Big Data – Empowering the Age of Agile Analytics

Big data is a buzzword, no question. Given that it is incumbent on practitioners – in particular architects – to tie the new Modern business conceptpatterns available in a “big data” enabled infrastructure to practical business benefits.

While there are a variety of business benefits that are enabled by big data infrastructure the single most tangible is Agile Analytics (also known as self service BI and data discovery and exploration). Here’s why:

1) Your business users never wanted reports.

What they really wanted was to be able to leverage data to answer questions. Traditional BI infrastructure did that well, provided you knew what questions you wanted answered in advance.

The problem is, you don’t. The world moves too fast to create a set of KPIs and instrument your business by those alone for 10 years.

2) Data Driven decision making requires empowered business users.

Business users must be empowered to use the the data directly – without intermediation by technical staff – in order to realize the benefit of data driven decision making.

This isn’t to say the technical staff doesn’t have a role – they do. They provide the platform and advanced support enabling business users to use data directly.

3) The prepared data can only answer known questions.

Business users need to follow the data to the important insights. They have the business knowledge to derive insights that matter, but they can only base those insights in data if they are empowered to explore in the data to follow the information to the value.

This means all the data – from the raw data, through each transformation or aggregation to the KPIs and rolled up analytics.

Big data infrastructure – properly deployed and governed – can provide a platform which solves the key problems preventing business users from engaging directly with the data and discovering valuable insights.

How to spot a fake Big Data Engineer

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Photo Credit: counterfitsoner on Flickr

I interview a lot of candidates… I mean a LOT.

And every resume I get these days has a “Big Data Project” listed.

So, naturally, my first question is – what is it that made it a “Big Data” project?

Top five immediate disquallification answers are:

  1. We were using Hadoop
  2. We had TONS of data
  3. We were running map reduce jobs
  4. The data was unstructured
  5. It wasn’t in our data warehouse

The truth is, no one knows what you mean when you say it was a “Big Data” project – and we all know it is on your resume as keyword search fodder – but if you are going to have it on there you better come with a better answer than one of the five above.

Big Data – Storage Isn’t Enough

We should have seen it coming. When we stopped even thinking about how we store data for our applications, when we just assumed some DBA would give us a database – and some SysAdmin would give us a file system. Sure, we can talk about W-SAN (what WLAN was to the LAN, but for storage) solutions like Amazon S3 and Rackspace Cloud – but they didn’t fundamentally change anything.

Big Data forces us to re-think storage completely. Not just structured/unstructured, relational/non-relational, ACID compliance or not. It forces us – at the application level – to rethink the current model exemplified by

I’m storing this because I may need it again in the future.

Where storage means physical, state aware object persistence and future means anywhere between now and the end of time.

Data Persistance – A Systemic Approach to Big Data for Applications

What Big Data applications require is a systemic approach to data. Instead of applications approaching data as only a set of if/then operations designed to determine what (if any) CRUD operations to perform it demands that applications (or supporting Data Persistence Layers) understand the nature of the data persistence required.

This is a level of complexity developers have been trained to ignore. The CRUD model itself explicitly excludes any dimensionally – or meta information about the persistence. It is all or nothing.

Data Persistance is primarily the idea that data isn’t just stored – it is stored for a specific purpose which is relevant within a specific time slice. These time slices are entirely analogous to those discussed in Preemption. Essentially, any sufficiently large real time Big Data system is simply a loosely aggregated computer system in which any data object may generate multiple tasks each of which have a specific priority.

For example, in a geo location game (Foursquare) the appearance of a new checkin requires multiple tasks which are prioritized based on their purpose, for example:

  1. Store the checkin to distribute to “friends” (real-time)
  2. Store the checking association with the venue (real-time)
  3. Analyze nearby “friends” (real-time)
  4. Determine any game mechanics, badges, awards, etc
  5. Store the checkin on the user’s activity
  6. Store the checkin object

NOTE: Many developers will look at this list above and ask: “Why not a database?” While a traditional database may suffice for a relatively low volume system (5k users, 20k checkins per day) it would not be sufficient at Big Data scale (as discussed here).

This Data Persistence solution is comprised of four vertical persistence types:

Big Data, Real Time Data Persistance

Transitory

Transitory persistance is for data persisted only long enough to perform some specific unit of work. Once the unit of work is completed the data is no longer required and can be expunged. For example: Notifying my friends (that want to be notified) that I’m at home.

Generally speaking (and this can vary widely by use case) Transitory persistence must be atomic, extremely fast and fault tolerant.

