Category Archives: Software

Big Data & Analytics – what’s ahead?

Recently I read somewhere this statement – As we end 2017 and look ahead to 2018, topics that are top of mind for data professionals are the growing range of data management mandates, including the EU’s new General Data Protection Regulation that is directed at personal data and privacy, the growing role of artificial intelligence (AI) and machine learning in enterprise applications, the need for better security in light of the onslaught of hacking cases, and the ability to leverage the expanding Internet of Things.

Here are the key areas as we look ahead:

  • Business owners demand outcomes – not just a data lake to store all kinds of data in its native format and API’s.
  • Data Science must produce results – Play and Explore is not enough. Learn to ask the right questions. Visualization of analytics from search.
  • Everyone wants Real Time – Days and weeks too slow, need immediate actionable outcomes. Analytics & recommendations based on real time data.
  • Everyone wants AI (artificial intelligence) – Tell me what I don’t know.
  • Systems must be secure – no longer a mere platitude.
  • ML (machine learning) and IoT at massive scale – Thousands of ML models. Need model accuracy.
  • Blockchain – need to understand its full potential to business – since it’s not transformational, but a foundational technology shift.

In the area of big data, a combination of new and long-established technologies are being put to work. Hadoop and Spark are expanding their roles within organizations. NoSQL and NewSQL databases bring their own unique attributes to the enterprise, while in-memory capabilities (such as Redis) are increasingly being utilized to deliver insights to decision makers faster. And through it all, tried-and-true relational databases continue to support many of the most critical enterprise data environments.

Cloud becomes the de-facto deployment choice for both users and developers. Serverless technology with FaaS (Function as a Service) is getting rapid adoption amongst developers. According to IDC, enterprises are undergoing IT transformation as they rethink their business operations, including how they use information and what technology to deploy. In line with that transformation, nearly 80% of large organizations already have a hybrid cloud strategy in place. The modern application architecture, sometimes referred to as SMAC (social, mobile, analytics, cloud) is becoming standard everywhere.

The DBaaS (database as a service) is still not as widespread as other cloud services. Microsoft is arguably making the strongest explicit claim for a converged database system with its Azure Cosmo DB as DBaaS. Cosmo DB claims to support four data models – key-value, column-family, document, and graph. However, databases have been slower to migrate to the cloud than other elements of computing infrastructure mainly for security and performance reasons. But DBaaS adoption is poised to accelerate. Some of these cloud based DBaaS systems – Cosmo DB, Spanner from Google, and AWS DynamoDB – now offer significant advantages over their on-premise counterparts.

One thing for sure, big data and analytics will continue to be vibrant and exciting in 2018.


AWS re:Invent 2017

In a few decades when the history of computing will be written, a major section will be devoted to cloud computing. The headline of the first section would read something like this – How did a dot-com era book-selling company became the father of cloud computing? While the giants like IBM, HP, and Microsoft were sleeping, Amazon started a new business eleven years ago in 2006 called AWS (Amazon Web Services). I still remember the afternoon when I had spent couple of hours with the CTO of Amazon (not Werner Vogel, his predecessor, a dutch gentleman) back in 2004 discussing the importance of SOA (Service Oriented Architecture). When I asked why was he interested, he mentioned how CEO Jeff Bezos has given a marching order to monetize the under-utilized infrastructure in their data centers. Thus AWS arrived in 2006 with S3 for storage and EC2 for computing.

Advance the clock by 11 years. At this week’s AWS Re-Invent event in Las Vegas it was amazing to listen to Andy Jassy, CEO of AWS who gave a 2.5 hour keynote on how far AWS has come. There were 43,000 people attending this event (in its 6th year) and another 60,000 were tuned in via the web. AWS has a revenue run rate of $18B with a 42% Year-to-Year growth. It’s profit is over 60% thus contributing significantly to Amazon’s bottom line. It has hundreds of thousands of customers starting from majority web startups to Fortune 500 enterprise players in all verticals. It has the strongest partner ecosystem. Garter group said AWS has a market share of 44.1% (39% last year), larger than all others combined. Customers like Goldman Sachs, Expedia, and National Football League were on stage showing how they fully switched to AWS for all their development and production.

