Data

Front End Developer – Infographic

FrontEndDeveloper

Front End Developer

 

 

 

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Digital Project Manager – Infographic

DigitalProjectManager

Digital Strategist

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Digital Project Management:

The nature of front office projects compare to traditional IT projects is one that generally involves a more co-operative project.

Not only do you need developers and testers but you might need artists, user experience and web analysts or more.

As a result THE key skill of a digital PM is the ability to be a strong scrum master who knows how to be a gatekeeper not just a facilitator.

 

 

 

 

 

 

Data Scientist – Infographic

Digital Strategist

Digital Strategist

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Digital Strategist:  There is still a lot of pushback from companies regarding embarking on digital campaigns and most of this derives from a lack of understandable direction.  The front office loves quantifiable measures (If I call 100 people I will get 10 meetings and 2 sales) and the digital universe isn’t setup to be conducive to quantifying results.  That is where the digital strategist comes in.  They are the purveyor of all things actionable and can translate what a business needs into a measurable digital strategy.

 

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Data Scientist – Infographic

Data Scientist

Data Scientist

The most mysterious of all positions.  The ability to write algorithms that find relationships in datasets is only usable if it provides actionable insight.
Consumer behaviour analysis allows front offices to better predict what and when consumers are buying. Data science provides the raw information that allows that to happen.

Capital markets riding the cutting edge of the digital revolution

Planet4IT has been a successful IT staffing agency for 15 years.  Recently we started to notice that the ever-changing technology landscape has spawned a subset of talented individuals who don’t fit perfectly into the traditional IT world.  P4Capital is our response.

It’s a division aimed at people who specialize at the point where capital markets and wealth management intersect with technology.

Data is where all of these specialists interact, create, and work. Think of data as the meeting point of Digital and Capital.

In 1964, Marshall McLuhan coined the term global village.

The new electronic independence re-creates the world in the image of a global village.” — Marshall McLuhan

50 years later we are finally arriving at the point where it’s becoming a reality.  It is technology that is enabling this, but if you stop and ask yourself why are we pursuing all these technological advances you will inevitably come to the conclusion that commerce is the driving force behind it.

Commercial society has always been the hallmark of North America, and with the fall of communism and the rise of democracy the rest of the world is pushing commercialism to a whole new level.

There is perhaps no better example of commerce than the traders of Capital around the world.

The major stock exchanges see more volume now than ever before, and those who can find advantages in investment can turn very serious profits.

Big data has provided those who can effectively understand it an advantage that rivals the speculation of films like Limitless.

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I see every scenario, I see 50 scenarios, that’s what it does Carl – it puts me 50 moves ahead of you.

With big data and good analytics come many advantages in the capital markets trading game:

  • The first is speed.  Real time databases such as Hadoop allow for information to be processed at a rate unprecedented in human history. We’re talking millions of information indices being turned into relevant and useful information in nanoseconds.
  • The second is depth.  Algorithmic programmers now have access to a huge volume of data they can sift through and collect relevant information from.   This has allowed traders to build significantly more accurate predictive models.    Now we are living in a world where data processing gives competitive advantage.
  • Thirdly and lastly for this post, is sales.  The world of commerce doesn’t exist without the private funding of people who are willing to put their money in other people’s hands.  The ability to maximize the amount entrusted is often an overlooked component of the trading game.  Sales teams and traders work side by side in banks around the world.  Big data analytics have given those sales teams ammunition to maximize their investment by building out customer profiles that predict who they should be contacting on any given day.

It’s easy to get distracted by the bright lights of the Digital world, but the reality is commerce is one of the biggest reasons these technological advances are useful to society.

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andrew

Guest Blogger: Andrew

Andrew is one of the newest members of Planet4IT, so he brings with him a fresh new perspective.

With one eye on the job market and the other on the IT world, he’s the man to go to for information on how the latest advancements in Data, Digital Marketing and Social Media are effecting business.

Andrew encourages you to reach out to him through not only telephone or email, but LinkedIn and Twitter as well

 

 

The Challenges of Scaling your Data Vertically

There are many reasons for which databases must be scaled. The majority of the time they must be scaled to accommodate for performance issues as the product grows. Though NoSQL is making a lot of noise these days, it is to no one’s surprise that SQL is still extremely popular. In general, the same principles are followed while scaling out any SQL product, be it MySQL, MsSQL, Oracle or even DB2. Scaling is often done to overcome performance issues as the product grows. However, when dealing with big data, scaling is often done to balance the data across multiple hardware nodes or clusters.

Most SQL products are scaled in clusters called shards. Each shard contains one or two masters and several slaves. Master servers are responsible for writing data whereas the slaves are responsible for reading data. NoSQL has become more popular over the years as it doesn’t require the complex infrastructures we see in SQL. In order to reduce calls made to the databases, a caching layer is added. These are often easy to put up and are cheap to run. As the product grows, the infrastructure can end up looking like this picture below.

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Source Infoq.com

I had to learn this graphic the hard way. Not expecting the product to be popular immediately after launch, we delayed scaling. To our surprise we hit 60k users after the second day. After learning the hard way, the game was scaled and was able to hit over 250k a few days later. Foresight is a great thing to have though often it is best to be ready to scale first. You must determine how you expect to grow. Is content going to be created constantly and the database is going to grow or will it remain stable over time?

A product design that doesn’t require a lot of data to be added to the databases will often benefit from a system that replicates databases and makes use of caching. Write-heavy applications will take the approach of growing their infrastructure vertically by splitting content up between shards and adding shards over time.

When dealing with partitioning, you will need to determine the key on which the data will be partitioned. With that key, an algorithm can be created that will be used to determine which shard the data will go to upon a read or write. For example: dealing with user registrations you have 3 shards. The 1st user is saved on the 1st shard, the 2nd user on the 2nd shard and so on. I often recommend storing values to identified shards based on a hash value of the key (like uid). One thing to keep in mind is that you will be growing and you likely will increase the number of shards, you will need to rebalance data when that happens.

There are a great many alternatives when trying to scale out your databases. Some are complex and take a lot of time to plan while others are as simple as setting up replication. I’ve been researching alternatives to scaling out SQL for years. With NoSQL coming up as a great solution, it did not meet my goals. There is a reason why relational databases are popular today. Joining tables, subqueries, stats and various other functions are often required. NoSQL attempts to accomplish several of those features through map-reduce, but it isn’t the same. This is why I have been working more closely with NewSQL solutions like VoltDB. They allow many SQL / relational database features all while being built from the ground up to scale.

 

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headshotFrancis Pelland is a born innovator and is experienced in building end-to-end technological solutions. He thrives on solving complex problems with elegant technical or product solutions, all the while improving user experience and building deeply embedded analytic solutions with Big Data.

One of our candidates here at P4Digital, if you want to contact him send a message to Archana Ravinder at aravinder@planet4it.com