Category Archives: cloud computing

When to think of Blockchain?

Blockchain is going thru its hype cycle. It’s not magic nor is it a solution looking for a problem. It is important to know what they can and can’t do. So let us revisit the definition again. Blockchain is a distributed ledgershared by untrusted participants with strong guarantee about accuracy and consistency. Now let us dissect the highlighted words.

  • Ledger: Manual ledgers go back to the 19th. century in which accountants entered transactions by hand. They are list of transactions: items sold/purchased, price, date, etc. Those transactions are dated (timestamped). Ledgers are strictly append only: transactions can be added, but old entries can neither be deleted or modified. Blockchain can have ledger entries that are significantly more complex, but the concept is the same.
  • Shared: Anyone with the appropriate software can put entries into a pool of entries that will eventually be checked for consistency and added to the ledger.
  • Distributed: Blockchains are not centralized. There is no central administration to decide who has access and what rules to follow. Hence there is no single point of control nor single point of failure.Many participants in the blockchain have copies of the entire ledger which gets updated whenever blocks are added. This dis-intermediation was fundamental when the Bitcoin movement started back in 2008.
  • Untrusted Participants: This is the most radical idea of blockchain. In enterprise applications, requiring a certain amount of trust allows some important optimizations, but the concept of “untrusted participants” is fundamental to a blockchain. Anyone can add entries. The protocol that produces agreement among untrusted partners is called BFT (Byzantine Fault Tolerance) or byzantine agreement.
  • Accuracy & Consistency: Despite untrusted participants, blockchain makes strong guarantees about ledger’s accuracy. The replicated copies are not always in agreement, but disagreements are quickly resolved automatically via algorithms and voting.

Blockchain is often a shorthand for “how Bitcoin is implemented”, but its scope is much broader than Bitcoin (like the first app. on blockchain much like email was the first app on the Internet). Blockchain introduces the era of “exchange of values/assets” whereas the Internet gave us the era of “exchange of information”.

If you are building applications that span enterprises and that need to keep accurate records in the presence of untrusted partners, you should be thinking about blockchains.

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Microsoft buys GitHub for $7.5B

This morning Microsoft announced the acquisition of Github for $7.5B. Why this makes immense sense? There are three main angles here: winning developers mindset and loyalty, pushing them closer to adopting Azure cloud at runtime, and a skills hookup with LinkedIn, another Microsoft acquisition from last year. Let’s see each point.

Developer love-fest

On the most surface level, the logic of buying GitHub is pretty clear. Developers love GitHub, and Microsoft needs the love of developers. Github is an online service that allows developers to host their software projects. From there, anyone can download those projects and submit improvements. That functionality has made GitHub the center of the open-source software development world. Microsoft offers a whole swath of tools for developers, including the increasingly popular Visual Studio Code software and the open-source .NET Core programming framework. The popularity of these kinds of tools provides a gentle, but apparently effective, funnel toward the Microsoft Azure cloud and other Microsoft products and services — if you like one Microsoft product, it’s more likely that you’ll choose other Microsoft products, especially if they integrate cleanly.

GitHub would just add to that strategy: Developers already love GitHub — in fact, in 2017, Microsoft killed Codeplex, its own GitHub competitor, saying GitHub’s popularity made its own efforts redundant and unnecessary. By owning GitHub, Microsoft would have a direct line to millions of highly engaged developers. We’ve already seen baby steps in this direction, as GitHub and Microsoft just this month announced integrations between their services.

Push Azure Cloud against AWS

AWS is the leader in cloud deployment with a run-rate of over $20B ($5.6B in first quarter revenue yielding 73% of Amazon’s total operating income). GitHub users get software developed and ready using the open source tools available, but running it is another story. Often they go to AWS as the default run-time platform.

Microsoft is laser-focused on the continued growth of its cloud-computing business. So the opportunity for Microsoft is fairly straightforward. If it can get the Microsoft Azure cloud tightly integrated with GitHub — basically, give developers an easy way to get a GitHub project up and running in the cloud — it can kill two birds with one stone. Developers could love GitHub even more, and it would drive more use of Microsoft Azure. It would be a weapon in Microsoft’s arsenal to close the gap between Azure and Amazon Web Services.

LinkedIn + GitHub

What does this mean? When Microsoft spent over $26 billion on LinkedIn last year, CEO Satya Nadella said the company was investing heavily in making sure that current and future workers had the skills they needed to succeed in the modern economy. In Silicon Valley, at least, it’s not uncommon for an employer to ask for a GitHub profile alongside — or instead of — a traditional resumé. If Microsoft is trying to understand the modern skills economy, GitHub could provide a compelling glimpse. So the GitHub push can be about helping developers work together as software becomes key to doing business at almost every company.

I am glad that Microsoft is making aggressive moves to give a fight to AWS. It’s time we see a tough competition for AWS as it gets bigger and stronger of monopolistic proportion.