Andy covered four major areas – computing, database, analytics, and machine learning with many new announcement of services. AWS already offers over 100 services. Here is a brief overview.

  • Computing – 3 major areas: Instances of EC2 including new GPU processor for AI, Containers (services such as Elastic Container Services and new ones like EKS – Elastic Kubernetes Services), and Serverless (Function as a Service with its Lambda services). The last one, Serverless is gaining fast traction in just last 12 months.
  • Database – AWS is starting to give real challenge to incumbents like Oracle, IBM and Microsoft. It has three offerings – AWS Aurora RDBMS for transaction processing, DynamoDB and Redshift. Andy announced Aurora Multi-Master for replicated read and writes across data centers and zones. He claims it is the first RDBMS with scale-out across multiple data centers and is lot cheaper than Oracle’s RAC solution. They also announced Aurora Serverless for on-demand, auto-scaling app dev. For No-SQL, AWS has DynamoDB (key-value store). They also have Amazon Elastic Cache for in-memory DB. Andy announced Dynamo DB Global Tables as a fully-managed, multi-master, multi-region DB for customers with global users (such as Expedia). Another new service called Amazon Neptune was announced for highly connected data (fully managed Graph database). They also have Redshift for data warehousing and analytics.
  • Analytics – AWS provides Data Lake service on S3 which enables API access to any data in its native form. They have many services like Athena, Glue, Kinesis to access the data lake. Two new services were announced – S3 Select (a new API to select and retrieve S3 data from within an object), Glacier Select (access less frequently used data in the archives).
  • Machine Learning – Amazon claims it has been using machine learning for 20 years in its e-commerce business to understand user’s preferences. A new service was announced called Amazon Sagemaker which brings storage, data movement, management of hosted notebook, and ML algorithms like 10 top commonly used ones (eg. Time Series Forecasting). It also accommodates other popular libraries like Tensorflow, Apache MxNet, and Caffe2. Once you pick an algorithm, training is much easier with Sagemaker. Then with one-click, the deployment happens. Their chief AI fellow Dr. Matt Wood demonstrated on stage how this is all done. They also announced AWS DeepLens, a video camera for developers with a computer vision model. This can detect facial recognition and image recognition for apps. New services announced besides the above two are – Amazon Kinesis Video streams (video ingestion), Amazon Transcribe (automatic speech recognition), Amazon Translate (between languages), and Amazon Comprehend (fully managed NLP – Natural Language Processing).

It was a very impressive and powerful presentation and shows how deeply committed and dedicated the AWS team is. Microsoft Azure cloud, Google’s computing cloud, IBM’s cloud and Oracle’s cloud all seem way behind in terms of AWS’s breadth and depth. It will be to customer’s benefit to have couple of AWS alternatives as we march along the cloud computing highway. Who wants a single-vendor lock-in?

Meet the new richest man on earth

This morning Jeff Bezos beat his nemesis from the same town Bill Gates as the richest man on the planet with his worth exceeding $90B. This was due to a huge surge in Amazon’s stock price (over $128 rise) to $1100 plus today. Their 3Q results came out yesterday and Amazon grew its revenue by 34% and profits inched up as well. There were fears that heavy investments in new warehouses and hiring workers would push it to a loss. This year Amazon’s stock started at $750. What a run!

Here are the numbers. Revenue soared 34% to a record $43.74B, a first for a non-holiday period, as the internet retail giant spread its ambitions with the acquisition of Whole Foods Market Inc. and widened its lead in cloud computing. Profit increased 1.6% to $256M, despite the costs bulging by 35%, a five-year high. I was surprised to know that Amazon employs 541,900 people, an increase from last quarter’s 382,400. Roughly 87,000 employees were added from Whole Foods. Now Amazon commands some 43.5% of e-commerce sales this year, compared with 38.1% last year.

