We got to keep more data

Guess which airport has won the most awards in the annual Skytrax list? Guess which airport won 480 awards since its opening in 1981? Guess how this airport did it?

Data Analytics gives the competive edge.

Serving and servicing more than 65 million passengers and travellers in 2018, and growing, Changi Airport Singapore sets a very high level customer service. And it does it with the help of technology, something they call Smart (Service Management through Analytics and Resource Transformation) Airport. In an ultra competitive and cut-throat airline business, the deep integration of customer-centric services and the ultimate traveller’s experience are crucial to the survival and growth of airlines. And it has definitely helped Singapore Airlines to be the world’s best airlines in 2018, its 4th win.

To achieve that, Changi Airport relies on technology and lots of relevant data for deep insights on how to serve its customers better. The details are well described in this old news article.

Keep More Relevant Data for Greater Insights

When I mean more data, I do not mean every single piece of data. Data has to be relevant to be useful.

How do we get more insights? How can we teach systems to learn? How to we develop artificial intelligence systems? By having more relevant data feeding into data analytics systems, machine learning and such.

As such, a simple framework for building from the data ingestion, to data repositories to outcomes such as artificial intelligence, predictive and recommendations systems, automation and new data insights isn’t difficult to understand. The diagram below is a high level overview of what I work with most of the time. Continue reading

The full force of Western Digital

[Preamble: I have been invited by GestaltIT as a delegate to their Tech Field Day for Storage Field Day 18 from Feb 27-Mar 1, 2019 in the Silicon Valley USA. My expenses, travel and accommodation were covered by GestaltIT, the organizer and I was not obligated to blog or promote their technologies presented at this event. The content of this blog is of my own opinions and views]

3 weeks after Storage Field Day 18, I was still trying to wrap my head around the 3-hour session we had with Western Digital. I was like a kid in a candy store for a while, because there were too much to chew and I couldn’t munch them all.

From “Silicon to System”

Not many storage companies in the world can claim that mantra – “From Silicon to Systems“. Western Digital is probably one of 3 companies (the other 2 being Intel and nVidia) I know of at present, which develops vertical innovation and integration, end to end, from components, to platforms and to systems.

For a long time, we have always known Western Digital to be a hard disk company. It owns HGST, SanDisk, providing the drives, the Flash and the Compact Flash for both the consumer and the enterprise markets. However, in recent years, through 2 eyebrow raising acquisitions, Western Digital was moving itself up the infrastructure stack. In 2015, it acquired Amplidata. 2 years later, it acquired Tegile Systems. At that time, I was wondering why a hard disk manufacturer was buying storage technology companies that were not its usual bread and butter business.

Continue reading

Greenplum looking mighty sweet

Big data is Big Business these days. IDC predicts that between 2012 and 2020, the spending on big data solution will account for 80% of IT spending and growing at 18% per annum. EMC predicts that the big data is worth USD$70 billion! That’s a very huge market.

We generate data, and plenty of it. In the IDC Digital Universe Report for 2011 (sponsored by EMC), approximately 1.8 zettabytes of data will be created and replicated in 2011. How much is 1 zettabyte, you say? Look at the conversion below:

                    1 zettabyte = 1 billion terabytes

That’s right, folks. 1 billion terabytes!

And this “mountain” of data and information is a Goldmine of goldmines, and companies around the world are scrambling to tap on this treasure chest. According to Wikibon, big data has the following characteristics:

  • Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible
  • Petabytes/exabytes of data
  • Millions/billions of people
  • Billions/trillions of records
  • Loosely-structured and often distributed data
  • Flat schemas with few complex interrelationships
  • Often involving time-stamped events
  • Often made up of incomplete data
  • Often including connections between data elements that must be probabilistically inferred

But what is relevant is not the definition of big data, but rather what you get from the mountain of information generated.  The ability to “mine” the information from big data, now popularly known as Big Data Analytics, has sparked a new field within the data storage and data management industry. This is called Data Science. And companies and enterprises that are able to effectively use the new data from Big Data will win big in the next decade. Activities such as

  • Business decision making
  • Gain competitive advantage
  • Drive productivity growth in relevant industry segments
  • Understanding consumer and business behavioural patterns
  • Knowing buying decisions and business cycles
  • Yielding new innovation
  • Reveal customer insights
  • much, much more

will drive a whole new paradigm that shall be known as Data Science.