Netflix Technology

I attended a meetup at Netflix last evening titled “Polyglot Persistence at Netflix”. The Cloud Development Engineering (CDE) team presented various aspects of building and maintaining a highly distributed system to meet its ever-growing customer needs. There are almost 160 million users and with its growing popularity of streaming movies and TV shows (many are produced now by Netflix), the demand on its systems is growing rapidly. Polyglot implies the coexistence of many databases and associated software systems.

Netflix cloud platform forms a layer of services, tools, frameworks and technologies that run on top of AWS EC2 in order to implement an efficient and nimble (fast reacting), highly available, globally distributed, scalable and performant solution. They switched over to AWS Cloud over a seven-year period starting back in 2009. It uses Amazon’s RDS and DynamoDB besides S3 for lower cost storage. The front-end is Node.js while the backend uses Java, Python, and Javascript. The team also described how they are using SSD (solid state device) besides memory cache. The main thrust of the evening talk was their use of Cassandra as the distributed database solution.

Apache Cassandra was originally developed at Facebook as a free, open-source, highly scalable, high performance distributed database to handle large amounts of data across many servers with no single point of failure. This global network of storage servers caches content locally to where it will be viewed. This local caching reduces bandwidth costs, reduces latency, and makes it easier to scale the service over a wide area, in this case globally. Here are the key reasons Netflix is a major user of Cassandra (besides others like eBay, Apple, Comcast, Instagram and Reddit):

  • Very large production deployment – 2500 nodes, 420 TB, over 1 Trillion user requests per day. Cassandra is a NOSQL, distributed, document-oriented database that scales horizontally and dynamically as more servers are added without need to re-shard or reboot.
  • Strong write performance with no network performance bottleneck.
  • It’s data model is highly flexible. A sparse 2-dimensional “super column family” architecture allows for rich data model representation (and better performance) beyond just key-value lookup.
  • It’s geographic capabilities – single global cluster can simultaneously replicate data asynchronously as well as service applications across multiple locations. The team last evening showed how users can seamlessly switch over to another data center if failure occurs. Cassandra has been a good choice for cross-data center and cross-regional deployment as customisable replication helps determine which cluster nodes to designate as replicas.

Like Youtube, Netflix has been growing its global reach and customers in providing streaming contents. The key success factor is the database technology to enable such high scale and performance. Other databases like RDS, DynamoDB and MySQL are providing varieties of function such as analytics and metadata store. One impressive part of last evening’s presentation was how they repair any damage to data on the fly by embedding it into the database itself.

Chaos Engineering

This phrase is new and it originated at Netflix back in 2010. I was listening to Nora Jones, a Netflix engineer at the AWS re-Invent conference few weeks back, where she talked about this. The principle of Chaos goes like this, “Chaos Engineering is the discipline of experimenting on a distributed system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” Distributed systems have too many moving parts and failures can occur at various levels – hard disks can fail, the network can go down, a sudden surge in customer traffic can overload a functional component—the list goes on. All too often, these events trigger outages, poor performance, and other undesirable behaviors. Chaos Engineering is a method of experimentation on infrastructure that brings systemic weaknesses to light. This empirical process of verification leads to more resilient systems, and builds confidence in the operational behavior of those systems.

Netflix moved its operation to the cloud back in 2008. They started some form of resiliency testing since that time. They introduced Chaos Monkey that systematically turned off services in the production systems. Then came Chaos Kong for large scale failures like shutting off a whole data center. Another tool called FIT (Failure Injection Testing) was introduced to test all scenarios between the small (Chaos Monkey) and very large (Chaos Kong). All these experiments culminated into what is called Chaos Engineering, a discipline now used across many large companies such as Google, Amazon, Microsoft, etc.

Applying Chaos Engineering improves the resilience of a system. By designing and executing Chaos Engineering experiments, you will learn about weaknesses in your system that could potentially lead to outages that cause customer harm. You can then address those weaknesses proactively, going beyond the reactive processes that currently dominate most incident response models.

So what is the difference between Chaos Engineering (experimentation) and testing? In testing, an assertion is made: given specific conditions, a system will emit a specific output. Tests are typically binary, and determine whether a property is true or false. Strictly speaking, this does not generate new knowledge about the system, it just assigns valence to a known property of it. Experimentation generates new knowledge, and often suggests new avenues of exploration. Examples of input for chaos experiments could span from maxing out cpu cores on an Elasticserach cluster to partially deleting kafka topics over a variety of instances to recreate an issue that occured in production. Numerous experiments can be performed to understand system behavior ahead of time and take corrective actions.

At Google, Kripa Krishnan leads a team that constantly breaks the system. So a small team of testers from other big companies have started to work together to share best practices. These folks are currently working on ways to automate some of the tests. “Right now, scale is our problem. We are doing hundreds of tests, but I cannot scale my team to hundreds of people. So we are exploring automating some of this. How do you constantly cause damage so systems are constantly recovering?”

As distributed systems get more complex with thousands of microservices providing various functions, chaos engineering is emerging as a key practice to make these systems more resilient to failure.

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 dot.com 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.