Volatile

Volatile persistance is for data that is useful but can be lost and rebuilt at any time. Object caching (how memcached is predominantly used) is one type Volatile persistence, but does not describe the entire domain. Other examples of volatile data include process orchestration data, data used to calculate decay for API Rate Limits, data arrival patterns (x/second over the last 30 seconds), etc.

The most important factor for Volatile data persistence is that the data can be rebuilt from normal operations or from long term data storage if it is not found in the Volatile dataset.

Generally speaking, data is stored in Volatile persistence because is offers superior performance, but limited dataset size.

ACID

Relational databases and atomicity, consistency, isolation and durability (ACID) are not obsolete. It is important for specific types of operations – done for specific purposes to maintain transactional compliance and ensure the entire transaction either succeeds in an atomic way, or fails. Examples of this include eCommerce transactions, Account Signup, Billing Information updates, etc.

Generally speaking, this data complies with the old rules of data. It is created/updated slowly over any given time slice, it is read periodically, there is little need to publish the information across a large group of subscribers, etc.

Amorphous

Amorphous persistence is the new kid on the block. NoSQL solutions fit nicely here. This non-volatile storage is amorphous in that the content (think property, not property value) of any object can change at any time. There is no schema, table structure or enforced relationship model. I think of this data persistence model as raw object storage, derived object storage and the transformed data that forms the basis of what Jeff Jones refers to as Counting Systems. Additionally, these systems store data in application consumable objects – with those objects being created on the way in.

Systems in this layer are generally highly scalable, fault tolerant, distributed systems with enhanced write efficiency. They offer the ability to perform the high volume writes required in real time Big Data systems without significant loss of read performance.

What Does All This Mean?

Most notably it means, that after years of obfuscating the underlying data storage from developers, we now need to re-engage application developers in the data storage conversation. No longer can a DBA define the most elegant data model based on the “I’m storing this because I may need it again in the future.” model and expect it to function in the context of a real time Big Data application.

We will hear a chorus of voices who will attack these dis-aggregated data persistence models based on complexity or the CAP Theorem or on the standard “the old way is the best way” defense of ACID and the RDBMS for everything. But all of this strikes me as a perfect illustration of what Henry Ford said:

If I had asked customers what they wanted, they would have told me they wanted a faster horse

Talking Big Data with IBM’s Jeff Jonas

I saw this on TechCrunch earlier today and thought it was an awesome add to my big data series. Jeff Jonas is clearly a big thinker and I agree with almost everything he says. The only thing I take issue with is the recurring theme in this interview that Big Data is primarily about commerce – and specifically ad targeting.

In my next post I’ll be talking about Big Data for One – which I think Jeff hints at but never fully develops.

Part 1: What is Data?

http://player.ooyala.com/player.js?deepLinkEmbedCode=JnZXZyMTpY5njnZyFbtL6owNeHSZaStK&width=630&height=354&embedCode=JnZXZyMTpY5njnZyFbtL6owNeHSZaStK

Part 2: Why data makes us more ignorant.

Data is actually evidence that you already knew, but failed to act on it. Amnesia.

http://player.ooyala.com/player.js?deepLinkEmbedCode=ZzZXZyMTrsb27oUZZDutt7A-TqGdPlXo&width=630&height=354&embedCode=ZzZXZyMTrsb27oUZZDutt7A-TqGdPlXo

Part 3: Why Big Data is the next big thing.

From pixels to pictures… This agrees with my idea of Big Data – it isn’t about the size of the dataset, but about using pieces of data in context by understanding context.

Why he goes to ad based is beyond me however…

http://player.ooyala.com/player.js?deepLinkEmbedCode=s4ZnZyMTrtWTaKSxWF2WEPPXkBtMjZc3&width=630&height=354&embedCode=s4ZnZyMTrtWTaKSxWF2WEPPXkBtMjZc3

Part 4: How data makes us average.

 

Very similar to the points I made here: Living In Public – Facebook, Privacy and Frictionless Distribution
http://player.ooyala.com/player.js?deepLinkEmbedCode=05ZnZyMToC-qRTChMHxO9jsDjOcJFdjo&width=630&height=354&embedCode=05ZnZyMToC-qRTChMHxO9jsDjOcJFdjo

Why the future is irresistible.

http://player.ooyala.com/player.js?deepLinkEmbedCode=JnZnZyMTpU1XeIWbGdtOrD96fWvhbDX6&width=630&height=354&embedCode=JnZnZyMTpU1XeIWbGdtOrD96fWvhbDX6