And EMC, having purchased Greenplum more than a year ago, has started their Data Computing Products Division immediately after the Greenplum acquisition. And in October of 2010, EMC announced their Greenplum Data Computing Appliance with some impressive numbers. Using 2 configurations of their appliance, noted below:

 

Below are 2 tables of the Greenplum performance benchmarks:

 

 

That’s what these big data appliance is able. The ability to load billions of either structured or unstructured files or objects in mere minutes is what drives the massive adoption of Big Data.

And a few days, EMC announced their Greenplum Unified Analytics Platform (UAP) which comprises of 3 Greenplum components:

  • A relational database for structured data
  • An enterprise Hadoop engine for the analysis and processing of unstructured data
  • Chorus 2.0, which is a social media collaboration tool for data scientists

The diagram below summarizes the UAP solution:

Greenplum is certainly ahead of the curve. Competitors like IBM Netezza, Teradata and Oracle Exalogic are racing to be ahead but Greenplum is one of the early adopters of a single platform for big data. Having a consolidation platform will not only reduce costs (integration of all big data components usually incurs high professional services’ fees) but will also reduce the barrier to entry to big data, thus further accelerating the adoption of big data.

Big Data is still very much at its infancy and EMC is pushing to establish its footprint in this space. EMC Education has already announce the general availability of courses related to big data last week and also the EMC Data Science Architect (EMC DSA) certification. Greenplum is enjoying the early sweetness of the Big Data game and there will be more to come. I am certainly looking forward to share more on this plum (pun intended ;-)) of the data storage and data management excitement.

Big data is big headache

IBM claims that we are responsible of for creating 2.5 quintillion bytes of data every day. How much is 1 quintilion?

 

According to the web,

1 quintillion = 1,000,000,000,000,000,000

After billion, it is trillion, then quadrillion, and then quintillion. That’s what 1 quintillion is, with 18 zeroes!

These data comes from everything from social networking updates, meteorology (weather reports), remote sensing maps (Google Maps, GPS, Geographical Information Systems), photos (Flickr), videos (YouTube), Internet search (Google) and so on. The big data terminology, according to Wikipedia, is data that are too large to be handled and processed by conventional data management tools. This presents a new set of difficulties when it comes to collected these data, storing them and sharing them. Indexing and searching big data would require special technologies to be able to mine and extract valuable information from big data datasets, within an acceptable period of time.

According to Wiki, “Technologies being applied to big data include massively parallel processing (MPP) databases, datamining grids, distributed file systems, distributed databases, cloud computing platforms, the Internet, and scalable storage systems.” That is why EMC has paid big money to acquire GreenPlum and IBM acquired Netezza. Traditional data warehousing players such Teradata, Oracle and Ingres are in the picture as well, setting a collision course between the storage and infrastructure companies and the data warehousing solutions companies.

The 2010 Gartner Magic Quadrant has seen non-traditional players such as IBM/Netezza and EMC/Greenplum, in its leaders quadrant.

 

And the key word that is already on everyone’s lips is “ANALYTICS“.

The ability to extract valuable information that helps determines what the next future trend is and personalized profiling will be something that may already arrived as companies are clamouring to get more and more out of our personalities so that they can sell you more of their wares.

Meteorological organizations are using big data analytics to find out about weather patterns and climate change. Space exploration becomes more acute and precise from the tons and tons of data collected from space explorations. Big data analytics are also helping pharmaceutical companies develop new biological and pharmaceutical breakthroughs. And the list goes on.

I am a new stranger into big data and I do not proclaim to know a lot. But terms such as scale-out NAS, distributed file systems, grid computing, massively parallel processing are certainly bringing the data storage world into a new frontier, and it is something we as storage professionals have to adapt to. I am eager to learn and know more about big data. It is a big headache but change is inevitable.