I remember during the crash, everyone wrote off Amazon. When they ridiculed Bezos for a no-profit company with a bleak future, he jokingly replied, ” I spell profit as ‘prophet'”. He has come a long way with his prophetic vision and masterful execution.

The best addition to Amazon’s two core businesses (books and e-commerce) was the introduction of AWS as the cloud computing infrastructure back in 2004. First came S3 (simple shared storage) when Bezos convinced start-up companies to rent storage at one-hundredth of the cost of buying from big vendors. Then EC2 (Elastic Computing Cloud) was added and that took off in a big way, especially with capital-starved startups with unpredictable computing needs. Pretty soon, Amazon took the credit of being the ‘father of cloud computing’ beating big incumbents like IBM, HP, etc. Now AWS is a huge business growing fast and bringing in about $16B revenue with over 60% profit. AWS is making a difference to the bottom line. Microsoft is trying hard to catch up with its Azure cloud and so is Google with its GCE (Google Computing Cloud). Today’s AWS is a very rich stack with its own database as a service (Redshift, Dynamo, and Aurora), elastic Map-Reduce, serverless offering with Lambda, and much more.There are predictions that AWS could one day be the biggest business for Amazon.

While the pacific north-west remains to be the home of the richest man on earth, the title shifts to Bezos from Gates.

Blockchain 101

There is a lot of noise on Blockchain these days. Back in May, 2015 The Economist wrote a whole special on Bockchain and it said, “The “blockchain” technology that underpins bitcoin, a sort of peer-to-peer system of running a currency, is presented as a piece of innovation on a par with the introduction of limited liability for corporations, or private property rights, or the internet itself”. It all started after the 2008 financial crisis, when a seminal paper written by Satoshi Nakamoto on Halloween day (Oct 31, 2008) caught the attention of many (the real identity of the author is still unknown). The name of the paper was “Bitcoin: A peer to peer electronic cash system”. Thus began a cash-less, bank-less world of money exchange over the internet using blockchain technology. Bitcoin’s value has exceeded $6000 and market cap is over $100B. VC’s are rushing to invest in cryptocurrency like never before.

The September 1, 2017 issue of Fortune magazine’s cover page screamed “Blockhain Mania”. The article said, “A blockchain is a kind of ledger, a table that businesses use to track credits and debits. But it’s not just any run-of-the-mill financial database. One of blockchain’s distinguishing features is that it concatenates (or “chains”) cryptographically verified transactions into sequences of lists (or “blocks”). The system uses complex mathematical functions to arrive at a definitive record of who owns what, when. Properly applied, a blockchain can help assure data integrity, maintain auditable records, and contracts into programmable software. It’s a ledger, but on the bleeding edge”.

So welcome to the new phase of network computing where we switch from “transfer of information” to “transfer of values”. Just as TCP/IP became the fundamental protocol for communication and helped create today’s internet with the first killer app Email (SMTP), blockchain will enable exchange of assets (the first app being Bitcoin for money). So get used to new terms like cryptocurrency, DLS (distributed ledger stack), nonce, ethereum, smart contracts, pseudo anonymity, etc. The “information internet” becomes the “value internet”. — Patrick Byrne, CEO of Overstock said, “Over the next decade, what the internet did to communications, blockchain is going to do to about 150 industries”. — In a recent article in Harvard Business Review, authors Joi Ito, Neha Narula, and Robleh Ali said, “The blockchain will do to the financial system what the internet did to media”.

The key elements of blockchain are the following:

  • Distributed Database – each party on a blockchain has access to entire DB and its complete history. No single party controls the data or the info. Each party can verify records without an intermediary.
  • Peer-to-Peer Transmission (P2P) – communication directly between peers instead of thru a central node.
  • Transparency with Pseudonymity – each transaction and associated value are visible to anyone with access to system. Each node/user has a unique 30-plus-character alphanumeric address. Users can choose to be anonymous or provide proof of identity. Transactions occur between blockchain addresses.
  • Irreversibility of records – once a transaction is entered in the DB, they can not be altered, because they are linked to every xaction record before them (hence the term ‘chain’).
  • Computational Logic – blockchain transactions can be tied to computational logic and in essence programmed.

