One of the benefits of coming to a greenfield job – like when I joined Mind Candy two years ago – was that you can jump several technological steps ahead as you don’t have any legacy to deal with. Essentially we could build from scratch based on lessons learned from traditional data architecture. One of the main ones was to establish a real-time path right away to avoid having to shoehorn it in afterwards. Another was to avoid physical hardware. And the most important one was to hold off on Hadoop as long as possible.
The last one might seem surprising, isn’t Hadoop the centre-piece of a data architecture? Unfortunately it creates a lots of admin overhead and it might be a full person’s (or more) workload to maintain. Not ideal in a small company where people resources are limited. AWS S3 can fulfil most of the storage function but requires no maintenance and is largely fast enough. Also while HDFS is important and will probably come back for us soon, MR1 or YARN is just not – there are better and more advanced execution systems that can use HDFS and we used one of those: Mesos.
Mesos is a universal execution engine for job and resource distribution. Unlike YARN it can not only run Spark but also Cassandra, Kafka, Docker containers and recently also HDFS. That works because Mesos just offers resources and let the framework handle the starting and management of the jobs. This finally breaks the link between framework and execution engine: in Mesos you can run not only different frameworks but different versions of the same framework. No more waiting for your infrastructure to upgrade to the latest Hadoop or Spark version, you can run it right now even when all your other jobs run on older versions. Combine that with a robust architecture and simple upgrading and Mesos can easily be seen as the successor to YARN (for more details on why Mesos beats Yarn, see Dean Wampler’s talk from Strata).
For the real-time path the obvious processing solution is Spark Streaming (so we have a simpler code base) running on Mesos with Kafka to feed data in and with Cassandra to store the results. You now have a so-called SMACK stack (Spark Mesos Apache Cassandra Kafka) for data processing which the Mesos folks call Mesosphere Infinity for some reason (aka marketing).
The last bit of a data architecture is the SQL engine. Traditionally this was Hive but we all know Hive is slow. While there are several open-source solutions out there that improve on good old Hive (Impala, Spark SQL) in the end we decided on AWS Redshift. It’s a column-oriented SQL-based data warehouse with PostgreSQL interface which fulfils most of the data analysis and data science needs while being reasonably fast and relatively easy to maintain with few people.
The resulting architecture looks like the above picture. We have an event receiver and a enricher/validate/cleaner, which were written in-house in Scala/Akka and are relatively simple programs using AWS SQS as a transport channel. The data is then send to Kafka and S3. Spark uses data straight from S3 to aggregate and put the processed data back into either Redshift or S3. On the real-time side of things we have Kafka going into Spark Streaming with an output into Cassandra.
What can be improved here? HDFS is still better than S3 for certain large scale jobs and we want to bring it back running on Mesos. Redshift could be replaced with Spark SQL hopefully soon. All in all the switch from tightly coupled Hadoop to an open architecture based on Mesos allowed us to have an unprecedented freedom as to which kind of data jobs we want to run and which frameworks to use, allowing a small team to do data processing in ways previous only possible on a large budget.
I’m giving a talk about Spark Streaming and probabilistic data structures this Monday at the London Hadoop Meetup. Sign up with the link below!
Looking again at the data science diagram – or the unicorn diagram for that matter – makes me realize they are not really addressing how a typical data science role fits into an organization. To do that we have to contrast it with two other roles: data engineer and business analyst.
What makes a data scientist different from a data engineer? Most data engineers can write machine learning services perfectly well or do complicated data transformation in code. It’s not the skill that makes them different, it’s the focus: data scientists focus on the statistical model or the data mining task at hand, data engineers focus on coding, cleaning up data and implementing the models fine-tuned by the data scientists.
What is the difference between a data scientist and a business/insight/data analyst? Data scientists can code and understand the tools! Why is that important? With the emergence of the new tool sets around data, SQL and point & click skills can only get you so far. If you can do the same in Spark or Cascading your data deep dive will be faster and more accurate than it will ever be in Hive. Understanding your way around R libraries gives you statistical abilities most analysts only dream of. On the other hand, business analysts know their subject area very well and will easily come up with many different subject angles to approach the data.
The focus of a data scientist, what I am looking for when I hire one, should be statistical knowledge and using coding skills for applied mathematics. Yes, there can be the occasional unicorn in a very senior data scientist, but I know few junior or mid-level data scientist who can surpass a data engineer in coding skills. Very few know as much about the business as a proper business analyst.
Which means you end up with something like this:
Data scientists use their advanced statistical skills to help improve the models the data engineers implement and to put proper statistical rigour on the data discovery and analysis the customer is asking for. Essentially the business analyst is just one of many customers – in mobile gaming most of the questions come from game designers and product designers – people with a subject matter expertise very few data scientists can ever reach.
But they don’t have to. Occupying the space between engineering and subject matter experts, data scientists can help both by using skills no one else has without having to be the unicorn.
If you are working as a data architect or a technical lead of a data team you are in a bit of thankless position at the moment. You could be working at or even founding one of the many data platform startups right now. Or work for the many enterprise consultancies that provide “big data solutions”. Both would mean directly profiting from you acquired technical skills. Instead, you are working in a company that actually needs the data you provide but also doesn’t care how you get it. There is the old business metaphor of selling shovels to gold diggers instead of digging for gold yourself. I think a closer metaphor is that the other guys are logistics and you are fighting with everybody in the trenches.
The particular trench for me is free-to-play mobile gaming which is closer to being a figurative battle field than say web or B2B. You either get big or you die. There is no meeting that goes by without people discussing performance metrics, mostly retention and ARPDAU. Because the business boils down to a mathematical formula: if you have a good retention and a good revenue per user and your acquisition costs are low you make a profit. If either of those is flailing, even just for a couple of days, you don’t. Fortunes can change very very quickly. Where metrics are this important, having people who can provide the metrics accurately is key. Hence front line data science.
The challenges you face in the trenches are of different nature. Real-time is very very important as everybody wants to see the impact of say an Apple feature right away. At the same time product managers and game designers want to crunch weeks of data to optimise say level difficulty. Spark Streaming query bugging out late night on Saturday and your inbox is overflowing with “What’s going on?” emails. Delays in a weekly Hadoop aggregation and a game release might be delayed as an A/B test could not be verified. In the trenches, the meaning within the data is much much more important than the technology you throw at it. But it’s also very limited from a data science point of you: you do a bit of significance testing here and a some revenue predictions there but most of the statistical methods are rather simple. Not what everybody was promised when taking up data science.
What does one gain being on the front lines? The data actually flows into the product every day, what you find during data mining is important to the survival of the game or app. Features live or die with your significance test which you hopefully picked the correct statistical method for. You could be making tools for data scientist or crunching large data sets for reports that one manager might read maybe – but that would be less chaotic, less rushed and less fun than throwing out some data and actually watching your game going up the charts. Welcome to the trenches.