineer, saying you have intermediate Python skills, what I would start to do is, I would start to maybe start looking at specialization.

Look at a couple different open source projects, maybe get involved, that revolve around Python. That can help you build your foundation, your portfolio, and yourself as a career. You still have options.

You’ve heard me talk about it before in the data engineer video. Take 30 minutes. Take 30 minutes a day and maybe two to three times a week, mix in some Scala research in there. That way, you’re learning it, and you’re starting to get it, but you’re also still contributing to your Python skills, which, like I said, you’re at the intermediate phase. There’s a way for you to specialize, because Python’s a pretty big language, and so there are different specializations that you can do. I would look at that.

The second part of the question that’s a little bit different, and I’m going to have somewhat of a different opinion here, is you’re asking about options for freelancing. Which one should you focus on if you want to freelance?

The way that I would look at that is, there are three important things that I look at for freelancing. If you’re freelancing, you’re focused on your brand. That’s how you’re going to get freelancing jobs. That’s how you’re going to be able to market yourself as a freelancer and continue to get that revenue there.

That starts with specialization. Like I was talking about a little bit with Python and starting to pick a specialization, I would really focus on it if that’s what I wanted to do. If I wanted to get into freelancing, wanted to be a freelance data engineer, you really want to start focusing and start specializing.

That’s not to say that you’re not going to pick up other skills and continue to learn those, but I think specialization, especially when you’re new to freelancing, is going to be very, very important, even more so important than just in your current career, where you’re working, maybe, at your company. Specialization is important there, but not as important as a freelancer. So, the second thing that’s really big with freelancing is the market. What is the total market need? Just looking based off that, if you were looking and asking that question, “Hey, I want to be a freelancer,” Python or Scala? There’s going to be a lot more jobs that are available right now, a lot more gigs to be able to get, that are going to revolve around Python, and I think that trend’s going to continue. I don’t think that Scala’s going to overtake that market.

Even segmented in the big data area, too, so even if we got outside of what’s going on with the big data community, I think those jobs are still going to continue to be maybe two to three times more for Python developers.

You’re looking at the brand. You’re looking at the market need, but then you’re also, the third key for freelancing is your reputation. Being new, you’re not going to have a lot of reputation, so what are some things that you can do to be able to get your name out there, and also get involved in some jobs?

It’s going to go back to the open source. I would look at some open source projects, maybe around the areas that you want to specialize in, and just get involved. Getting involved in those is sometimes just as easy as joining the user discussion list, and maybe answering some questions there. Getting on the developer list, and just maybe reading the emails, and see how it’s going.

I’m telling you, just getting on those email lists and seeing what’s going on, and as you start to contribute on there, that’s a great way for you to be able to, one, have code that you can point back to, or that you’ve been a part of the community. Two, that helps your reputation, so it’s going to be easier for you to find jobs. There’s a lot of times that you’ll see jobs, or requests for podcast interviews, or just a lot of different things, all come from that open source email list. I would get involved there.

Also, too, another thing that you do, just being new to the area of freelancing, is maybe you look for one of the gig, one of the freelancing places online, and start doing some of those jobs. A lot of times, those jobs, because you’re just starting out, they’re going to be very competitive, and you’re going to maybe be doing it for 10 or 20 bucks, or 30 bucks an hour, for a project, but that helps you build that portfolio. I wouldn’t focus too much on being a part of that, and saying that’s going to be your revenue for freelancing, but I think it’s a great way for you to get started, and start to see how it works for freelancing, and if it’s something that you’re interested in, too.

It’ll help you build that reputation, but I think the open source, long term, is going to really build that reputation for a lot longer than even the gig sites, and some of the other things. Three things for freelancing. Definitely, for that question around Python versus Scala, I would continue to focus on Python for your freelancing gigs, because I think that’s going to be your fastest way to be able to build that specialization. I think the market need’s there, and then also being able to brand yourself as well. That’s today’s episode of Big Data Big Questions.

Make sure you subscribe, so that you never miss an episode. Also, go, if you have any questions for Big Data Big Questions, check it out, thomashenson.com, I’ve got a place out there for you to just fill out. I can answer any questions you have.

Thanks again. See you!

Big Data Beard Podcast Announcement

November 7, 2017 by Thomas Henson Leave a Comment

How do you keep up with all the news going on in the Big Data community?
Announcing the Big Data Beard Podcast, a Podcast devoted to Big Data news, architecture, and the software powering the big data ecosystem. Watch the video below to learn how I feel about the new podcast.

Transcript – Big Data Beard Podcast Announcement

Hi, folks! Thomas Henson here with thomashenson.com. Today, I’m in a different location. Looks like a construction site, right?

That’s because changes are coming. I’m building an office right now, and at some point in the future, I’m going to have a video maybe showing that office off.

With all these changes coming, I wanted to announce another big change. That’s a new podcast for you to listen to. If you follow me on Twitter, you’ve probably heard about the Big Data Beard Podcast. Look at all these tweets.

That’s a good way to keep in touch, but the Big Data Beard Podcast just released a couple weeks ago. Big Data Bears Podcast? This is going to be epic!

Beard. Check. Talks about big data, check. The Big Data Beard Podcast is a Podcast with a group of engineers I’ve been working with, and what we decided was, we decided we should take our conversations that we have over coffee, or beers, or just at conferences, start recording those, and maybe have some guests along the way. This is a great way for you to be able to find out what’s going on in big data and data analytics, and also a great way to get more information.

I’m all about learning, and all about finding ways to get more involved in the community, and find out what’s going on. This is a great way, in under an hour, once a week, to be able to be involved, have some information, and then even interact with us.

If you’d like to appear on the show, or you have any ideas for the show, post them in the comments here. Send them on Twitter. However, you can get in touch with me, just give us those ideas, and we’ll be sure to field those questions.

Make sure you subscribe and check out the Big Data Beard Podcast. Thanks, folks!