The heart of the system is a distributed database that is write-once, read-many with a copy replicated at each node. It is transaction processing in a highly distributed network with guaranteed data integrity, security, and trust. Blockchain also provides automated, secure coordination system with remuneration and tracking. Even if it started with “money transfer” via Bitcoin, the underpinnings can be applied to any assets. The need for a central coordinating agency such as bank becomes unnecessary. Assets such as mortgages, bonds, stocks, loans, home titles, auto registries, birth and death certificates, passport, visa, etc. can all be exchanged without intermediaries. The Feb, 2017 HBR article said, “Blockchain is a foundational technology (not disruptive). It has the potential to create new foundations for our economic & social systems.”

We did not get into the depth of the technology here, but plenty of literature is available for you to read. Major vendors such as IBM, Microsoft, Oracle, HPE are offering blockchain as an infrastructure service for enterprise asset management.

Apache Drill + Arrow = Dremio

A new company just emerged from stealth mode yesterday, called Dremio, backed by Redpoint and Lightspeed in a Series A funding of $10m back in 2015. The founders came from MapR, but were active in Apache projects like Drill and Arrow. The same VC’s backed MapR and had the Dremio founders work out of their facilities during the stealth phase. Now the company has around 50 people in their Mountainview, California office.

Apache Drill acts as a single SQL engine that, in turn, can query and join data from among several other systems. Drill can certainly make use of an in-memory columnar data standard. But while Dremio was still in stealth, it wasn’t immediately obvious what Drill’s strong intersection with Arrow might be. But yesterday the company launched a namesake product that also acts as a single SQL engine that can query and join data from among several other systems, and it accelerates those queries using Apache Arrow. So it is a combo of (Drill + Arrow): schema-free SQL for variety of data sources plus a columnar in-memory analytics execution engine.

Dremio believes that BI today involves too many layers. Source systems, via ETL processes, feed into data warehouses, which may then feed into OLAP cubes. BI tools themselves may add another layer, building their own in-memory models in order to accelerate query performance. Dremio thinks that’s a huge mess and disintermediates things by providing a direct bridge between BI tools and the source system they’re querying. The BI tools connect to Dremio as if it were a primary data source, and query it via SQL. Dremio then delegates the query work to the true back-end systems through push-down queries that it issues. Dremio can connect to relational databases (DB2, Oracle, SQL Server, MySQL, PostgreSQL), NoSQL stores (MongoDB, Amazon Redshift, HBase, MapR-FS), Hadoop, cloud blob stores like S3, and ElasticSearch.

Here’s how it works: all data pulled from the back-end data sources is represented in memory using Arrow. Combined with vectorized (in-CPU parallel processing) querying, this design can yield up to a 5x performance improvement over conventional systems (company claims). But a perhaps even more important optimization is Dremio’s use of what it calls “Reflections,” which are materialized data structures that optimize Dremio’s row and aggregation operations. Reflections are sorted, partitioned, and indexed, stored as files on Parquet disk, and handled in-memory as Arrow-formatted columnar data. This sounds similar to ROLAP aggregation tables).

Andrew Brust from ZDNet said, “While Dremio’s approach to this is novel, and may break a performance barrier that heretofore has not been well-addressed, the company is nonetheless entering a very crowded space. The product will need to work on a fairly plug-and-play basis and live up to its performance promises, not to mention build a real community and ecosystem. These are areas where Apache Drill has had only limited success. Dremio will have to have a bigger hammer, not just an Arrow”.

A conference in Bangalore

I was invited to speak at a conference called Solix Empower 2017 held in Bangalore, India on April 28th, 2017. It was an interesting experience. The conference focused on Big Data, Analytics, and Cloud. Over 800 people attended the one-day event with keynotes and parallel tracks on wide-ranging subjects.

I did three things. First, I was part of the inaugural keynote where I spoke on “Data as the new Oxygen” showing the emergence of data as a key platform for the future. I emphasized the new architecture of containers and micro-services on which are machine learning libraries and analytic tool kits to build modern big data applications.

Then I moderated two panels. The first was titled, ” The rise of real-time data architecture for streaming applications” and the second one was called, “Top data governance challenges and opportunities”. In the first panel, the members came from Hortonworks, Tech Mahindra, and ABOF (Aditya Birla Fashion). Each member described the criticality of real-time analytics where trends/anomalies are caught on the fly and action is taken immediately in a matter of seconds/minutes. I learnt that for online e-commerce players like ABOF, a key challenge is identifying customers most likely to refuse goods delivered at their door (many do not have credit cards, hence there is COD or cash on delivery). Such refusal causes major loss to the company. They do some trend analysis to identify specific customers who are likely to behave that way. By using real-time analytics, ABOF has been able to reduce such occurrences by about 4% with significant savings. The panel also discussed technologies for data ingestion, streaming, and building stateful apps. Some comments were made on combining Hadoop/EDW(OLAP) plus streaming(OLTP) into one solution like the Lambda architecture.

The second panel on data governance had members from Wipro, Finisar, Solix and Bharti AXA Insurance. These panelists agreed that data governance is no longer viewed as the “bureaucratic police and hence universally disliked” inside the company and it is taken seriously by the upper management. Hence policies for metadata management, data security, data retirement, and authorization are being put in place. Accuracy of data is a key challenge. While organizational structure for data governance (like a CDO, chief data officer) is still evolving, there remains many hard problems (specially for large companies with diverse groups).

It was interesting to have executives from Indian companies reflect on these issues that seem no different than what we discuss here. Big Data is everywhere and global.

The end of Cloud Computing?

A provocative title for sure when everyone thinks we just started the era of cloud computing. I recently listened to a talk by Peter Levine, general partner at Andreessen Horowitz on this topic which makes a ton of sense. The proliferation of intelligent devices and the rise of IoT (Internet of Things) lead us to a new world beyond what we see today in cloud computing (in terms of scale).

I have said many times that the onset of cloud computing was like back to the future of centralized computing. We had IBM mainframes, dominating the centralized computing era during the 1960s and 1970s. The introduction of PCs created the world of client-server computing (remember the wintel duopoly?) from 1980s till 2000. Then the popularity of the mobile devices started the cloud era in 2005, thus taking us back to centralized computing again. The text message I send you does not go from my device to your device directly, but gets to a server somewhere in the cloud first and then to your phone. The trillions of smart devices forecasted to appear as sensors in automobiles, home appliances, airplanes, drones, engines, and almost any thing you can imagine (like in your shoe) will drastically change the computing paradigm again. Each of these “edge intelligent devices” can not go back and forth to the cloud for every interaction. Rather they would want to process data at the edge to cut down latency. This brings us back to a new form of “distributed computing” model – kind of back to a vastly expanded version of the “PC era”.

Peter emphasized that the cloud will continue to exist, but its role will change from being the central hub to a “learning center” where curated data from the edge (only relevant data) resides in the cloud. The learning gets pushed back to the edge for getting better at its job. The edge of the cloud does three things – sense, infer, and act. The sense level handles massive amount of data like in a self-driving car (10GB per mile), thus making it like a “data center on wheels”. The sheer volume of data is too much to push back to the cloud. The infer piece is all machine learning and deep learning to detect patterns, improve accuracy and automation. Finally, the act phase is all about taking actions in real-time. Once again, the cloud plays the central role as a “learning center” and the custodian of important data for the enterprise.

Given the sheer volume of data created, peer-to-peer networks will be utilized to lessen load on core network and share data locally. The challenge is huge in terms of network management and security. Programming becomes more data-centric, meaning less code and more math. As the processing power of the edge devices increases, the cost will come down drastically. I like his last statement that the entire world becomes the domain of IT meaning we will have consumer-oriented applications with enterprise-scale manageability.

This is exciting and scary. But whoever could have imagined the internet in the 1980s or the smartphone during the 1990s, let alone self-driving cars?